1085 results about "System parameters" patented technology

The invention relates to the technical field of power system monitoring, and discloses a wire and cable fault early warning system based on intelligent monitoring, which comprises a data sensing module, a multi-mode fusion module, a characteristic evolution module, an abnormal early warning module and a dynamic optimization module. The data sensing module collects multi-source heterogeneous data, the multi-modal fusion module processes the data to generate a spatial-temporal feature matrix, the feature evolution module extracts cable degradation features, the abnormity early warning module performs fault early warning based on the cable degradation features, and the dynamic optimization module optimizes system parameters by using federal learning. In addition, the system also comprises a digital twin mapping and topology analysis module for assisting decision making and enhancing positioning. According to the invention, real-time monitoring, accurate fault early warning and system performance optimization of the operation state of the wire and cable are realized, the stability and reliability of power transmission are improved, and the system has the advantages of comprehensive multi-source data acquisition, efficient data processing, accurate early warning, data privacy protection and the like.

The invention discloses a heterogeneous sensing early warning system based on decoupling perception and adversarial robust learning, and the system comprises a feature extraction module which processes heterogeneous sensor original data collected in real time through a multi-layer decoupling encoder, separates target related features and environment interference features, and suppresses noise pollution from the source; the multi-dimensional collaborative fusion module adopts a cross-domain adversarial robustness learning framework to carry out space-time sequence alignment and deep fusion on decoupling features to generate high-robustness joint representation, and a data missing problem is processed through a cross-modal generative feature completion mechanism; and the cognitive enhancement closed-loop decision module constructs a cognitive heuristic confidence evaluation model based on joint representation, realizes graded early warning by combining real-time quality scoring and behavior prediction, and dynamically optimizes system parameters through a feedback mechanism. According to the method, the problems of poor target detection robustness, high delay and low accuracy in a complex dynamic environment are solved, the detection precision is remarkably improved, the false alarm rate is reduced, and the all-weather adaptive capacity is enhanced.

The invention relates to the technical field of machining precision control, in particular to a laser carving precision control method and system.The laser carving precision control system comprises a feature collecting unit, a model building and analyzing unit, a dynamic threshold value adjusting unit and an online incremental learning unit, and the feature collecting unit collects vibration, current and temperature data through a multi-source sensor array; the model construction analysis unit realizes dynamic prediction of processing parameters by combining a bidirectional long-short-term memory network with an attention mechanism, and the dynamic threshold adjustment unit dynamically updates parameters of a numerical control system based on a material hardness real-time detection and thermal coupling model. The online incremental learning unit automatically generates training samples through error data, continuously optimizes model parameters and constructs a'data acquisition-intelligent modeling-dynamic compensation-model evolution 'closed loop, so that accurate prediction and adaptive adjustment of machining parameters are realized, and the adaptability of the manufacturing process to multi-variety and small-batch working conditions is remarkably improved.

The invention discloses a numerical control machining path control system based on artificial intelligence, particularly relates to the field of numerical control machining, is used for solving the problems of continuity and stability of a curvature mutation area in cutter path planning, and accurately captures global and local geometric characteristics of a curved surface through a curvature constraint feature matrix and a curvature adaptive convolutional network. A machining track meeting the differential geometric continuity is generated, and the problem of path breakage of a curvature sudden change area is effectively avoided; meanwhile, energy scale criteria and curvature manifold constraints are introduced, the machining track is dynamically repaired, geometric repair and physical stability are balanced, cutting force sudden change and stress concentration are reduced, and therefore the machining reliability is enhanced; in addition, through finite element simulation of the digital twin platform and synchronous updating of servo system parameters, dynamic closed-loop matching of a control instruction and a machining state is achieved, and the control efficiency and the surface quality are further optimized. And therefore, high-precision, high-stability and high-efficiency collaborative optimization is realized in complex curved surface processing.

The invention discloses a multi-modal data processing method and system, computer equipment and a readable storage medium, which can realize deep association and complementarity mining of multi-modal information and improve the accuracy and robustness of multi-modal understanding. The method comprises the following steps: an environment sensing module adjusts an environment sensing strategy according to feedback information transmitted by a self-adaptive decision module, and acquires multi-modal data according to the environment sensing strategy; the multi-modal encoding module encodes the multi-modal data into multi-modal feature vectors of the same dimension; a cross-modal fusion module fuses the multi-modal feature vectors to obtain fusion features; the self-adaptive decision-making module selects a decision-making network matched with the task type from a predefined network library according to the task type of the current decision-making task, inputs the fusion features into the decision-making network, and generates feedback information according to the decision-making process of the decision-making network; and the meta-learning controller evaluates the system performance of the current multi-modal data processing system and adjusts system parameters according to an evaluation result.

The invention relates to the technical field of aircraft intelligent detection and maintenance systems, and discloses an aircraft defect intelligent evaluation system and method based on multi-modal fusion, and the system comprises a multi-modal data collection module, a tensor construction and decomposition module, a meta-prototype relation network module, a multi-target game optimization module, and a closed-loop feedback module. The method comprises the steps of constructing a five-order feature tensor through multi-modal data synchronous acquisition and space-time alignment, extracting low-rank features through hypergraph block item decomposition, dynamically generating a defect prototype set in combination with meta-learning, generating a maintenance decision by adopting a Nash equilibrium strategy and fusing multi-constraint conditions, and optimizing system parameters through closed-loop feedback. The whole-process intelligentization of aircraft defect detection and maintenance is realized; according to the method, high-precision defect detection is realized through multi-modal data fusion and hypergraph modeling, an intelligent decision is generated in combination with dynamic prototype learning and multi-target game optimization, and continuous self-optimization is performed by means of a closed-loop feedback mechanism, so that the operation and maintenance efficiency and safety of the aircraft are improved automatically in the whole process.

The invention relates to the technical field of valve opening control, and discloses a valve opening control method for user-side hydraulic balance. The method comprises the following steps: carrying out parameter acquisition on the buried pipe cold storage system to obtain dynamic system parameter data; performing cold load fluctuation spectrum analysis and multi-scale decomposition based on the dynamic system parameter data to obtain a user demand prediction model and a hydraulic balance ideal flow distribution proportion; generating a comprehensive system state evaluation index according to the user demand prediction model and the hydraulic balance ideal flow distribution proportion; constructing a global optimization objective function based on the comprehensive system state evaluation index, and solving to obtain a time-phased valve adjustment strategy; and a valve flow characteristic model is constructed according to the time-phased valve adjusting strategy and the valve historical response data, the target opening degree of each adjusting valve is calculated, and a valve control execution instruction is output. The hydraulic balance strategy can be dynamically adjusted according to different working conditions, and flexible coordination of hydraulic balance and cooling requirements is achieved.

The invention discloses a micro-grid intelligent scheduling method and system based on an AI large model, and the method comprises the steps: collecting and processing the real-time output data of a photovoltaic power station and a wind power station, and obtaining a standardized micro-grid operation data set; a discrete time micro-grid dynamic model is established and a recursive least square method is adopted to carry out system parameter online estimation so as to obtain a robust scheduling scheme oriented to uncertainty interference; and in combination with real-time operation state monitoring, real-time micro-grid intelligent scheduling is carried out by deploying edge computing nodes. According to the method, the Lyapunov stability theory and the control barrier function are combined, a safety reinforcement learning framework oriented to micro-grid dispatching is constructed, and the absolute safety of system operation in the dispatching process is ensured.

The invention relates to the field of heat energy engineering and automatic control, and discloses a multi-mode optimization system for combustion efficiency of a thermal power boiler. The system comprises a multi-modal data perception and space-time alignment module, a tensor manifold modeling and physical constraint feature extraction module, a space-time coupling dynamic prediction and uncertainty quantification module, a quantum optimization decision and DCS cooperative control module and a combustion state derivative early warning and optimization feedback module. Through multi-modal data space-time alignment, five-order tensor physical constraint modeling, PDE deep network prediction, quantum optimization decision and a closed-loop feedback mechanism, space-time unified fusion and physical feature extraction of combustion data are realized, the reliability of combustion state prediction is improved, an optimal control instruction is efficiently solved, system parameters are dynamically corrected, and the reliability of combustion state prediction is improved. The problems that in the prior art, data fusion is difficult, modeling physical constraints are lacked, optimization real-time performance is poor, and adaptivity is weak are solved, and the combustion efficiency and the intelligent control level of the thermal power boiler are remarkably improved.

The invention provides a lightning protection system effectiveness evaluation method and system based on multi-dimensional research and judgment, and relates to the technical field of lightning protection, and the method comprises the steps: collecting system index data through a bidirectional feature extraction network, employing a separable convolution layer with an attention mechanism and multi-scale Fourier transform to extract features, and carrying out the analysis of the features; an index transfer function is constructed in combination with a causal reasoning network, evaluation grade division is performed by adopting kernel principal component analysis and density clustering, and system parameters are optimized through a hierarchical reinforcement learning framework, so that accurate evaluation and dynamic optimization of the state of the lightning protection system can be realized, and the reliability and the protection effect of the system are improved.

The invention discloses a KCBMA algorithm-based windmill bridge system safety assessment method, which relates to the technical field of windmill bridge coupling systems, combines a Kepler optimization algorithm (KOA) with a convolutional neural network-bidirectional long-short-term memory network (CNN-Bi-LSTM-MA) under a multi-head attention mechanism, adaptively searches an optimal hyper-parameter combination of the CNN-Bi-LSTM-MA network through the KOA, and provides a safety assessment method for a windmill bridge system. Constructing a neural network prediction model with dynamic adaptive capacity; the Bi-LSTM unit performs modeling by fusing the randomness characteristics of wind load excitation and system parameters, so that a numerical model can effectively represent the randomness of a wind-vehicle-bridge system and predict the random response of the wind-vehicle-bridge system. According to the method, the dynamic interaction among the wind-vehicle-bridge structures is integrated into the neural network model, the modeling time is remarkably shortened by using the KOA, the modeling accuracy is improved, the calculation cost is reduced, and the system response is more accurately predicted.

The systems and methods disclosed herein enable dynamic selection of a routing model for generation of an output in response to a provided input (e.g., a prompt for a large-language model). Based on the selected routing model, the data generation platform can evaluate the input and / or other suitable system parameters (e.g., system resource usage) to determine a suitable model for processing the provided input. For example, the routing model can determine a technical application associated with the input and dynamically determine to modify the input prior to generation of the output based on system resource measurement values and / or other suitable information, thereby conferring efficiency, security, and accuracy benefits while preserving system resilience.

According to the tray cycle scheduling system and the application method, historical data and a production plan are fused, an improved algorithm is adopted to predict tray requirements, and an attention mechanism is introduced to improve precision; the method comprises the following steps: acquiring multi-dimensional state information of a tray through a multi-modal sensor, eliminating noise by using a data fusion algorithm, and constructing a digital twin model to realize state synchronization; a double-layer optimization architecture is constructed, an upper layer solves a global scheme by combining an improved particle swarm and a simulated annealing algorithm, and a lower layer dynamically adjusts a path through reinforcement learning; an instruction is generated based on a digital twin model, an event triggering mechanism is adopted to reduce communication load, and virtual-real interaction closed-loop control is realized; a real-time evaluation index system is established, a meta-learning algorithm is utilized to quickly adapt to a new environment, and system parameters are continuously optimized. Multi-module collaborative innovation is achieved, the tray scheduling efficiency and the intelligent level are remarkably improved, production logistics whole-process collaborative optimization is achieved, and core support is provided for cost reduction and efficiency improvement of enterprises.

The invention discloses a multi-agent collaborative decision-making system and method for neural symbol fusion, and relates to the technical field of artificial intelligence, and the system comprises a neural symbol fusion engine which constructs a knowledge double-layer representation architecture, and achieves the organic fusion of symbol reasoning accuracy and neural learning adaptability; the intelligent agent coordination optimizer quantifies the intelligent agent difference through cognitive state mapping, constructs a consensus feasible region, carries out hybrid verification and constraint optimization, and selects an optimal decision scheme; and the adaptive interpretation system constructs a decision evidence chain and realizes continuous optimization of system parameters through feedback learning. The technical challenges of symbol reasoning and neural learning fusion, multi-agent cognitive difference coordination, decision reliability and interpretability and the like are effectively solved, and the method is suitable for complex decision scenes of medical treatment, finance, intelligent manufacturing and the like.

The invention discloses a data collaborative directory management method and system, and relates to the technical field of government affair informatization, and the method comprises the steps: generating a directory snapshot containing a global hash value; calculating a directory node hash value of each edge directory node based on the directory snapshot identifier; eliminating clock drift interference through time sequence alignment and dynamic tolerance filtering; inputting the Hash difference time sequence into an isolated forest model to judge a substantial change node; based on the difference entry number and the historical calling weight, combining a dual-threshold rule and an online dichotomy model to hierarchically synchronize requirements; according to a grading result, matching an incremental push mode or a full pull mode, and constructing a synchronous transaction context containing an exponential backoff retry mechanism; synchronous operation is executed through the two-stage state model, and compensation rollback is triggered when the synchronous operation fails; and calculating a health index of the substantially changed node, dynamically selecting a self-healing action and optimizing system parameters. The problem of misjudgment caused by time sequence drift is effectively solved, the synchronization efficiency is improved, and the consistency of directory versions is guaranteed.

The invention relates to the technical field of artificial intelligence application, and discloses an information technology auxiliary consultation system based on artificial intelligence. The system comprises a data acquisition module, a knowledge graph construction module, an intention analysis module, a decision engine module, a strategy optimization module and a feedback correction module. The data acquisition module acquires multi-dimensional data such as a semantic type, an intention label and a historical interaction record of a user consultation request in real time; the knowledge graph construction module dynamically generates a hierarchically associated domain knowledge graph according to the domain database; and the intention analysis module completes user intention classification and analysis through a multi-level attention mechanism. The decision engine module combines the analysis result and the knowledge graph to generate candidate strategies, and the strategy optimization module screens out target strategies meeting real-time response requirements through an adaptive weighting algorithm. The feedback correction module utilizes user interaction data to update system parameters, improves service precision, and is suitable for various information technology consultation scenes.

The invention discloses a revocable attribute-based encryption method with strategy hiding, which is based on ciphertext strategy attribute encryption, solves the problem that user privacy is leaked due to disclosure of an access strategy, realizes revocation of fine-grained user attribute access authority, and is proved to be completely safe. In a system establishment stage, system parameters are disclosed, a public key and a master key are generated, an authoritative authority authorizes a data user, distributes a private key and generates an AGK tree and an attribute group key, a data owner generates a ciphertext according to the public key, an access structure set by the data owner and a selected secret value, and the ciphertext is transmitted to the data owner. And then hiding the mapping function by using a cuckoo filter, positioning the attribute by a data user through the cuckoo filter, updating a private key by using an attribute group key, and finally decrypting the ciphertext to obtain the wanted information.

The invention relates to the technical field of encoders, in particular to an incremental optical encoder signal error compensation system and method.The incremental optical encoder signal error compensation system comprises a signal acquisition module, a preprocessing module, an error analysis module, a compensation operation module, a data storage module and an output control module; the preprocessing module preprocesses the original signal; the error analysis module analyzes and identifies the error type and characteristics of the processing signal in a multi-dimensional manner, and calculates compensation values of the scribing error, the subdivision error and the eccentric error through the compensation operation module; a compensation value is superposed with an original signal, a compensated high-precision signal is obtained, the output control module outputs the high-precision signal to a subsequent device, meanwhile, system parameters are adjusted according to an external instruction, a high-precision error model is constructed by the system, and the high-precision error model is obtained by combining an advanced digital signal processing technology and an intelligent algorithm. And the scribing error, the subdivision error, the eccentric error and the like of the encoder are effectively compensated.

The invention discloses an LCA-based fabricated building carbon neutralization analysis management system, and particularly relates to the field of analysis management, and the system comprises a data collection module, a life cycle analysis module, a carbon emission calculation module, a carbon neutralization strategy generation module, a visual display module, and a user feedback and optimization module. According to the invention, through integration of an Internet of Things sensor, a BIM model and a block chain technology, full life cycle links are automatically collected; the system quantifies the environmental influence of each stage by using a dynamic LCA model, calculates the carbon emission in real time, and generates a carbon neutralization strategy based on a multi-objective optimization algorithm, including renewable energy application and carbon compensation measures; through a visual tool and dynamic simulation, the system visually displays an analysis result and a strategy effect, and supports user interaction and parameter adjustment; a user feedback mechanism is combined with a reinforcement learning algorithm, system parameters and strategies are dynamically optimized, a'feedback-optimization-re-feedback 'closed loop is formed, and analysis precision and strategy feasibility are improved.

The invention provides a multi-model fusion collaborative energy-saving optimization control method and system based on a knowledge graph, and is applied to the technical field of industrial equipment optimization control. The method comprises the steps that target knowledge graph information and real-time data of the cooling capacity requirement of a target air conditioning system are obtained, wherein the target knowledge graph information is used for representing entities, attributes and corresponding relations matched with the target air conditioning system; feature extraction processing is conducted on the real-time data information of the cooling capacity requirement of the target air conditioning system, and real-time environment parameter features and equipment running state features are generated; the real-time environment parameter features and the equipment operation state features are processed based on a target load prediction model, and cooling capacity demand information and load prediction information in the target time period are generated; and the target knowledge graph information, the cooling capacity demand information of the target time period and the load prediction information are processed based on the target DQN model, global optimization control strategy information is generated, and the global optimization control strategy information is used for adjusting system parameters of the target air conditioning system.

The invention discloses a multi-dimensional monitoring and early warning system and method for displacement, axial force and water level in a deep foundation pit. The system comprises a data acquisition module, a data transmission module, a data processing module, a risk assessment module, an early warning issuing module and a user interaction module. The data acquisition module is composed of a displacement monitoring sub-module, an axial force monitoring sub-module and a water level monitoring sub-module and is used for respectively acquiring displacement, axial force and water level data in the foundation pit; the data transmission module is responsible for transmitting data to the data processing module; the data processing module cleans, fuses, analyzes and processes the data; the risk assessment module performs risk assessment on the processed data according to a preset standard; the early warning issuing module issues early warning information according to the risk assessment result; the user interaction module is used for displaying information and configuring system parameters. The method has the advantages that real-time, continuous and multi-dimensional monitoring of the construction process of the deep foundation pit is achieved, high-risk points can be accurately recognized, early warning can be conducted in time, and the construction safety of the foundation pit is guaranteed.

The invention relates to the technical field of equipment parameter optimization, and discloses a liquid cooling system parameter optimization method and system for a data center. The method comprises the steps of performing wavelet noise reduction processing on thermal load data of equipment, and constructing a time sequence heat generation model; the thermodynamic model and the time sequence heat generation model are fused to generate a comprehensive heat model, an objective function and constraint conditions are set, and a parameter combination is output; the parameter optimization model generates a standard parameter combination based on the parameter combination, the standard parameter combination is substituted into the parameter optimization model for evaluation, and an optimal parameter combination is output; the operation parameters of the liquid cooling system are adjusted in real time, the actual system operation data of the liquid cooling system are collected, the actual system operation data are compared with the expected equipment thermal load data of the data center, the parameter deviation is obtained, and the operation parameters are dynamically adjusted based on the parameter deviation. According to the invention, the efficiency and accuracy of parameter optimization of the liquid cooling system are improved.

The invention discloses an intelligent numerical control machine tool contour error prediction compensation system and a control method thereof, and belongs to the technical field of industrial control and intellectualization, the intelligent numerical control machine tool contour error prediction compensation system comprises a parameter setting module used for setting system parameters and processing parameters of a numerical control machine tool and importing external planning data and configuration files; the online planning module is used for acquiring planning data online; the core control module is used for controlling the executing mechanism to realize multiple motion modes; the data monitoring module is used for dynamically collecting real-time monitoring data in the machining process of the executing mechanism; the neural network prediction module is used for performing tracking error prediction on the plurality of processing single axes based on a neural network model; and the error intelligent control module is used for carrying out iterative optimization on the processing track. Through modular design, all links of numerical control machining are integrated into a unified system, so that the technical problems that a control system of a traditional numerical control machine tool is poor in function expansibility, low in resource utilization rate, high in development threshold, tedious in process and the like are solved.

The invention discloses a combined heat and power generation system control strategy based on an AME-TD3 algorithm, and belongs to the technical field of combined heat and power generation system optimization control. Comprising the following steps: S1, collecting system parameters and initializing a CHP running state; s2, when a target # imgabs0 # value is calculated, an entropy reward mechanism is introduced; s3, realizing exploration and utilization of an adaptive noise adjustment mechanism and a dynamic balance strategy based on Critic network evaluation; and S4, optimizing the control strategy in real time. Aiming at the problem that an existing TD3 algorithm is insufficient in exploratory performance, an entropy correction item is introduced into calculation of a target # imgabs1 # value, structural correction is conducted on a target value function, strategy updating is more efficient, and the effectiveness of overall strategy optimization is improved; aiming at the problem that random noise in a traditional TD3 algorithm cannot adapt to a complex environment, a dynamic self-adjusting noise generation function is added in a strategy updating process, so that the convergence speed is increased, the stability is enhanced, and the self-adaptive adjustment capability of a system to load fluctuation and environmental condition change is enhanced. The method has good expandability.

The invention relates to the technical field of unmanned aerial vehicle cluster control, in particular to an unmanned aerial vehicle cluster distributed optimal formation control method considering multiplicative noise and a computer readable medium, and the method comprises the following steps: constructing a communication topological graph of an unmanned aerial vehicle cluster, each edge represents a communication relationship between two adjacent unmanned aerial vehicles; constructing a Laplacian matrix of the communication topological graph; establishing an unmanned aerial vehicle kinetic equation considering the multiplicative noise influence; defining an objective function needing to be optimized and constraint conditions; constructing a Hamilton function of the target function; and designing a distributed optimal control strategy by applying a random optimal control theory, setting operation time, system parameters and initial state information of the unmanned aerial vehicle cluster, and obtaining a formation operation track and a final formation state of the unmanned aerial vehicle cluster under optimal control. According to the invention, the global optimality and robustness of the unmanned aerial vehicle cluster control algorithm are ensured.

The intelligent control method comprises the steps that a system architecture of the multi-heat-source coupling heat supply system is constructed, and the system architecture comprises a composite heat source module, an intelligent energy storage unit and an edge computing and internet-of-things platform; constructing a heat source energy efficiency dynamic evaluation matrix based on meteorological data and system monitoring data; constructing a multi-objective optimization model of the system, and solving the multi-objective optimization model in combination with the heat source energy efficiency dynamic evaluation matrix and an optimization algorithm to obtain an optimal solution; and dynamically distributing the output of each heat source according to the optimal solution. Controlling the system to operate in combination with multiple operation modes according to the external environment parameters and the actual operation parameters of the system; continuously optimizing related parameters of the system by adopting a reinforcement learning algorithm according to historical operation data and a reward function of the system; according to the scheme, the energy efficiency, flexibility, reliability and adaptability of the heat supply system are improved.

The invention relates to the technical field of power system monitoring, in particular to a power system parameter dynamic verification method, system and device based on digital twinning and a storage medium. Constructing a digital twinborn model of the power system, establishing a state mapping relation between a physical system and a digital model, realizing bidirectional dynamic mapping between the physical system and the digital model through a digital mapping mechanism, and obtaining system operation characteristics; constructing a parameter verification model based on the operation characteristics, obtaining a verification objective function by fusing physical constraints and data driving, and dynamically adjusting parameter values to generate parameter correction values by evaluating model errors in real time by adopting an adaptive parameter correction algorithm; based on a dynamic optimization mechanism of multi-objective optimization, an optimization strategy is adaptively adjusted according to the running state, and the verification process is continuously optimized; and performing multi-dimensional evaluation on the verification result through the verification precision evaluation system to generate a parameter verification result. The technical problems that a traditional method is inaccurate in modeling, low in response speed and poor in adaptability are effectively solved.

The invention belongs to the technical field of multi-body dynamics analysis, and discloses a wind driven generator transmission chain rigid-flexible coupling multi-body dynamics analysis method based on dynamic mode decomposition, and the method comprises the steps: firstly, enabling multi-degree-of-freedom time series data to be non-linearly embedded into a high-dimensional feature space through an encoder neural network; extracting a dominant mode by utilizing intrinsic orthogonal decomposition (POD), and constructing a low-dimensional feature space; parameterized dynamic mode decomposition and radial basis function regression are adopted, a mapping relation between system parameters and Koopman operators is established, and accurate prediction of dynamic characteristics under variable working conditions is achieved; and finally, reconstructing a physical response through a decoder, and optimizing model parameters in combination with an error driving mechanism. The problems that a traditional method is low in calculation efficiency, poor in nonlinear adaptability and difficult in multi-parameter coupling prediction are effectively solved, the efficiency and precision of transmission chain dynamic analysis are remarkably improved, and reliable technical support is provided for state monitoring and service life prediction of the wind turbine generator.

The invention relates to the technical field of industrial automatic monitoring, and provides a mining equipment remote monitoring method and system and a storage medium. The method comprises the following steps: dividing mining equipment into core equipment and general equipment based on equipment operation parameters; performing fault risk assessment on the core equipment by adopting a dynamic threshold value adaptively generated based on historical data, and performing deviation detection on the general equipment by adopting a fixed threshold value; fusing the fault risk assessment result of the core equipment and the deviation detection result of the general equipment to generate global maintenance decision information; optimizing a field maintenance path according to the geographic position and the emergency degree of the maintenance task; and continuously calibrating system parameters through a closed-loop feedback mechanism. According to the invention, through hierarchical monitoring and adaptive optimization, the problems of uneven distribution of monitoring resources, rigid threshold setting and isolated maintenance decision in the prior art are solved, and accurate configuration of mining equipment monitoring resources and remarkable improvement of fault early warning capability are realized.

The invention discloses a hydropower station AGC intelligent optimization control method and system based on multi-unit dynamic load distribution and prediction and early warning, and relates to the technical field of AGC control, and the method comprises the steps: initializing system parameters, including a unit power range and upper and lower limits of a vibration region, and designing a dynamic vibration region crossing control algorithm, a prediction model and a multi-unit intelligent load distribution algorithm; acquiring real-time load and operation data, inputting the real-time load and operation data into the prediction model to obtain a prediction result, and triggering a load adjustment or early warning mechanism according to the prediction result; calling a vibration area crossing algorithm, and adjusting the unit power to a safe range; and a multi-unit intelligent load distribution algorithm is used, multi-unit loads are dynamically distributed, a control instruction is output, and real-time adjustment is completed. The method reduces manual intervention, improves the operation efficiency, prolongs the service life of equipment, reduces the operation cost through the optimization model, improves the economic benefits of a power plant, adapts to various working conditions, is short in response time, and adapts to complex scheduling scenes.