1366 results about "System parameters" patented technology

The invention discloses a data analysis platform management method and system, and belongs to the technical field of computer data processing, and the method comprises the steps: obtaining multi-source heterogeneous data from a medical information system; establishing a cross-system data association model, performing dynamic feature extraction and weight distribution on multi-source heterogeneous data, and outputting a resource supply-demand relationship graph; analyzing the resource supply-demand relation graph, and identifying business conflicts in medical resource scheduling; generating a conflict solution of the business conflict based on a multi-objective optimization algorithm, and generating an execution instruction; and issuing an execution instruction to the target medical system, verifying an execution result of the execution instruction, and updating system parameters. According to the method, a cross-system dynamic modeling and multi-objective optimization strategy is adopted, and a closed-loop verification mechanism and a multi-source heterogeneous data fusion technology are combined, so that intelligent prediction, accurate scheduling and self-learning optimization of medical resource conflicts can be realized, full-process traceable execution of high-value consumables is ensured, and the resource utilization efficiency and the emergency response capability are comprehensively improved.

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 provides a DCS system autonomous controllable upgrading and system fault prediction method and system, and relates to the technical field of fault prediction, and the method comprises the steps: obtaining historical operation data and real-time operation data of a DCS system, carrying out the preprocessing, and generating an autonomous controllable upgrading scheme through a deep reinforcement learning algorithm; the method comprises the following steps of: setting specified time for upgrading time, carrying out consistent backup on the state of the DCS, executing software updating, hardware configuration adjustment and system parameter optimization according to upgrading steps, analyzing indexes of the DCS in real time in the process of executing the autonomous controllable upgrading scheme, and recovering the DCS to the state before upgrading when an abnormal condition is detected. And verifying and optimizing the real-time fault prediction result based on a knowledge graph, and generating an explanatory report including a fault prediction and upgrade decision process according to the optimized fault prediction result and the autonomous controllable upgrade scheme.

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.

Power flow in a power distribution system is controlled using control commands generated based on values of system parameters. In this regard, generation prediction data, load prediction data, and real-time device status of equipment are utilized to generate scheduling data for power generation and power consumption and a network topology of the system is also obtained. The values of the system parameters are generated as an outcome of an unbalanced load flow analysis of the power distribution system using the scheduling data and the network topology. The unbalanced load flow analysis utilizes a compact multi-bus based nodal admittance model to represent relationships between nodal injected currents and nodal voltages at a plurality of non-overlapped phases of buses for a section formed by zero impedance branch connected with impedance branches in the power distribution system.

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 invention discloses a secondary ripple optimization method based on star cascade H bridge injection three-time zero sequence voltage. The method comprises the following steps: acquiring real-time voltage and current data of a system, performing dynamic harmonic analysis on the acquired data, generating a harmonic compensation signal, and generating an optimized control signal; constructing a multi-objective optimization model based on the optimized control signal and pre-stored system parameters, solving the model to obtain a Pareto optimal solution set, and selecting an optimal cubic zero-sequence voltage parameter from the Pareto optimal solution set; based on the optimal cubic zero-sequence voltage parameters, constructing a system dynamic model, constructing an adaptive prediction controller, generating an optimal control sequence, and performing closed-loop feedback adjustment; the ripple suppression effect is evaluated, the ripple suppression proportion is calculated by comparing the secondary ripples before and after the third zero-sequence voltage is injected, and the control parameters are dynamically adjusted according to the result. And the adaptability and precision of harmonic analysis are greatly improved. The problem that secondary ripples rise when the star cascade device compensates reactive power is effectively solved.

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 relates to the field of intelligent optimization systems and application thereof, in particular to a grain storage and transportation disorder plan autonomous scheduling system and method. Comprising the following steps: carrying out calibration and time synchronization on collected data, and carrying out data cleaning, screening, redundancy removal and anomaly detection processing; further performing encryption and structuring processing; constructing a prediction model based on the encrypted structured data, generating prediction output, dynamically optimizing and updating model parameters, and evaluating the prediction performance of the prediction model; an optimization task scheduling scheme is output based on the data after anomaly detection and prediction; calculating performance evaluation indexes and generating optimization suggestions; a new resource allocation strategy and a task scheduling scheme are provided, and functions of monitoring system performance in real time, adjusting an interface of system parameters and the like are provided for users. The problem that the accuracy and the adaptability are low when dynamic data are processed is solved. And in the face of complex and dynamic tasks, resource allocation cannot be adjusted in real time to adapt to constantly changing requirements.

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 wind power unmanned ship path tracking control method based on active disturbance rejection linear quadratic regulation control. The method comprises the following steps: building kinematics and dynamics models based on a ship body physical structure and a system parameter identification method; according to the method, a linear quadratic regulator (LQR) is introduced into an active disturbance rejection control framework, and the strong disturbance compensation capability of the ADRC and the control accuracy of the LQR are combined; an unmanned ship guidance system is constructed based on the model, a dynamic curvature navigation control (DCNC) algorithm is adopted to dynamically adjust the navigational speed, and path tracking is ensured by controlling the course angle. The method for calculating the weight proportion of the LQR coefficient in real time overcomes the system instability caused by environment change in actual engineering application to a certain extent; the ADRC control strategy adopted by the invention effectively improves the robustness and optimality of the system.

The invention provides a high-speed train dynamic response simulation method and device, and the method comprises the steps: building a surface element dynamic model of a to-be-tested high-speed train through a potential energy theory based on the boundary dimension parameters and panel parameters of the to-be-tested high-speed train, and building a surface element dynamic model of the to-be-tested high-speed train based on the train body structure parameters and suspension system parameters of the to-be-tested high-speed train; constructing a multi-body dynamic model of the high-speed train to be tested; aiming at the target flow test working condition, performing simulation driving on the high-speed train to be tested through a time domain serial sequence type coupling method so as to perform online coupling on the surface element dynamic model and the multi-body dynamic model, and obtaining dynamic response data of the high-speed train to be tested under the target flow test working condition; and based on the dynamic response data, obtaining a simulation result of the to-be-tested high-speed train under the target flow test working condition through function analysis. Through the method, the problem of waste of simulation calculation resources is solved, and the accuracy and efficiency of dynamic response simulation of the high-speed train are improved.

The invention relates to the technical field of laser radar identification and cargo warehouse management, and provides a cargo location identification cargo warehouse management system based on a laser radar, and the system is characterized in that a laser radar module obtains the data information of cargoes and cargo locations in a warehouse; the data analysis module analyzes and processes the goods allocation data information acquired by the laser radar module, and identifies and classifies the goods; the dynamic adjustment module dynamically allocates the identified and classified results according to the cargo allocation data information acquired by the laser radar module; the system management module is connected with the dynamic adjustment module and is used for configuring and managing the system, including adjusting system parameters and providing a user interface; according to the invention, the data analysis module carries out weighted fusion based on the features of the three-dimensional point cloud and the image features, and carries out recognition and classification with historical data, thereby achieving the accurate recognition of the appearance of the goods, achieving the dynamic adjustment of the module for dynamic distribution, improving the efficiency and accuracy of warehouse management, and solving a problem that the space utilization rate is reduced.

The invention discloses a multi-effect rectification system control method and a control system thereof.The method comprises the steps that technological parameter data in a multi-effect rectification system are collected in real time, and the technological parameter data comprise the steam flow, the feeding flow, the reflux flow and the recovery quantity of each stage of rectification tower; the temperature and the pressure of each tower plate in each stage of rectifying tower are measured; for each stage of rectifying tower, acquiring static operation parameters of the rectifying tower in a set load section based on a full-automatic load regulation algorithm; the static operation parameters comprise the steam flow, the reflux quantity and the extraction quantity of each stage of rectifying tower; for each stage of rectifying tower, on the basis of the static operation parameters, dynamic operation parameters in the load change process are obtained through a load transition algorithm, and the multi-effect rectifying system is controlled according to the dynamic operation parameters. The method can automatically adapt to feeding change and production load adjustment, optimizes energy efficiency and product quality in the rectification process, automatically adjusts system parameters when the production load fluctuates, and realizes optimal control of energy efficiency and product quality.

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.