4581results about How to "Improve running stability" patented technology

The invention belongs to but not limited to the technical field of fault diagnosis and maintenance of power equipment, and discloses an intelligent diagnosis and maintenance system and method for power equipment based on a knowledge graph. A data acquisition and preprocessing module acquires real-time operation data and historical data of the power equipment; the knowledge graph construction module associates various kinds of information of the power equipment to construct a knowledge graph; the intelligent diagnosis module performs association analysis on power equipment faults based on a knowledge graph and a machine learning algorithm, identifies fault modes and potential faults, and predicts possible faults of the equipment; the maintenance suggestion module automatically generates a detailed maintenance scheme according to the diagnosis result; the operation and maintenance visualization module provides visual fault diagnosis results and maintenance suggestions, supports multi-level data display, and helps operation and maintenance personnel to clearly understand the equipment state. The operation safety of the power equipment is improved, the operation and maintenance cost is remarkably reduced, and the overall efficiency of equipment management in an intelligent power grid environment is improved.

The invention relates to a multi-objective optimization scheduling method and system for a multi-AGV flexible job shop. The method comprises the steps of collecting job shop layout data, AGV operation parameters and production task data, and constructing a scheduling system basic model; performing recurrent neural network training and physical constraint model integration to obtain a hybrid scheduling prediction model; subtask decomposition and path search processing are carried out, and a dual-objective optimization model is obtained through constraint linearization conversion; performing particle swarm optimization solution on the dual-objective optimization model, and obtaining a non-dominated solution set through two-stage iterative calculation of an adaptive weight strategy and constraint conversion; performing multi-dimensional evaluation calculation on the non-dominated solution set to obtain an optimal scheduling execution scheme; and performing model feedback updating on the hybrid scheduling prediction model to obtain a target scheduling prediction model. According to the invention, multi-target balance of task completion time and energy consumption is realized, and the real-time response capability and operation stability of the system are improved.

The invention provides a power system operation state intelligent monitoring method and system, and relates to the technical field of power data monitoring, and the method comprises the steps: obtaining multi-source heterogeneous operation data, obtaining a standard operation data set, adaptively extracting high-order multi-modal feature representation through a confidence weighted fusion method, and obtaining an operation state multi-modal feature set. Constructing an intelligent monitoring cognitive map by learning topological association characteristics and semantic association rules of the running state; mapping the real-time operation data into an intelligent monitoring cognitive map, dynamically sensing an operation state spatio-temporal evolution rule through a spatio-temporal dependence modeling unit and a graph feature propagation unit in an operation state graph learning model, and generating a prediction and diagnosis result; and learning and generating an optimal control strategy of the power system through a depth deterministic strategy gradient algorithm, decoupling the optimal control strategy of the power system into a plurality of local sub-strategies, issuing the local sub-strategies to corresponding power control equipment, and obtaining a distributed cooperative control scheme through a federated learning algorithm.

The invention discloses a central air conditioner energy-saving optimization system based on load prediction, and relates to the technical field of air conditioner energy saving.According to the central air conditioner energy-saving optimization system based on load prediction, a VMDTransformerGRU hybrid deep learning model is introduced, through multi-source data fusion, the precision of cold load prediction is remarkably improved, and the prediction model can recognize the load change trend in advance. According to the improvement, the dynamic response capacity of the system is effectively enhanced, pre-regulation and control can be carried out before load changes, energy waste is reduced, a water chilling unit, a cooling tower and a pump are modeled to be independent intelligent agents, multi-agent deep reinforcement learning (MADRL) is adopted, and all the intelligent agents can autonomously learn and optimize control strategies of the intelligent agents. The intelligent agents cooperate with one another, efficient operation of the whole system is achieved through autonomous decision, and operation conflicts between devices are avoided. In this way, dynamic coordination and joint optimization among all the devices are achieved, invalid energy consumption is reduced, and the overall energy-saving effect and operation stability of the system are remarkably improved.

The invention discloses an operation regulation and control method and system for a distributed new energy photovoltaic power station, and relates to the technical field of new energy, and the method comprises the steps: recognizing a power station, building a monitoring system, collecting and monitoring key equipment data through the system, so as to obtain historical and real-time operation data, and predicting photovoltaic output and load based on the data to obtain predicted values, calculating power deviation, inputting the deviation into an optimal scheduling model to obtain a strategy, and adaptively regulating and controlling the operation state of the power station according to the strategy. The technical problems of unbalanced power generation and power utilization, low operation efficiency, poor stability, high cost and poor power grid compatibility caused by the fact that efficient and self-adaptive optimal scheduling cannot be performed according to accurate photovoltaic output and load prediction in the prior art are solved, accurate balance of power generation and power utilization is achieved, and the power generation efficiency and the power utilization efficiency are improved. The operation efficiency and stability of the power station are remarkably improved, the operation cost is reduced, and the compatibility with a power grid is enhanced.

The invention relates to the technical field of power systems, in particular to a power transmission and transformation network power distribution station room monitoring device based on the Internet of Things, which comprises a data acquisition module, a data preprocessing module, a heterogeneous data fusion power grid health assessment module, a fault prediction and alarm module and an intelligent maintenance module, the data acquisition module acquires real-time operation data and environmental data of power grid equipment; the data preprocessing module is used for preprocessing the acquired operation data and environment data; the heterogeneous data fusion power grid health assessment module assesses the health condition of the power grid in real time; the fault prediction and alarm module identifies potential faults of power grid equipment in advance and generates fault early warning signals. And the intelligent maintenance module automatically generates an equipment maintenance plan based on the generated fault early warning signal. According to the invention, quick response can be realized under different operation conditions, false alarms are reduced, and stable operation of a power grid is effectively guaranteed.

The invention relates to the field of transformer parameter regulation and control, in particular to a parameter optimization method and device for a coupling type transformer. The method comprises the following steps: acquiring multi-dimensional real-time working monitoring parameters and real-time working environment state parameters of the coupling type transformer; performing nonlinear dynamic analysis and global state sensing modeling on the multi-dimensional real-time working monitoring parameters so as to construct a transformer global state sensing model; performing multi-parameter combination definition on the multi-dimensional real-time working monitoring parameters, performing dynamic operation simulation based on a transformer global state sensing model, and collecting operation simulation response data of a plurality of schemes; performing multi-target dynamic balance analysis on the operation simulation response data of the plurality of schemes to obtain an optimal transformer working parameter combination; and carrying out environment dynamic parameter change analysis on the real-time working environment state parameters, and constructing a real-time environment condition distribution field. According to the invention, efficient and self-adaptive adjustment transformer parameter optimization is realized.

The invention relates to the technical field of power management, in particular to a charging pile group energy management method, which comprises the following steps: S1, predicting the power demand of each charging pile in a short period and the available amount of renewable energy; s2, adjusting the charging power of each charging pile through a dynamic allocation algorithm; s3, encouraging a user to charge in an off-peak period based on a preset pricing algorithm; s4, predicting a user charging demand mode; s5, monitoring electric energy quality in a charging process in real time, and automatically adjusting parameters of charging equipment by using a preset electric energy adjusting technology; and S6, automatically activating a preset standby energy supply scheme under the emergency condition of the power grid fault. According to the invention, energy distribution is optimized through real-time monitoring and intelligent adjustment of the charging strategy, the power grid efficiency is improved, the cost is reduced, and the continuity and safety of the charging service are ensured through the standby energy system, so that the operation efficiency of the charging infrastructure and the user satisfaction are remarkably improved.

The invention belongs to the field of power distribution network low-carbon economic optimization scheduling, and provides a power distribution network source network load storage low-carbon optimization scheduling increment reinforcement learning method and system, and the method comprises the steps: carrying out the intelligent agent division of a power grid, obtaining a plurality of intelligent agent regions, and collecting the observation data of each intelligent agent region; on the basis of the observation data, constructing a low-carbon economic dispatching model of source-grid-load-storage cooperation by taking the operation cost and carbon emission of each agent regional power grid as objective functions; designing the source-network-load-storage coordinated low-carbon economic dispatching model into a reinforcement learning model in a multi-agent environment according to a state space, an action space, an environment and a reward function; and solving the reinforcement learning model in the multi-agent environment to obtain an optimal scheduling solution. According to the incremental optimization method, the environment can help the intelligent agent to detect environment changes in training, excessive exploration risks are avoided, the training time is shortened, and meanwhile the flexibility of the system is improved.

The invention belongs to the technical field of condensation prevention of power equipment, and discloses an intelligent induction type condensation prevention system and method for the power equipment, and the system comprises a multivariate data collection unit, an intelligent control unit and an execution unit, and achieves the comprehensive real-time monitoring of the internal and external environments of the equipment through the integration of the multivariate data collection unit; the intelligent control unit can accurately predict the condensation risk based on real-time and historical data by using a pre-trained machine learning model, and dynamically adjust the condensation prevention strategy according to the condensation risk, thereby ensuring that condensation can be effectively prevented under various complex environmental conditions; the accurate execution of the execution unit further guarantees the condensation prevention effect, the operation stability and safety of the power equipment are integrally improved, the equipment fault risk caused by condensation is reduced, the service life of the equipment is prolonged, and the unnecessary maintenance cost is reduced; the system has self-learning and self-adaptive capabilities, can continuously optimize a control strategy to adapt to different environmental conditions, and ensures an optimal anti-condensation effect.

The utility model relates to a multi-stroke wire pushing rod mechanism of a wire drawing machine, which belongs to the technical field of wire drawing machines and comprises a wire drawing machine frame, a plurality of machine heads are arranged at the front end of the wire drawing machine frame, yarns which are wound with the machine heads to form a barrel are arranged on the machine heads, and composite wire feeding rings which are fixedly connected with the machine heads are arranged at the front ends of the machine heads. A yarn pushing rod mechanism fixedly connected with a rack of the wire drawing machine through bolts is arranged above the machine head, and the yarn pushing rod mechanism drives yarn to complete the creeling and head feeding process on the composite yarn feeding ring. The device has the characteristics of compact structure, good operation stability and high use efficiency. The problem that the amount of yarn falling into a deep groove is large is solved. The problem that large and small silk threads cannot be automatically fed and automatically reeled on the same drawing machine is solved. The front end of the wire pushing air cylinder is jacked, and any position control is achieved through the stroke positions of the two air cylinders.

The invention relates to the technical field of data distributed storage, and discloses a distributed storage method and system for clinical test data, and the method comprises the steps: carrying out the ring signature encryption processing of the patient test data of a plurality of data centers, and obtaining a patient anonymization identifier and standardized clinical data; performing two-stage random storage node configuration to obtain an optimal storage node configuration scheme; constructing an LPPMM-DA data aggregation model based on the optimal storage node configuration scheme, and performing fragmentation processing on the patient test data to obtain a plurality of target data fragments; performing dynamic operation envelope analysis and alliance graph game calculation on the multiple target data fragments to obtain a storage resource scheduling scheme; according to the storage resource scheduling scheme and the anonymization identifier, distributed data block storage and access authentication are executed on the multiple target data fragments, data block storage distribution information is generated, a complete data access control system is constructed, and safe and reliable storage of clinical test data is ensured.

The invention discloses intelligent production automatic wire drawing equipment which comprises a pay-off mechanism, a wire furnace, a mold furnace, a wire drawing mechanism, a take-up mechanism and a control system, and the pay-off mechanism comprises a pay-off wheel, a swing pulley, a buffer pulley and a pay-off speed measurement pulley; the pay-off wheel is driven by a pay-off servo motor to rotate and pay off The buffer pulley can vertically and linearly float and is provided with a floating detection sensor; the paying-off speed measurement pulley is provided with a first speed measurement sensor; the take-up mechanism comprises a take-up wheel and a take-up servo motor; the tension speed measurement mechanism comprises a first guide pulley and a post-drawing speed measurement pulley, the first guide pulley is connected with a second swing arm, the second swing arm is connected with a torsion control servo motor, and the control system controls the pay-off speed in real time according to the floating condition of the buffer pulley; meanwhile, the torque control servo motor is controlled to provide corresponding torque according to the current angle of the second swing arm, and the rotating speed of the take-up servo motor is controlled according to the angle change trend and the wire drawing speed of the second swing arm.

The invention relates to the technical field of micro-grid distributed control, in particular to a micro-grid cluster distributed control method and system based on information interconnection. The method comprises the following steps: identifying a micro-grid cluster in a region, and extracting a plurality of micro-grid nodes; obtaining a plurality of microgrid operation state parameters; performing filtering standardization processing on the plurality of micro-grid operation state parameters, and constructing a micro-grid time sequence load change curve; discrete wavelet transform is carried out on the micro-grid time sequence load change curve, load fluctuation frequency trend mining is carried out, and micro-grid load fluctuation frequency trend characteristics are generated; carrying out data stream encapsulation on the load fluctuation frequency trend characteristics of the micro-grid, and constructing a cloud environment distributed computing framework; carrying out dynamic load demand calculation on the plurality of micro-grid nodes based on a cloud environment distributed calculation framework so as to obtain multi-period load demand prediction data of each micro-grid; according to the invention, high-efficiency microgrid cluster control with high energy utilization rate is realized.

The invention relates to the technical field of wind power integration control, and discloses a wind power integration system safe operation method aiming at subsynchronous oscillation and a system thereof. The method comprises the following steps: performing time-frequency feature extraction processing on an original data set to obtain an oscillation mode feature quantity; performing stability analysis processing on the oscillation modal characteristic quantity to obtain a subsynchronous oscillation risk early warning signal; performing multi-time-scale decoupling processing on the sub-synchronous oscillation risk early warning signal to obtain a mechanical-electrical coordination control instruction; performing task decomposition processing on the coordination control instruction to obtain a real-time control quantity of each execution mechanism; and performing evaluation processing and correction on the execution effect of the real-time control quantity of each execution mechanism to obtain a correction control parameter set, and performing real-time control on the wind power integration system through the correction control parameter set. According to the invention, coordinated control of the electrical subsystem and the mechanical subsystem of the wind power grid-connected system is realized, subsynchronous oscillation is effectively suppressed, and the operation stability of the system is improved.

The invention discloses a spinning equipment real-time monitoring system based on an itinerant robot and stroboscopic control, particularly relates to the field of spinning equipment monitoring, is used for solving the problems of low monitoring precision and insufficient inspection efficiency, and is used for accurately evaluating the state of spinning equipment and generating a score based on historical operation data and real-time feedback. And then planning and dynamically adjusting an inspection path by adopting an ant colony optimization and reinforcement learning algorithm to ensure that high-risk equipment is preferentially monitored. Resource allocation is optimized and inspection efficiency is improved through accurate simulation of path time consumption and a coverage range; meanwhile, handshake connection is encrypted through multiple protocols, equipment operation parameters are extracted in real time, stroboscopic control is dynamically adjusted, and the definition of image collection and data safety are guaranteed; high-frame-rate high-definition camera shooting and self-adaptive stroboscopic control are combined, high-quality image data are collected and preprocessed, deep analysis is carried out by using a convolutional neural network and a multivariable correlation model, abnormal early warning is generated in real time, and an inspection path is optimized.

The invention relates to an intelligent integrated modular cabinet control method, system and device and a medium, and the method comprises the steps: monitoring the external environment parameters of a cabinet in real time through a sensor group in the cabinet, and carrying out the preprocessing of the external environment parameters through an edge computing device, and obtaining multi-dimensional environment parameters; integrating and analyzing the multi-dimensional environmental parameters and the internal operation data of the cabinet by using a multi-modal data fusion algorithm, and outputting a fusion analysis result; according to a fusion analysis result, generating a digital twinborn model of the current environment and the equipment state of the cabinet in the virtual environment, and performing rapid fusion simulation analysis on different adjustment schemes through simulation drift and a genetic algorithm to obtain an optimized adjustment scheme; and adjusting the resource configuration of each module in the cabinet and the operation state of the heat dissipation equipment based on the optimization adjustment scheme. The equipment cabinet has the effect of improving the operation efficiency of the equipment cabinet.

The invention provides a control method and device for dehumidification heat compensation of a constant-temperature and constant-humidity air conditioning unit, and the method comprises the steps: firstly monitoring indoor and outdoor temperature and humidity data sets in a current time period T, and obtaining a historical temperature and humidity data set; then, the indoor temperature prediction value and the indoor humidity prediction value are input into the trained convolutional neural network model, and after the convolutional neural network model outputs the indoor temperature prediction value and the indoor humidity prediction value, the indoor temperature prediction value and the indoor humidity prediction value are compared with a preset temperature range and a preset humidity range; when the temperature predicted value or the humidity predicted value exceeds the upper limit or the lower limit of the corresponding preset range, a humidity adjusting mode or a temperature adjusting mode is started, and when the temperature predicted value or the humidity predicted value is within the preset range, the system enters a high-precision fine adjustment mode; and finally, the display displays the current temperature and humidity. An improved convolutional neural network model is adopted, temperature and humidity predicted values are calculated in real time and compared with a preset range, and a humidity adjustment mode or a temperature adjustment mode or a high-precision fine adjustment mode is selected to be started; the temperature and humidity adjusting accuracy is greatly improved, and the user experience is greatly improved.

The invention provides an AI-based servo driver fault on-line rapid diagnosis method and system, and the method achieves the quantitative evaluation of fault data through obtaining the candidate system fault monitoring data of a servo driver control system, and converting the candidate system fault monitoring data into a diagnosis difficulty index and a fault description knowledge attribute. When the diagnosis difficulty index exceeds the preset index and the fault data lacks dynamic change information, the method can further utilize a plurality of fault penetration derivation data and the associated fault knowledge networks matched with the data, and effectively identifies and integrates similar fault modes by calculating the state feature distance between the associated fault knowledge networks, thereby improving the fault diagnosis accuracy. Compared with the prior art, the method has the advantages that the accuracy and efficiency of fault diagnosis are remarkably improved, particularly in a complex and changeable fault scene, the fault reason can be quickly positioned, and data support is provided for instant maintenance and recovery of the servo driver, so that the downtime of a system is shortened, and the overall operation reliability and stability of equipment are improved.

The invention discloses a power grid anomaly detection and diagnosis method based on artificial intelligence and topological data analysis. The method comprises the steps of S1, forming power operation data; s2, constructing a topological network model of the power grid based on the physical structure of the power grid; s3, using a first generator to generate abnormal data in the simulation power grid based on the power operation data; s4, optimization abnormal data generation is carried out; s5, embedding the power operation data and topological structure features of the power grid into the anomaly detection model through a graph convolutional network; s6, power grid anomaly detection parameters are dynamically adjusted through the adaptive learning model, so that the anomaly detection model is continuously optimized along with the change of the power grid operation condition; and S7, based on the topological information and abnormal data generated by the generative adversarial network, combining topological data analysis, and positioning a fault node or an abnormal line in the power grid. According to the invention, the accuracy of fault prediction and positioning is improved.

The invention provides a dynamic load electric energy metering optimization method and system. Belongs to the technical field of data processing, and comprises the following steps: acquiring voltage and current signals in a dynamic load environment in real time by using a high-precision sensor, and discretizing the acquired signals to obtain a voltage value and a current value of a sampling point; and performing statistical analysis on the voltage and current values of the sampling points, extracting key parameters, and constructing a dynamic load feature vector based on the extracted key parameters. By collecting voltage and current signals in real time and carrying out accurate statistical analysis and feature extraction, the electric energy error value can be calculated more accurately, so that the accuracy of electric energy metering is improved.

The invention provides a collaborative optimization scheduling method and system for an integrated energy system comprising an electric heavy truck. The method comprises the following steps: randomly generating key driving characteristics of the electric heavy truck; by analyzing the key features, the charging demand, the schedulable time period and the charging power range of the electric heavy truck are evaluated so as to construct an adjustable potential model of the electric heavy truck; based on the adjustable potential model and the current running state of the electric heavy truck, a daily charging load curve is generated by establishing a load prediction model of the electric heavy truck; establishing a power output model of photovoltaic power generation and a charging and discharging model of an energy storage system; an upper-layer optimization model of electric heavy truck charging scheduling based on IGDT and a lower-layer optimization model of energy storage system and power grid combined scheduling are established to generate a double-layer optimization model; and solving the double-layer optimization model by adopting an iterative solving method so as to generate a collaborative optimization scheduling strategy of the integrated energy system. According to the invention, the energy supplement problem of the new energy heavy truck is effectively solved, and the peak load shifting capability of the power grid is improved.

The invention relates to the technical field of energy scheduling, in particular to an intelligent power utilization system based on an optical storage direct flexible technology. The photovoltaic power generation unit is used for collecting solar energy by installing a solar photovoltaic panel and converting the solar energy into electric energy; the energy storage unit is used for storing excess electric energy, and when the electric energy generated by the photovoltaic power generation unit is insufficient or has no energy, the electric energy of the storage unit is used for supplying power; the power utilization management unit is used for monitoring and managing distribution of direct current in the photovoltaic power generation unit and then dynamically adjusting the direct current according to actual requirements; according to the system, factors such as temperature, seasons and system loss are comprehensively considered, so that the actual power generation capacity of the photovoltaic system can be more comprehensively reflected, and the prediction precision and reliability of the system are improved; through real-time data acquisition and processing, the system can predict future electric energy supply and demand conditions in advance, so that the overall operation efficiency and stability of the system are improved.

The invention discloses a power battery management method and system based on data driving, particularly relates to the technical field of battery degradation monitoring, and is used for solving the problem that hidden degradation of a battery in a microcirculation state is difficult to monitor in real time in the prior art. According to the invention, microcirculation energy spectrum is analyzed through a transient spectrum clustering algorithm, and risks of spectrum concentration abnormity and structure degradation are identified; the internal resistance deviation trend of the battery is analyzed, and the influence of internal resistance fluctuation on active material granulation is evaluated; judging whether microcirculation abnormity risks exist or not based on spectrum abnormity and internal resistance fluctuation conditions; when an abnormal risk exists, analyzing a stress accumulation condition through tensor decomposition and singular value decomposition, and evaluating an attenuation gradient of an electrode polarization signal in combination with variational mode decomposition and a recurrent neural network so as to determine an overall degradation trend of the battery; management of the power battery can be dynamically optimized, the service life is prolonged, and safety and reliability are improved.

The invention relates to the technical field of underground gas production, and discloses a metal sealing gas production wellhead device which comprises a body. A housing; the filtering assembly is used for circularly filtering gas in the wellhead; the self-discharging assembly is used for automatically discharging impurity particles attached to the filtering assembly; the filtering assembly comprises a screening device and an indexing rotating device; and the screening device is used for filtering gas impurities in the well. According to the metal sealing gas production wellhead device, drum-type double-layer filtering can be achieved through the arranged filtering assembly, so that it can be guaranteed that impurity particles in mined gas can be greatly filtered out, and the purity of the extracted gas is guaranteed. And the indexing rotation of the circular filter cartridge can be controlled by utilizing the indexing rotation device, so that the gradually blocked circular filter cartridge can be rotated, and clean filter holes face the underground again, and therefore, circulating automatic anti-blocking screening is carried out, and the operation stability of the gas production wellhead device is ensured.

The invention relates to the technical field of textile, in particular to a digital textile equipment management method based on big data, and the method comprises the following steps: 1, an equipment connection and data initialization stage: checking the data interface type of equipment in each link of a textile process, and connecting the data interface type to a workshop data collection network, establishing an electronic file for each device, setting initial data acquisition parameters, and carrying out pre-operation test and calibration on the devices; 2, a production process monitoring and data recording stage; according to the scheme, the equipment performance evaluation model is established by using a big data analysis technology, multiple factors such as energy consumption efficiency, a production stability index and an equipment failure rate trend are comprehensively considered, and the equipment performance is comprehensively and accurately evaluated, so that sudden equipment failures are effectively avoided, the risk of production interruption is reduced, and the production efficiency is improved. The overall management efficiency and the operation stability of the equipment are improved, the continuity of the production process is guaranteed, and the cost increase caused by equipment faults is reduced.

The invention discloses a fuel cell system modeling and control-oriented joint simulation design method, which comprises the following steps of: based on an electrochemical and heat transfer mechanism, dividing an electric pile into a plurality of nodes along a reaction gas flowing direction by adopting a distributed parameter method, and establishing a one-dimensional solid oxide fuel cell dynamic model by applying a multi-field unified modeling language Modelica; establishing a dynamic model of each auxiliary machine part of the solid oxide fuel cell system; all the components are connected according to a unified interface to form a system overall dynamic model, and dynamic simulation is carried out; carrying out multi-output multi-input state space model identification in SIMULINK by applying dynamic simulation data of the solid oxide fuel cell system; a model prediction controller KF-MPC based on Kalman filtering is developed; and establishing a joint simulation platform. According to the invention, rapid and accurate load tracking and temperature control are realized, and the operation safety of the system is maintained at the same time.

The invention provides a floating attitude adjustment and optimization system for a floating type offshore wind power equipment group, and relates to the technical field of offshore wind power equipment, and the system comprises a sensing monitoring module which collects an equipment operation attitude parameter set and a marine environment data set in real time; the information is transmitted to the central control unit through the data transmission module, and is analyzed and processed by the equipment attitude prediction module to generate equipment attitude prediction information; the adjustment strategy generation module obtains a target attitude adjustment strategy based on the equipment attitude prediction information and the construction information of the buoyancy attitude adjustment device; the adjustment parameter generation module obtains attitude dynamic adjustment parameters; and the attitude adjustment execution module performs multi-point attitude adjustment and dynamic feedback optimization on the equipment group. The technical problem that an existing floating type wind power equipment set lacks a flexible floating posture adjusting means and is difficult to cope with floating posture changes caused by a complex marine environment is solved, the operation stability of the equipment set is improved, and therefore the power generation efficiency is improved, and the service life of the equipment is prolonged.

The invention discloses a circulating fluidized bed boiler bed temperature optimization control method and system, and the method comprises the steps: obtaining and preprocessing the historical operation data of a circulating fluidized bed boiler, analyzing the preprocessed data, and determining the operation characteristic parameters affecting the bed temperature of the circulating fluidized bed boiler; building a bed temperature prediction model based on the operation characteristic parameters and a preset neural network model, and performing prediction according to the bed temperature prediction model to obtain bed temperature prediction data; acquiring a bed temperature requirement for the circulating fluidized bed boiler, and determining a deviation characteristic between the bed temperature prediction data and the bed temperature requirement; analyzing and evaluating the bed temperature difference of the circulating fluidized bed boiler based on the deviation characteristics to obtain a bed temperature difference evaluation value; and determining an operation optimization coefficient according to the bed temperature difference evaluation value, and performing optimization control on the bed temperature of the circulating fluidized bed boiler according to the operation optimization coefficient. The running efficiency and stability of the circulating fluidized bed boiler can be improved, energy consumption and emission are reduced, and therefore the benefits of energy conservation, emission reduction and cost reduction are achieved.

The invention discloses a construction method and system for a membrane material database, and relates to the technical field of database construction.The construction method comprises the following steps that in the operation process of a database management system, multi-dimensional parameter information generated by the system is collected in real time, collected original data is cleaned and preprocessed, different types of data are normalized, and a database management result is obtained; and the consistency and the validity of the data are ensured. According to the method, the multi-dimensional parameters of the database management system are collected in real time, cleaning and anomaly analysis are carried out, the system intelligently evaluates the operation state, the operation anomaly coefficient is generated, and early warning and fault prediction are achieved. Based on the comparison between the abnormal coefficient and the multi-level threshold, the system divides the risk into low, middle and high levels, and takes corresponding measures. According to the refined management mechanism, the stability of the database is effectively improved, data loss, inconsistency and crash are prevented, and the accuracy and efficiency of fault processing are improved.