236656 results about "Testing Methods" patented technology

The invention relates to the technical field of power equipment, and discloses a digital twin operation monitoring system for power equipment, which comprises a data sensing and acquisition system for acquiring key operation parameters of temperature, current, voltage, partial discharge, vibration and humidity of the power equipment in real time, and performing multi-dimensional data acquisition through a sensor and a data transmission module; the state evaluation and prediction system is used for performing equipment health evaluation and residual life prediction by using a prediction model LSTM based on the collected data, and updating a prediction result in real time; provided is a digital twin modeling system. Through the combination of edge calculation, an LSTM model and a digital twinning technology, the precision and real-time performance of health management of power equipment are improved, data quality is optimized through edge calculation, the LSTM model captures an equipment degradation trend, virtual-real fusion is realized through digital twinning, and accurate monitoring and early warning of the health state of the equipment are ensured, so that intelligent operation and maintenance decisions are optimized, the failure rate is reduced, and the safety of power equipment health management is improved. The equipment life is prolonged.

The invention relates to the technical field of industrial automation control, and discloses a multi-machine collaborative industrial robot intelligent scheduling system and an application method.The system comprises a data acquisition module for acquiring real-time motion data through a multi-modal sensor; the feature fusion module is used for generating environment dynamic feature data based on graph neural network fusion data; the strategy generation module is used for generating collaborative strategy parameters by utilizing a collaborative reinforcement learning model; the trajectory optimization module is used for constructing a multi-target trajectory optimization model to plan an optimal synchronous trajectory; the motion control module is used for establishing a layered motion control model to realize cooperative motion control; a dynamic resource allocation module can also be included to optimize resource allocation. The application method sequentially executes corresponding steps according to the modules of the system. According to the method, the environment can be accurately sensed, the cooperation strategy can be optimized, the efficient track can be planned, accurate motion control and reasonable resource allocation can be realized, and the cooperative motion control capability and the production efficiency of the industrial robot in the complex environment can be effectively improved.

The present application relates to an on-line evaluation layered model including a monitoring data layer, a diagnosis method layer, an evaluation indicator layer, a monitored item layer, a key part layer, and an overall project layer. The method further includes collecting same-type multi-measuring-point monitoring data and multi-type multi-measuring-point monitoring data of a concrete dam, determining various indicator analysis results of the concrete dam based on the same-type multi-measuring-point monitoring data, determining various indicator operation scoring results of the concrete dam based on the multi-type multi-measuring-point monitoring data. In some embodiments, the method includes performing, based on the various indicator analysis results and the various indicator operation scoring results, analysis of an impact effect of external special loads on the dam caused by one or more risk events, and giving operation decision support suggestions of the dam in combination with overall performance graded evaluation of the dam.

Disclosed is a microgrid optimal scheduling method taking into consideration a system operation risk and user satisfaction, comprising: generating a power prediction curve of wind power and photovoltaic new energy output in a microgrid, using a Monte Carlo simulation method to obtain a prediction error range to generate a typical scene set, and by means of a data fitting method, acquiring a wind-photovoltaic prediction error probability density distribution function; using value-at-risk and conditional value-at-risk theories to quantify a microgrid operation risk which is increased by slightly high or low output caused by wind power and photovoltaics; obtaining the scheduling cost of an adjustable load in a microgrid system; taking the minimum sum of conditional value-at-risk cost and microgrid system operation cost as an objective function and a user satisfaction level under a demand-side response as a constraint, establishing a microgrid optimal scheduling model taking into consideration the operation risk and the user satisfaction; and using a genetic algorithm to solve the model, and obtaining an optimal result of grid-connected microgrid system optimal scheduling. The safety, economic efficiency, stability and reliability of the microgrid are enhanced.

The invention discloses an intelligent management system for energy consumption optimization and fault self-diagnosis of cleaning equipment, and relates to the field of intelligent maintenance of the cleaning equipment, and the system comprises the steps: obtaining three groups of core parameters, i.e., a historical vibration spectrum, a motor current harmonic component and a bearing temperature gradient, constructing a dynamic failure mode knowledge graph, performing time sequence correlation analysis on the historical fault data to obtain failure mode analysis data; establishing a multi-dimensional analysis platform, identifying a high-risk component, and updating a fault threshold value; introducing a service time attenuation factor and a working condition correction coefficient, establishing an aging degree quantitative model, and calculating an aging coefficient; and constructing and developing an energy consumption-reliability joint optimization module, and adjusting equipment operation parameters. The method has the advantages that the dynamic knowledge graph and the time sequence analysis model are constructed by integrating multi-source sensor data, precise diagnosis and self-adaptive threshold adjustment of the coupling fault of the cleaning equipment are achieved, aging evaluation and task scheduling optimization are combined, the energy consumption efficiency is improved, and the maintenance cost is reduced.

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

A dynamic evaluation method for an extreme rainstorm waterlogging disaster risk for disaster prevention and reduction. The method comprises: investigating and surveying urban system data and disaster prevention and reduction data, using GIS technology to divide disaster-bearing objects into refined risk units on the scale of urban buildings and road networks, and determining the spatial distribution of the disaster-bearing objects; on the basis of an extreme rainstorm waterlogging scene, simulating the disaster influence of a dynamic change process of a flood ponding depth on the disaster-bearing objects; developing refined dynamic evaluation on a waterlogging risk by combining the two methods of waterlogging process simulation and an indicator system; using a spatial complex network and a shortest path plan to calculate a traffic capacity and emergency service accessibility of a road network system; and on this basis, taking into comprehensive consideration the rational allocation of disaster prevention emergency drainage and emergency rescue services to a high-risk area, and proposing dynamic evaluation technology for a waterlogging risk that integrates a disaster evolution process and a disaster prevention response process, and ultimately realizing the dynamic evaluation of the waterlogging risk of each disaster-bearing unit during the waterlogging disaster evolution.

The invention relates to the technical field of intelligent energy management, in particular to an energy consumption prediction and optimization system for energy-saving management and control, which comprises a data acquisition core module, a dynamic energy consumption prediction core module, an intelligent optimization control core module, a self-adaptive calibration core module, a user interaction core module and the like. The data acquisition module acquires energy consumption, equipment state and environment data from multiple sources; the dynamic energy consumption prediction module fuses improved time series decomposition and a multi-modal LSTM model to realize accurate prediction; the intelligent optimization control module is combined with strategies such as time-of-use electricity price and equipment linkage to generate an optimal instruction; the adaptive calibration module dynamically optimizes the model through Kalman filtering and incremental learning; the user interaction module supports visual display and strategy self-definition; in addition, the system is provided with an edge computing node to guarantee offline operation, an SM4 algorithm and a block chain technology are adopted to guarantee data security, and the system is compatible with various industrial protocols. The energy utilization efficiency is effectively improved, the operation cost is reduced, and the system safety and reliability are enhanced.

The invention provides a defect detection method and system for integrated circuit manufacturing. Wherein in the integrated circuit packaging stage, packaged chips are divided into a first type of chips meeting a preset physical characteristic threshold value and a second type of chips exceeding the physical characteristic threshold value through a pneumatic sorting device; performing scanning electron microscope imaging on the surface of the second type of chip, generating a three-dimensional topological structure by using an image reconstruction technology, and positioning a micron-sized particle defect area by comparing reference type differences; synchronously applying high-frequency electric signal excitation in the sorting process, detecting impedance response of the second type of chips, and recording an abnormal signal sequence exceeding a preset frequency domain range; performing space-time correlation matching on the micron-sized particle defect area and the abnormal signal sequence, and judging that the defect is a multi-layer composite defect when a preset coupling condition is met; and dynamically adjusting the mesh diameter and vibration frequency parameters of the pneumatic sorting device. According to the technical scheme provided by the invention, accurate identification of the multi-layer composite defect of the chip is realized, and the defect interception efficiency is remarkably improved.

The invention relates to the technical field of cable operation state detection, and particularly discloses a multi-source data driven cable operation state comprehensive evaluation method, which comprises the following steps of S1, adopting a layered distributed sensing network architecture, and deploying three types of core sensors at key nodes of a cable, through space-time calibration of the multi-source heterogeneous sensor, data consistency is improved, fusion deviation is eliminated, the problem of data islands of a traditional system is solved, and a precise evaluation foundation is laid; noise suppression and dynamic correlation modeling are adopted, environmental interference is stripped, a vibration and displacement coupling relation is quantified, limitation of a single parameter is broken through, heterogeneous fault features are captured, and evaluation comprehensiveness and sensitivity are improved; a self-adaptive threshold mechanism is constructed based on environment weight and historical data, the bottleneck of a fixed threshold is broken through, an evaluation standard is corrected along with equipment aging and environment change, misjudgment is avoided, and diagnosis robustness in different scenes is enhanced.

The invention discloses a dynamic sensitive data outbound risk assessment method and system based on multi-source risk information in the technical field of data cross-border. The system is mainly composed of a multi-source risk information acquisition module, a dynamic security identifier generation module, a risk collaborative assessment engine, a dynamic weight adjustment module, a disposal range dynamic calculation module and a flexible emergency disposal module, and an acquisition-identifier-assessment-calculation-disposal full-process closed-loop architecture is formed. A full-process closed-loop processing framework of collection-identification-evaluation-calculation-disposal is innovatively proposed, and by combining a dynamic risk evaluation model, a reinforcement learning intelligent technology and a block chain evidence storage technology, the problems of insufficient dynamic nature, lack of collaboration and lack of closed-loop capability in the prior art are systematically solved; and high-precision evaluation, real-time response and traceable management and control of the cross-border data flow risk are realized.

The invention discloses a method, a system and a device for monitoring multifunctional parameters of a direct-current drilling machine, and relates to the technical field of manufacturing of industrial automatic control system devices. The method, system and device for monitoring the multifunctional parameters of the direct-current drilling machine comprises the steps that S1, various data are collected and subjected to standardization and normalization processing, and a standardized working condition data set is constructed; s2, multi-dimensional disturbance characteristics are analyzed, and the stability level of a drilling system is quantified; s3, evaluating a dynamic evolution trend of a working condition, and updating a risk level, a response strategy and a monitoring priority; and S4, identifying an abnormal state based on the key disturbance value and the trend evolution value, and generating a monitoring report. The problems that an existing direct current drilling machine display device is insufficient in key working condition feature extraction capacity, deep understanding and trend analysis of the equipment operation state are difficult to support, and then the early warning timeliness and judgment accuracy of the abnormal state are limited are solved.

The invention provides a thermal power equipment real-time monitoring method and system based on edge computing, and relates to the technical field of thermal power equipment real-time monitoring, and the method comprises the steps: deploying an edge computing node array to collect multi-source heterogeneous data of thermal power equipment, and carrying out the preprocessing through data screening, multi-scale adaptive filtering and wavelet packet decomposition, a conditional variation auto-encoder is used to extract features, a hierarchical attention mechanism and a deep feature fusion network are combined to generate mixed feature representation, refined distribution estimation and abnormal mode recognition are performed on equipment states, cooperative monitoring modeling is performed based on a multi-scale spatial-temporal feature fusion network and a hierarchical depth deterministic policy gradient network, and a multi-scale spatial-temporal feature fusion network is established. The real-time monitoring accuracy and efficiency of the thermal power equipment can be effectively improved, the equipment failure rate is reduced, and safe and stable operation of a thermal power plant is guaranteed.

The invention relates to the technical field of airport safety management, in particular to an airport safety management method and system, and the method comprises the steps: obtaining various data of airport operation in real time through a multi-source heterogeneous data collection module, carrying out the feature fusion processing through a dynamic risk assessment model, generating a three-dimensional risk thermodynamic diagram, and outputting a risk level and a type identifier; triggering a hierarchical response mechanism, generating a resource configuration scheme through a resource scheduling optimization algorithm, and sending a scheduling instruction; in the emergency disposal stage, a multi-terminal collaborative platform is used for monitoring the disposal effect, when the risk diffusion coefficient exceeds a preset threshold value, a cross-department collaborative disposal protocol is started, and the resource allocation weight is dynamically adjusted. According to the method, the risk detection precision is improved through multi-source data fusion and space-time correlation analysis, multi-target optimization configuration is realized by adopting a dynamic resource scheduling matrix, a closed-loop mechanism of hierarchical response and cross-department co-processing is constructed, the traceability of the process is enhanced in combination with digital twin and block chain technologies, and the airport safety management level is effectively improved.

The invention relates to the field of power equipment data processing, in particular to a biomass power generation combustion parameter deep learning method and system, and aims to solve the problem of phase mismatch caused by sampling frequency difference and clock reference offset of multi-source heterogeneous time sequence data. A parallel multi-scale convolution and bidirectional long-short-term memory network hybrid model is constructed, transient fluctuation and long-period trend features are extracted, and combustion stage feature weights are dynamically distributed through a gating attention mechanism. The optimization control module generates a multi-target constraint condition, an operation instruction is output in combination with a fuzzy inference engine, and a digital twin platform simulates an extreme working condition to enhance model robustness. A closed-loop feedback mechanism dynamically adjusts model parameters through combustion efficiency monitoring data and simulation results, and a two-stage fault-tolerant strategy realizes sensor abnormity compensation and historical control strategy backtracking. The problem of asynchronous data stream feature misalignment is effectively solved, and the combustion efficiency prediction precision and the control decision reliability are improved.

The invention discloses a swivel bridge attitude monitoring system based on dynamic feedback control, and belongs to the technical field of bridge construction. Comprising the following modules: a multi-source sensor integration module for constructing an omnibearing attitude monitoring network and realizing high-precision real-time monitoring of key parts of a bridge; the edge calculation and data fusion module realizes real-time on-site processing and fusion of sensor data, effectively reduces data transmission delay and improves monitoring stability; the multi-physics field coupling modeling module is used for constructing a multi-physics field coupling model comprising structural mechanics, a temperature field, contact friction and fluid action, and realizing accurate prediction of the attitude change of the swivel bridge; the self-adaptive control strategy module is used for realizing high-precision self-adaptive control on the attitude of the swivel bridge; the grading early warning and cooperative control module automatically matches a corresponding control strategy and realizes risk grading response and intelligent cooperative control; and the digital twinning interaction verification module is used for constructing a high-precision digital twinning model of the swivel bridge.

The invention provides a power cable joint insulation state intelligent monitoring method based on a complex environment. The method belongs to the technical field of power system monitoring, and comprises the steps that related data are collected through multiple means, and the related data comprise environment temperature data, partial discharge signals and temperature distribution images; collected environment parameters and partial discharge signals are input to an intelligent analysis system, preprocessing and feature extraction are carried out, an insulation state prediction model is established based on a machine learning algorithm, and the aging degree and potential fault points of a cable joint insulation layer are predicted according to historical data and real-time monitoring data. Environment temperature data, partial discharge signals and temperature distribution images are collected through multiple means, and an intelligent analysis system and a machine learning algorithm are combined, so that the aging degree and potential fault points of the cable joint insulating layer can be predicted more accurately.

The invention relates to the technical field of flow measurement calibration, in particular to a flow instrument intelligent calibration system based on multi-sensor fusion, which comprises a data acquisition unit, a deep coupling compensation unit and a closed loop verification unit, a data acquisition unit obtains a differential pressure value, an environment temperature, a pipeline pressure, a vibration frequency spectrum and sensor accumulated working time, a depth coupling compensation unit constructs an aging prediction model based on a Weibull distribution life model, and a temperature-pressure coupling equation and a vibration compensation mechanism are combined to obtain an aging real-time value and a time sequence deviation. Multi-parameter coupling characteristics are extracted through a neural network, an environment disturbance compensation coefficient matrix is constructed, a joint compensation amount is generated through dynamic weight distribution, a closed-loop verification unit optimizes model parameters, the problems that multi-source disturbance coupling analysis is insufficient and calibration precision is low in the prior art are solved, accurate calibration of a flow instrument under complex working conditions is achieved, and the calibration precision is improved. The metering stability is improved.

The invention relates to the technical field of servo motor control, and discloses an intelligent servo motor control method and device and computer equipment. The method comprises the following steps: carrying out multi-dimensional data acquisition and preprocessing on a position loop, a speed loop and a current loop of the intelligent servo motor to obtain standard structured parameter data; identifying a working condition characteristic parameter set of the intelligent servo motor; establishing a two-stage optimization architecture comprising a motor parameter layer and a control algorithm layer, and generating a dynamic parameter adjustment mapping table; a depth deterministic strategy gradient network is trained based on the characteristic parameter set and the dynamic parameter adjustment mapping table, and an optimal control strategy model for different working conditions is generated; and the optimal control strategy model is utilized, control parameters are automatically switched according to the working condition types recognized in real time, and smooth transition control of the intelligent servo motor among different working conditions is achieved. Smooth switching among different working conditions is achieved, system oscillation caused by sudden change of control parameters is avoided, and the disturbance influence in the working condition switching process is greatly reduced.

The invention provides a pressure sensor data precision compensation method and system for industrial control, and the method comprises the steps: firstly obtaining an original pressure signal sequence outputted by a target pressure sensor in a preset collection period, the original pressure signal sequence containing a plurality of pressure signals arranged according to a timestamp; meanwhile, a real-time environment parameter set, covering temperature, humidity and mechanical vibration parameters in an acquisition period are detected, then the real-time environment parameter set is input into a pre-trained compensation parameter generation model, a corresponding dynamic compensation parameter set is generated, then each signal of an original pressure signal sequence is adjusted step by step according to the dynamic compensation parameter set, and a dynamic compensation parameter set is generated; and finally, the compensated pressure signal sequence is output to a signal processing module of an industrial control system to drive an execution mechanism, so that the data precision of the pressure sensor can be dynamically and accurately compensated by integrating multiple environmental factors, and the accuracy and stability of industrial control are improved.

The invention relates to the technical field of engineering progress management, in particular to an engineering construction digital project management method and system, and the method comprises the steps: database building, real-time data collection, and model building: generating a visual construction progress model; progress deviation judgment: calculating progress deviation and performing judgment; deviation analysis: calculating a resource gap of an affected process, and generating a resource allocation priority list; resource adjustment: pushing an adjustment instruction to the construction terminal according to the priority list; simulation: simulating the adjusted construction progress, and if the deviation is not eliminated, executing a redistribution step; redistribution: executing the deviation analysis step again; and optimization: optimizing subsequent project progress plan generation logic. The system comprises a database building module, a real-time data acquisition module, a model building module, a progress deviation judgment module, a deviation analysis module, a simulation module, a redistribution module and an optimization module. The method and the device have the effect of facilitating fine management of the engineering project.

The invention discloses a mobile phone anti-theft system and method based on a camera and a GPS (Global Position System) of a mobile phone. The mobile phone anti-theft system comprises a mobile phone CPU (Central Processing Unit), a microprocessor, a camera driving module, an input module and an unlocking detection module, wherein the input module is connected with the unlocking detection module; the unlocking detection module is connected with the mobile phone CPU; and the mobile phone CPU is connected with the camera driving module through the microprocessor. Compared with the prior art, the mobile phone anti-theft system and method provided by the invention have the advantages as follows: an unlocking operation is carried out when the mobile phone is used after the mobile phone is lost or stolen, and a background operation program which is installed in the mobile phone is triggered and started when the unlocking operation is inaccurate; and a user who currently uses the mobile phone is positioned and photographed through a GPS module and the camera of the mobile phone; meanwhile, obtained information is sent to a mailbox or other addresses which are specified by the user in time, so that the user is convenient to alarm and can find the mobile phone back accurately in time, and the problems of the mobile phone loss and personnel information release of the user can be solved.

The invention relates to the technical field of power supply or power distribution circuit systems, in particular to a photovoltaic module fault monitoring system and method, and the system comprises an acquisition module, a power supply dynamic compensation module, a control optimization module, a power supply module and a digital twin module. The acquisition module acquires multi-source data through the infrared thermal imaging sensor, the electroluminescent detection unit and the current and voltage characteristic curve acquisition module, the power supply dynamic compensation module dynamically adjusts wavelet basis function parameters based on an improved whale optimization algorithm, and the power supply module fuses hot spot distribution, current and voltage abnormity and aging trend data. And driving the dynamic adjustment of the protection threshold. And the digital twin module synchronizes real and virtual system data through transfer learning, rehearses a fault path to generate a maintenance instruction and feeds back the maintenance instruction to an optimization prediction model, so that a monitoring, protection and self-optimization closed-loop system is formed, and the fault diagnosis precision and the system reliability in a complex environment are improved.

The invention relates to the technical field of battery monitoring, in particular to an electric vehicle battery state real-time monitoring system based on deep learning. Comprising a battery data acquisition module, a feature extraction module, a battery state prediction module, a fault monitoring module and a safety regulation and control module. The battery data acquisition module acquires battery parameters in real time, the feature extraction module extracts key features through deep learning, and the battery state prediction module predicts battery capacity, health state and performance degradation based on a neural network and a differential equation. The fault monitoring module adopts integrated learning to detect abnormity and output an alarm signal, and the safety regulation and control module optimizes a charging and discharging strategy through reinforcement learning and fuzzy control to guarantee safe and stable operation of the battery. According to the invention, accurate prediction and intelligent monitoring of the battery state are realized through the deep learning technology, comprehensive state evaluation and fault early warning are provided, and the accuracy and stability of battery state monitoring are effectively improved, so that the operation safety of the electric vehicle is improved, and the service life of the battery is prolonged.

The invention relates to the field of power equipment data processing, in particular to a steam turbine vibration fault diagnosis system fused with deep learning. Comprising a dynamic knowledge base construction module, a working condition adaptive data synchronization module, a knowledge-guided heterogeneous feature fusion module, a dynamic structure neural network module, an online self-optimization weight distribution module and a knowledge-enhanced coupling fault reasoning module. The dynamic knowledge base construction module updates the multi-modal knowledge graph through an incremental knowledge distillation mechanism, and generates an interpolation strategy template and a frequency band sensitivity matrix; the working condition self-adaptive data synchronization module dynamically calls an interpolation algorithm based on a rotating speed fluctuation mode to realize time sequence alignment optimization of multi-source sensor data; the system effectively solves the problems of time scale asynchronism and feature heterogeneity in multi-source heterogeneous data fusion through a knowledge-driven and data-driven closed-loop interaction mechanism, and realizes accurate diagnosis and early warning of steam turbine vibration faults under complex working conditions.

The invention relates to the technical field of multi-axis servo systems, and discloses a synchronous control method, device and equipment for a multi-axis servo system, and the method comprises the steps: measuring a mechanical coupling relation parameter, a motor dynamic characteristic parameter and a sampling delay time parameter of the multi-axis servo system, and substituting the parameters into a state equation modeling formula, calculating to obtain a discrete time system state matrix; performing Z transformation processing on the discrete time system state matrix and calculating frequency response characteristics to obtain a transfer function matrix and a target coupling axis pair set; executing hierarchical optimization to obtain a preliminary control instruction; a trajectory tracking error and an inter-axis synchronization error are calculated and substituted into the optimization objective function for sampling delay compensation processing, and a compensation control instruction is obtained; the compensation control instruction is processed in real time, synchronous control signals are output to the servo shafts to achieve multi-shaft coordinated movement, and the synchronous control precision in the multi-shaft servo system is improved by accurately recognizing the key coupling shaft pairs and the sensitive frequency points.

The present invention provides an energy storage synchronous coordinated management method and system based on virtual synchronization technology. The method comprises: selecting an appropriate energy storage device according to the requirements of a power grid and collecting related parameters; building a dynamic model of a virtual rotor, and setting rotational inertia of the rotor and torque parameters; building an adaptation layer; configuring a line monitoring apparatus, carrying out a safety operation when the fault occurs in the adaptation layer, or carrying out dynamic control and predictive planning in the absence of faults. The present invention utilizes intelligence to reduce reliance on experience, and enables dynamic optimization to improve the automation level of energy storage control.

The invention provides a BIM-based fabricated building component management method and system, and the method comprises the steps: firstly obtaining a building information model data set of a target fabricated building project, a component attribute parameter set covering a prefabricated component unit, and a component geometric feature set, and then carrying out the multi-level component feature analysis of the building information model data set, obtaining a standardized geometric feature sequence and a dynamic construction state feature set containing a transportation positioning offset, a hoisting stress distribution gradient and an installation collision risk coefficient, and then performing cross-stage collaborative analysis on the features based on a preset construction stage matching strategy to generate a component matching strategy set; and the component dynamic optimization module is used for indicating component installation sequence adjustment and connection node precision compensation, finally generating a component dynamic optimization instruction set according to the component matching strategy set, and synchronizing the component dynamic optimization instruction set to the building information model data set to update component attributes, so that efficient management of the fabricated building components is realized.

The invention relates to the technical field of power systems, provides an association rule analysis-based power load prediction method and system, and aims to solve the problem of hidden fault response lag caused by lack of an equipment health state and load fluctuation dynamic coupling mechanism in the prior art. And the problem of load prediction compensation deviation caused by insufficient weight quantization of the fault propagation path is solved. The method comprises the following steps: generating equipment state data according to a vibration spectrum and an insulation aging index of power equipment; performing fusion analysis, generating an equipment health degree evaluation index, and establishing a dynamic influence model of the equipment abnormal event on the power grid load fluctuation according to the association rule; identifying potential abnormal equipment, performing logic mapping, and generating a fault propagation path weight; and dynamically adjusting according to the weight to obtain an adjusted power load predicted value. According to the technical scheme provided by the invention, equipment vibration and insulation aging data are fused, a health assessment and fault propagation model is constructed, and load prediction is dynamically corrected to prevent and control power grid risks.

The invention discloses a building energy consumption dynamic optimization method and system based on BIM and reinforcement learning, and belongs to the technical field of building energy management and intelligent control, and the method comprises the steps: building a BIM containing building component physical attribute parameters, and generating a building digital twinborn body with dynamic thermal attribute evolution; extracting the spatial topological relation and the physical property parameters of the components, and constructing a multi-dimensional state space of a preset reinforcement learning model; embedding physical constraint conditions, and training the reinforcement learning model to generate a multi-objective optimization strategy of the energy equipment; and analyzing the multi-objective optimization strategy into an equipment control instruction set, and feeding back the equipment control instruction set to the building digital twin for real-time physical attribute simulation. According to the method, the physical accuracy of the BIM and the self-adaptive decision-making ability of reinforcement learning are combined, adversarial training under physical constraints is introduced, an energy consumption optimization strategy which conforms to actual operation limitation and dynamically adapts to environmental changes can be generated, and the energy utilization efficiency and the system response speed are remarkably improved.