201 results about "Load analysis" patented technology

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

The invention discloses a photovoltaic power station distributed construction management method and system based on load analysis, and the method comprises the steps: outputting a dynamic load intensity thermodynamic diagram according to the terrain elevation data, meteorological historical data and assembly mechanical parameters of a photovoltaic power station planning region; based on the dynamic load strength thermodynamic diagram, generating a construction zoning scheme for wind pressure resistance optimization; outputting a conflict-free construction resource allocation matrix according to the construction partitioning scheme; on the basis of the construction resource allocation matrix, construction parameters are dynamically corrected by adopting an adaptive particle swarm optimization algorithm, and an anti-deformation construction instruction set is generated; and driving the construction machinery to execute operation according to the anti-deformation construction instruction set, and iteratively updating the global load distribution map by using the reinforcement learning model to form a closed-loop construction management link. By utilizing the embodiment of the invention, the balance of construction precision and efficiency in a complex environment can be realized by fusing multi-source data, optimizing partition planning, intelligently scheduling equipment and controlling closed-loop quality.

The invention relates to the technical field of hoisting equipment monitoring, and discloses a hoisting equipment digital real-time monitoring system based on the Internet of Things. A dynamic load analysis module of the system collects multi-dimensional operation parameters in real time through distributed edge computing nodes; the risk situation assessment module executes tensor decomposition operation on the parameters, extracts feature vectors and generates a three-dimensional risk map; the self-adaptive safety control module dynamically adjusts the working state of the equipment according to the risk map; the digital twin mapping module is used for realizing time-space synchronous mapping of real-time parameters and a three-dimensional model and outputting holographic running state projection; and the cloud collaborative diagnosis module fuses the historical fault case library to generate a preventive maintenance strategy and returns the preventive maintenance strategy. The system can comprehensively monitor the equipment state, accurately assess the risk, realize dynamic safety control and preventive maintenance, and improve the safety and reliability of the operation of the hoisting equipment.

The invention discloses a linear guide rail optimization design method and device, a medium and a program product, and the method comprises the steps: (1) constructing a simulation model and a MaOP design model which can optimize the rigidity, modal and weight at the same time based on the linear guide rail structure and static load analysis; (2) generating an elite population based on a Latin hypercube and diversity criterion, obtaining target values of the elite population, establishing a database, and constructing a Gaussian process function model; (3) designing mixed mutation operation based on OCC to generate a filial generation guide rail set; (4) designing a DEP-driven DPM evolutionary strategy to generate a candidate guide rail set, and selecting an optimal candidate guide rail; and (5) obtaining each target value of the optimal candidate guide rail, updating the database and the Gaussian process function model, returning to the step (3) until all optimization targets meet requirements, and outputting an optimal parameter value. According to the method, the MaOP process aiming at the rigidity, the modal and the weight of the linear guide rail can be effectively balanced, and higher precision and better comprehensive performance are achieved.

The invention belongs to the field of data processing, particularly relates to a wind turbine generator gear box tooth surface strength optimization method considering multi-working-condition load distribution, and aims to solve the problem that the optimization effect is affected due to the fact that strength optimization checking is carried out only for a single working condition in the prior art. The method comprises the following steps: acquiring working condition data of the gearbox under various preset working conditions; load spectrum decoupling processing is conducted on the working condition data, a dynamic load distribution matrix synchronous with the tooth surface meshing period is determined, stress field conversion is conducted on the dynamic load distribution matrix, and a tooth root bending stress field and a tooth surface contact stress field are obtained; inputting the tooth root bending stress field and the tooth surface contact stress field into a pre-constructed double-constraint optimization model to obtain a tooth surface strengthening parameter tensor output by the model; and inputting the tooth surface strengthening parameter tensor into a preset process mapping system. Based on the method provided by the invention, the multi-working-condition operation data of the gearbox are synchronously obtained for optimization calculation, and the limitation of traditional single load analysis is broken through.

The invention provides a simulation analysis method and system for tunnel structure response under the action of multi-source power. The simulation analysis method comprises the following steps: S1, constructing a numerical solution system for coupling load analysis; s2, constructing a track-tunnel-soil body implicit dynamic analysis finite element model based on the numerical solution system, and forming a structure-soil body dynamic coupling system; s3, setting a plurality of types of working conditions according to preset requirements, wherein the working conditions comprise actual measurement and simulation scenes of a vehicle body and an earthquake; s4, analyzing a structure response result to obtain a response rule under the action of multi-source power; a track-tunnel-soil body three-dimensional finite element implicit dynamic analysis model is constructed, key technologies such as static-dynamic ground stress balance conversion, viscoelastic artificial boundaries, seismic oscillation equivalent node force input and train vibration function loading are comprehensively adopted, and a reliable simulation system of tunnel structure response under the multi-source dynamic effect is established.

The invention provides a historical load analysis-based power supply path dynamic generation and evaluation method, which comprises the following steps of: acquiring historical load data of a commercial district and topological structure data of a power distribution network, extracting load space distribution characteristics, and determining a dynamic change trend of load space distribution by adopting a clustering algorithm; performing time sequence analysis on the offset distance of the load center and the change rate of the power utilization density, and identifying the change period of the load in the commercial district and the sudden characteristics of sudden increase or sudden decrease of the load; key section distribution is identified according to the section identification standard, the load matching degree of the topological structure of the power distribution network is evaluated according to the key section distribution, and the load distribution balance and the line bearing capacity between topological nodes are calculated; and identifying weak nodes and areas with unbalanced load distribution according to the load distribution balance among the topological nodes and the line bearing capacity, and adjusting the line layout according to the identified weak nodes.

The invention discloses an offshore wind turbine supporting structure reliability evaluation method based on a reduced-order digital twin model, and the method comprises the steps: obtaining the response data of an offshore wind turbine supporting structure through multi-dimensional load analysis; and finally, setting a limit state function of the offshore wind turbine support structure in an extreme state, and calculating the failure probability of the offshore wind turbine support structure through a first-order reliability method under the condition of combining response data of the offshore wind turbine support structure so as to evaluate the reliability of the offshore wind turbine support structure. According to the method, the problems of insufficient model precision, calculation efficiency bottleneck, reliability evaluation limitation and the like of an existing reliability evaluation method for the offshore wind turbine supporting structure can be solved, and the requirements of offshore wind power engineering for high precision, high efficiency and comprehensiveness of reliability evaluation of the supporting structure can be met.

The invention discloses a machine room energy efficiency optimization simulation system and method based on digital twinning, and relates to the technical field of machine room energy efficiency optimization. The system comprises a model construction and load analysis module, an equipment combination design module, an energy efficiency simulation calculation module and an optimal scheme adaptation and output module. According to the method, related parameters of a machine room are collected to construct a digital twinborn simulation model, different levels of load intervals are divided, a differentiated combination scheme composed of a high-voltage fixed-frequency large cold source unit and a low-voltage variable-frequency small cold source unit is designed for each interval, an energy efficiency optimal scheme is matched through simulation, a correlation database is established, and a result is output. Dynamic adaptation of the cold source equipment and the load interval is achieved, low-load energy efficiency attenuation is avoided, the full-working-condition energy efficiency of a machine room is remarkably improved, and reliable support is provided for optimal configuration of a cold source system.

The invention discloses a self-adaptive scheduling method and system for multi-process production, and the method comprises the steps: generating a production plan according to basic data of a multi-process production task, executing the plan, monitoring a production field data flow in real time, and when an abnormal event is monitored, extracting abnormal event information including an abnormal source, abnormal occurrence time and predicted duration; inputting the abnormal event information into a preset rule engine, positioning an influenced scheduling block set according to a preset influence evaluation rule, marking the influenced scheduling block set as a to-be-rearranged task, and outputting the to-be-rearranged task; local rescheduling optimization is carried out in the affected time-resource range, and candidate rescheduling schemes are generated; and screening out an optimal scheme from the candidate rescheduling schemes, updating and integrating the optimal scheme into the production plan, and automatically updating the associated productivity load analysis data. According to the method, the scheduling block set influenced by abnormity can be accurately positioned, local rearrangement optimization is carried out, and the stability of a production plan is guaranteed on the basis of low computing power consumption.

The invention discloses an electrical load analysis method and system based on multi-source data fusion. The method comprises the steps of obtaining load data and a meteorological data sequence of a target area; carrying out high-fidelity interpolation on the load missing data by adopting a strategy of combining form matching and meteorological verification; adaptively splitting the complete load sequence into a plurality of internally stable load data subsequences based on a preset change rate threshold; calculating a time weighted average value of the meteorological data corresponding to each sub-sequence as target meteorological data; performing two-stage clustering according to the data quantity and the target meteorological data to form a homogeneous load sub-sequence set; a representative sub-sequence with the meteorological closest to the central value is extracted from each set and input into the load analysis model to obtain a reference result; and efficiently deriving the reference result to all other subsequences in the set according to the meteorological difference by using a preset load correction coefficient. According to the method, on the premise of ensuring the analysis precision, the processing efficiency and the result consistency of the mass load data are remarkably improved.

The invention discloses a dynamic edge resource adaptive optimization method in a heterogeneous environment, which relates to the technical field of edge computing, and comprises the following steps: extracting a resource state feature set from a global resource state map through sliding segmentation, and carrying out coding mapping to generate a multi-dimensional resource state vector; according to the multi-dimensional resource state vector, executing interpolation operation, generating a dynamic resource thermodynamic diagram, performing comparative analysis on the dynamic resource thermodynamic diagram and historical edge resource data, and identifying a current high-load area; performing fusion analysis on the current high-load area and the matching relationship between the real-time monitoring task request and the node resource occupation to obtain a conflict task set, inputting the conflict task set into the quantum optimization model, executing quantum annealing calculation, and generating a task-node mapping table; unified modeling of distributed edge resources is realized, space-time relevance among the resources can be dynamically captured, and a high-precision global view is provided for subsequent load analysis and task scheduling.

The invention provides a task scheduling method and device based on dynamic priority and resource awareness, a computer system and a storage medium, and the method comprises the steps: calculating the numerical priority of each task through a pre-constructed dynamic priority evaluation model based on the related factors of each task; acquiring a current resource index of each computing node of the system, and performing load analysis based on the current resource index by using a pre-constructed resource sensing module to obtain a load condition of each computing node; and performing task scheduling by using an improved genetic algorithm and a multi-agent negotiation mechanism based on the numerical value priority of each task and the load condition of each computing node, and generating a task scheduling scheme. According to the scheme, the fundamental transformation of task scheduling from static state and isolation to dynamic state and collaboration is realized, and the comprehensive technical effects of reducing key task delay, improving resource utilization efficiency and guaranteeing stable operation of the system are achieved.

The invention discloses a cloud computing resource dynamic allocation system and management method oriented to a heterogeneous environment, and relates to the technical field of cloud computing. The system comprises a resource monitoring module, a load analysis module, a dynamic allocation module, a heterogeneous resource adapter and a strategy management module, and can collect heterogeneous resource states in real time, generate a demand prediction result through a time sequence prediction model, generate a resource allocation scheme by adopting a multi-target optimization algorithm, and realize cross-platform unified management by means of the adapter. The management method comprises the steps of resource state monitoring, demand prediction, initial allocation, secondary allocation and task lossless migration, the prediction precision and the resource utilization rate are improved through an optimization algorithm, and the service continuity is guaranteed in combination with a check point migration mechanism. According to the method, the problems of low resource allocation efficiency, poor compatibility and the like in a heterogeneous environment are solved, the resource utilization rate is improved by more than 25%, the cost is reduced by 18-22%, and the method is suitable for complex scenes such as mixed clouds and edge clouds.

The invention discloses a front subframe structure MaOP method and device based on TPT, a medium and a program product, and the method comprises the steps: (1) constructing a MaOP design model capable of optimizing the weight, modal, strength and rigidity at the same time based on front subframe longitudinal beam and load analysis; (2) generating an elite population based on a diversity criterion and Latin hypercube, obtaining target values of the elite population, establishing a data set, and constructing a radial basis function model; (3) designing co-evolution operation driven by TPT to generate a candidate frame set; (4) an individual potential evaluation criterion is constructed based on the advantage and disadvantage target sets, and an optimal candidate frame is screened; and (5) obtaining each target value of the optimal candidate frame, updating the data set and the radial basis function model, returning to the step (3) until all optimization targets meet requirements, and outputting an optimal parameter value. According to the method, a TPT-driven coevolution mechanism is adopted, radial basis function model prediction is combined, and the multi-performance index optimization process for the front auxiliary frame can be effectively balanced.

The invention relates to the technical field of kitchen appliance control, and discloses a kitchen appliance integrated control system based on dynamic power distribution, which comprises a power monitoring module, a load analysis module, a dynamic distribution module, a safety protection module, a user interaction module and a network integration module. The current, voltage and instantaneous power consumption value of each device are collected in real time through a power monitoring module, and whether a safe power interval is exceeded is judged; analyzing a total load state and a dynamic demand trend based on a load analysis module, and predicting a potential overload risk; the dynamic allocation module dynamically adjusts the power allocation proportion of key and non-key equipment in combination with a user priority rule; the safety protection module monitors in real time and automatically executes graded protection measures; and the user interaction module provides a graphical interface to support priority configuration and power upper limit setting so as to realize strategy closed-loop optimization.

The invention discloses a load curve decomposition method and system based on a curvature equipartition rational B-spline basis function, and the method comprises the steps: calculating the curvature of a historical load sequence, generating a continuous curvature function, determining a group of B-spline node sequences meeting the cumulative absolute curvature equipartition condition, and enabling the node distribution to be adaptive to the load fluctuation intensity; a B-spline primary function is generated based on the node sequence, and a rational B-spline primary function is constructed through weight optimization so as to finely describe a continuous time fluctuation mode; a fitting model is established in combination with time period virtual variables, and a load curve is decomposed into two parts, namely a time period characteristic load level representing different time period reference power consumption intensity and a time characteristic load curve representing a continuous change fluctuation form. The defect that a traditional uniform spline is poor in adaptability to a non-uniform load form is overcome, self-adaptive decomposition with definite physical significance is achieved, and a better-quality data basis is provided for load analysis and prediction.

The invention relates to the field of transaction electricity price prediction, in particular to an electricity spot market transaction electricity price prediction method. According to the invention, on the basis of the power capacity data of the power spot market, the historical accidental outage capacity of each unit time period in each historical day is obtained and the periodicity is analyzed, and the total estimated available capacity is determined by combining the environmental parameters of each unit time period of the prediction day and the total load analysis accidental outage capacity of the system. Calculating a spinning reserve rate and obtaining a corresponding initial electricity price in combination with the total system load of each unit time period of the prediction day, constructing a temperature trajectory vector of each unit time period of each historical day according to the sensible temperature of the same month over the years of the prediction day, and counting the electricity price change rates corresponding to different temperature trajectory vectors; according to the sensible temperature variable quantity of each unit time period of the prediction day, all the time periods to be corrected are screened, the initial electricity price is corrected according to the electricity price change rate corresponding to the temperature trajectory vector of each time period to be corrected, and the accuracy of electricity price prediction in a complex environment is improved.

The invention discloses a tracked vehicle loading wheel dynamic load analysis method, system and device and a medium, and relates to the field of tracked vehicle dynamics, and the method comprises the steps: constructing a road surface unevenness time domain model considering front and rear wheel input variable time difference correlation and left and right wheel input coherence, and outputting a road surface excitation input signal; establishing a parameterized kinetic model of the tracked vehicle suspension system and further determining a state-space equation; according to the road surface excitation input signal and the state-space equation, obtaining a dynamic load and a dynamic load coefficient of each loading wheel; establishing a tracked vehicle action system simulation model, performing dynamic load simulation based on different driving speeds, different road surface grades and different track shoe parameters by adopting a multi-body dynamics simulation method, and determining a change rule of a dynamic load of a loading wheel; the structure of the tracked vehicle loading wheel is optimized according to the change rule of the loading wheel dynamic load, the loading wheel dynamic load can be accurately analyzed, and a reliable basis is provided for design optimization of the tracked vehicle.

This invention discloses a method for establishing an integrated calculation data model for power distribution networks based on real-time measurement data, relating to the field of power distribution networks. It addresses the problem of existing methods lacking regional specificity in establishing integrated calculation data models for power distribution networks. The method includes steps S1: periodically monitoring load changes at power nodes in a sample power network, obtaining the load calculation complexity corresponding to each power node based on the monitoring results, and obtaining time-period load analysis data; step S2: periodically monitoring frequency changes at power nodes in the sample power network based on the time-period load analysis data, obtaining the frequency control complexity corresponding to each power node based on the monitoring results, and obtaining time-period frequency analysis data; and step S3: matching the calculation data model of the sample power network based on the time-period frequency analysis data and the time-period load analysis data. This invention improves the regional specificity of the integrated calculation data model establishment method for power distribution networks.

The invention relates to the technical field of irradiation transmission line automatic control, and discloses an irradiation transmission line tray dynamic scheduling method and system.The irradiation transmission line tray dynamic scheduling method comprises the steps that an integrated sensor module is installed on each tray, and data are transmitted to a data acquisition server in a wireless communication mode; performing filtering processing and standardization processing on the original sensor data; collecting product information and identifying product characteristics according to the opportunity when the product arrives at the transmission line inlet, and establishing a mapping relation between the product characteristics and irradiation requirements; predicting an equipment operation state and a production load; constructing a multi-objective optimization model by adopting an improved non-dominated sorting genetic algorithm; generating a scheduling strategy by adopting fuzzy logic decision and deep reinforcement learning; a predictive control algorithm is adopted to realize trajectory tracking and dynamic adjustment; according to the invention, through equipment health monitoring, system load analysis, energy consumption monitoring and a fault prediction algorithm, the operation state can be comprehensively evaluated and the future trend can be predicted.

This invention discloses a method, apparatus, equipment, and medium for power system load clustering, relating to the field of power system load analysis technology. First, this invention dynamically extracts load characteristic indicators to take into account the spatiotemporal differences in user electricity consumption behavior, making it more consistent with the periodic nature of load behavior. In weight allocation, the in-class-outside-class variance ratio weighting method is adopted, directly extracting the consistency within each feature and the separability between classes from the preliminary clustering results. This allows the weights to directly serve the clustering objective; that is, through the natural filtering of the in-class-outside-class variance ratio, the weights of interfering features are reduced, improving the ability to identify real electricity consumption patterns. Finally, during clustering, high-density data points are preferentially selected as initial cluster centers. Furthermore, based on the density requirement, the point furthest from the selected center is selected as the new cluster center for iterative clustering to promote class dispersion and ensure cluster stability, ultimately achieving highly accurate load clustering.

The invention discloses a sliding formwork construction load prediction method and system based on a neural network, and relates to the field of formwork displacement prediction and control. The method comprises the steps of collecting multi-dimensional parameters such as concrete pouring height, template vibration frequency, concrete flowability and wind load, establishing a support vector regression model to predict template displacement, judging whether a prediction result exceeds an error threshold value or not, if yes, constructing a load characteristic matrix based on finite element analysis and optimizing a compensation factor, and if not, calculating the displacement of the template. And then adjusting the control gain to generate a control instruction to drive a hydraulic system to realize displacement compensation, and finally collecting the actual displacement of the template to realize feedback adjustment. The system comprises a data acquisition module, a prediction module, a load analysis module, a control module, an execution module and a feedback module. The prediction precision and the control response capability of the slip form template are improved, and the system stability and the construction quality are enhanced.

The application relates to the technical field of unmanned aerial vehicle structure design and optimization, and discloses a layout optimization method based on an unmanned aerial vehicle landing gear system. The method comprises the following steps: a three-dimensional design model of the landing gear is constructed; real flight attitude data and a load spectrum are imported; stress nephograms and displacement fields under multiple working conditions are dynamically generated to accurately identify a region to be optimized; preset layout rule library is used for iterative adjustment; component layout is optimized until mechanical indexes are met; the optimized model is compared with a historical failure case database; a risk component list with geometric or mechanical similarity is extracted; and local reinforcement or topological reconstruction is carried out according to the risk component list to generate a final scheme. The method fuses dynamic load analysis and historical failure data, improves the accuracy of layout design, and identifies and prevents potential structural failure risks.

This invention discloses a linear guide rail SOMC design method, equipment, medium, and program product, including: (1) constructing a simulation model and SOMC design model with static stiffness as the target and weight and maximum contact stress as constraints based on the structural characteristics and static load analysis of the linear guide rail; (2) constructing a design space, generating an initial population and establishing a database, and obtaining a set of high-influence continuous variables and a set of high-influence discrete variables through SRC and CEA sensitivity analysis methods; (3) generating corresponding candidate sets based on the two sets of variables, and obtaining a complete set of candidate guide rails through individual pairing; (4) establishing a random forest prediction model, screening excellent subsets and selecting the best offspring guide rails; (5) updating the database and prediction model after simulation evaluation, returning to step (3) until the indicators meet the standards, and outputting the optimal parameter values. This invention can effectively balance the SOMC process with stiffness as the optimization target and weight and maximum contact stress as constraints, achieving higher accuracy and better overall performance.

The application discloses a tornado-affected power distribution conductor wind load calculation method and system and a storage medium. The method comprises the following steps: constructing a tornado wind field model, simplifying the wind field into a main leading tangential wind speed distribution; extracting a conductor segment center position under a span configuration, and measuring a radial spatial relationship of the conductor segment center position relative to a tornado center; establishing a wind load integral calculation model of each span segment of the conductor, and calculating an aerodynamic load distribution of adjacent span conductors; converting the conductor aerodynamic force into an external load of a power distribution pole structure through load direction decomposition and structural stress analysis; and superimposing conductor transmission loads and pole body wind pressure, and calculating a total wind load response of the power distribution pole. The method realizes accurate modeling and distribution of the conductor aerodynamic force in the space and structure layers. The method is suitable for wind load analysis of a power distribution line under the action of tornadoes and other local strong winds, and provides fine input parameters for safety evaluation of a power distribution system structure.

The invention relates to the technical field of goods shelf stability safety early warning, in particular to a goods shelf stability safety early warning system based on multi-sensor fusion, which comprises nodes for monitoring and acquiring state information, recording position time, screening signal changes, registering node labeling structural parts and generating a state change set; semantic identification is carried out to extract change nodes, identify inclined load change, position a connection area and generate instability characteristics, transition judgment is carried out to analyze bearing combination and mark response abnormity to generate an unbalanced combination set, hierarchical balance is carried out to extract staggered nodes, check a stress direction, identify an offset part and generate a track drift record. And early-warning response screening misplaced number record behavior sequence summarizing triggers nodes to generate a stability early-warning list. According to the method, the structure state of the goods shelf is tracked through real-time monitoring and data fusion, signal abnormity and direction changes are identified, stability risks are analyzed and evaluated in combination with inclined loads, track drifting is positioned, the early warning accuracy and response timeliness are improved, and missing detection and false alarm are avoided.

The invention discloses a pavement load analysis method based on discrete elements and a related device, and the method comprises the steps: collecting geometric features and material parameters of an asphalt pavement, and constructing a discrete element model; based on the constructed discrete element model, creating a rigid load plate in the target area, and simulating a tire-road contact surface; a target load is applied through a rigid load plate, displacement field distribution under the action of the load is calculated based on a discrete element model subjected to the load, and settlement response analysis is achieved. The discrete element model of the asphalt pavement is established on a physical scale closer to reality, and interlayer and particle contact conditions are flexibly defined through fine unit division; on the premise of reducing the cost, the transmission path and the distribution characteristic of the dynamic load in an actual pavement structure and the complex internal mechanical response caused by the transmission path and the distribution characteristic can be more accurately captured, and powerful support is provided for comprehensive improvement of scientificity, reliability and precision of pavement engineering design and accurate prediction of pavement diseases.

The invention relates to a power grid power utilization load analysis system based on novel power utilization demand equipment, and the system comprises a load data collection module and a load feature analysis module which are used for carrying out the multi-dimensional feature extraction of collected original power utilization data; the scene mode recognition module is used for recognizing the scene category to which the power consumption behavior of the user belongs through a mode matching algorithm based on the analyzed load characteristics; the impact load evaluation module is used for quantitatively evaluating the impact load intensity and duration possibly caused by multi-device linkage or remote centralized control according to the identified specific scene; and the adaptive prediction engine is used for generating a load prediction curve of a specific time scale in the future. A complete technical chain from data acquisition, feature analysis, scene recognition and impact evaluation to adaptive prediction and scheduling decision is constructed, and the problem of load characteristic complication caused by novel electric equipment is systematically solved.

The invention relates to the technical field of smart tourism management, and discloses a dynamic management system based on smart scenic spot resources. The system establishes a resource real-time state portrait through a state portrait module, and a load analysis module extracts a periodic load waveform of each resource point according to a historical operation log and calculates a current theoretical load phase. The offset calculation module compares a real-time state with a standard state under a theoretical phase to generate a real-time offset vector, and a global offset field is constructed through the global fusion module. And the phase calibration module reversely corrects a theoretical load phase according to the space gradient and intensity distribution of the biased field to obtain a predicted load phase after feedback calibration. And the load prediction module extracts a load prediction value sequence of a plurality of time slices in the future. According to the method, through periodic phase modeling and full-field dynamic calibration, finer time sequence description and more collaborative space prediction of scenic spot resource states are realized, and the perspectiveness and adaptability of management are improved.