318 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 field of floating type fan load simulation, in particular to a floating type fan simulation analysis method considering atmospheric stability in a stormy wave environment, which comprises the following steps of: simulating WRF wind field data; carrying out WRF-SWAN wave field simulation; calculating key parameters of wind field simulation; carrying out WRF-TurbSim wind field simulation; and carrying out OpenFAST simulation and analysis. On the basis of traditional wind field driving, a Richardson number calculation and atmospheric stability classification method is introduced, a spectrum inversion and adaptive fitting mechanism is combined, key parameters of an engineering wind speed spectrum model are extracted, and a parameter mapping relation between WRF output and TurbSim input is established. A disturbance construction module driven by Richardson numbers is developed in TurbSim, adaptive generation of wind speed disturbance under the multi-stability condition is achieved, and the physical consistency and engineering adaptability of a simulated wind speed field on the aspects of a section structure and spectrum characteristics are improved. And finally, finishing floating type fan load analysis under wind wave coupling input by combining with an OpenFAST platform, and supporting system response evaluation and structural design optimization under complex sea conditions.

The invention relates to the technical field of computing power sharing, in particular to a computing power sharing system and method in a multi-tenant environment, and the system comprises a computing power monitoring module, a load analysis and prediction module, a task priority judgment module, a resource optimization configuration module and a fault management module. According to the invention, by implementing real-time resource monitoring and data analysis and accurately capturing the speed and mode of resource consumption, the resource allocation is more accurate, the waste of computing power resources is avoided, the response capability is improved, the load prediction is generated by using historical and real-time data, the computing power resources can be ensured to be fully utilized in the peak period of demand, overload is avoided, and the service life of the system is prolonged. Through intelligent analysis of the resource utilization rate and the predicted completion time and optimization of priority ranking of the tasks, the emergency tasks can be rapidly responded, implementation of a real-time computing power distribution strategy is achieved, high-efficiency computing power resource use is guaranteed, the stability and reliability of the system are improved, and the cost problem caused by fault response delay is reduced.

The invention relates to the field of heterogeneous computing resource management, in particular to a CPU (Central Processing Unit), GPU (Graphic Processing Unit) and NPU (Network Processing Unit) resource allocation method and system of a training and reckoning all-in-one machine. The invention discloses a CPU, GPU and NPU resource allocation system of a training and reckoning all-in-one machine. The system comprises a schedulable resource analysis module, a load analysis module and a resource allocation module. According to the method, the reference parameters and the real-time dynamic indexes of heterogeneous hardware are fused, so that deep perception and efficient quantification of computing power resources are realized; on the basis of an NPU temperature attenuation experiment, calibrating a computing power loss coefficient, and dynamically correcting available weight values of a CPU, a GPU and an NPU in combination with suitability analysis of a GPU stream processor utilization rate and a task batch processing demand and collaborative efficiency evaluation of a CPU dominant frequency and an IPC value; and the accuracy of resource availability prediction is improved, so that the system can still guarantee the stability of computing power output in complex environments such as high temperature and high concurrency.

The invention relates to the technical field of ship control, in particular to a fishing boat control system based on data analysis, which comprises a traction load analysis module, a fluctuation condition division module, an attitude disturbance estimation module, a propulsion power regulation module and a rudder angle correction output module. According to the method, by analyzing the traction force load sequence value and the moment arm length of the fishing net traction point, calculating the left and right side offset torque and marking the action direction, the attitude adjustment precision of the ship is optimized, the operation sea condition level is adjusted according to data, and the ship can be subjected to more reasonable attitude estimation and adjustment under the dynamic working condition; the wind pressure change rate and the pitch angle rate are combined to judge the direction resultant force trend, and the navigation attitude is adjusted according to the sea condition grade, so that the ship can more accurately resist the influence of sea surface storm waves and maintain the course stability, and the rudder angle is corrected by adjusting the propulsion power in real time, limiting the adjustment amplitude of the propulsion power and correcting the rudder angle according to the real-time data of the navigation attitude. And the propulsive efficiency and the navigation precision are optimized.

The invention relates to the technical field of remote control, in particular to an intelligent device remote cooperative control method and system based on the Internet of Things, and the method comprises the following steps: monitoring and adjusting the CPU utilization rate and memory usage of an intelligent device based on an Internet of Things platform, optimizing the data collection and network resource distribution, and improving the intelligent device remote cooperative control efficiency. By analyzing equipment performance and a network load balance index, equipment connection priority ranking and task load management are realized, and task execution and connection frequency is dynamically adjusted, so that real-time task requirements are met, and system efficiency is improved. According to the method, the performance index of the equipment is monitored in real time, the data transmission interval is dynamically adjusted, the data processing speed is remarkably increased, resource waste is reduced, efficient use of network resources is ensured through real-time load analysis and optimization of network nodes, the problem of data delay caused by network congestion is avoided, and in the aspect of equipment connection management, the data transmission efficiency is improved. An intelligent priority decision mechanism allows the key device to quickly access the network in an emergency.

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 relates to the field of new energy system dynamic load shedding, in particular to a new energy system dynamic load shedding method based on real-time load analysis. Comprising the following steps: acquiring power grid real-time load data and new energy system output, and performing time domain analysis on the power grid real-time load data to obtain a load fluctuation rate and a load change acceleration; predicting and standardizing a load fluctuation rate at a future moment to obtain a normalized load characteristic parameter at the future moment; predicting the output of the new energy system at the future moment, and calculating the deviation between the output of the new energy system at the future moment and the load power; introducing a normalized load characteristic parameter at a future moment, calculating an initial output constraint factor, and carrying out amplitude limiting processing to obtain an output constraint factor; and the adjusted new energy system output is calculated through a dynamic load shedding adaptive adjustment algorithm. The technical problem that the analysis result is distorted due to the fact that low-frequency sampling is mostly adopted to obtain load data in the traditional technology and the rapid change characteristic of the load is difficult to capture is solved.

The invention relates to the technical field of production management in the injection molding industry, and discloses a method and system for automatically generating a production scheduling plan based on production in the injection molding industry, and the method comprises the steps: obtaining a production order, an equipment state and material inventory data, generating original production parameter data, and deploying a distributed production scheduling network; collecting and analyzing multi-stage equipment load data and equipment state data, and generating corresponding influence factors; order priority data are collected, and production task areas are divided; and optimizing and calculating a production scheduling plan based on the task area data, integrating the multi-factor data to generate comprehensive production scheduling data, and further generating and outputting a production scheduling instruction. The system comprises a production scheduling network deployment module, an equipment load analysis module, an equipment state analysis module, a production task area analysis module and a production scheduling instruction generation module. Through multi-data integration and scientific modeling, the intelligent precision of injection molding production scheduling is realized, the equipment management efficiency, the order delivery capability and the overall production efficiency are improved, and the system is suitable for automatic production scheduling management in the injection molding industry.

The invention provides a fault diagnosis system of an intelligent bamboo cutting machine, which relates to the technical field of mechanical engineering and comprises a feature extraction module used for collecting current and voltage of a motor, a spindle vibration frequency spectrum and dynamic parameters of a quadrilateral monitoring area formed by a spindle bearing, a transmission gear box, a cutter clamping end and a base supporting part in real time, and a current waveform characteristic value, a voltage harmonic component, a vibration frequency domain energy distribution parameter and an abnormal parameter combination in the quadrilateral region are extracted. Through cooperation of multiple modules, parameter acquisition, material-load analysis, dynamic threshold setting, graded early warning and accurate fault diagnosis are realized, and the operation and production continuity of the bamboo cutting machine can be efficiently guaranteed.

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 technical field of power equipment monitoring, and particularly relates to a transformer load remote monitoring and detecting system. Comprising a data acquisition module, an edge calculation module, a load adjustment module, a load prediction module and a load analysis module. The data acquisition module acquires transformer secondary side load power, current, vibration signals and winding temperature data; the edge calculation module processes load early warning including data denoising and the like; the load adjusting module calculates and adjusts the primary side load power; the load prediction module constructs a model to predict future primary side load power; the load analysis module analyzes the future load condition according to the prediction result. The system realizes remote real-time monitoring, improves data processing precision, load adjustment accuracy and prediction capability, and ensures safe and efficient operation of the transformer.

The invention discloses a host load adjustment processing method and device, a server and a storage medium, and relates to the technical field of servers, and the method comprises the steps: carrying out the data processing of a plurality of read operation data of a host, so as to obtain the processed operation data; inputting the one or more processed operation data into a pre-generated target clustering model for load analysis to obtain a load analysis result; performing load adjustment on the corresponding one or more elements according to the load analysis result to obtain one or more pieces of adjusted operation data; the one or more adjusted operation data is output to the display terminal, load adjustment can be automatically carried out on the operation state of one or more corresponding elements only by obtaining the load analysis result, stable operation of the system is guaranteed, and the energy consumption adjustment of the host is more flexible.

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 relates to the technical field of wireless communication networks, in particular to a vehicle-mounted multi-device Bluetooth communication cooperative management system, which comprises a connection priority scheduling module, a dynamic channel allocation module, an interference suppression optimization module, a load balancing adjustment module and a frequency switching coordination module. According to the invention, through extracting the device connection state and the communication requirement, comparing the communication priority in real time, improving the connection scheduling efficiency, and dynamically adjusting the bandwidth and the power in combination with the load and the channel occupation, the channel is prevented from being occupied by a single device for a long time, the channel utilization rate is improved, the interference abnormal device combination is rapidly identified when the interference occurs, and the interference suppression is executed; the method is advantaged in that interruption caused by signal conflicts is reduced, flow redistribution is carried out on high-load equipment through load analysis, uneven communication pressure is alleviated, a frequency switching path is optimized, delay and interruption caused by unreasonable switching are reduced, stable connection, resource balance and interference suppression under multi-equipment concurrent communication are realized, and communication efficiency is improved.

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 an energy storage dynamic adjustment and frequency modulation system and method based on peak-valley price difference, and relates to the technical field of power systems. The system comprises a data acquisition and preprocessing module, a load analysis and priority evaluation module, a dynamic parameter calculation module and a frequency modulation task processing module. The data acquisition and preprocessing module acquires and preprocesses power grid operation data and energy storage system state data; the load analysis and priority evaluation module divides a power grid load time period, evaluates the available capacity of an energy storage system and divides the priority of a battery compartment; the dynamic parameter calculation module calculates the discharge depth and the charge-discharge power threshold value of each battery compartment; and after receiving the frequency modulation task signal, the frequency modulation task processing module decomposes the frequency modulation task signal into sub-tasks, distributes tasks according to the state of the battery compartment and calculation parameters, monitors and executes the tasks in real time, outputs prompt information, and realizes efficient dynamic adjustment and frequency modulation of the energy storage system.

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 present invention discloses a method and system for operation and maintenance management of the status of 5G communication base station equipment, which relates to the technical field of data processing. The method includes: preprocessing real-time operation status data; constructing an equipment degradation model based on historical maintenance data to determine the real-time degradation status of each unit in the 5G communication base station equipment; identifying prone-to-failure points based on the historical maintenance data combined with the real-time operation status data, and identifying potential failure points based on their real-time degradation status using an abductive reasoning network; monitoring the status of the prone-to-failure points and target potential failure points; judging whether equipment maintenance is required based on the status monitoring results. If so, a target maintenance plan is formulated based on the operation and maintenance model combined with equipment load analysis; equipment maintenance is carried out on the 5G communication base station equipment based on the target maintenance plan, and the equipment maintenance information is encrypted and uploaded to the cloud platform. The present invention enables the operation and maintenance management of 5G communication base station equipment to achieve a more ideal effect.

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 provides a power grid cable wiring path optimization method and device, and the method comprises the steps: carrying out the load analysis of geographical distribution data and power utilization data of a power grid system, and generating load region types and weight data corresponding to each region type; performing fault propagation risk assessment on power grid nodes in the power grid system according to the load area type and the weight data, and generating a cable capacity scheme according to a risk assessment result; generating a candidate path set through a multi-target balance mechanism and an adaptive path growth algorithm according to the load area type, the weight data and a cable capacity scheme; and carrying out toughness evaluation and optimization on the candidate path set under various preset simulation scene conditions to generate a power grid cable wiring scheme. According to the method, the power grid load distribution characteristic, the fault propagation risk and the system toughness are comprehensively considered, a cable wiring scheme which adapts to a complex load environment and has fault resistance can be generated, and the reliability and stability of a power grid system are remarkably improved.

The invention provides a design and construction method of an adjustable fabricated floor support plate temporary supporting system, and belongs to the technical field of building construction.The design and construction method of the adjustable fabricated floor support plate temporary supporting system comprises the steps that firstly, engineering data are collected; then finite element analysis is conducted based on a critical buckling load analysis equation to determine component specifications, then horizontal components and vertical components are assembled, an adjusting system and a prestress system are installed, and then a supporting system is hoisted in place and fixed to an original steel beam; then the optimal prestress value is determined through a multi-level game optimization prestress configuration model to enhance the stability of the system, and after laying of the steel bar truss floor support plate and concrete pouring are completed, the supporting system is dismantled when the concrete meets the strength requirement; the technical problem that in the prior art, the adjustment stability of an assembly type floor support plate temporary supporting system is not fully optimized is solved.

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.

According to the QLC medium write amplification optimization method and device, by responding to a data write-in request, metadata of a RocksDB database is written into a metadata pool formed by TLC media, and user data is written into a data pool formed by QLC media. The service load monitoring module is used for collecting operation state data, and the intelligent load analysis module is used for judging whether the current state is in a non-service or low-service load state or not. In a low-load or non-business state, closing a garbage collection (GC) mechanism, and only executing Purge operation to recycle invalid data; and under a normal load, the GC trigger condition is controlled according to the storage space utilization rate and the data block life cycle set by the dynamic threshold configuration module. And obtaining the write-in data volume of the QLC medium through the SMART interface, and calculating the write magnification to evaluate the optimization effect. The method can be applied to electronic equipment such as a server, the write magnification of the QLC medium is effectively reduced, the service life of the QLC medium is prolonged, and the performance and stability of a storage system are improved.

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 belongs to the technical field of analysis of load fluctuation behaviors, and particularly relates to an electrical load analysis method, system and device based on subspace clustering-CatBoost and a storage medium. Electrical load data are collected and subjected to standardization processing; clustering the standardized data through a subspace clustering algorithm to generate a clustering label; the clustering labels are classified through a CatBoost classifier, and the fluctuation behavior of the electrical load is analyzed; by performing standardization processing on the electric load data, the inconsistency of data scales is eliminated, the stability of data input is improved, different types of load modes are automatically identified through a subspace clustering technology, the classification of the load data is more accurate, meanwhile, the calculation efficiency of data processing is improved, and through a CatBoost classifier, the calculation efficiency of data processing is improved. Different types of load modes are accurately classified, the intelligent level of electricity consumption prediction is improved, and the interpretability of the model is enhanced.