512 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 relates to the technical field of building energy consumption management, in particular to a building energy consumption management system and method for analyzing the load of a power grid, and the system comprises an acquisition processing unit, a load analysis unit and an energy management unit. A load prediction module of the load analysis unit predicts a load by using a long short-term memory network and a graph convolutional neural network, a decomposition traceability module decomposes a traceability load by using an attention mechanism and a hidden Markov model, and an energy management unit regulates and controls adjustable equipment in peak and valley periods according to load prediction. According to the method and the system, the abnormal energy consumption equipment is pre-warned according to the load decomposition and traceability result, multiple technical means are integrated, the traceability load can be accurately predicted and decomposed, the energy management is optimized, the complex building environment is adapted, and the problems that the equipment relationship is difficult to grasp, the load change reason is difficult to track and the energy consumption is difficult to accurately manage in a traditional mode are effectively solved.

The invention relates to the technical field of data analysis, in particular to a wind tunnel test analysis method and system for a large-span warehouse structure. The method comprises the following steps: executing a wind tunnel simulation test operation, and collecting wind tunnel test data of the wind tunnel simulation test operation; performing wind pressure quantitative distribution simulation analysis of multi-scale wind direction angles based on wind tunnel test data to generate multi-scale wind pressure quantitative distribution data; designing a wind tunnel test large-span warehouse model according to the wind tunnel simulation test operation; based on the multi-scale wind pressure quantitative distribution data, carrying out wind load analysis on each node of the warehouse on the wind tunnel test large-span warehouse model to generate warehouse wind load data; based on the warehouse wind load data, large-span warehouse structure design parameter analysis is carried out, and large-span warehouse structure design parameters are generated; and transmitting the large-span warehouse structure design parameters to a terminal to execute large-span warehouse structure design parameter feedback operation. According to the method, optimization design of large-span warehouse structure parameters is realized through wind tunnel simulation test analysis.

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 embodiment of the invention discloses a numerical optimization method and device considering bearing capacity of a steel frame and a concrete precast slab, equipment and a medium. The method comprises the following steps: acquiring geometric parameters, material attributes and connection parameters between the steel frame and the concrete precast slab corresponding to the steel frame and the concrete precast slab respectively, constructing a geometric model based on the geometric parameters, performing finite element mesh division, assigning the material attributes and the connection parameters to generate a finite element model, and performing finite element mesh division on the finite element model; static load analysis and dynamic load analysis are conducted on the finite element model to obtain bearing capacity response, continuous optimization is conducted by changing set parameters, material attributes and connection parameters, the influence of different parameters on the bearing capacity is analyzed, an optimal parameter combination is found, the bearing capacity is maximized, and the bearing capacity is improved. Marking stress concentration and weak areas based on the optimal parameter combination; according to the technical scheme, the accuracy of the steel frame-concrete prefabricated slab model can be improved, and the effectiveness and efficiency of parameter optimization on stress concentration and weak areas are improved.

The invention relates to the technical field of food supply chain traceability, in particular to a food supply chain quality safety traceability method and system. The method comprises the following steps: realizing feature fusion of food data through multi-source data acquisition and time sequence alignment, then identifying a food logistics mode and carrying out load analysis, carrying out adaptive fragmentation storage on fusion features based on logistics load data, constructing a storage network topology structure, and utilizing a block chain technology to carry out self-adaptive fragmentation storage on the fusion features. And performing chain structure connection on the fragmented data, deploying an intelligent contract, finally constructing a traceability coding system according to the contract and a chain structure, and realizing whole-course quality safety accurate monitoring and tracking of the food supply chain through a traceability code set. The invention realizes a more efficient and more reliable food supply chain quality safety traceability method.

The invention discloses an electric power system real-time simulation optimization scheduling method based on edge computing, and particularly relates to the technical field of edge computing management, and the method comprises the following steps: dividing an electric power system into a plurality of regional power grids according to a preset strategy, collecting the load information of each region, and carrying out the load analysis, activating a distributed computing potential evaluation mechanism after identifying abnormal load fluctuation; according to the method, resource utilization difference and task allocation imbalance analysis is carried out by obtaining regional power grid simulation cooperative task allocation information, and efficient and low-efficiency cooperative operation power grids are identified; for a low-efficiency collaborative power grid, task allocation of edge nodes and a center node is optimized, optimal scheduling of analogue simulation tasks is achieved, finally, a regional power grid is adjusted to be a high-efficiency collaborative operation power grid, and the power grid operation efficiency is improved; according to the method, tasks can be reasonably allocated according to the load and computing power of each node, overload or idle of some nodes is avoided, computing resources of edge and center nodes are fully utilized, and the simulation real-time performance and efficiency are improved.

The invention provides a segmental bridge wind vibration performance evaluation method and system, and relates to the technical field of segmental bridge wind vibration performance. Laser scanning is used for obtaining geometric parameters of a segmental bridge and collecting material characteristics, the geometric parameters and the material characteristics are imported into finite element software, a basic model is established, and boundary conditions are set; modal analysis is executed in finite element software, the natural vibration frequency and the vibration mode are calculated through a Lanczos method, and basic data are provided for wind load analysis; simulating a wind load by using a Davenport wind spectrum, setting a wind speed and a wind direction, and then applying the wind speed and the wind direction to the model; finally, the wind load condition is adjusted, the performance of the bridge under different wind loads is tested, and the displacement, acceleration and stress distribution of the bridge are analyzed and calculated through dynamic response to complete the wind vibration performance evaluation of the segmental bridge.

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 closed-loop control, in particular to an irrigation area water gate automatic adjusting system based on fuzzy logic control, which comprises a load monitoring module, a gate parameter optimization module, an environment response adjusting module, a water level threshold setting module, a flow prediction module and a gate feedback control module. According to the method, accurate calculation of the inter-regional load difference is performed by integrating the water resource flow and pressure data, the irrigation efficiency and the response speed are remarkably improved, water requirements can be accurately matched through refined regional load analysis, water resource waste is avoided, and the adaptability of the system to environmental changes is enhanced; environment variables are monitored in real time, gate operation is adjusted, the optimal water level is maintained under the constantly changing condition, agricultural requirements and sustainable utilization of water resources are ensured, predictive analysis of flow data enhances preventive regulation and control of the system, the system can respond to future requirement changes in advance, resource allocation is optimized, and the system is suitable for large-scale popularization and application. The water resource utilization rate and the automation level of irrigation area management are effectively improved.

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 ship propulsion system monitoring, in particular to a shafting load analysis method based on real-time data monitoring, and the method comprises the following steps: S1, collecting a pressure parameter and a displacement parameter of a shafting; s2, performing timestamp alignment and data frame encapsulation to generate a synchronous data stream; s3, shafting natural vibration noise and real load signals are separated, and shafting real-time torque distribution is calculated; s4, if the torque distribution deviates from the correction threshold value, graded early warning is triggered; and S5, generating an interactive report according to the triggered early warning level and the torque distribution result. According to the invention, through multi-sensor synchronous acquisition, edge calculation data alignment, vibration noise decoupling, dynamic safety threshold adjustment and intelligent fault tracing analysis, high-precision monitoring, abnormity early warning and intelligent maintenance of the shafting load are realized, and the safety and operation and maintenance efficiency of ship operation are improved.

The invention relates to the technical field of power load analysis, and discloses an intelligent ammeter power load analysis system and method, and the method comprises the steps: obtaining the current overall power consumption of a user, judging whether the power consumption of the user is abnormal or not, forming abnormal analysis data, and carrying out the corresponding abnormal prompt for the user according to the abnormal analysis data. According to the intelligent electric meter power load analysis system and method, whether the electric equipment is abnormal or not is judged according to the power utilization data collected by the electric meter, the abnormal reason of the electric equipment is judged according to the power utilization data, and a user is timely notified to carry out adjustment according to the abnormal reason of the electric equipment; therefore, the service life of the electric equipment is prevented from being shortened due to the fact that the use condition of the electric equipment cannot be adjusted in time, the equipment is prevented from being damaged due to the fact that equipment abnormity cannot be perceived in time, the service life of the equipment is prolonged, property loss of a user is reduced, fire catching of the electric equipment is reduced, and personal injury to personnel due to fire disasters is avoided.

A floating type wind turbine generator ballast water control method based on wind power medium and long term prediction comprises the steps that firstly, based on historical meteorological data, long-term wind regime distribution and probability distribution of a wind power plant are obtained through a data processing and clustering algorithm; and predicting the wind condition by using historical weather and external forecast data and a random forest algorithm, and optimizing a ballast water control strategy and generating a wind power time sequence in combination with wave flow load calculation and unit attitude and load analysis. Measuring the position of the impeller of the unit in real time, determining an operation strategy by means of a neural network, and optimizing the attitude and load of the unit by using a particle swarm algorithm; a ballast water capacity range can be determined according to unit parameters and control system performance, a state space model is established, an optimization problem is solved, and a strategy is corrected. And finally, a ballast water system is adjusted through deep learning and feedback control, a real-time optimization model is constructed for cooperative control, and the unit stability and the power generation efficiency are improved.

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 relates to an adaptive control method, system and device for a switching power supply and a storage medium, and the method comprises the steps: obtaining power supply parameter data in real time, and carrying out the preprocessing of the power supply parameter data, and obtaining standard state data; performing load analysis on the standard state data to obtain real-time load data, and determining a corresponding power supply modulation mode according to the real-time load data; obtaining standard state data in a preset time period, and inputting the standard state data into a pre-trained load prediction model to obtain a load prediction result; and determining a corresponding prediction modulation mode based on the load prediction result, and pre-inputting the prediction modulation mode by adopting a smooth switching algorithm so as to improve the response speed and stability of the system. The invention has the effect of improving the stability of the switching power supply.

The invention, which relates to the technical field of building construction monitoring, discloses a tall template construction safety monitoring system comprising a template data acquisition module, a dynamic load analysis module, an interpolation module, a time window analysis module, an adjacent region comprehensive analysis module and an early warning module. The template data acquisition module collects stress data of a basic area in real time through a sensor network, the dynamic load analysis module dynamically adjusts division of the stress area according to the stress data, the interpolation module performs smoothing processing on the adjusted data, the time window analysis module calculates area stress fluctuation, and the time window analysis module analyzes the area stress fluctuation. The adjacent region comprehensive analysis module evaluates the stress influence of adjacent regions, the early warning module generates risk levels through fuzzy logic, and the construction report generation module is used for recording and generating a safety report; according to the invention, a local dangerous area can be better controlled and accurately positioned, and large-range blind troubleshooting is avoided, so that the safety management efficiency of a construction site is improved.

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 relates to the technical field of hydraulic engineering management, in particular to a hydraulic engineering management method and system based on digital twinning. The method comprises the following steps of obtaining hydraulic engineering design data, hydraulic engineering operation data and hydraulic engineering environment data, and performing digital twin space modeling on the corresponding hydraulic engineering to obtain a hydraulic engineering digital twin space model; performing operation condition virtual simulation analysis and water flow distribution change analysis on the hydraulic engineering digital twin space model, and performing engineering structure stress load analysis and engineering operation risk assessment to obtain hydraulic engineering simulation operation process risk data under different operation and environment change conditions; and performing risk decision management processing on the corresponding water conservancy project to generate a water conservancy project operation process risk management decision so as to execute corresponding water conservancy project operation risk management adjustment work. According to the invention, the management effect and response capability of the hydraulic engineering can be greatly improved.

The invention discloses an efficient Transform model reasoning accelerator deployment method based on Versaal ACAP, and belongs to the field of software and hardware collaborative acceleration numerical calculation. Firstly, load analysis is carried out on the model, and meanwhile, according to hardware performance, the types and scales of operators needing to be borne by hardware of all parts are determined; then, hardware is equipped with a data transmitter, a data receiver, a nonlinear operator and other modules; then, the modules are reasonably deployed and connected in hardware, pipeline parallelization is completed, and calculation of a multi-head attention stage and a feedforward neural network stage is achieved; and finally, integrating the two stages to realize accelerated calculation of the whole encoder / decoder layer so as to accelerate the reasoning speed of the upper layer model. Experiments prove that the throughout of the model is effectively improved, the reasoning delay is reduced, meanwhile, the accelerator has huge advantages in the aspect of batch reasoning, and the accelerator has wide application prospects in the aspects of reducing the model reasoning cost and improving the model reasoning speed.

The invention discloses a ship distributed hybrid energy system and an evaluation method thereof, the system comprises a solar photovoltaic module, a wind driven generator module, a diesel generator module, an energy storage system, a converter module, a combined heat and power generation module and a thermal load controller, and the method comprises the steps of obtaining ship data information; performing analysis according to the ship data information; according to the power load analysis, heat energy converted by the residual electric quantity in the energy storage system through the combined heat and power generation module is obtained; obtaining an optimal combination according to an analysis result of the thermal load analysis; performing power demand-based dynamic pricing analysis, environmental influence evaluation analysis and social factor evaluation analysis on the optimal combination; according to the evaluation analysis result, determining that the corresponding optimal combination is a solution for comprehensive and sustainable use of the ship energy; the problems that in the prior art, many defects exist in the aspects of an energy structure, intelligent management and system integration, and efficient utilization, intelligent management and sustainable development of an energy system cannot be achieved are solved.

The invention relates to the technical field of cloud computing, in particular to a power distribution network protection method and system based on cloud computing, and the method comprises the following steps: receiving and analyzing state data from a transformer substation based on a real-time data flow of a cloud computing platform, carrying out the threshold comparison of a current load, and adjusting the current configuration according to a comparison result. And generating real-time load analysis data. According to the invention, transformer substation data is analyzed in real time through the cloud platform, the speed and accuracy of data processing are directly enhanced, load unevenness and fault risks are identified and responded, a network structure is updated in real time, the adaptive capacity of a power grid and the response speed of a system are enhanced, network simulation is carried out by using differentiated load scenes, and node performance is accurately analyzed and compared. The operation efficiency of the power grid is improved, fault point prediction and load fluctuation analysis are carried out by utilizing trend data, the perspectiveness and accuracy of fault processing are remarkably improved, the power grid can quickly and accurately respond in an emergency, and meanwhile, the operation efficiency and the power supply stability are optimized.

The invention discloses a modularly-designed new energy automobile charging pile and a charging method, and relates to the technical field of new energy automobiles. Battery parameters, power grid parameters and media parameters in the automobile charging process are collected, collected battery data features are extracted, a battery health assessment mechanism is established, load analysis is carried out, and a fuzzy rule is established; according to the method, social media real-time event data are introduced, the data dimension is expanded, logic judgment is innovated, multi-source data such as batteries, power grids and social media are fused, the load state of the power grids is comprehensively described, and the method is high in practicability and high in practicability. By means of the self-adaptive fuzzy control algorithm, the weight is dynamically adjusted, and the charging power is precisely regulated and controlled, so that the power supply safety is balanced, the charging cost of a user is reduced, the capacity of the charging pile for coping with complex scenes is enhanced, and the intelligence and comprehensive performance of the charging pile are remarkably improved.

The invention relates to the technical field of power regulation, and discloses a central air-conditioning refrigeration system load flexible regulation method which comprises the following steps: performing multi-modal feature extraction and short-term load analysis on a historical sensing data sequence to obtain short-term load data; and performing multi-scale expansion convolution, bidirectional time sequence feature extraction and long-term load analysis on the historical sensing data sequence to obtain long-term load data, and obtaining real-time sensing data according to the pre-obtained real-time sensing data. The short-term load data and the long-term load data are used for carrying out fitness calculation and global optimal iteration on the adjustment strategy set to obtain a primary adjustment strategy; and state space construction and state feedback updating are conducted on the primary adjusting strategy according to the real-time sensing data and the analysis load data, a standard adjusting strategy is obtained, and load adjusting is conducted on the central air conditioner through the standard adjusting strategy. The invention further provides a device and equipment for flexibly adjusting the load of the central air-conditioning refrigerating system. The sales volume of air conditioner load adjustment can be increased.

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.