83 results about "Energy efficient scheduling" patented technology

The invention belongs to the technical field of iron phosphate preparation, and discloses an energy-saving control system for iron phosphate preparation based on an energy consumption scheduling model. The system is composed of a data acquisition module, an energy consumption sensing module, a preparation process modeling module, an energy consumption prediction module, an energy-saving scheduling module, an intelligent execution module, a feedback correction module, a man-machine interaction module and a remote operation and maintenance module. The energy consumption sensing module intelligently senses an energy consumption state, the preparation process modeling and energy consumption prediction module accurately predicts energy consumption, the energy-saving scheduling module generates an optimal scheduling strategy, the intelligent execution module accurately executes an instruction, and the feedback correction module realizes closed-loop adaptive regulation and control; all the modules cooperatively operate, process parameters are adjusted in real time according to actual working conditions of iron phosphate preparation, energy consumption in the preparation process is remarkably reduced, the energy utilization rate is increased, and energy-saving optimization of iron phosphate preparation is achieved.

The invention discloses a multi-modal load prediction computing power center dynamic energy-saving scheduling method and system, and relates to the technical field of energy-saving scheduling, and the method comprises the following steps: executing long-tail fractal filtering based on service load data in a steady state for a long time in a computing power center, extracting a steady-state energy spectrum, and constructing a zero-drift contrast base plane; applying micro-amplitude random phase disturbance on the zero-drift contrast base plane, and enhancing weak disturbance characteristics in the steady-state energy spectrum in combination with an entropy gradient amplification strategy; and converting the enhanced precursor micro-fluctuation characteristics into a time sequence difference energy cloud picture. According to the method, zero-drift contrast base surface enhancement perturbation identification is constructed, and multi-scale energy analysis and Bayesian confidence correction are combined, so that accurate prediction and quantification of the burst load are realized; during surge triggering, a fractal slicing and low-power-consumption parallel scheduling strategy is adopted, high response is ensured, meanwhile, energy consumption is reduced, and the method is suitable for a computing power center scene with high performance and energy efficiency requirements.

The invention provides an energy efficiency prediction and energy-saving scheduling optimization system and method for a food cold chain system, and relates to the technical field of cold chain system energy-saving scheduling, and the system comprises a data recognition module which is used for recognizing cold chain loading state data, and analyzing the space influence characteristics of the loading state data of a cold chain loading space; the strategy obtaining module is used for carrying out energy efficiency optimization on cold chain energy supply according to the space energy efficiency relation between the cold chain space and the link energy supply parameters and the space influence characteristics; the state chain establishment module is used for carrying out loading state dynamic analysis based on the cold chain vehicle path and predicting a path switching node; and the optimization strategy obtaining module is used for carrying out path state node scheduling optimization according to the cold chain path state chain by taking a space energy efficiency strategy as an initial parameter based on the vehicle energy supply coupling relationship and the space energy efficiency relationship of the cold chain system. The technical problem of energy consumption waste in cold chain transportation in the prior art can be solved, and the technical effect of reducing cold chain energy consumption is achieved.

The invention provides a numerical control multi-objective optimization energy-saving method and system based on an improved wolf pack algorithm, and the method comprises the steps: constructing a mixed integer linear programming model of multi-objective flexible job shop scheduling, taking machine tool distribution, a machining sequence, tool distribution and a machining speed as decision variables, and taking total weighted tardiness and total energy consumption as optimization targets; the method comprises the following steps: constructing an MOGWO-ALNS algorithm for solving a mixed integer linear programming model based on an MOGWO algorithm and an adaptive large neighborhood search mechanism, constructing a three-stage collaborative optimization architecture by the MOGWO-ALNS algorithm through a dual-threshold trigger mechanism, solving the mixed integer linear programming model based on the MOGWO-ALNS algorithm, obtaining a Pareto optimal solution set of the mixed integer linear programming model, decoding the Pareto optimal solution set, and finally obtaining the mixed integer linear programming model. And generating a multi-target balanced energy-saving scheduling scheme. According to the method, collaborative optimization of the delivery date and the energy consumption in numerical control machining is achieved, the solving efficiency and the optimization effect are improved, and the method has good engineering application value.

The invention provides an air compressor unit energy-saving scheduling optimization method and system based on SSA-LSTM dynamic load prediction, and relates to the technical field of industrial energy efficiency management. The method comprises the following steps: decomposing gas load data to perform characteristic analysis; firstly, parameters with physical significance are qualitatively screened based on the working principle of the air compressor, and then multi-dimensional parameters are selected as input through Pearson correlation coefficient analysis; in combination with shipyard production data characteristics, LSTM is adopted to carry out dynamic load prediction, SSA optimization hyper-parameters are introduced, and an SSA-LSTM load prediction model is constructed; sSA-LSTM load prediction is taken as key input, constraint conditions of multiple time scales of air compressors are integrated, and an air compressor unit optimization scheduling model is systematically constructed. According to the method, the space-time heterogeneity and the dynamic fluctuation characteristics in the working condition data are effectively captured through the SSA-LSTM model, efficient global tuning is realized, and the prediction precision and the calculation efficiency are remarkably improved; the method is suitable for a high-dynamic, real-time and accurate load prediction industrial scene represented by a shipyard, and provides key support for scheduling decision making.

The invention discloses a dynamic energy-saving cluster resource collaborative allocation method. The method comprises the following steps: acquiring environment sensing data: acquiring environment data and node state data in real time through an unmanned aerial vehicle sensor; predicting a task demand: predicting a future task demand change condition by adopting a deep learning method according to historical data and environment change; optimizing the resource utilization rate: generating a resource allocation strategy, and dynamically adjusting calculation and communication resources of each node; making an energy-saving scheduling decision: generating an energy-saving scheduling strategy by using deep reinforcement learning, wherein the scheduling strategy considers two targets of task completion rate and energy consumption at the same time; feedback is collected, and self-adaptive adjustment is carried out: according to a task execution result and a node state feedback condition, an allocation strategy and energy-saving parameters are dynamically adjusted. By monitoring, predicting and optimizing resource allocation in real time and dynamically responding to environment changes, the task efficiency is improved, meanwhile, energy consumption is reduced, and the node operation time and service life are prolonged.

The invention discloses a time-sharing energy-saving scheduling method and device for a marine water supply system based on intelligent prediction, and relates to the technical field of data processing, the method comprises the following steps: constructing a hydraulic network based on a marine water supply pipeline and a time-sharing water supply scene water law gene pool, the scene being defined by time heterogeneity, space gradient and burst disturbance; through valve opening and water pump adjustment, a double-end decision maker is deployed to be embedded into a water supply system; and the auxiliary decision-making device performs primary and secondary pipeline classification and key valve and water pump motor parameter adjustment decision making, executes double-end balance adjustment to determine an adjustment strategy, decouples the strategy and issues the strategy to the equipment to complete water supply scheduling control. Therefore, the technical effects of accurate and stable water supply scheduling, and improvement of system operation stability and energy-saving efficiency are achieved.

The invention provides a smart city super high-rise building night variable-speed pump energy-saving scheduling method and device and a medium, belongs to the technical field of building energy saving, and particularly relates to the steps that parameter measurement is conducted on a pipeline and a variable-frequency pump, vertical nodes of the pipeline are created, preset parameters are written in, and any frequency sequence is mapped into pressure increment of each vertical node; calculating night accumulated electric charge and tail end pressure margin for each skeleton, and outputting a global optimal pump speed curve and a pressure margin track; and positioning and calling the compensation frequency of which the pressure is lower than a preset pressure threshold time period, and re-evaluating the compensation frequency to obtain the electric charge and pressure margin. According to the invention, collection of field data is realized, a double-domain digital twin network is constructed, and intelligent scheduling control of a night water supply system is realized through frequency skeleton generation screening and global search optimization and through fine adjustment and convergence of local sensitivity. And the economical efficiency and the reliability of the night water supply system of the high-rise building are improved.

The invention provides a water plant raw water and clear water lifting pump station joint optimization energy-saving dispatching method and system for solving the problems that traditional pump station dispatching depends on artificial experience, global optimization is lacked, and energy waste is serious. Comprising the steps that a flow-lift and flow-efficiency relation model is established, and a variable frequency pump deduces a performance formula under any frequency based on the similarity law; in the first-stage optimization scheduling, the optimal flow of a water intake pumping station and the optimal water storage level of a clean water tank in each time period are determined by adopting a dynamic programming algorithm and taking the minimum total work of the system as a target, and the conditions of flow balance, water level constraint and the like are met; and in the second-stage optimization scheduling, the genetic algorithm is adopted to optimize the combination and operation frequency of the water pumps in each time period by taking the minimum total power of the pump set as a target, and a flow and lift deviation penalty term is introduced. According to the method, water taking, water storage and water supply whole-process collaborative optimization is achieved, the energy saving rate exceeds 10%, the operation cost is reduced, the method adapts to dynamic changes of the water source tide level and the water demand, and the dispatching intelligent level is improved.

The invention belongs to the technical field of power grids, and particularly relates to a power grid equipment operation mode intelligent switching method for energy-saving dispatching. The method comprises the following steps of: 1, establishing a monitoring point table, reading a current state table, establishing an operation constraint table and a control command table, automatically generating an action fragment catalog, dividing a region set, extracting an initial value of a coupling boundary quantity, and writing the initial value into a consistency alignment result table as all inputs of subsequent circulation; step 2, by taking the region set as a parallel unit, carrying out iteration according to an execution sequence of local decision construction, near-end alignment, consistency alignment, approaching instruction issuing and convergence judgment around a coupling boundary quantity and consistency alignment result table under the control of a coordinator; 3, sequentially issuing the action fragments in the progressive execution windows according to the mode switching instruction set; according to the method, the interlocking labels are arranged in the action fragment catalog, and the reference and influence range is withdrawn, so that the whole switching process not only can be repeatedly executed, but also has definite traceability and auditing performance.

The invention discloses a flexible job shop energy-saving scheduling optimization method considering a light storage condition and a load characteristic, and relates to the technical field of industrial scheduling, and the method comprises the steps: building a flexible job shop energy-saving scheduling problem model based on mixed integer programming, and setting an optimization target and a constraint condition; an ant colony algorithm is improved, an ant colony is divided into dynamic multi-level search, and a pheromone matrix is optimized; the updating effect of pheromones in the ant colony algorithm in the iteration process is optimized; optimizing the optimal solution of each generation of the ant colony algorithm by adopting a graph neural network off-line learning neighborhood search method; and stopping iteration when a preset termination condition is met, and outputting an optimal scheduling scheme. According to the method, a more efficient and flexible energy-saving scheduling strategy is developed to effectively coordinate the productivity and the energy efficiency, energy optimization and cost reduction in the production process are achieved, actual technical support is provided for energy-saving scheduling of the flexible job shop, and a method system for the workshop scheduling problem is enriched.

The invention discloses an automatic cleaning and energy-saving dispatching control method for a commercial kitchen oil fume system, and belongs to the field of kitchen oil fume equipment control. The method comprises the following steps: S1, collecting current environment characteristic parameters and equipment operation parameters, inputting the parameters into a pre-trained lampblack prediction model, and outputting a lampblack concentration prediction value and an equipment state parameter prediction result in a future preset time period; s2, the lampblack concentration predicted value is called, and the lampblack purifier is controlled to operate in a corresponding working condition mode so as to control energy-saving dispatching of the lampblack purifier; and S3, based on the equipment state parameters monitored in real time and the predicted equipment state parameter prediction result, triggering an automatic cleaning process of the oil fume purifier, and performing consumable service life management according to the environment characteristic parameters and the equipment operation parameters, so that the effectiveness of automatic cleaning and energy-saving scheduling control of the oil fume purification equipment is improved, and the service life of the oil fume purifier is prolonged. The problem that in the prior art, the effectiveness of automatic cleaning and energy-saving dispatching control of oil smoke purification equipment is low is solved.

The invention discloses a data center dynamic energy-saving scheduling method and system based on AI prediction, electronic equipment and a storage medium, and the method comprises the steps: collecting load data of a data center server and external service flow data in real time to construct an original data set, and processing the original data set through employing a long short-term memory network to obtain a load change trend; according to the load change trend, determining distribution characteristics of the thermal load trend in a future time window, extracting idle computing resource data from the server group based on the distribution characteristics, and computing a computing resource distribution proportion needing to be adjusted; carrying out iterative optimization on the calculation resource distribution proportion by adopting a genetic algorithm to obtain an optimized server load distribution scheme; through a server load distribution scheme, obtaining associated refrigeration system parameter data, determining an adjustment vector of a refrigeration strategy, fusing real-time energy consumption data of a power supply module, and judging a correction demand of power supply scheduling; and obtaining the operation configuration of the overall energy efficiency optimization according to the correction demand.

The invention discloses a boiler energy-saving performance prediction, regulation and control method based on industrial big data analysis. The method comprises the following steps: S1, collecting time sequence data; s2, generating a standardized training data set; s3, based on the improved N-BEATS model, obtaining a boiler thermal efficiency prediction result; s4, identifying potential abnormity in the boiler thermal efficiency prediction result, and automatically triggering an early warning identifier; s5, if the heat efficiency predicted value is lower than a set threshold value, it is judged that the current operation state has an efficiency reduction trend; and S6, constructing a depth deterministic strategy gradient algorithm to generate boiler operation parameters, and constructing a multi-dimensional reward function based on the thermal efficiency predicted value and the boiler operation parameters. The method is suitable for industrial boiler energy efficiency management and intelligent optimization control scenes, has the advantages of being high in prediction precision, high in periodical recognition and intelligent in regulation and control response, and can achieve pre-judgment and strategic energy-saving scheduling of boiler operation energy efficiency in advance.

The invention discloses a hybrid learning-based flexible job shop energy-saving batch scheduling method, which relates to the technical field of shop scheduling and comprises the following steps of: establishing a flexible job shop energy-saving scheduling model by taking minimization of maximum completion time and total energy consumption of a machine as optimization objectives; the method comprises the following steps of: constructing a mapping relationship between an operation process and machine selection by adopting a three-layer coding mode, initializing a population through multiple initialization strategies, decoding a coding part, and generating an initial solution; constructing a neighborhood structure, performing population evolution through an adaptive crossover and mutation operator, and adjusting the crossover and mutation probability according to the population evolution degree; searching an optimal solution through a plurality of search strategies; traversing the population to perform non-dominated sorting, and adjusting the crossover mutation rate according to a non-dominated sorting result; and judging whether an iteration termination condition is met or not, if not, continuing iteration, and if so, outputting the optimal Pareto frontier solution. According to the invention, the energy consumption is reduced while the processing time is minimized.

The application aims at providing a multi-stage multi-level assembly job shop energy-saving scheduling multi-objective optimization method, relates to the technical field of assembly shop scheduling, and establishes a mathematical model of the problem in stages, and designs a problem-driven energy-saving strategy triggering mechanism according to the individual state, so that the quality and search efficiency of the solution are improved. Secondly, two heuristic rules and a random generation method are used to construct an initialization population that takes into account high quality and diversity, and a dynamic self-adaptive adjustment strategy for the assimilation operator parameters is realized through Q-learning, so that the convergence speed is improved while the population diversity is ensured, so that the exploration and mining ability of the algorithm is better balanced. A revolutionary operation guided by a hyper-heuristic variable neighborhood search is designed, and a high-quality colony found is searched in detail. The joint empire invasion operation is used to replace the competition, realizes the cooperative evolution and information interaction sharing of multiple empires, and thus finds non-dominated solutions with more uniform distribution.

The invention discloses an oil tank truck energy-saving dispatching method, device and equipment, a storage medium and a product, and relates to the technical field of low carbon and energy conservation, and the oil tank truck energy-saving dispatching method comprises the steps that oil tank truck dispatching path information and oil tank truck liquid load information are acquired; the oil tank truck scheduling path information and the oil tank truck liquid load information are input to a pre-trained oil tank truck energy-saving scheduling model, an optimal energy-saving scheduling scheme is generated, and the oil tank truck energy-saving scheduling model is obtained by combining a path optimization sub-model and an energy-saving optimization sub-model. According to the method, the energy-saving optimization sub-model is adopted to carry out scheduling decision optimization on the scheduling path according to the liquid load, the optimal path is generated according to the path optimization sub-model, finally, the scheduling decision and the optimal path are considered to generate the optimal energy-saving scheduling scheme, and scheduling strategy optimization of energy conservation and emission reduction is completed.

Disclosed in the embodiments of the present application are an energy saving control method applied to FTTR, and an apparatus and a system. A main device sends to a first sub-device a first message for instructing the first sub-device to enable an energy saving feature. On the basis of the first message, the first sub-device can acknowledge enabling the energy saving feature, and acquire its own energy saving capability. Then, by means of a second message, the first sub-device sends its own energy saving capability to the main device. It should be understood that the first sub-device acknowledging enabling the energy saving feature indicates that the first sub-device supports a switch to an energy saving state, which is equivalent to supporting energy saving scheduling by the main device. In this manner, because the main device can learn whether each sub-device can enable the energy saving feature and can learn the energy saving capability of each sub-device that can enable the energy saving feature, the main device can properly perform energy saving scheduling on the plurality of sub-devices on the basis of the learned information, thus better optimizing the energy saving effect.

Hardware distribution and energy efficient scheduling using digital emissions data may include determining an emissions load target for an asset, wherein the emissions load target indicates a limit on digital emissions related to operation of the asset; determining, based on energy utilization data, a future energy utilization projection for the asset; generating, based on the future energy utilization projection, a digital emissions forecast for the asset based on digital emissions attributable to the asset; and alleviating an energy demand of a workload executing on the asset in response to determining that the digital emissions forecast exceeds the emissions load target

The invention provides a photovoltaic base station energy-saving scheduling method and device, electronic equipment and a storage medium, and belongs to the technical field of base station energy scheduling, and the method comprises the steps: determining the nominal photovoltaic power generation capability parameter of a photovoltaic base station based on the geographical climate environment parameter of the photovoltaic base station and the power generation capability parameter of the photovoltaic base station; and determining an energy-saving scheduling scheme of the photovoltaic base station based on the nominal photovoltaic power generation capacity parameters. Different photovoltaic base station energy-saving scheduling schemes are adopted according to different conditions such as geography, weather and photovoltaic power generation and storage efficiency, the problems of unstable energy-saving operation and station breakage of the photovoltaic base station in the high-altitude area can be avoided, and the working continuity of the photovoltaic base station in the plateau area under different geographical conditions and different weather conditions is ensured.

The application discloses a power center dynamic energy-saving scheduling method and system for multi-modal load prediction, relates to the technical field of energy-saving scheduling, and comprises the following steps: based on the business load data of the power center in a stable state for a long time, performing long-tail fractal filtering, extracting the steady-state energy spectrum, and constructing a zero-drift reference surface; applying a small random phase disturbance to the zero-drift reference surface, and combining an entropy gradient amplification strategy to enhance the weak disturbance characteristics in the steady-state energy spectrum; and converting the enhanced precursor microwave fluctuation characteristics into a time series difference energy cloud chart. The application enhances micro-disturbance identification by constructing a zero-drift reference surface, combines multi-scale energy analysis and Bayesian confidence correction, realizes accurate prediction and quantification of sudden load, adopts fractal slicing and low-power parallel scheduling strategies when the load surges, ensures high response while reducing energy consumption, and is suitable for power center scenes with high performance and energy efficiency requirements.

The application relates to the technical field of elevator dispatching optimization, in particular to an elevator group control energy-saving dispatching method and system based on load prediction, which comprises the following steps: analyzing actual passenger flow with the same time property as the current time in elevator historical operation data, constructing a first prediction factor, taking the first prediction factor as a benchmark prediction value, analyzing the similarity of the first prediction factor with all historical operation data, assigning a contribution weight to the historical operation data of each day, constructing a second prediction factor, obtaining a passenger flow prediction value, and finally correcting the passenger flow prediction value in real time according to the difference between the passenger flow prediction value and the actual passenger flow load of the day; and constructing an adaptive inertia weight of a particle swarm algorithm based on the passenger flow prediction correction value, and finding an optimal solution for response scheduling. The application aims to improve the dispatching efficiency of the whole elevator group.

The application discloses an energy-saving scheduling method for self-hanging real-time tasks, and belongs to the field of real-time system scheduling and energy efficiency management. The method gives a task division strategy by considering the load difference of tasks and processors on a multi-core CPU platform. On a GPU platform, the real-time performance and energy consumption are balanced by selecting a suitable execution core. Through the combination of the two, energy-saving scheduling and task division for a multi-core CPU and GPU heterogeneous computing platform are realized. The energy consumption of the CPU and the GPU can be optimized synergistically on the premise of guaranteeing the real-time task deadline. A system-level energy efficiency perception scheduling framework is constructed, which is suitable for real-time cyber-physical systems, automatic driving, embedded vision and other scenes with strict requirements for energy efficiency and timing.

The invention discloses an energy-saving control system and method for water supply-water distribution cooperative scheduling of a water plant, and relates to the technical field of intelligent water affairs. The method comprises the following steps: constructing a water supply mixed prediction model and a tide-coordinated water intake distribution model, solving in real time to obtain an optimal water intake flow, and controlling and adjusting a water intake pump. The system is provided with a multi-source data acquisition layer, a data storage management layer, an intelligent analysis layer and a collaborative execution layer. Through the real-time rolling optimization model fusing the tidal liquid level, the water supply amount prediction and the clear water tank liquid level constraint, the purposes of improving the prediction accuracy and maximally utilizing the tidal potential energy to save energy and reduce consumption are achieved; the water intake amount distribution model maximizes the utilization of tidal potential energy, reduces the power consumption of the water intake pumping station, achieves the cooperative scheduling of water supply and water inlet distribution of the water supply pumping station of the water plant, guarantees the water supply safety and operation efficiency, and provides core decision support for the energy-saving scheduling of the water plant.

The present invention provides a collaborative control method applicable to servers and cold plate liquid cooling systems in an intelligent computing center, comprising the following steps: Step 10, obtaining IT system scheduling data and liquid cooling system data of the intelligent computing center within a preset time period, and establishing a server real-time power consumption model, a cooling tower power consumption model, a water pump power consumption model, and a server temperature rapid prediction model respectively; Step 20, based on the server real-time power consumption model, the cooling tower power consumption model, the water pump power consumption model, and the server temperature rapid prediction model, adopting an optimization method combining real-time joint and timing sequence to collaboratively control virtual machine deployment and the cold plate liquid cooling system. The collaborative control method applicable to servers and cold plate liquid cooling systems in an intelligent computing center provided by the present invention can solve the overcooling technical problem that is prone to occur in energy-saving scheduling of the intelligent computing center, so as to improve the overall energy efficiency and operational stability of the intelligent computing center.

The invention discloses a multi-dimensional energy-saving scheduling system and energy-saving scheduling method based on the Internet of Things, and the method comprises the steps: collecting various types of energy consumption and environment data through an Internet of Things sensor network, and carrying out the primary processing of the data; multi-dimensional data are fused through big data analysis, and future energy demands are predicted by adopting deep learning and a time sequence prediction model; based on the prediction result and the equipment state, an intelligent scheduling algorithm is adopted for optimization, and optimization is continuously carried out in combination with a mixed strategy of rule scheduling and machine learning; according to real-time feedback, an energy supply plan is automatically adjusted, and if equipment fails or is abnormal, the system is automatically switched to a standby mode; by evaluating the energy-saving effect and continuously optimizing, a user checks and manually adjusts the energy-saving effect through the intelligent terminal; according to the multi-dimensional energy-saving scheduling system and the energy-saving scheduling method based on the Internet of Things, edge calculation, deep learning, an intelligent scheduling algorithm and multi-dimensional data fusion are introduced, and the accuracy and the energy-saving effect of energy scheduling are improved.

Methods, systems, and devices for wireless communications are described. The described techniques may enable a user equipment (UE) prevent a baseband processing duration associated with an energy efficient scheduling mode of one or more wideband communication resources from overlapping with one or more uplink slots reserved for uplink communications. For example, the UE may refrain from entering the energy efficient scheduling mode for a configured duration or until the UE transmits one or more uplink messages in the one or more uplink slots. In some examples, the UE may determine whether to enter the energy efficient scheduling mode based on a priority associated with the uplink messages or based on a capability of the UE to perform uplink and downlink communications in different power states.

The invention relates to the technical field of computer computing resource scheduling, and discloses a DAG task energy-saving scheduling method and device and a storage medium, through dynamic analysis of a DAG structure, utilization of elastic time recovery on a non-critical path and combination of DVFS adjustment frequency, the real-time deadline requirement is finally met, and meanwhile the power consumption of a system is greatly reduced. In combination with the mapping schematic from the task to the core, the most necessary hardware resources are preferentially used in the execution process of the strategy, and the idle cores are timely combined and closed, so that the scheduling characteristics of high efficiency and energy conservation are embodied; the problems that in an existing technical scheme, a non-critical path dynamic elastic space cannot be utilized at the same time, DVFS frequency adjustment and idle core combination cannot be combined to achieve real-time scheduling, and the energy consumption cannot be reduced while the SLA requirement is guaranteed are solved.

This invention relates to an energy-saving scheduling method for inaccurate hybrid critical tasks based on a genetic algorithm, comprising the following steps: establishing an inaccurate hybrid critical task scheduling model; encoding the mapping of inaccurate hybrid critical tasks to multiple processors; formulating a fitness function to evaluate and select task mapping schemes to processors; performing crossover and mutation operations on existing task mapping schemes; and calculating the energy-efficient speed of each processor's task set. This invention, based on a genetic algorithm, seeks task mapping schemes that balance the load across multiple processors and calculates the optimal speed for calculating the energy consumption of each processor, ensuring that all tasks meet deadline requirements and reducing system energy consumption.

This invention provides a control method for an integrated strong and weak current IoT intelligent control box, belonging to the field of electrical control technology. The method includes: collecting strong current parameters and weak current environmental parameters within the control box via an edge computing unit; dynamically adjusting control parameters based on the collected multi-source data using a fuzzy inference algorithm; generating control commands according to the adjusted control parameters; and controlling the operation of the actuators through an intelligent device controller. Simultaneously, it achieves functions such as predictive energy-saving scheduling, fault precursor detection and fault-tolerant control, and multi-control box collaborative optimization. This invention solves the problems of slow system response, low energy efficiency, and poor reliability caused by traditional separate strong and weak current control cabinets, realizing integrated strong and weak current intelligent control and improving system operating efficiency and reliability.