54 results about "Energy efficient scheduling" patented technology

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 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 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 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.

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.

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 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 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 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.

Methods, systems, and devices for wireless communications are described. For example, a user equipment (UE) may receive, from a network entity, first control signaling indicating a scheduling configuration associated with a first active period for shared channel communications, one or more scheduling gaps that occur after the first active period and during which shared channel communications are not scheduled, and a second active period. The UE may communicate the one or more first shared channel communications during the first active period. The UE may determine, during or after the second active period, an expiration of a timer associated with the shared channel communications, where the expiration of the timer is in accordance with the one or more scheduling gaps.

The invention discloses an energy-saving strategy generation method based on rail transit operation mode recognition, and belongs to the technical field of rail transit operation. Multi-source data are collected and preprocessed, time features, passenger flow features, operation features, energy consumption features and vehicle and environment features are extracted, and a feature data set is formed after standardization processing; selecting an initial clustering center based on probability distribution of sample points of the feature data set, obtaining a typical operation mode set through sample point clustering distribution and cluster center correction, and identifying noise points as an abnormal operation mode; and customizing a full-scene energy-saving operation strategy for each typical operation mode, and executing a corresponding energy-saving strategy or triggering an early warning strategy through real-time data acquisition, standardized processing and mode matching. Typical and abnormal mode classification is combined to cover a full operation scene, the energy-saving scheduling precision and scene adaptability are remarkably improved, and technical support is provided for efficient and energy-saving operation of rail transit.

This invention relates to energy-saving scheduling and control technology for industrial circulating water supply networks, and discloses a method for constructing an industrial fluid transport and distribution control model, a control method, and a device based on deep deterministic strategy gradient. First, a circulating water supply network experimental platform is built, a hydraulic simulation model of the circulating water supply network is constructed, and the model is iteratively corrected. Then, a DDPG reinforcement learning framework is constructed, and a composite reward function that considers energy consumption, continuous penalty for node pressure deviation, and penalty for flow exceeding limits is designed. Training is completed under the condition that the action space is a continuous adjustment of pump frequency. The trained DDPG model is encapsulated and deployed to an industrial PC. Terminal pressure and flow demand and actual operating parameters are collected via Ethernet as input, and pump frequency adjustment commands are output, which are executed in a closed loop via PLC and frequency converter. This invention can realize real-time energy-saving scheduling of industrial circulating water supply networks within a continuous action space, significantly reducing computation time and operating energy consumption, and improving the efficiency and safety of network operation.

The application discloses an energy-saving strategy generation method based on rail transit operation mode recognition, and belongs to the technical field of rail transit operation. The application collects and pre-processes multi-source data, extracts time characteristics, passenger flow characteristics, operation characteristics, energy consumption characteristics and vehicle and environment characteristics, and forms a characteristic data set after standardization processing. The initial clustering center is selected based on the probability distribution of the sample points of the characteristic data set, the typical operation mode set is obtained through sample point clustering distribution and cluster center correction, and the noise points are recognized as abnormal operation modes. The all-scene energy-saving operation strategy is customized for each typical operation mode, the corresponding energy-saving strategy or the early warning strategy is executed through real-time data acquisition, standardization processing and mode matching. The application covers all operation scenes by combining typical and abnormal mode classification, significantly improves the energy-saving scheduling accuracy and scene adaptability, and provides technical support for realizing efficient and energy-saving operation of rail transit.

Certain aspects of the present disclosure provide techniques for data transmission in energy efficient scheduling gaps. An example method performed by a user equipment (UE) includes receiving a first control configuration that configures a first peak throughput for a first downlink transmission to be received by the UE and receiving a second control configuration that configures at least one or more gap slots and a second peak throughput, for at least a second downlink transmission to be received by the UE, that is less than the first peak throughput. The method further includes receiving at least the second downlink transmission in a first slot in accordance with the second control configuration and receiving at least a third downlink transmission in the one or more gap slots based on a scheduled downlink transmission throughput associated with the first slot being less than the second peak throughput.

The invention provides a subway train energy-saving scheduling optimization method and system, and belongs to the technical field of energy-saving scheduling, and the method specifically comprises the steps: carrying out the recognition processing of an energy-saving optimization strategy of an energy-saving scheduling interval through an energy-saving scheduling recognition strategy, and obtaining a recognition processing result, determining an optimized control interval in the energy-saving dispatching interval according to the identification processing result and the coincident data of the trains in the energy-saving dispatching interval and other energy-saving dispatching intervals in different operation periods, and determining the energy-saving dispatching interval based on the data of the optimized control interval and the coincident data of the trains in the energy-saving dispatching interval and the optimized control interval. And the energy-saving scheduling optimization method of the energy-saving scheduling interval is determined, so that the recognition processing efficiency of the energy-saving strategy of the subway train is improved.

The application relates to an information-based cold chain operation energy-saving scheduling method, device, equipment and medium. The method collects cold chain node sensing data and performs edge preprocessing to generate standardized data packets, performs channel quality assessment on the standardized data packets and constructs a dynamic transmission network, transmits the standardized data packets to a cloud platform for multi-dimensional fusion processing, generates a cold chain digital twin model, and then simulates and predicts the cold chain environment temperature field distribution and equipment energy consumption, and finally generates equipment control instructions according to the obtained prediction analysis results. The method can realize intelligent energy-saving scheduling of the cold chain system, improve the energy efficiency of the system and reduce the operation cost.

The application discloses an energy-saving scheduling method, device and equipment and a storage medium, relates to the technical field of storage, and the energy-saving scheduling method comprises the following steps: obtaining the working state, load data and to-be-responded workload of a hardware acceleration unit; in the case that the working state of the hardware acceleration unit is not the shutdown state, determining the busy degree of the hardware acceleration unit according to the load data and to-be-responded workload of the hardware acceleration unit; then, judging whether the hardware acceleration unit meets the condition of entering the shutdown state according to the busy degree and to-be-responded workload of the hardware acceleration unit; and finally, switching the hardware acceleration unit to the shutdown state in the case that the hardware acceleration unit meets the condition of entering the shutdown state, so as to realize the energy-saving scheduling of the hardware acceleration unit. The application can realize energy consumption optimization on the premise of guaranteeing the stable performance of a RAID system.

This invention discloses an energy-saving control method and device for a bird tracker based on sensor fusion. The method uses an accelerometer to collect triaxial acceleration data to identify the bird's behavioral state, activating the heart rate and blood oxygenation sensors only when the bird is stationary and deactivating them during movement. It combines illumination data, location information, and the device's real-time time to determine signal obstruction environments and dynamically adjusts the operating strategies of the positioning and communication modules. Before positioning calculation, it detects the number of satellites in a low-power mode, performing full positioning only when the target is met. Power management independently controls the on / off operation of the positioning, communication, and storage modules, with standby power supplied only to the real-time clock module. The corresponding device includes a main control unit, a sensor data acquisition and tracking unit, and an energy-saving control unit, integrating multiple types of sensors and a power management module. This invention significantly reduces the tracker's ineffective power consumption and extends its battery life in the field through multi-sensor fusion and dynamic energy-saving scheduling, adapting to the long-term monitoring needs of birds.

Facilitating Multiple-Tenant Energy Efficient Radio Access Network Sharing In Advanced Communication Networks is provided. A method includes facilitating, by a system comprising at least one processor, network energy savings in a communications network that is deployed in a shared radio access network architecture. The facilitating includes implementing, at a network operator level of the communications network according to a defined energy efficiency criterion, energy efficient scheduling of user equipment within the communications network. The facilitating also includes implementing, at an infrastructure provider level of the communications network, network energy savings actions based on respective measured quality of service levels of the user equipment being retained at or above a defined quality of service level. In an example, network equipment comprised by the communications network is configured to operate according to at least a fifth generation radio network communication protocol.

The invention 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.The method comprises the steps that the actual passenger flow with the same time property as the current moment in elevator historical operation data is analyzed, and a first predictive factor is constructed; the method comprises the following steps: acquiring a pedestrian flow load, taking the pedestrian flow load as a reference prediction value, analyzing the similarity between the reference prediction value and all historical operation data, distributing contribution degree weight to the historical operation data of each day, constructing a second prediction factor, obtaining a pedestrian flow prediction value, and finally correcting the pedestrian flow prediction value in real time according to the difference between the pedestrian flow prediction value and the actual pedestrian flow load of the current day; and constructing an adaptive inertia weight of a particle swarm algorithm based on the human traffic prediction correction value, and searching an optimal solution to perform response scheduling. The invention aims to improve the dispatching efficiency of the whole elevator group.