513 results about "Group control" patented technology

The invention provides an air conditioner energy-saving control method and system based on a virtual power plant, and the method comprises the steps: determining an air conditioner energy-saving control model corresponding to a plurality of air conditioner devices through obtaining an operation parameter set and real-time power grid load data of the plurality of air conditioner devices in a target region, and carrying out the energy-saving control of the plurality of air conditioner devices according to the real-time power grid load data and load prediction data; and determining a power grid load fluctuation threshold value of the target area, dynamically optimizing the operation parameter set of the plurality of air conditioning equipment through a parameter optimization model, generating an optimized equipment operation parameter subset, and adjusting temperature set values and operation modes of the plurality of air conditioning equipment in a future time period. And generating an air conditioner group control strategy corresponding to a future time period, obtaining operation feedback data, updating weight parameters of the load prediction model according to a deviation value between actual power grid load data and load prediction data, calibrating an optimization rule of the parameter optimization model, and generating a calibrated model. According to the invention, the fine regulation and control capability and the energy coordination efficiency of the air conditioner group can be improved.

The invention discloses an elevator taking optimization system and method based on artificial intelligence, and relates to an intelligent elevator control technology combining multi-mode perception, prediction model dynamic adjustment and reinforcement learning scheduling strategies. The method comprises the steps that firstly, elevator running states, environment information and passenger behavior data are collected through multiple types of sensors, and multi-modal scene perception vectors are generated; and secondly, elevator loads and floor requirements are predicted in real time through a dynamically-adjusted prediction model, the reward function weight in reinforcement learning is adjusted in a self-adaptive mode on the basis, and accurate response and intelligent scheduling of different scenes are achieved. The system forms a perception-prediction-scheduling closed loop, significantly reduces the waiting time of passengers through multi-objective optimization, reduces the energy consumption, and improves the safety and emergency processing capability. The method is suitable for various high-rise building elevator group control systems, and has high intelligence, flexibility and wide application value.

The invention relates to the technical field of air conditioner control, and discloses an intelligent building air conditioner group control energy-saving system and method based on multi-source data fusion, and the method comprises the steps: collecting heterogeneous tracks of the activities of a person in a building, and constructing a space-time energy demand coupling map after cross-modal coupling and abnormal outlier suppression processing; an optimized hang compensation factor beta'is obtained based on the atlas, and an energy demand significance region and a non-energy demand significance region are divided; and finally, making an intelligent air conditioner group dynamic energy-saving control strategy according to a region division result and the optimized hang compensation factor. According to the method, the air conditioner can be accurately regulated, the energy utilization efficiency of the intelligent building air conditioning system is remarkably improved, and energy consumption is reduced.

The invention discloses a building heating, ventilating and air conditioning control method with dynamic energy efficiency optimization, and belongs to the technical field of heating, ventilating and air conditioning control. The method comprises the following steps: step 1, acquiring temperature, humidity, carbon dioxide concentration and personnel distribution data in real time, and performing preliminary data processing through an edge computing node; step 2, realizing accurate prediction of future load change of the building; step 3, generating an optimal control strategy by using a reinforcement learning algorithm, and realizing dynamic optimization control of equipment operation; 4, the compliance of equipment operation, network communication and control instructions is monitored in real time, and a closed-loop dynamic tuning mechanism is constructed to continuously optimize control strategy parameters; step 5, dividing a control task into a group control layer, a region layer and a terminal layer, and establishing communication among the layers through a standardized interface so as to realize accurate transmission and execution of an instruction; and step 6, designing a friendly human-computer interaction interface, displaying a building operation state, energy consumption analysis and a control strategy, and supporting multi-terminal access and remote monitoring.

The invention relates to the technical field of elevator group control, in particular to an elevator group control method and system based on deep learning, and the method comprises the steps: building an elevator taking behavior time sequence data set through collecting historical elevator taking behavior data and combining with an elevator calling request and the number of people waiting for an elevator obtained through image recognition; the elevator taking frequency, the elevator calling interval and the passenger number fluctuation are modeled by adopting a long-short-term memory network, passenger flow periodical characteristics in workdays, holidays and peak periods are extracted, the passenger flow of each floor in the future is predicted, and pre-scheduling deployment of idle elevators is completed in advance. In the scheduling stage, state input containing the elevator running state and predicted flow is constructed, a reinforcement learning model is introduced for strategy optimization, a reward function is constructed based on passenger waiting time, response timeout and the service completion number, a scheduling strategy is driven to continuously converge, elevator response distribution is optimized, and position presetting and path adjustment are preset. And the scheduling intelligence and the service efficiency are improved.

The invention discloses an elevator group control method and device and a storage medium, and is applied to a group control system of multiple elevators in a building, and the group control system comprises a sensing layer, a decision-making layer and an execution layer. Calling a sensor in each elevator to sense the state information related to the waiting time of the elevator riders, the energy consumption of the plurality of elevators and the comfort level of the elevator riders in the historical tense, the current tense and the future tense; in the decision-making layer, near-end strategy optimization under reinforcement learning is executed according to the state information, so that one action is selected from the mixed action space to serve as a decision-making strategy; the mixed action space comprises discrete actions and continuous actions related to elevator calling control of each elevator; and in the execution layer, the multiple elevators are controlled to execute decision strategies, so that the elevator waiting time of the elevator passengers and the energy consumption of the multiple elevators are reduced, and the comfort level of the elevator passengers is improved. And through multi-level cooperation, the elevator operation efficiency is optimized, the user experience is improved, and the energy consumption is reduced.

The invention relates to the technical field of air conditioner temperature adjustment, in particular to an energy-saving control method, device and equipment for machine room air conditioner group control, a storage medium and a computer program product. The method comprises the following steps: acquiring temperature and humidity data in a machine room, power consumption data of load equipment and operation parameters of air conditioning equipment; synchronizing the data according to timestamps to generate a corresponding multi-source feature data matrix; constructing spatial thermodynamic model parameters based on the multi-source feature data matrix; on the basis of the space thermodynamic model parameters, the temperature evolution trend in the target time domain is predicted, and a control instruction sequence of the air conditioning equipment is generated on the basis of a prediction result; and if the control instruction sequence meets the preset temperature range constraint, the preset temperature change rate constraint and the preset control time interval constraint, the corresponding control instruction is sent to the air conditioner equipment, and the air conditioner group control energy-saving efficiency is improved.

The method is mainly applied to the technical field of intelligent robots. The invention discloses a robot group control method, system and device capable of walking randomly and a medium, and the method comprises the steps that a robot group is divided into a plurality of robot groups, and each robot group comprises a plurality of robots; determining a virtual leader in each robot group; aiming at each robot group, each robot moves by taking the virtual leader as a target; each robot group performs search action in a random walk mode; and when the robot group finds the target object in the search action, each robot in the robot group moves by taking the target object as a target so as to execute a hunting task aiming at the target object. According to the method and the device, the task of exploring and hunting multiple targets can be executed, and the task execution efficiency is improved.

The invention discloses a source load risk management and control method for cooperative operation of multiple types of adjusting power supplies and new energy, and the method comprises the steps: depicting a prediction error tail dependency relationship through employing an extreme value theory and a Copula function, constructing a distribution robust optimization model in combination with a Wasserstein distance, and obtaining the standby capacity sharing, triggering threshold and risk index of the multiple types of adjusting power supplies; performing conformal prediction based on the prediction residual error to generate a dynamic confidence interval, and mapping the dynamic confidence interval into a pipeline radius of model prediction control; executing rolling MPC containing information bottleneck regularization under the constraint of the reserve capacity and the pipeline, and generating a robust scheduling strategy; and constructing a polyhedral feasible region model of the flexible load group according to the scheduling strategy and the risk index, and performing scaling to obtain a group control action. And the new energy consumption level and the system operation safety are obviously improved.

The invention relates to an elevator group control system scheduling method based on improved YOLOv10, and belongs to the technical field of intelligent elevator control and computer vision crossing. The elevator group control system scheduling method comprises the following steps that S1, pedestrian images of indoor elevator corridor scenes are collected, and a training set and a test set are constructed; s2, the YOLOv10 network is improved; s3, training the improved YOLOv10 network by using the training set to obtain a pedestrian detection model, and testing by using the test set; s4, the trained pedestrian detection model is used for actual pedestrian detection in the elevator floor; and S5, counting the number of pedestrians on each floor according to a detection result, and dynamically adjusting an elevator scheduling strategy according to the distribution of the number of pedestrians on high and low floors. According to the method, the detection capability of the network on the shielded pedestrians is enhanced, the pedestrian detection precision is improved, and the operation efficiency of the elevator and the riding experience of passengers are remarkably improved.

The utility model discloses a group control cabinet for a refrigerating machine room, which comprises a cabinet body, and the front side of the cabinet body is connected with a cabinet door through a hinge; the moving plate is mounted on the lower side of the interior of the cabinet body, a mounting frame is fixedly mounted on the upper side of the moving plate, a fixed plate is fixedly connected to the inner side of the mounting frame, and a placement plate is connected to the middle side of the mounting frame; the cooling liquid pipe is installed on the inner side of the rear end of the cabinet body, the right side of the interior of the cabinet body is connected with a heat dissipation structure, the upper surface of the cabinet body is provided with a heat dissipation opening, the interior of the heat dissipation opening is connected with a second filter screen, a fixing block is fixedly installed on the outer side of the upper end of the cabinet body, and the interior of the fixing block is connected with an installation block. A rainproof plate is fixedly mounted on the outer side of the upper end of the mounting block, and a fixing rod is connected to the outer side of the lower end of the mounting block. According to the group control cabinet of the refrigerating machine room, heat dissipation of the control cabinet is facilitated, electrical equipment can be conveniently installed, and rain prevention of the control cabinet is facilitated.

The invention belongs to the crossing field of artificial intelligence and industrial automation, particularly relates to an aeration fan frequency conversion group control energy-saving method based on an intelligent optimization algorithm, and aims to solve the problems of high energy consumption, response lag and large equipment loss of a traditional control mode. According to the method, parameters such as dissolved oxygen, flow and pressure are collected in real time, a nonlinear optimization model with minimum energy consumption, accurate oxygen supply and minimum start and stop as multiple targets is constructed, an optimal rotating speed distribution scheme is solved online by adopting an improved particle swarm algorithm, and a smooth slope instruction output and closed-loop feedback correction mechanism is combined, so that the optimal rotating speed distribution scheme is obtained. And dynamic self-adaptive adjustment of fan group control is realized. The energy-saving efficiency is remarkably improved by 18%-25%, the annual electricity is saved by more than 800000 kilowatt-hour, meanwhile, the load unevenness is reduced to below 8%, fan start and stop is reduced by 60%, the service life of equipment is prolonged by 2-3 years, hardware does not need to be newly added, the deployment period is short, investment recovery is fast, and the method is successfully applied to multiple sewage plants.

The invention belongs to the technical field of motor control, and relates to a four-motor fault-tolerant working topological structure and method. The topological structure comprises twelve phases of bridge arms which are respectively connected with motors I to IV, and each motor is provided with A, B and C three-phase windings. The structure also comprises four bidirectional thyristors which are connected with the C-phase bridge arms of the four motors. During working, the duty ratio of the C-phase bridge arm is taken as a reference, the optimal duty ratio of the motor with the open-circuit fault is calculated, and fault-tolerant control is carried out on the four three-phase star-connected winding permanent magnet synchronous motors. The method not only can prolong the service life of the system, but also considers fault-tolerant control of a large number of motors, effectively improves the reliability of the motor group control system, and has important application value in the field of motor control.

The invention discloses a multi-target cooperative control method for a production system, and the method comprises the steps: collecting and preprocessing multi-source data of wire harness production, and constructing a multi-dimensional structure containing processes, materials, carriers and time phases; establishing a time-phase mapping and dynamic evolution mechanism based on the multi-dimensional structure, and generating a target time-phase association set; takt, material in-place and carrier circulation signals are extracted, a multi-frequency phase set is constructed, and a comprehensive deviation signal is formed; constructing a multi-frequency phase-locked group control module, and generating three types of multi-target control instruction sets of takt, material and carrier; inputting the target association set and the control instruction set into an improved Longform model to generate a prediction control vector of the next period; and according to the predictive control vector output parameter, dynamically updating the plan and path configuration and rolling and correcting the phase locking parameter. According to the invention, through multi-dimensional time phase modeling and the improved Longform model, cooperative control optimization of the wire harness production takt, the material and the carrier is realized.

The invention belongs to the technical field of mold area temperature control, and particularly relates to an automobile part mold area temperature control method, which is characterized in that a temperature control area of a mold cavity channel is finely divided based on the wall thickness and heat distribution of the mold cavity channel, so that the fine division of the temperature control area is realized; and each temperature control area is accurately adjusted by combining the planned cooling time, so that the occurrence of events of non-uniform cooling of a finished product and poorer forming quality caused by not considering the wall thickness can be avoided or reduced, the cooling uniformity is optimized, the injection molding quality is improved, and meanwhile, according to the temperature value of each temperature area and the distance between the areas, the injection molding quality is improved. According to the control method, the mode of combining independent control, grouping control and overall control is adopted for the area needing to be controlled, the defects existing in the current single temperature control mode are overcome, and therefore the preheating requirement can be rapidly met, the control time is saved, the control efficiency is improved, and energy waste is avoided.

The invention relates to a water chilling unit group control method and system based on an ESC. The method comprises the steps that meteorological parameters, air conditioner area equipment heat dissipation, personnel loads and the like are obtained in real time for load prediction; establishing a water chilling unit energy efficiency model based on the mechanism; a model self-updating device is designed, operation data can be processed and stored, the characteristics can be changed due to aging and other reasons when a host operates for a period of time, a model updating strategy verifies the model precision according to the actual operation data, and model parameters are corrected if the deviation is too large; the group control strategy establishes an objective function and constraint conditions, collects cooling water parameters, outdoor environment wet bulb temperature and other parameters based on a load prediction result, performs optimization through an escape optimization algorithm ESC, and outputs the number of on-off hosts, the load rate of each host and the like.

The invention relates to the technical field of hydraulic turbine set accessory equipment redundancy control, in particular to a drainage pump multi-lift redundancy control method and system. The method comprises the steps that the pump starting water level and the pump stopping water level of a top cover drainage pump of a hydraulic turbine set are defined, and the difference between the tail water level of the hydraulic turbine set and the water level in a top cover is H water head; the hydraulic turbine set comprises a first lift water pump set and a second lift water pump set with the lift lower than that of the first lift water pump set, and the switching water level of the first lift water pump set and the second lift water pump set is H; judging whether the H water head is greater than the H switching water level or not; if not, executing a second lift water pump group control logic; if yes, first lift water pump set control logic is executed; and judging whether the water level in the top cover of the hydraulic turbine set is lower than the pump stopping water level, if so, triggering pump stopping of the corresponding lift water pump set, and completing safe operation of the hydraulic turbine set at different water levels. By means of the method, the reliability and economical efficiency of hydraulic turbine set equipment operation can be effectively improved.

The invention discloses a marshalling train end screening method, device and equipment and a storage medium, and relates to the technical field of rail transit, and the method comprises the steps: obtaining marshalling plan information of a marshalling train when the marshalling train does not meet an end screening condition; when a head idle instruction is received, the head ends of the marshalling trains are screened based on the marshalling plan information, and marshalling train head end screening is completed; and when a tail idle instruction is received, the tail ends of the marshalling trains are screened based on the marshalling plan information, and marshalling train tail end screening is completed. Through the train end screening method and device, the technical problem that the train marshalling function cannot be completed due to the fact that the group control equipment cannot complete the end screening function for the marshaled train once the marshaled train cannot meet the set end screening condition due to some scenes in the related technology is solved.

The invention discloses a water chilling unit AI energy-saving group control module and method based on IOE, and the module comprises a prediction model building unit, a water chilling unit screening unit and a screening equipment energy-saving adjustment unit, relates to the technical field of water chilling unit control, and solves the technical problems that the real-time load demand cannot be met, and the energy consumption is wasted or the comfort is reduced. According to the method, the number of equipment is dynamically adjusted according to the predicted load, a traditional extensive mode of'fixed number + manual start and stop 'is avoided, for example, a high-energy-efficiency unit is started in advance before a high-load period is predicted, and energy consumption impact caused by equipment overload or temporary start and stop when the load is sharply increased is avoided; meanwhile, high-energy-efficiency equipment is screened as equipment to be analyzed by setting a COP threshold value, it is ensured that follow-up adjustment only aims at an efficient unit, the comprehensive energy efficiency ratio of the system is increased through accurate load matching and a high-energy-efficiency equipment priority strategy, and the frequent starting and stopping time and the low-load operation time of the equipment are shortened.

The invention aims to provide an elevator group control dynamic scheduling method and system based on reinforcement learning, electronic equipment and a storage medium. The method comprises the steps that S100, elevator demand data is obtained; s200, elevator taking behavior temporal and spatial characteristics are obtained according to the elevator demand data; and S300, an elevator resource dynamic scheduling strategy is generated according to the elevator taking behavior spatio-temporal characteristics. According to the method, an intelligent closed loop of data acquisition, space-time modeling, dynamic strategy generation and continuous evolution is constructed, so that the autonomous evolution of the elevator dispatching logic along with the building passenger flow rule is realized.

The invention discloses an engineering vehicle and a steering control system and method.The system can be used for a new energy architecture and a fuel architecture, a steering control valve set of the system is arranged on an oil supply oil way, the oil supply oil way is divided into a steering oil way and a centering oil way through the steering control valve set, the steering oil way communicates with a steering execution element, and the centering oil way communicates with the steering execution element; the centering oil path is communicated with the centering execution element, and the steering control valve group has a first switching state and a second switching state for controlling an oil liquid flowing path; the centering control valve group is arranged on the centering oil path; in the first switching state, the steering oil way supplies oil to the steering execution element, and the centering control valve group controls the centering oil way to unload; and in the second switching state, the steering oil way is unloaded, and the centering control valve group controls the centering oil way to supply oil to the centering execution element. Interlocking of vehicle steering and centering can be achieved, namely the vehicle can only be in a steering or centering state, and the phenomenon that high-pressure oil exists in steering and centering at the same time due to component faults is avoided.

The invention relates to a group control optimization method and system for a cold station system, and belongs to the technical field of automatic control, and the method comprises the steps: carrying out the minimum optimization of a first-stage group control optimization objective function through a mixed integer programming algorithm with the start-stop state of each device as an optimization variable during the operation of the cold station system, and obtaining a second-stage group control optimization objective function; the start-stop state of each device of the cold station system is controlled during first-stage group control; and on the basis of the first-stage group control, performing minimum optimization on the second-stage group control optimization objective function by taking chilled water outlet water temperature, chilled pump frequency, cooling pump frequency and cooling tower frequency as optimization variables through a model prediction control algorithm, and controlling the operation state of operation equipment when the cold station system is in the second-stage group control. Therefore, energy consumption is reduced as much as possible and energy efficiency is improved.

A group control charging power distribution method comprises a first module cabinet, a second module cabinet and a charging controller, N first charging modules and N first switch control modules are arranged in the first module cabinet, and N second charging modules and N second switch control modules are arranged in the second module cabinet. Each first charging module and each second charging module are correspondingly connected with each first switch control module and each second switch control module respectively, and each first charging module and each second charging module are connected with each first terminal and each second terminal; the first terminals are connected with the second terminals in a one-to-one correspondence mode through the cabinet combination relays. The charging controller is connected with the first charging modules, the second charging modules, the first switch control module, the second switch control module, the positive pole cabinet combination relay and the negative pole cabinet combination relay. According to the invention, the defects in the prior art are overcome, and the problems of high cost and non-uniform power distribution in the prior art are solved by optimizing the structure and control logic of the module cabinet.

The invention discloses a data center active preventive operation and maintenance method based on intelligent group control, and relates to the technical field of telecommunication technologies. Comprising the steps of collecting data center environment parameters and equipment operation parameters in real time and performing data preprocessing; preliminarily defining a state space, and constructing a state incidence matrix and a state space model between devices; defining a control action space, and establishing an optimization function by taking minimization of energy consumption efficiency and avoidance of equipment overheating as targets; a state space is improved by fusing a historical mean value and a real-time state and quantifying a system dynamic trend, an intelligent agent is trained based on Markov decision, and a dynamic adjustment strategy is embedded; the intelligent agent generates a control instruction, and the experiment platform executes the instruction and feeds back the instruction to form a dynamic closed loop. The problems that in the prior art, real-time dynamic adjustment is insufficient, and the operation and maintenance effect and the response capacity are poor are solved, and the purposes of dynamic adjustment, environment and parameter change adaptation, high fault recognition rate and high response capacity are achieved.

The invention discloses an intelligent air conditioner group control method and system based on cloud computing, and the method comprises the steps: dividing air conditioners in a hotel hall into a plurality of regions according to the distribution positions of air supply outlets, setting heat isolation layers after customers enter a hotel gate, dividing the plurality of regions into the levels of the heat isolation layers, the different levels of the heat isolation layer are controlled to be within the set temperature value when the customers pass by the corresponding areas, the real-time temperature of the different areas in the hall after the customers enter the hotel hall is monitored and analyzed, and the influence condition of the external temperature on the different areas in the hall can be accurately predicted; different areas are divided and screened according to heat isolation layers, the control and adjustment capacity of air conditioner group control on the temperatures of different positions is improved, finally, an air supply outlet is controlled to adjust the temperature based on the division result, and the possibility that customers feel uncomfortable due to the temperature difference is reduced. The system has the characteristics of high monitoring precision and high humanization degree.

The invention discloses a group control method and system for a multi-group machine room air conditioner group. The multi-group machine room air conditioner group comprises at least one room-level air conditioner group and at least one inter-column air conditioner group. Air supply temperature data collected by the inter-column air conditioner group, return air temperature data collected by the room-level air conditioner group and cold channel temperature and humidity values collected by a cold channel temperature and humidity sensor are obtained; the group control configuration mode of the multi-group machine room air conditioner group is determined according to one or more of the air supply temperature data, the air return temperature data and the cold channel temperature and humidity value; the group control configuration mode comprises an operation control parameter and an operation control mode; and the inter-column air conditioner group and / or the room-level air conditioner group are / is controlled to execute the group control configuration mode. According to the method, the corresponding group control configuration mode is determined according to the detected current environment state, so that high-energy-consumption events are avoided, the operation safety of the multi-group machine room air conditioner group is improved, the energy consumption is reduced, and the efficiency of the air conditioner is improved.

The invention relates to the technical field of refrigeration air conditioners and automatic control, in particular to a fixed frequency compressor group control energy regulation system based on outdoor temperature and humidity coupling, which comprises a data acquisition unit, a reheating demand judgment unit and an anti-icing protection control unit, a data acquisition unit acquires outdoor temperature and humidity coupling data and return air temperature data of all compressors through an outdoor temperature and humidity sensor and a return air temperature sensor, and a reheating demand judgment unit implements temperature feedforward control based on outdoor temperature and humidity and sets an adjustable limit level to constrain the input proportion of the compressors. When the compressors are fully input and the humidity exceeds the standard, the moisture content of indoor, target and outdoor air is calculated, the reheating requirement is judged by combining the transient temperature when the integral speed of the cooling and dehumidification PID controller and the adjustable limiting level and the floating gear are adjusted according to the moisture content, and the anti-icing protection control unit periodically monitors the return air temperature and takes measures. The problems that temperature and humidity control of a traditional system is not coordinated, and anti-icing protection is not accurate are solved.

The invention discloses a method for realizing a mobile robot with intelligent behaviors, which comprises the following steps of: firstly, establishing a feature database of a workpiece to be processed manually or by a robot, and then, sending a task instruction comprising task data, a target machine tool, an equipment number and processing time to the robot by a workshop digital management system through a group control scheduling system; and the robot autonomously recognizes the to-be-machined workpiece according to the instruction, if recognition fails, the next to-be-machined workpiece continues to be scanned, and if recognition succeeds, a route is autonomously planned, and the to-be-machined workpiece is transferred to the target machine tool to be machined. Compared with the prior art, the method has the advantages that the robot is endowed with self-judgment and self-behavior generation capabilities under certain constraint conditions, and efficient and autonomous operation tasks can be realized; the robot has the intelligent capability, so that the robot does not depend on scheduling of a group control scheduling system when executing the operation task, the scheduling system is mostly in a monitoring state, a behavior track of the robot is monitored and the like, and the robot is endowed with more intelligent characteristics.

The invention provides a GRPO-RL driven data center multi-area cooling system group control method, which comprises the following steps of S1, implementing environment arrangement at inlets of machine room air conditioning units, a hot channel and a cold channel in each area of a controlled data center, and collecting multi-dimensional operation data required by air conditioning system group control and reinforcement learning; s2, data center cold source system strategy optimization modeling is carried out, and the control problem of each air conditioning system of a data center is approximately modeled as a Markov decision process; and S3, performing reinforcement learning control strategy online learning in combination with a group relative strategy optimization algorithm to obtain optimal energy-saving and environment control effects. According to the invention, the generalization ability and stability of the control strategy in the multi-region data center control can be improved, the cooperative control of the global energy conservation of the large data center and the comfort of each region can be realized, and the memory and computing resource occupation in the RL training process can be obviously reduced through the optimization of the multi-region sharing strategy.

The invention relates to the technical field of heat pumps, in particular to a heat pump energy-saving group control method and a heat pump system. The heat pump energy-saving group control method comprises the following steps: an information acquisition step: acquiring placement position information of a heat pump unit; and a grouping step, specifically, the heat pump units are grouped according to the placement position information of the heat pump units, the heat pump units which are not adjacent in the transverse direction and the longitudinal direction are divided into the same mother group, and at least two mother groups which are not adjacent to each other are obtained. And a loading and unloading control step: carrying out loading and unloading control on the heat pump unit in one mother group according to the loading and unloading requirements. The heat pump units are divided into the main units which are not adjacent to each other, so that the problem of airflow interference when the adjacent heat pump units operate at the same time is effectively avoided, airflow distribution is more uniform, mutual interference is reduced, mutual influence between the adjacent heat pump units is reduced, the phenomenon of frequent defrosting of the heat pump units is reduced, and the heat exchange efficiency and the energy efficiency ratio of the heat pump units are improved.