649 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 relates to the field of unmanned aerial vehicle group control, in particular to an intelligent cooperative control method for an unmanned aerial vehicle group. Comprising the steps of receiving and preprocessing environment data, unmanned aerial vehicle state data and task data, and performing data fusion processing on the preprocessed data; performing unmanned aerial vehicle group task allocation by adopting a multi-objective optimization genetic algorithm to generate a task allocation scheme; generating an optimal path based on the task allocation scheme; the unmanned aerial vehicle executes a task according to the optimal path, and the flight path of the unmanned aerial vehicle is adjusted by using a dynamic obstacle avoidance function; a new optimal path is generated according to the current position of the unmanned aerial vehicle, the unmanned aerial vehicle continues to execute the task according to the new optimal path, and the unmanned aerial vehicle returns to the landing point after the task is completed and uploads the collected flight data and environment data. The problems that an existing selection mechanism usually depends too much on a single standard, and most of existing path planning methods only consider a static environment; and the problem that the unmanned aerial vehicle is easy to collide or deviate in the task execution process due to the lack of real-time obstacle avoidance capability is solved.

The invention discloses an RGV multi-vehicle cooperative control method and device applied to the field of storage and transportation, and the method comprises a centralized vehicle group management strategy and a distributed inter-vehicle cooperative strategy. The centralized vehicle group management strategy comprises the steps that a vehicle group control center is arranged, and the vehicle group control center is responsible for task RGV selection and task RGV path planning; the distributed inter-vehicle cooperation strategy comprises setting an inter-vehicle communication module and a local control unit for each RGV; each RGV exchanges information with other RGVs through the inter-vehicle communication module so as to obtain real-time states of all the RGVs, and the RGVs are independently controlled through the local control unit to realize speed coordination, anti-collision avoidance and task path avoidance; the vehicle group control center sends information to the inter-vehicle communication module, and the local control units adjust the speed and the driving state of the corresponding RGVs according to the information received by the inter-vehicle communication module. The instantaneity of information exchange between the RGVs is enhanced, and efficient cooperative control of the RGVs is realized.

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 invention relates to the technical field of water purification, in particular to a water surface robot group control method, device and system, the method is applied to a control unit in a group water surface robot system, and the control unit is in communication connection with a plurality of water surface robots; the method comprises the following steps: acquiring a water quality index of a water environment; judging whether the water quality index exceeds a preset threshold range or not; if yes, planning a water surface robot inspection rule according to the water quality index; and controlling the water surface robot to inspect the water environment according to the inspection rule to determine the polluted area. According to the method, the comparison relation between the water quality index of the water environment and the preset threshold range is obtained, the inspection path of the water surface robot is planned under the condition that the water quality does not meet the preset requirement so as to determine the polluted area, and then the target water surface robot is controlled to operate so as to adjust the water quality of the water environment. Therefore, dynamic water quality adjustment of the water body environment can be realized.

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 discloses an air conditioning equipment group energy-saving control method and system based on big data driving, and relates to the technical field of air conditioning equipment group control. According to a target environment where an air conditioning equipment group is located, arrangement information corresponding to the target environment is obtained; the temperature can be adjusted by X1 DEG C within the set time to determine the initial coverage area of any air conditioner, then the initial coverage area of the air conditioner is updated according to the cooling speed for the areas which are not divided, and therefore the approved coverage area of each air conditioner is obtained, and the number of seats in the approved coverage area is synchronously obtained; and then the number of started air conditioners is determined according to the number of students in the next class of the current time, the corresponding number of started air conditioners is determined, and the started objects are determined, so that the number of started air conditioners and the specific number of started air conditioners are reasonably arranged.

The invention discloses a rod-pumped well group control method based on a flexible load, and belongs to the technical field of rod-pumped well group control. The method is characterized by comprising the following steps that 1, modeling is conducted on an oil pumping system; step 2, establishing a flexible load model suitable for a rod-pumped well group; 3, taking the minimum difference value between the well group power and the input power as an objective function, considering the power constraint of each pumping unit and the frequency constraint of a motor, and solving the power constraint and the frequency constraint to obtain a pumping adjustment degree scheme between pumping unit well groups; and 4, forming a rod-pumped well group prediction control algorithm. According to the rod-pumped well group control method based on the flexible load, the flexible load characteristic of the rod-pumped well group is fully utilized, and green electric power resources can be efficiently absorbed and utilized. Dynamic matching of load and energy supply is realized by constructing an accurate flexible load model of the rod-pumped well group and performing scheduling optimization control on the system.

The invention discloses a cluster air conditioner load grouping regulation and control method and device based on user comfort preference, and relates to the field of electric power. The method comprises the steps that firstly, cluster air conditioner data of a target area is obtained, cluster air conditioners in the target area are divided into a plurality of air conditioner sets according to the cluster air conditioner data, the user comfort preference of each air conditioner set is determined, and the user comfort preference comprises the air conditioner use temperature and the pre-refrigeration temperature; then a regulation and control target of the target area is determined according to the transformer area load rate in the target area, and a regulation and control model is constructed according to the regulation and control target and the user comfort preference of each air conditioner set; and finally, based on the regulation and control model, a regulation and control strategy of each air conditioner set is obtained, and the regulation and control strategies are used for controlling each air conditioner set to conduct pre-refrigeration or shutdown in different time periods. According to the method, the user experience is improved while load regulation is met.

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.

The invention provides a micromodule data center air conditioner group control method and system. The method comprises the steps that a clustering algorithm is adopted to divide a plurality of cabinets into a plurality of cabinet groups, wherein each air conditioner corresponds to one cabinet group. And determining a group target of each cabinet group according to the average temperature of the air inlet or the average temperature of the air outlet of each cabinet group. And the air conditioner corresponding to each cabinet group performs area control aiming at the group target. According to the area control target completion condition of the air conditioner corresponding to the cabinet group and / or the operation condition of the air conditioner, whether the air conditioner needs refrigeration cooperation or not is judged; if yes, refrigeration cooperation requests are sent to the air conditioners corresponding to the other cabinet groups, and the air conditioners receiving the refrigeration cooperation requests conduct refrigeration cooperation operation. According to the method, the defects of independent operation, frequent start and stop, excessive check-in and large idle electric energy consumption of each air conditioner in a traditional control mode are overcome by adopting a refrigeration cooperative control mode, the overall operation efficiency of the micromodule is remarkably improved, and the energy consumption of the micromodule air conditioner is effectively reduced.

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.