1638 results about "Cooling capacity" patented technology

The invention discloses an accurate flow control method and system for a two-phase cold plate cooling data center, and relates to the technical field of two-phase cold plate cooling, and the method comprises the following steps: S1, collecting multi-mode operation monitoring data in real time, and carrying out the data preprocessing; s2, constructing a multivariable short-time-sequence prediction model, predicting the cooling demand, and performing optimization regulation and control on a cooling demand prediction result; s3, the target regulation and control flow of the cooling liquid is predicted, and flow and cooling execution measures are taken according to the target regulation and control flow prediction result; the opening degree of the valve is accurately adjusted and evaluated in real time, and accurate flow control is achieved; s4, integrating multi-mode operation monitoring data, a cooling demand prediction result, a target regulation and control flow prediction result and a valve opening accurate regulation evaluation result, and constructing a parameter optimization and safety fault-tolerant mechanism; the problems of chip safety and energy consumption risks caused by cold plate temperature overshoot and cooling capacity regulation lag under high-load fluctuation of the server are solved.

The invention provides a liquid cooling fusion energy consumption adjustment optimization method and system based on dynamic adjustment, and the method comprises the steps: S1, obtaining the thermal load data of calculation equipment, the state data of a liquid cooling system, the state data of an air cooling system, and the constraint data of the system, and constructing a cooling fusion real-time state map; s2, performing calibration operation on the cooling prediction model based on the cooling fusion real-time state atlas, after calibration is completed, extracting cooling key dynamic feature vectors, obtaining short-time cooling prediction data based on the cooling prediction model and the cooling fusion real-time state atlas, and constructing a cooling conflict objective function; s3, obtaining a cooling random sample set, performing initialization operation on a PSO algorithm based on the cooling random sample set, and generating an optimal control parameter combination; and S4, the cooling system is controlled in real time based on the optimal control parameter combination, the step S1 to the step S4 are executed in a circulating mode, and by means of the method, optimal energy consumption control can be found in real time under the condition that the cooling capacity of the cooling system is kept.

The invention discloses a control method and device for a fusion scheduling efficient refrigerating machine room system and a storage medium, and relates to the technical field of central air conditioner control. The method comprises the steps that operation data of all edge end devices and sensor data in all areas are obtained; calculating the energy efficiency index of each subsystem based on the operation data, the distribution weight of the subsystem corresponding to each equipment type, the comfort coefficient associated with each region and the sensor data in each region; according to the energy efficiency index, a control strategy of each subsystem is formulated in combination with the cooling capacity prediction data associated with each region; and performing reverse decomposition on the control strategy to obtain control parameters of the edge end equipment in each region, and issuing the control parameters to the edge end equipment in the corresponding region. The technical defect that when the cold source machine room is dispatched, the refrigeration effect of the central air conditioner is poor is overcome, and the overall energy utilization rate and the refrigeration effect are improved.

The invention discloses an energy-saving control method and system based on a large refrigeration house, and the method comprises the steps: S1, obtaining cargo attribute information in real time through a cargo label, and inputting a dynamic load prediction model to generate a cooling capacity demand prediction signal in a future time period; s2, generating a multi-device cooperative control signal based on the cooling capacity demand prediction signal in the future period; s3, a shelf-level cooling capacity demand distribution signal is generated in combination with the cooling capacity demand prediction signal; s4, generating a directional cold airflow path signal matched with goods shelf distribution according to the goods shelf level cold capacity demand distribution signal; and S5, closed-loop feedback adjustment is conducted on the compressor frequency, the refrigerant flow and the air valve opening through an edge calculation module, and a dynamic balance control instruction of cooling capacity supply and space distribution is generated. The intelligent air quality monitoring method and system based on sensing data feedback can solve the problems of excessive refrigeration energy consumption waste caused by inaccurate cold capacity demand prediction of a large refrigeration house and extra energy loss caused by low cooperative efficiency of multiple devices.

The invention provides an intelligent monitoring system of a liquid cooling cabinet and processing equipment thereof, and relates to the technical field of data processing, and the system comprises a cold source self-control module which is used for dynamically adjusting the operation state of a cooling tower of primary side circulation and the flow of a circulating water pump according to cooling water parameters, and achieving the variable flow control of cooling water; the dynamic environment monitoring module is used for monitoring the secondary side pipeline state and the cabinet environment in real time through the cooling capacity distribution unit and the distributed sensor network; and the comprehensive monitoring sub-module is used for realizing leakage positioning through a built-in liquid leakage sensor, a temperature sensor and a pressure sensor, and automatically supplementing liquid by using a liquid storage tank. Through multi-mode sensor fusion, dynamic threshold value self-adaption and multi-parameter collaborative analysis, accurate leakage positioning and graded alarm are achieved, automatic liquid supplementing closed-loop control and variable flow adjustment are combined, the system reliability is improved, and energy consumption is reduced.

The invention discloses a cold and heat supply unit and process capable of deeply utilizing a heat source in the technical field of absorption refrigeration. The cold and heat supply unit comprises a refrigerating unit, a heating unit, a heat source flash tank and a high-pressure absorber. The refrigerating unit utilizes an external heat source to produce cold energy, the heating unit utilizes the heat source used by the refrigerating unit to produce cold energy so as to cool circulating water of the refrigerating unit, and the heat source flash tank utilizes the heat source discharged by the high-pressure absorber to heat; the absorption refrigerating unit is optimally designed, so that the absorption refrigerating unit can deeply utilize a heat source and supply cold and high-level heat energy at the same time, the problem that the heat source utilization rate of the unit is not high is solved, and the requirement for high-temperature heat energy in the production process is met; and low-grade waste heat is converted into cold energy and high-level heat energy, transition and upgrading of energy quality can be achieved, the unit has the cold and heat combined supply function, and the application scene is greatly expanded.

The invention discloses an air conditioner supply and demand collaborative optimization scheduling method, which relates to the technical field of air conditioner supply and demand scheduling, and comprises a sensing prediction module, an acquisition comparison module, a deviation correction driving module, a control adjustment module, a cold source control module, a strategy adjustment module and an anomaly detection module. When the abnormality detection module detects that the air conditioner terminal breaks down, the control adjusting module enhances the power of the peripheral equipment to fill the cooling capacity gap, if the water chilling unit breaks down, the cold source control module starts the standby unit to switch the cooling capacity output path, and when the cooling capacity is insufficient, the control adjusting module is in linkage to reduce the air volume of the low-priority area to preferentially guarantee cooling of the core area; meanwhile, a strategy adjusting module automatically matches a predefined strategy chain according to the fault type, for example, global average temperature control is switched when temperature control fails, a load balancing strategy is triggered to be distributed to a standby unit when equipment is overloaded, and the strategy is gradually rolled back to the optimal strategy based on historical data after the fault is repaired; and strategy conflicts and equipment fluctuations are avoided through progressive improvement of cold energy supply.

The invention belongs to the technical field of power system control, and discloses an intelligent adjusting system and method for a transformer. The method comprises the following steps: inputting transformer operation data and weather data collected in real time into a pre-trained load prediction model to obtain predicted load data; inputting the acquired physical parameters and operation data and preset calibration parameters into a pre-constructed digital twinborn model to obtain a cooling capacity index; calculating the total heat source power of the copper loss and the iron loss according to the predicted load data and the electrical parameters of the transformer collected in real time, and dynamically quantifying the balance state of the total heat source power and the cooling capacity index through a matching degree evaluation function in combination with the cooling capacity index to obtain a matching degree evaluation index; and the problem of dynamic unbalance between the heat dissipation capability of the transformer and the load demand in an extreme environment is systematically solved.

The invention belongs to the technical field of intelligent building energy management, and particularly discloses and provides a dual-mode cold storage air conditioning system based on human flow dynamic cooperative regulation, which comprises the following steps: acquiring dynamic human flow thermodynamic diagram data in real time, and accurately calculating a multi-region linkage cold load demand value based on a density change correlation coefficient and a heat conduction model; the real-time sensing of the flow density fluctuation and the dynamic refrigeration demand matching are realized; the phase change state of the coolant of the cold storage tank and the power consumption data of the refrigerating unit are collected in real time, the cold storage release proportion, the refrigerating power increment and other key parameters are dynamically adjusted, and the energy utilization rate is increased; the inherent defect of non-uniform cold and heat in zone control is overcome by calculating inter-zone cooling capacity migration parameters; temperature feedback data and a people flow thermodynamic diagram are collected in real time, the proportionality coefficient of the fan rotating speed and the air valve opening degree is dynamically adjusted, a closed-loop learning mechanism is formed, and the matching precision of a temperature control strategy and people flow distribution is continuously optimized.

The invention provides a high-efficiency control method and system for a central air conditioner refrigeration machine room, and relates to the technical field of automatic control, and the method comprises the steps: 1, obtaining chilled water supply and return water temperature difference and flow data, and calculating the dynamic cold load demand of a building in combination with the personnel distribution thermal imaging data of each floor; and 2, on the basis of the dynamic cooling load requirement, a dynamic adjustment model for the number of running water chilling units is built, matching degree calculation is conducted on the actual cooling load value and the single refrigerating capacity of the water chilling units through a fuzzy PID control algorithm, and a final unit running combination scheme is generated. By calculating the cooling load requirement, dynamically adjusting the operation parameters of the water chilling unit, the chilled water pump and the cooling tower and balancing the equipment operation time, the system energy efficiency is effectively improved, the energy consumption and the operation and maintenance cost are reduced, and stable operation of the refrigerating system is guaranteed.

The invention relates to the technical field of adaptive control, in particular to an AI-based adaptive phase change liquid cooling fault control method and system, and the method comprises the following steps: obtaining the temperature, flow velocity and phase change state data of a cooling liquid, monitoring the phase change time from a liquid state to a gel state under different flow velocities and temperature conditions in real time, calculating the phase change delay time increment of adjacent periods, and calculating the phase change delay time increment of the adjacent periods. And obtaining a phase change delay time change trend analysis result. According to the method, the phase change time of the cooling liquid under different flow velocity and temperature conditions is monitored in real time, the heat exchange behavior of the cooling agent can be predicted more accurately, the descending trend of the cooling capacity has quantitative analysis capacity, and on the basis of construction of the heat absorption rate time sequence and change rate analysis, the abnormal fluctuation degree of the heat absorption rate is combined. And the pump speed, flow distribution and flow paths of the coolant are dynamically adjusted, the heat dissipation requirements under different working conditions can be met, and the cooling scheme can be adaptively optimized according to the running trend.

The invention discloses an intelligent operation and maintenance robot real-time state monitoring system and method based on artificial intelligence, and belongs to the technical field of artificial intelligence. An intelligent operation and maintenance robot arrives at each equipment monitoring point of a transformer substation, a communication machine room or a data center to acquire multi-dimensional data; when arriving at a monitoring point, acquiring real-time electrical parameter data, and fusing the real-time electrical parameter data with the multi-dimensional data to obtain a data set; constructing a main analysis model by combining a convolutional neural network with a long short-term memory network, inputting the trained main analysis model, and outputting a current state evaluation result of the equipment; cooling medium parameters are measured, enthalpy difference changes in the cooling process are calculated, and the heat exchange efficiency of the cooling system is quantified; constructing a transformer cooling system performance evaluation model by using a support vector regression algorithm, and outputting a current heat dissipation capability coefficient of the cooling system; and judging whether the cooling system meets the heat dissipation requirement or not by combining the current heat dissipation capability coefficient of the cooling system with the current state evaluation result of the equipment.

The invention discloses a second-level regulation and control method and system for a refrigeration system of a data center, and the method comprises the steps: firstly carrying out the validity verification, type recognition and scale evaluation of a data request, outputting a request type and a task scale, distributing a hardware number, predicting the pre-refrigeration capacity through a pre-refrigeration model, and regulating and controlling the equipment combination of the refrigeration system to complete the pre-refrigeration; a load-energy consumption prediction model is utilized to predict a load curve of each component of a server and convert the load curve into a thermal power consumption curve, a cold load conversion model is utilized to predict a cold load of a data center, a real-time cold quantity difference value is combined to dynamically supplement and adjust a refrigeration system, and multi-stage verification and correction are performed according to temperature, cold quantity and energy efficiency data of a measuring point to generate an optimal control strategy. Finally, the optimal strategy is fed back to the pre-refrigeration model, the load-energy consumption prediction model and the cold load conversion model, and iterative optimization of the models is achieved. The response speed, the cooling capacity distribution precision and the overall energy efficiency of the refrigeration system are improved through task perception preposed pre-cooling, component-level precise regulation and control and closed-loop feedback optimization.

The invention discloses an energy-saving economic dispatching method for a data center energy system based on a prediction model. The method comprises the following steps: establishing a digital twinborn model of the data center energy system; dividing the data center into a plurality of energy consumption partitions, and installing a temperature acquisition device in each energy consumption partition; after a cooling capacity demand prediction model of the data center under each time period partition is established, target combination parameters and target energy efficiency indexes PUE of the chilled water temperature and the air supply temperature of a refrigeration subsystem under each time period are obtained through calculation; establishing a data center energy efficiency index PUE prediction model, and establishing an upper-layer refrigeration subsystem optimization scheduling model by taking the minimum deviation between an energy efficiency index PUE prediction value and a target energy efficiency index PUE, the minimum deviation between actual combined parameters of chilled water temperature and air supply temperature and target combined parameters and the minimum cold capacity supply and demand deviation of each partition as targets; and establishing a lower-layer data center optimization scheduling model by taking the minimum power utilization operation cost of the system and the minimum load migration cost of the IT equipment as a target.

The invention discloses a cryogenic step-by-step preparation unit and process based on injection condensation and enhanced heat absorption in the technical field of absorption refrigeration. The cryogenic step-by-step preparation unit comprises a generation assembly, an evaporation absorption assembly, an injection assembly and a condenser. The evaporation and absorption assembly comprises a medium-pressure evaporator, a low-pressure evaporator, a medium-pressure absorber and a low-pressure absorber; the generating assembly utilizes a heat source to produce a cold source, the medium-pressure evaporator utilizes part of the cold source produced by the generating assembly to primarily cool a secondary refrigerant, and the low-pressure evaporator utilizes the remaining cold source discharged by the medium-pressure evaporator to cool the secondary refrigerant again. The condenser produces cold energy which is used as cold sources of the medium-pressure absorber and the low-pressure absorber respectively; the absorption type refrigerating unit is optimally designed, single-stage refrigeration is replaced by stage-by-stage refrigeration, the problem that the cost of single-stage refrigeration is too high due to the fact that equipment is too large is solved, and then application of the unit in the deep cooling scene is expanded.

The invention discloses a liquid cooling adaptive control method and system based on a single-phase immersion liquid cooling data center, and relates to the technical field of liquid cooling control, and the method comprises the steps: reading real-time power consumption data of the data center, and synchronously starting a temperature monitoring sensor to read chip temperature data and environment temperature data; configuring a short-time predictor by using the historical load sequence, and executing information gain ratio and prediction uncertainty analysis of preset candidate granularity; performing matching evaluation of single-phase immersion liquid cooling and the data center by using the calibrated prediction granularity; and if the triggering threshold value is met, self-adaptive optimization under local-global control coordination constraint of single-phase immersion liquid cooling control is executed, and a liquid cooling response scheme is output. The technical problems that in the prior art, the cooling capacity of single-phase immersion liquid cooling is low in matching degree with the load requirement, response lags behind, and energy consumption control is insufficient are solved, and the technical effects of self-adaptive optimization control based on real-time and prediction data, energy consumption reduction and heat dissipation efficiency and temperature control precision improvement are achieved.

The invention relates to the technical field of multi-split air conditioners, and discloses a control method for a multi-split air conditioner system. According to the indoor environment parameters, the current required cooling capacity is obtained; according to the current required cooling capacity, the average energy efficiency ratio is optimized through a heuristic algorithm, and the target load rate corresponding to each compressor is obtained; and according to the target load rate corresponding to each compressor, the operation frequency of each compressor is adjusted. According to the method, the average energy efficiency ratio of the outdoor unit is optimized through the heuristic algorithm, so that the outdoor unit load rate combination optimization result when the overall average energy efficiency ratio is optimal is obtained through optimization. And the respective operation frequency is adjusted in combination with the target load rate corresponding to each compressor, so that the outdoor unit can be ensured to operate in an efficient interval, the energy-saving potential of the system is fully explored, and the energy-saving effect of the system is improved. The invention further discloses a control device for the multi-split system, the multi-split system and a computer readable storage medium.

The invention provides a distributed energy-saving control method and system for refrigeration equipment, and the method comprises the steps: obtaining physical parameters in an environment, fusing the preprocessed data, and generating an environment thermal field model through the mapping of an environment thermal field; according to a time sequence input sequence of the environment thermal field model, obtaining corresponding temperature field time sequence data, performing correction processing on the temperature field time sequence data, generating corresponding future temperature distribution, and forming a predicted future thermal field model; temperature deviation calculation analysis is conducted on future temperature distribution, the cooling capacity demand and the global cooling capacity demand of unit volume air are obtained, and an optimal refrigeration power distribution scheme is designed by minimizing the total energy consumption and meeting the power distribution constraint; and executing the optimal refrigeration power distribution scheme, determining the overall load balance degree of the system according to the current load ratio of each device, performing global optimization scheduling according to the overall load balance degree, and generating a final refrigeration device power distribution scheme.

The invention provides an intelligent cooling capacity distribution method and system for a data center refrigeration system, and relates to the technical field of data center refrigeration. Through the thermal model based on data driving, airflow distribution and temperature field change are accurately simulated, thermal load prediction errors are effectively reduced, and high-precision data support is provided for cold distribution, so that the energy utilization efficiency and the refrigeration efficiency are improved, and energy efficiency improvement and reliable operation of a data center refrigeration system are achieved. And meanwhile, the interaction among heat transfer, power consumption and a control strategy is comprehensively considered, the overall efficiency loss caused by local optimization is avoided, and the comprehensive energy efficiency of the refrigerating system is improved. Besides, the cooling capacity distribution optimization model is constructed based on the power-cooling capacity characteristic curve, so that the optimal balance between the cooling power and the cooling index is realized, and the PUE is effectively reduced.

The invention discloses a public building cold load short-time prediction method fusing physical information, and the method comprises the steps: collecting and preprocessing the historical cooling capacity, indoor environment, outdoor weather and equipment operation state data of a public building at a fixed time interval, and obtaining multi-dimensional input features; respectively establishing a workday sub-model and a holiday sub-model according to workday and holiday scene division; the workday sub-model and the holiday sub-model jointly form a cold load prediction model, the workday sub-model adopts a long short-term memory (LSTM) network, and the holiday sub-model adopts a light gradient elevator (Light GBM); a physical constraint loss function based on building energy balance and heat conduction residual error is introduced in the training process, and the physical constraint loss is fused into a total loss function according to a weighting coefficient so as to constrain that the output of each sub-model accords with the law of energy conservation and thermal inertia; monitoring the prediction error MAPE in real time and performing online calibration; and outputting a short-time cold load prediction result.

The invention relates to the technical field of thermal management of power adapters, and discloses a multi-mode power management method and device of a gallium nitride adapter. The multi-mode power management method of the gallium nitride adapter comprises the following steps: dividing the interior of the adapter into a plurality of cooling priority regions according to the thermal sensitivity of elements, and integrating a three-dimensional layered micro-channel structure; monitoring the temperature of each area and the state of a cooling system in real time; constructing a self-adaptive flow distribution algorithm, and dynamically adjusting the flow distribution of the cooling liquid according to the real-time temperature state of each region; the maximum allowable power under the current cooling capacity is calculated, and power parameters are dynamically adjusted; dynamically switching the working mode of the adapter; through the synergistic effect of the regional differentiation micro-channel cooling structure and multi-mode power management, the technical effects that the temperature management effect is improved, the reliability is improved, the service life is prolonged, and the power output capacity is enhanced are achieved.

The invention discloses a wafer bearing device, a temperature control method thereof and semiconductor process equipment. The wafer bearing device comprises a heating assembly, the heating assembly comprises a heater body, the heater body comprises an inner heating area and an outer heating area, the outer heating area is located outside the inner heating area in the radial direction, and an inner area heating part and an inner area temperature measuring part are arranged in the inner heating area; an outer area heating part and an outer area temperature measuring part are arranged in the outer heating area; an annular protruding structure is arranged on the edge of the water cooling disc, the water cooling disc is connected with the heater body in a sealed mode through the annular protruding structure, the width and the height of the annular protruding structure are set according to the working condition temperature needed by the wafer bearing device, an inner cooling area is arranged in the annular protruding structure, and the annular protruding structure is an outer cooling area. And a gap is formed between the inner cooling area and the heater body. The cooling device has high cooling capacity under the high-temperature working condition and the low-temperature working condition, and the high-temperature working condition and the low-temperature working condition can be considered simultaneously.

The invention provides a data center energy saving method and device based on artificial intelligence, and a server, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining equipment operation data, and machine room space layout information and environment parameters of a modular machine room; performing model updating processing on the initial self-adaptive real-time energy consumption prediction model through historical equipment operation data to obtain a target self-adaptive real-time energy consumption prediction model, and performing energy consumption prediction processing on the equipment operation data by using the target self-adaptive real-time energy consumption prediction model to determine an energy consumption prediction value; carrying out refrigerating capacity calculation processing on the machine room space layout information, the environmental parameters and the energy consumption predicted value through a basic heat load calculation model and an air enthalpy difference method cooling capacity calculation model, and determining a target refrigerating capacity; and according to the target refrigerating capacity, operation parameters of all refrigeration equipment of the data center are adjusted, so that all the refrigeration equipment operates with the lowest energy consumption meeting the refrigeration requirement of the data center. The energy utilization rate of the data center can be remarkably improved.

The condensation pump type dilution refrigerating machine with the booster pump comprises the pre-cooling module and the dilution module, the pre-cooling module provides the required pre-cooling temperature for the dilution module, the dilution module uses the booster pump working at 1K, 3He circulation is achieved through cooperation of the booster pump and the condensation pump, no room-temperature circulation system is arranged, and therefore the dilution effect is good. And the defects of high heat loss and high compression power consumption in a room temperature circulation mode are overcome. Meanwhile, due to the fact that the booster pump is used, the system can obtain larger circulating flow so as to achieve larger cooling capacity, and the defect that a condensation pump type dilution refrigerator obtains large refrigerating power is overcome.

The invention relates to an energy efficiency optimization method for a dynamic load of a central air conditioner, which comprises the following steps of: establishing a response relationship among a cold and heat source, a transmission and distribution system and end equipment, identifying a nonlinear decoupling characteristic of a cold and heat response behavior by monitoring operation deflection of each level of equipment under a low-load working condition, and defining and updating a cooling capacity response deviation threshold in real time; on the basis of the cooling capacity response deviation threshold, delay compression control is conducted on response actions of the refrigerant side and the air medium side, and by adjusting the air side response time sequence in advance, dynamic thermodynamic equilibrium is formed between refrigerant transfer delay and air medium response in advance; by establishing the response relationship among the cold and heat source, the transmission and distribution system and the end equipment and introducing a cold capacity response deviation threshold dynamic updating mechanism, nonlinear coupling mismatch in a cold and heat transmission chain can be accurately identified, so that the system is more flexible to operate under low-load and variable-load conditions, frequent start and stop or redundant operation of the cold and heat source is avoided, and the system reliability is improved. And therefore, the overall COP value of the system is remarkably increased, and unit cooling capacity consumption is reduced.

The invention discloses a linkage control system and method for cold energy storage and release in an energy storage low-temperature interval, belongs to the technical field of energy storage system temperature control, and aims to solve the problems that traditional energy storage cold energy regulation is lagged, cold energy waste is serious, and dynamic changes of working conditions are difficult to adapt. According to the system and method, cold release judgment is triggered based on the environment temperature and the temperature of an energy storage module, a cold release interval is defined by predicting the environment temperature and the temperature of the energy storage module in a fitting mode, a temperature prediction curve is dynamically corrected in combination with real-time working conditions, cold release subintervals are divided, and a cold release control instruction is generated; and meanwhile, the temperature of the energy storage module is monitored in real time to correct the cooling capacity release control instruction, and a cooling capacity recovery instruction is generated by monitoring the temperature of the cooling liquid. Accurate steady-state control over the temperature of the energy storage module and efficient circulation of the cooling capacity are achieved, and the operation economical efficiency and safety of the energy storage system are remarkably improved.

The invention provides a cooperative distribution method, system and device for vehicle-mounted double-loop liquid cooling refrigerating capacity and a medium, and relates to the technical field of electric vehicle liquid cooling. Determining the confidence value of the refrigerating capacity in the battery cell cooling loop according to the thermal power net increment in the electric vehicle and the temperature distribution gradient in the battery cell; when the temperature of the power device in the power conversion module is larger than a preset safety threshold value, balance adjustment is conducted on the real-time refrigerating capacity of the power conversion module, and the balance characteristic of the refrigerating capacity in a cooling loop of the power conversion module is obtained; and the liquid cooling refrigerating capacity in the double cooling loops is cooperatively redistributed according to the balance characteristic of the refrigerating capacity in the cooling loops of the power conversion module and the confidence value of the refrigerating capacity in the battery cell cooling loops. Based on the scheme, balanced distribution of the refrigerating capacity between the battery cell cooling loop and the power conversion module cooling loop in the electric automobile can be achieved.

The invention provides a control method for an indirect evaporative cooling system. The control method comprises the steps that an indirect evaporative cooling system refrigeration model and an indirect evaporative cooling system operation power consumption model are established; obtaining training data; training an indirect evaporative cooling system refrigeration model and an indirect evaporative cooling system operation power consumption model based on the training data to obtain the determined indirect evaporative cooling system refrigeration model and indirect evaporative cooling system operation power consumption model; and optimizing the operation cost of the indirect evaporative cooling system at least based on the determined model to obtain data of output parameters of the refrigeration model of the indirect evaporative cooling system when the operation cost is optimized so as to control the operation of the indirect evaporative cooling system. According to the method, the optimal operation cost of the indirect evaporative cooling system under different climates and different places can be guaranteed, operation of the indirect evaporative cooling system is controlled based on the indoor air supply temperature and the indoor air return temperature, and therefore it is rapidly and accurately guaranteed that the refrigerating capacity meets the requirement of the cooled unit.

The utility model relates to the technical field of air conditioning, in particular to an air supply structure, an air supply system and a koji room. The air supply structure comprises an air supply branch pipe, a first air valve assembly and a second air valve assembly. One end of the air supply branch pipe is an air inlet end, and an air supply section with a plurality of air supply holes in the pipe wall is formed on the air supply branch pipe; the first air valve assembly comprises a first driving mechanism and a first air valve arranged in the air supply section, and the first driving mechanism is configured to drive the first air valve to move in the air supply section so as to be close to or away from the air inlet end; the second air valve assembly comprises a second air valve arranged in the air supply section, and the second air valve is located between the first air valve and the air inlet end. According to the method, targeted air supply can be conducted on the local heating area in the space, unnecessary whole-area air supply is reduced, cooling capacity is concentrated in the hot spot area, the method can adapt to constantly-changing hot spot positions, and the air supply area and the air supply amount can be adjusted.

The invention provides a refrigeration house multi-fan intelligent cooperative defrosting control system and method. The refrigeration house multi-fan intelligent cooperative defrosting control system comprises an air-cooled condenser, a throttling valve, a compressor, a refrigerating chamber, a bypass valve, an air speed sensor, a temperature and humidity sensor, a temperature sensor, a pressure sensor, a flow meter, an air cooler tail end evaporator, data acquisition equipment, a central processing unit and a control output port. According to the cold storage scene that hot gas bypass defrosting is adopted, and one refrigerating unit is provided with a plurality of air coolers, the ratio (COP) of the accumulated refrigerating capacity to the total energy consumption in the operation period of each air cooler is calculated. And a comprehensive performance coefficient dynamic model of the air cooler is constructed by combining the temperature difference between the air supply and the air return, so that optimal energy efficiency of the air cooler and multi-fan defrosting control are realized. Compared with the prior art, the defrosting control logic is reconstructed from the perspective of the performance coefficient of the air cooler, the frosting condition of the surface of the evaporator at the tail end of the multiple fans of the refrigeration house can be accurately judged, it is ensured that the unit maintains high efficiency in the operation process, and development of the efficient and energy-saving control technology of a refrigeration house refrigerating system is facilitated.