2290 results about "Cooling capacity" patented technology

The invention provides a cold machine and water pump linkage energy-saving control method under a low-load working condition, which relates to the technical field of information and comprises the following steps of: dynamically predicting load requirements under the low-load working condition by analyzing historical operation data of a refrigeration system and combining weather forecast information, equipment operation parameters and data of building personnel activity rules; obtaining a cooling capacity demand curve and a load demand prediction result in future preset time; establishing a system low-load operation mode optimization model, taking the screened control combination meeting the preset energy-saving operation target as model input, and obtaining an optimized system low-load operation mode through the training model; the optimal energy efficiency interval is determined by analyzing the relation between equipment energy consumption and cold storage capacity, and during load demand prediction and control optimization, the interval serves as a target constraint to guide a refrigerator and water pump equipment to operate in a high-energy-efficiency area. And the operation efficiency and the energy-saving effect of the refrigerating system under the low-load working condition are improved.

The invention provides an energy-saving control method and device for a central air-conditioning system and a medium, and the method comprises the steps: constructing a cooling load prediction model of the central air-conditioning system, and predicting the cooling load per hour of the next day; under the conditions that the hourly cooling load of the next day is met and the power utilization cost is the lowest, the cooling capacity in a fixed time period is distributed; and an energy efficiency model and a function model of the central air-conditioning system are constructed, and the operation strategy of the central air-conditioning system corresponding to the given cooling capacity is optimized with the purpose of minimizing the power consumption cost, so that the comprehensive energy consumption of the central air-conditioning system is minimized. According to the method, the cooling load prediction result is improved by comprehensively considering various cooling load influence factors and effectively capturing the long-term dependency relationship in historical cooling load data, the comprehensive energy consumption minimization and the power consumption cost minimization of the central air-conditioning system serve as targets, the operation strategy is optimized through simulation combination and energy efficiency comparison, and the energy efficiency of the central air-conditioning system is improved. Therefore, the requirement of a target environment for an efficient and energy-saving central air conditioning system is met.

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.

A cooling capacity prediction method and system for heating, ventilation and air conditioning and a related device. The method comprises: by means of a denoising algorithm, decomposing an original signal into multiple intrinsic modes and a residual, the multiple intrinsic modes constituting an intrinsic mode function; using the Pearson correlation coefficient to classify the intrinsic modes into a first intrinsic mode dominated by an effective signal and a second intrinsic mode dominated by noise; decomposing the second intrinsic mode by means of wavelet soft thresholding denoising to obtain a third intrinsic mode; reconstructing the first intrinsic mode and the third intrinsic mode to finally obtain denoising data; taking the denoising data as an input of a preset neural network to finally obtain a cooling capacity prediction model, and using the cooling capacity prediction model to predict the cooling capacity of heating, ventilation and air conditioning to obtain a prediction result of the cooling capacity of the heating, ventilation and air conditioning. Compared with the prior art, the present invention solves the problems of nonlinearity, coupling, and long-term dependency in a cooling capacity prediction process, and increases the speed and accuracy of the cooling capacity prediction model.

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 belongs to the technical field of composite fiber materials, and relates to a functional thermal management fabric, in particular to a phase change microcapsule doped flexible cooling fiber fabric and a preparation method thereof. The fabric has a controllable double-layer structure and is formed by assembling nanofibers and micro-nanofibers doped with a large number of phase change microcapsules. Wherein the micro-nano fiber layer has relatively high sunlight reflectivity and atmospheric window wave band emissivity, and shows an excellent radiation cooling effect; the microcapsule content of the nanofiber layer doped with a large amount of phase change microcapsules is not less than 10 wt%, and the nanofiber layer has no rupture phenomenon, has high heat storage capacity, not only makes up for the deficiency of radiation cooling capacity, but also effectively slows down temperature fluctuation. The radiation cooling technology and the phase change latent heat technology are combined, so that the long-time temperature control performance of the fabric is realized, and the thermal shock resistance is enhanced. In addition, the fabric also has excellent mechanical property, flexibility, washability, air permeability, moisture penetrability and sunscreen property.

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 provides an air-liquid homologous liquid cooling system, a control method, equipment and a storage medium, the air-liquid homologous liquid cooling system comprises an outdoor heat dissipation device, a cooling water pump, a cooling capacity distribution unit, a cold plate type liquid cooling cabinet and a tail end air conditioner, the cooling capacity distribution unit is used for managing distribution of cooling liquid, and the cooling capacity distribution unit comprises a liquid cooling heat exchanger and a water fluorine heat exchanger; the primary side of the liquid cooling heat exchanger is connected with the outdoor heat dissipation device, the secondary side of the liquid cooling heat exchanger is connected with the cold plate type liquid cooling cabinet, the primary side of the water fluorine heat exchanger is connected with the outdoor heat dissipation device, and the secondary side of the water fluorine heat exchanger is connected with a refrigerant pipe of the tail end air conditioner; the cold plate type liquid cooling tail end and the air cooling air conditioner tail end in the machine room adopt the same outdoor primary side cold source, stepless adjustment of the cooling capacity is achieved, and the problem that two cold sources need to be deployed at the same time in a traditional cold plate type liquid cooling machine room is solved.

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 provides a two-phase cold plate liquid cooling cabinet, a control method and a data center, the two-phase cold plate liquid cooling cabinet comprises a plurality of two-phase cold plates, an electro-hydraulic integrated distribution module, a liquid cooling heat exchange module, a system detection module and a system control module, the plurality of two-phase cold plates are used for heat transfer connection with a to-be-cooled part; the electro-hydraulic integrated distribution module is arranged on the rear side of the liquid cooling cabinet and connected with the two-phase cold plate and the to-be-cooled part. The data acquisition module is used for acquiring power consumption information corresponding to to-be-cooled equipment of each node and / or cooling capacity information of a two-phase cold plate of each node; the flow of fluid in the two-phase cold plate is controlled according to the power consumption information and / or the cooling capacity information; the liquid cooling heat exchange module comprises an accommodating cavity arranged at the bottom of the liquid cooling cabinet and is connected with the electro-hydraulic integrated distribution module, and at least three stages of heat exchange units are arranged in the accommodating cavity; and the system detection module is used for acquiring parameters of a cabinet circulation system in operation of each module and each pipeline in the liquid cooling cabinet.

Disclosed is an integrated thermal and electrical system with self-regulating capabilities to provide enhanced cooling capacity and auxiliary power. The system includes two cooling loops, a primary cooling loop whose cooling capacity is fixed and a secondary cooling loop that supplements the primary cooling loop when the cooling capacity of the primary cooling loop is insufficient. The two cooling loops may use a phase change fluid whose vapor pressure is monitored to control the cooling capacity of the secondary cooling loop to respond to fluctuating thermal load. The system includes two types of energy sources such as a photovoltaic system and a power storage. The photovoltaic system may power the secondary cooling loop to control the fluid flow rate or the airflow rate through the secondary cooling loop based on the vapor pressure. The photovoltaic system may charge the power storage when not powering the secondary cooling loop.

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.

A heat-transfer system includes a cooling circuit configured to convey heated coolant from one or more cooling nodes to one or more heat-rejection devices, and to convey the cooled coolant from the one or more heat-rejection devices to the one or more cooling nodes. Each cooling node facilitates a transfer of heat to the coolant, the heat being from one or more heat-dissipation devices and a corresponding heat load on the respective cooling node. Each heat-rejection device facilitates heat transfer from the coolant to another medium. The heat-transfer system also has a selectively operable flow-control device configured to control a flow rate of the coolant through a segment of the coolant circuit. A control logic selectively operates the flow-control device responsive to an output from one or more sensors to tailor a cooling capacity available to each cooling node to the real-time heat load on the respective cooling node.

The invention belongs to the technical field of refrigeration control, and discloses a refrigeration system optimization method and device based on error correction, and the method comprises the steps: obtaining a target required cooling capacity and a current environment parameter, inputting an optimization model, and obtaining an optimal control parameter and prediction power; constructing a performance curve; the performance curve comprises a temperature refrigeration curve, a temperature correction curve and a load correction curve; inputting the optimal control parameters into the performance curve to obtain actual power; subtracting the actual power from the predicted power to obtain a power error value; judging whether the absolute value of the power error value is smaller than a preset error threshold; if yes, the refrigerating system is made to operate according to the optimal control parameters; and if not, the cooling water inlet temperature in the optimal control parameters is adjusted according to the power error value. According to the method and the device, the error of the optimal control parameter output by the optimization model can be corrected, and the refrigerating system can be ensured to operate at the optimal power energy consumption while the target required cooling capacity is met.

The invention relates to the technical field of temperature control, and provides an indoor temperature control method and device, electronic equipment and a readable storage medium. The method comprises the steps that operation state data, corresponding to current time, of temperature control equipment is obtained according to the current time, indoor and outdoor environment characteristic data corresponding to the current time are obtained, and the temperature control equipment is cold station equipment or hot station equipment; predicting an indoor load demand in the next time period according to the indoor and outdoor environment characteristic data to obtain an indoor target load in the next time period; wherein the load demand is a cooling capacity demand or a heat demand; predicting operation parameters of the temperature control equipment according to the target load, the operation state data and the indoor and outdoor environment characteristic data, and generating target operation state data of the temperature control equipment; and the temperature control equipment is controlled according to the target operation state data, so that the performance of the temperature control equipment is remarkably improved, and more refined and personalized temperature control is realized.

The invention relates to the technical field of refrigerating system control, discloses a load distribution control method, system and equipment for a refrigerating host parallel system, and aims to solve the problem of how to find the number of operating refrigerating host nodes corresponding to an optimal energy efficiency value in the parallel system. The method comprises the following steps: acquiring a three-dimensional performance array table corresponding to inlet and outlet temperatures, load rates and energy efficiency values of each refrigeration host; the real-time refrigerating capacity of each refrigerating main machine is obtained, the real-time total refrigerating capacity of the system is determined, and the range of the number of the main machines needing to operate is determined by combining the rated refrigerating capacity and the minimum refrigerating capacity of the main machines; determining the energy efficiency value in the table corresponding to each host under each corresponding number of host combinations in the number range, calculating the total energy efficiency value of each corresponding number of host combinations, and obtaining the number of hosts corresponding to the maximum total energy efficiency value as the optimal number of running hosts; and loading and unloading are carried out based on the current operation number and the optimal operation number. By means of the method, the system meets the refrigeration requirement and meanwhile reaches the optimal energy efficiency value.

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 relates to a device and a method for simulating influence of underground water seepage on an existing tunnel and a freezing wall, the device comprises a test box body, a tunnel model, a seepage system, a data acquisition monitoring system, a saline water circulation cooling system and a saline water circulation heating system, the test box body is formed by splicing three box bodies with different structures; a formed space area is used for laying a test soil body and a broken stone hardcore; the tunnel model is arranged in the hollow area in the test box body; the seepage system is used for providing an underground water source for simulating seepage; the data acquisition monitoring system is used for monitoring and recording the soil pressure and displacement of the soil body around the tunnel model and the temperature change of the soil body near the freezing pipe; the saline water circulating cooling system is used for providing cooling capacity required by soil body freezing; and the saline water circulation heating system is used for providing heat required for pulling out the freezing pipe. The device can be used for simulating the influence of groundwater seepage on soil deformation and soil pressure around the existing tunnel, adjusting the seepage direction of groundwater and restoring more real groundwater seepage movement.

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.