A refrigeration control system, method, apparatus and electronic device

Abstract

The present disclosure provides a refrigeration control system, method, device and electronic equipment, and relates to the technical field of refrigeration. The refrigeration control system comprises an ice making module, an ice storage pool, a circulation system and a control unit. The ice making module is used to prepare ice blocks in a natural cooling manner and deliver the ice blocks to the ice storage pool for storage. The ice storage pool adopts a composite structure of a thermal insulation layer and a phase change material and is used to store the ice blocks. The circulation system is used to crush the ice blocks in the ice storage pool into ice slurry and control the circulation of the ice slurry to achieve a refrigeration effect. The control unit is used to dynamically adjust the operating parameters of the circulation system according to the amount of stored ice in the ice storage pool to optimize the refrigeration effect. The present disclosure can effectively reduce the energy consumption of a data center, reduce the use of traditional refrigerants and achieve environmental protection.

Description

Technical Field

[0001] This disclosure relates to the field of refrigeration technology, and in particular to a refrigeration control system, method, apparatus and electronic equipment. Background Technology

[0002] With the rapid development of information technology, data centers, as the core carriers of information processing and storage, are facing increasingly prominent energy consumption issues, making energy conservation and environmental protection key research areas. Existing technologies primarily improve data center operational efficiency from two dimensions: energy monitoring and optimization, and intelligent data control. On the one hand, energy management systems based on edge computing and adaptive adjustment are constructed through energy data acquisition and dynamic configuration mechanisms. On the other hand, control frameworks capable of predicting cooling demand and optimizing energy consumption based on environmental changes are designed using predictive models and reinforcement learning algorithms. These solutions provide technical support for the safe operation and energy efficiency improvement of data centers.

[0003] Existing technologies propose energy-saving predictive control methods suitable for data center water-cooling systems, aiming to address the difficulties in modeling and insufficient energy consumption optimization in traditional control systems. A method for storing and retrieving refrigerator data in the cloud is also proposed, solving the problem of low data retrieval efficiency in existing refrigerator systems by performing time calibration on the refrigerator data in the cloud. However, these technologies cannot be directly applied to the cooling needs of data centers, making it difficult to solve their high energy consumption and environmental problems. Summary of the Invention

[0004] This disclosure provides a cooling control system, method, apparatus, and electronic equipment to overcome the problems of high energy consumption and environmental unfriendliness in data center cooling.

[0005] In a first aspect, a refrigeration control system is provided, including an ice-making module, an ice storage tank, a circulation system, and a control unit, wherein:

[0006] The ice-making module is used to prepare ice blocks using natural cooling and to transport the ice blocks to the ice storage tank for storage. The ice storage tank, with a composite structure of insulation layer and phase change material, is used to store ice blocks; A circulation system is used to break up ice blocks in the ice storage tank into ice slurry and control the circulation of the ice slurry. The control unit is used to dynamically adjust the operating parameters of the circulation system based on the amount of ice stored in the ice storage tank.

[0007] In conjunction with the first aspect, in some possible implementations of the first aspect, the ice storage tank includes: an insulation layer and a phase change material; wherein, The insulation layer is made of polyurethane foam material; Phase change materials are used to absorb and release heat, and the phase change temperature of phase change materials is related to the freezing point.

[0008] In conjunction with the first aspect, in some possible implementations of the first aspect, the control unit includes: an ice volume prediction model and a control module; wherein, Ice volume prediction models, using support vector machines or neural network algorithms, are used to predict the amount of ice stored in ice storage tanks. The control module dynamically adjusts the operating parameters of the circulation system based on the predicted ice storage volume and ambient temperature using a fuzzy rule base.

[0009] In conjunction with the first aspect, in some possible implementations of the first aspect, the control module is also used for: Based on the predicted ice storage volume and ambient temperature, the rotational speed of the circulating pump and / or the heat exchange efficiency of the heat exchanger in the circulation system are dynamically adjusted using a fuzzy rule base.

[0010] In conjunction with the first aspect, in some possible implementations of the first aspect, the ice-making module includes an ice-making unit, a temperature sensor, a cooling water circulation unit, an ice-making tank, and a temperature control unit; The cooling water circulation unit is used to control the water circulation flow in the ice-making tank; Temperature sensor is used to monitor water temperature data in ice-making tank and transmit the water temperature data to temperature control unit; The ice-making unit is used to make ice by controlling the flow rate of cooling water and the water temperature data sent by the temperature control unit.

[0011] In conjunction with the first aspect, in some possible implementations of the first aspect, the circulation system further includes: an ice slurry crushing unit, an ice slurry preparation unit, an ice slurry circulation unit, and an ice slurry recovery unit; wherein, The ice slurry crushing unit is used to crush ice blocks in the ice storage tank into ice slurry; An ice slurry preparation unit is used to mix ice slurry with cooling water through a mixer to form an ice slurry mixture; The ice slurry circulation unit is used to control the circulation of the ice slurry mixture. The circulating ice slurry mixture removes heat and achieves a cooling effect. The ice slurry recovery unit is used to recycle and reuse ice slurry mixtures.

[0012] Secondly, a refrigeration control method is provided, the method being applied to a refrigeration control system as described in any one of the first aspects, comprising: A target prediction algorithm is used to predict the amount of ice stored in the ice storage pool; Obtain the ambient temperature; By using a fuzzy rule base, the operating parameters of the refrigeration control system are dynamically adjusted based on the predicted ice storage volume and ambient temperature.

[0013] In conjunction with the second aspect, in some possible implementations of the second aspect, the operating parameters of the refrigeration control system are dynamically adjusted based on the predicted ice storage volume and ambient temperature using a fuzzy rule base, including: Based on the predicted ice storage volume and ambient temperature, the operating parameters of the circulation system are dynamically adjusted through a fuzzy rule base. The operating parameters of the circulation system include the speed of the circulation pump and / or the heat exchange efficiency of the heat exchanger.

[0014] Thirdly, a refrigeration control device is provided, the device being used in a refrigeration control system as described in any one of the first aspects, comprising: The first prediction unit is used to predict the amount of ice stored in the ice storage pool using a target prediction algorithm. The first acquisition unit is used to acquire the ambient temperature; The first control unit is used to dynamically adjust the operating parameters of the refrigeration control system based on the predicted ice storage quantity and the ambient temperature using a fuzzy rule base.

[0015] In conjunction with the third aspect, in some possible implementations of the third aspect, the first control unit is further configured to: Based on the predicted ice storage volume and the ambient temperature, the operating parameters of the circulation system are dynamically adjusted through the fuzzy rule base, wherein the operating parameters of the circulation system include the rotational speed of the circulation pump and / or the heat exchange efficiency of the heat exchanger.

[0016] Fourthly, an electronic device is provided. It includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of any one of the second aspects.

[0017] Fifthly, a non-transitory computer-readable storage medium is provided that stores computer instructions. The computer instructions are used to cause a computer to perform the method according to any one of the second aspects.

[0018] This disclosure provides a refrigeration control system, method, apparatus, and electronic device. The refrigeration control system includes an ice-making module, an ice storage tank, a circulation system, and a control system. The ice-making module prepares ice blocks using natural cooling and transports them to the ice storage tank for storage. The ice storage tank employs a composite structure of insulation layer and phase change material for storing the ice blocks. The circulation system breaks the ice blocks in the ice storage tank into ice slurry and controls the circulation of the ice slurry. The control system dynamically adjusts the operating parameters of the circulation system based on the amount of ice stored in the ice storage tank. By utilizing natural winter cooling energy (i.e., using natural cooling) to make ice and storing it in the ice storage tank, summer cooling of the data center is achieved. This not only effectively reduces data center energy consumption but also reduces the use of traditional refrigerants and minimizes environmental impact. Attached Figure Description

[0019] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 This is a schematic diagram of the structure of a refrigeration control system provided in an embodiment of this disclosure; Figure 2 This is a schematic diagram of another refrigeration control system provided in an embodiment of this disclosure; Figure 3 This is a schematic diagram of a refrigeration control method provided in an embodiment of this disclosure; Figure 4 This is a schematic diagram of a refrigeration control device provided in an embodiment of this disclosure; Figure 5 This is a schematic diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0020] The technical solutions in this disclosure will now be described with reference to the accompanying drawings.

[0021] To make the objectives, technical solutions, and advantages of this disclosure clearer, the disclosure will be further described in detail below with reference to the accompanying drawings. The specific operating methods in the method embodiments can also be applied to the device embodiments or system embodiments. In the description of this disclosure, unless otherwise stated, "multiple" means two or more.

[0022] In the various embodiments of this disclosure, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions between different embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0023] It is understood that the various numerical designations used in this disclosure are merely for descriptive convenience and are not intended to limit the scope of this disclosure. The order of the process numbers does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.

[0024] The terms first, second, third, fourth, and other various terminology (if present) in this disclosure, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms include and have, and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] To facilitate understanding of the embodiments of this disclosure, the terminology involved in the embodiments of this disclosure will be briefly explained below.

[0026] Phase change materials: Materials used to absorb and release heat to maintain the stable temperature of ice blocks. Their phase change temperature is close to the freezing point, ensuring the stability of ice blocks in the ice storage tank during storage.

[0027] Polyurethane foam: a high-efficiency insulation material used as an insulation layer in underground ice storage pools to reduce heat transfer and ensure the stability of ice during storage.

[0028] Support Vector Machine (SVM): A machine learning algorithm used in ice quantity prediction models to predict the amount of ice stored in ice storage tanks by analyzing historical data, thereby optimizing refrigeration control strategies.

[0029] Fuzzy rule base: A logical structure in a refrigeration control system used to dynamically adjust the operating parameters of the circulating system. Based on changes in ice storage and ambient temperature, fuzzy logic is used to adjust the speed of the circulating pump and the efficiency of the heat exchanger.

[0030] Ice slurry circulation system: It consists of ice slurry crushing, preparation, circulation and recycling units. It achieves a high-efficiency cooling effect by crushing ice into ice slurry and mixing it with cooling water, and then circulating it to remove heat.

[0031] Based on this, this disclosure provides a cooling control system, method, apparatus, and electronic equipment to overcome the lack of specific solutions for data center cooling using underground ice blocks in the prior art, which cannot be directly applied to the cooling needs of data centers, making it difficult to solve the problems of high energy consumption and environmental unfriendliness. At the same time, it does not provide a cooling control method based on a combination of natural cooling and ice slurry circulation, and cannot achieve the technical problems of dynamic adjustment of ice storage and optimization of cooling effect.

[0032] Figure 1 This is a schematic diagram of the structure of a refrigeration control system provided in an embodiment of this disclosure. Figure 1 As shown, the refrigeration control system includes an ice-making module 1, an ice storage tank 2, a circulation system 3, and a control unit 4. The ice-making module 1 prepares ice blocks using natural cooling and transports them to the ice storage tank 2 for storage. In some embodiments, this is accomplished through an integrated natural ice-making module 1 and a specially structured underground ice storage tank 2. First, the natural ice-making module 1 operates in low-temperature winter environments. The ice storage tank is typically constructed of insulating materials to reduce unnecessary heat exchange. The water in the ice storage tank is cooled by a closed cooling water circulation system. The working fluid in this system can optionally be a mixture of ethanol and sodium chloride. This mixture effectively lowers the freezing point of the cooling water, ensuring effective and normal circulation for ice-making even in extremely cold winter weather. When the water temperature drops below the freezing point, the water begins to freeze. The ice-making module 1 controls the flow rate and temperature of the cooling water to ensure the stability and efficiency of the ice-making process. After the ice blocks are formed, they are broken to a suitable size by a chain conveyor or screw conveyor and transported to the underground ice storage tank 2 for storage.

[0033] Ice storage tank 2 employs a composite structure of insulation layer and phase change material for storing ice blocks. In some embodiments, a highly efficient heat-insulating environment is constructed to minimize the transfer of ambient heat into ice storage tank 2, ensuring that the ice blocks remain solid during storage periods lasting several months (especially during the high-temperature summer months), thus providing a stable cold source for subsequent refrigeration cycles. The composite insulation structure of ice storage tank 2, composed of insulation layer and phase change material, achieves long-term ice storage stability through the synergistic effect of physical barrier and active heat absorption.

[0034] The circulation system 3 is used to break the ice blocks in the ice storage tank 2 into ice slurry and control the circulation of the ice slurry to achieve a cooling effect. During the cooling demand period, the computer room cooling system based on underground ice storage converts the stored solid cold energy (ice blocks) into a highly fluid and efficient liquid cooling medium (ice slurry) during the core operation phase of the cooling demand period (usually summer or when the computer room needs active cooling). The system then circulates the ice slurry to accurately and efficiently deliver the cooling capacity it carries to the heat source in the computer room, achieving a continuous and stable cooling effect.

[0035] Control unit 4 is used to dynamically adjust the operating parameters of the circulation system according to the amount of ice stored in ice storage tank 2, so as to optimize the cooling effect. Based on the prediction of the amount of ice in the underground ice storage tank 2 and the judgment of the cooling demand of the computer room, the circulation strategy of the ice slurry is dynamically adjusted. This relies on the collaborative work of the ice amount prediction model and the fuzzy control algorithm, aiming to achieve the optimal balance between cooling efficiency and ice consumption.

[0036] This disclosure provides a refrigeration control system, including an ice-making module 1, an ice storage tank 2, a circulation system 3, and a control unit 4. The ice-making module 1 prepares ice blocks using natural cooling and transports the ice blocks to the ice storage tank 2 for storage. The ice storage tank 2 employs a composite structure of insulation layer and phase change material for storing the ice blocks. The circulation system 3 breaks the ice blocks in the ice storage tank into ice slurry and controls the circulation of the ice slurry. The control unit 4 dynamically adjusts the operating parameters of the circulation system based on the amount of ice stored in the ice storage tank. By utilizing natural winter cooling energy (i.e., using natural cooling) to make ice and storing it in the ice storage tank, summer cooling of the data center is achieved. This not only effectively reduces data center energy consumption but also reduces the use of traditional refrigerants and minimizes environmental impact.

[0037] The ice-making module 1 is used to prepare ice blocks through natural cooling and then transport the ice blocks to the ice storage tank 2 for storage. Figure 2 As shown, the ice-making module 1 includes an ice-making unit 11, a temperature sensor 12, a cooling water circulation unit 13, an ice-making tank 14, and a temperature control unit 15. The cooling water circulation unit 13 controls the water circulation flow within the ice-making tank. The water in the ice-making tank is cooled by a closed cooling water circulation system. The working fluid circulating in this system is optionally a mixed solution of ethanol and sodium chloride. This ratio lowers the freezing point of the working fluid to, for example, below -20°C, thereby ensuring that the circulation pump and pipelines can still operate normally at extreme low temperatures (up to -30°C) and preventing freezing. As the cooling water flows through a heat exchanger exposed to cold air, it releases heat from the water in the tank into the environment, causing the tank water temperature to continuously decrease. In some embodiments, the freezing point of the cooling working fluid needs to be set with a safety margin based on the local historical extreme minimum temperature, for example, a freezing point of -25°C to cope with an environment of -20°C. Specifically, this disclosure does not specifically limit the freezing point.

[0038] Temperature sensor 12 is used to monitor water temperature data in ice-making tank and transmit the water temperature data to temperature control unit 15; temperature control system monitors water temperature data in real time through PT100 platinum resistance temperature sensors 12 (such as multi-point PT100 platinum resistance temperature sensors) arranged at multiple points in ice-making tank.

[0039] The ice-making unit 11 is used to make ice by controlling the flow rate of cooling water and the water temperature data sent by the temperature control unit 15. The prepared ice blocks are transported to the underground ice storage tank for storage through the conveying system. When the water temperature is detected to be close to the freezing point (such as 0°C), the cooling water flow rate is controlled by adjusting the three-way valve, or low-power stirring is used to prevent overcooling, thereby starting and maintaining a stable freezing process.

[0040] In some embodiments, the ice storage tank 2 is used to store ice. The ice storage tank 2 may specifically include an insulation layer 21 and a phase change material 22, wherein the insulation layer 21 is made of polyurethane foam material; the phase change material 22 is used to absorb and release heat, and the phase change temperature of the phase change material 22 is related to the freezing point.

[0041] From a technical implementation perspective, the outermost layer of the ice storage tank 2 is an insulation layer, which is in direct contact with the surrounding soil or backfill material. Its main function is to block heat flow caused by changes in ground temperature and ambient temperature. In some implementations, this insulation layer uses a high-efficiency insulation material such as polyurethane (PU) foam, which is applied to the side walls and top cover of the ice storage tank 2 in the form of on-site spraying or prefabricated panels. Polyurethane foam has a high closed-cell rate, which can effectively suppress air convection. Furthermore, a phase change material (PCM) layer is set inside the insulation layer. The selection of the phase change material is crucial; its phase change temperature must be close to the phase change point of ice (e.g., 0°C). For example, a specific salt hydrate or paraffin mixture with a melting point in the range of -2°C to 2°C can be selected. When a slight increase in the local temperature of the ice storage pool is caused by a minor heat intrusion, the PCM layer will first undergo a solid-liquid phase transition, absorbing a large amount of latent heat, thereby maintaining the temperature in the pool near the phase transition point, providing a dynamic temperature buffer barrier for the ice blocks inside, and preventing the melting of the ice blocks from starting.

[0042] In some embodiments, the thickness of the insulation layer 21 is typically determined by thermal calculation based on the average ground temperature of the hottest month in the local area, the design storage period (e.g., ≥8 months), and the thermal conductivity of the material; no specific limit is imposed.

[0043] The storage period of underground ice storage pool 2 should be greater than or equal to 8 months to ensure that the ice remains stable in the high-temperature environment of summer. Specifically, the formula for calculating the storage period is as follows:

[0044] in, To assess the heat storage capacity of ice cubes, This is the temperature difference between the freezing point and the ambient temperature. The system inputs heat to the environment. Using the above formula, the storage period of the underground ice storage tank can be calculated, ensuring the stability of the ice during storage. Specifically, this disclosure does not specifically limit the storage period.

[0045] Ice storage tank 2 maintains the physical state of the cold source medium (ice blocks), ensuring that the ice blocks do not melt or only melt slightly on the surface during the storage period, thus preserving their latent heat of solidification. This is a prerequisite for the efficient operation of the ice slurry refrigeration system. Secondly, it realizes the time transfer of energy, storing the abundant natural cold energy in winter as solid ice for use during the peak demand period in summer, solving the problem of the mismatch between cooling supply and cold source in time. Compared with the traditional method of simply thickening the insulation layer, the introduction of a phase change material buffer layer improves the system's temperature stability and thermal inertia, making it more robust in the face of short-term extreme high temperatures or local heat intrusion. Thus, while ensuring long-term storage reliability, it may reduce the dependence on the maximum thickness of the insulation layer and optimize construction costs.

[0046] The circulation system 3 is used to break the ice in the ice storage tank into ice slurry and control the circulation of the ice slurry to achieve a cooling effect. Specifically, the circulation system 3 includes an ice slurry breaking unit 31, an ice slurry preparation unit 32, an ice slurry circulation unit 33, and an ice slurry recovery unit 34; wherein... The ice slurry crushing unit 31 is used to crush the ice blocks in the ice storage tank 2 into ice slurry; during periods of high cooling demand, typically summer or when the computer room requires active cooling, Ice slurry preparation unit 32 is used to mix ice slurry with cooling water through a mixer to form an ice slurry mixture; and to extract pre-stored ice blocks from underground ice storage tank 2. In some implementations, the ice blocks are fed into a crusher via a conveyor belt or gripping device. The crusher, for example using roller crushing or hammer crushing technology, breaks large solid ice blocks into fine ice particles with uniform particle size. The typical diameter range of the ice particles can be controlled within 5-10 mm (different diameter ranges can also be set according to actual needs) to ensure good flowability and heat exchange surface area. Subsequently, the crushed ice particles enter the mixer and are mixed with cooling water (such as, but not limited to, ethylene glycol solution to lower the freezing point) as a cooling medium in a predetermined ratio to form a solid-liquid two-phase flow ice slurry mixture. The mixer ensures that the ice particles are uniformly suspended in the liquid through mechanical stirring or jet technology to prevent sedimentation.

[0047] The ice slurry circulation unit 33 is used to control the circulation of the ice slurry mixture, and the circulating ice slurry mixture removes heat to achieve a cooling effect; The ice slurry recovery unit 34 is used to recover and reuse the ice slurry mixture.

[0048] The prepared ice slurry is driven by the circulation pump of the ice slurry circulation unit 33 and flows through the terminal heat exchange devices (such as fan coil units or cold plates) laid in the machine room. The ice slurry mixture absorbs the heat generated by the equipment in the machine room through phase change heat absorption (ice particle melting) and sensible heat exchange. The ice slurry mixture after heat absorption (at this time, it has basically melted into low-temperature water or low-temperature solution) is returned to the system. It can be filtered and cooled (such as through an auxiliary cooler) before participating in the mixing cycle again, or enter the recovery system for subsequent processing and reuse, thus forming a closed or open cold energy transfer loop.

[0049] In some embodiments, the control unit 4 is used to dynamically adjust the operating parameters of the circulation system based on the amount of ice stored in the ice storage tank to optimize the cooling effect. Specifically, the control unit may include an ice quantity prediction model 41 and a control module 42; wherein, Ice volume prediction model 41 uses support vector machine or neural network algorithm to predict the amount of ice stored in ice storage pool 2. The control module 42 dynamically adjusts the operating parameters of the circulation system based on the predicted ice storage volume and ambient temperature using a fuzzy rule base.

[0050] Sensors (such as weight sensors, level sensors, or visual recognition devices) deployed in the underground ice storage tank 2 monitor the physical ice volume in real time, and input historical storage data and environmental temperature time-series data into a pre-trained ice volume prediction model. This ice volume prediction model can optionally employ machine learning algorithms such as Support Vector Machine (SVM) or Neural Network (NN) to predict the ice volume change trend (predicted ice storage volume) for a specific future period (such as the next 24 hours or week) by learning from historical data. Simultaneously, by monitoring temperature sensors inside the computer room and the power consumption or load rate of server racks, the system determines the level of cooling demand in real time. Furthermore, the system inputs the ice volume prediction results and the real-time cooling demand judgment value into a fuzzy control algorithm. This algorithm internally defines fuzzy sets and membership functions related to ice volume status (such as sufficient, moderate, insufficient) and cooling demand (such as high, medium, low), and presets a series of IF-THEN fuzzy rules (e.g., IF sufficient ice volume and high cooling demand, THEN increase ice slurry flow rate). Through fuzzy reasoning and defuzzification processes, the algorithm outputs dynamic adjustment commands for key operating parameters of the ice slurry circulation system.

[0051] In some implementations, the prediction accuracy of the ice volume prediction model is a key indicator, requiring its prediction error (e.g., root mean square error, RMSE) to be typically controlled within 5% to ensure decision reliability. Cooling demand is determined based on specific parameter thresholds. For example, high cooling demand can be identified when the temperature in the core area of ​​the computer room exceeds a set value (e.g., 25°C) or when the overall load rate of the server cluster consistently exceeds 80%. The output parameters of the fuzzy control algorithm directly correspond to the adjustable variables of the ice slurry circulation system, mainly including: the speed adjustment of the circulation pump (e.g., stepless adjustment within the range of 30%-100% of the rated speed), and the mixing ratio of ice slurry and cooling water controlled by the regulating valve (e.g., dynamically adjusted ice slurry mass ratio between 10% and 40%). The adjustment range and frequency of these parameters are determined by the algorithm in real time, and the specific values ​​are not limited.

[0052] The system operates continuously throughout the data center's cooling season. During normal summer operation, the system may continuously collect data, perform predictions and judgments, and fine-tune circulation strategies on a minute or ten-minute cycle. When a sudden surge in IT load (such as during peak business periods) causes a sharp increase in cooling demand, the system can respond quickly, increasing the ice slurry circulation flow and heat exchange intensity within seconds to prevent the data center from overheating. Conversely, at night or during off-peak periods, the system automatically reduces the ice slurry supply to conserve ice storage. In addition, if ice volume forecasts indicate that the remaining ice volume may not be sufficient until the end of the cooling season, the system will activate energy-saving control mode in advance, smoothly reducing the ice slurry consumption rate by appropriately increasing the upper limit of the data center's allowable temperature.

[0053] The dynamic adjustment of the circulation system's operating parameters enables a leap from extensive cooling to precise, on-demand cooling. Through forward-looking ice quantity prediction and real-time demand sensing, the system intelligently allocates limited natural cold sources (ice blocks), preventing premature ice depletion or the waste of large amounts of residual ice after the cooling season. This improves the utilization efficiency of underground ice storage, a natural cold source, and optimizes the refrigeration system's energy efficiency ratio to an even lower level. Simultaneously, dynamic adjustment ensures a stable thermal environment in the computer room while meeting equipment operating requirements, enhancing the system's adaptability to load fluctuations and overall reliability.

[0054] In some implementations, the control module 42 is also used to dynamically adjust the speed of the circulating pump and / or the heat exchange efficiency of the heat exchanger in the circulation system based on the predicted ice storage amount and ambient temperature, thereby further improving the response speed and energy efficiency ratio of the refrigeration system.

[0055] The control module 42 uses the predicted ice storage volume and ambient temperature as input variables, and the speed of the ice slurry circulation pump and the opening degree of the heat exchanger regulating valve as output variables, to achieve continuous and smooth dynamic adjustment of the ice slurry circulation strategy.

[0056] The precise input values ​​are fuzzified. Specifically, the predicted ice quantity is quantified into linguistic variables such as sufficient, moderate, and insufficient, and corresponding membership functions are defined for them, such as triangular or trapezoidal functions. Ambient temperature is fuzzified into high temperature, moderate temperature, and low temperature. Output variables are also fuzzified: pump speed is categorized as high speed, moderate speed, and low speed; valve opening is categorized as fully open, moderately open, and slightly open. Subsequently, the system calls the fuzzy rule base for inference, with rules in the form of IF-THEN statements, for example: IF predicted ice quantity is sufficient AND ambient temperature is high temperature, THEN pump speed is high speed AND valve opening is fully open; IF predicted ice quantity is insufficient AND ambient temperature is moderate temperature, THEN pump speed is low speed AND valve opening is slightly open. The inference process integrates all activated rules, and finally, a defuzzification method (such as the center-of-gravity method) is used to convert the fuzzy output values ​​into precise values ​​that can be used to directly control the actuators.

[0057] The dynamic adjustment of the circulation pump speed and / or heat exchanger efficiency in the circulation system is a closed-loop, real-time process. The refrigeration control system periodically (e.g., every 5 minutes) obtains the latest ice storage volume from the ice quantity prediction model and reads the ambient temperature from a network of temperature sensors deployed around the air inlets of the computer room and the ice storage tank. Subsequently, the fuzzy controller instantaneously calculates the optimal circulation pump speed and heat exchanger control valve opening based on these two inputs and sends this information to the frequency converter and electric control valves via an industrial bus (e.g., Modbus, PROFIBUS) for execution. This allows the ice slurry flow rate and heat exchange intensity to precisely match the real-time heat load of the computer room with future ice storage expectations. For example, during nighttime low temperatures and when ice volume is sufficient, the circulation rate can be moderately increased for pre-cooling, while a conservative strategy is adopted to extend the cooling time during afternoon high temperatures and when ice consumption is high.

[0058] This represents a leap from static, coarse-grained setting of cooling strategies to dynamic, fine-grained optimization. It effectively solves the control challenges caused by the time-varying nature of ambient temperature and the limited ice storage capacity. By handling uncertainties and nonlinearities through fuzzy logic, it can smoothly transition between different operating conditions, avoiding the oscillations or response lags that may occur with traditional PID controllers. It ensures that, while meeting the stringent temperature and humidity requirements of data centers, it maximizes the utilization rate of natural cooling sources, significantly reduces the start-up and shutdown frequency of mechanical refrigeration backup systems, directly contributes to improving the overall system power usage effectiveness (PUE), and ensures that ice storage resources can be sustainably supplied throughout the entire design cycle (e.g., ≥8 months).

[0059] This disclosure also provides a refrigeration control method, which is applied to, for example... Figure 1 In the refrigeration control system shown, Figure 3This is a schematic diagram of a refrigeration control method provided in an embodiment of this disclosure. Figure 3 As shown, the method includes: Step 201: Use a target prediction algorithm to predict the amount of ice stored in the ice storage pool.

[0060] Specifically, sensors deployed in underground ice storage tanks collect parameters such as ice mass and ice density, and input this data into the ice quantity prediction model (target prediction algorithm) in the refrigeration control system. The ice quantity prediction model uses machine learning algorithms such as support vector machines or neural networks to perform modeling and analysis based on historical data, current operating status, and the current number of ice blocks in the ice storage tank, and predicts the amount of ice blocks stored in the ice storage tank in the future, providing a basis for subsequent refrigeration strategy adjustments.

[0061] In some embodiments, the ice volume prediction model described in this disclosure may optionally employ machine learning algorithms such as support vector machine (SVM) or neural network (NN) to predict the trend of ice volume change (predicted ice storage volume) in a specific future period (such as the next 24 hours or week) by learning from historical data.

[0062] Step 202: Obtain the ambient temperature.

[0063] Specifically, ambient temperature data is collected in real time by temperature sensors placed inside or outside the computer room or around the ice storage tank, and the ambient temperature data is transmitted to the refrigeration control system as an important input condition for dynamically adjusting the operating parameters of the refrigeration control system.

[0064] Step 203: Using a fuzzy rule base, dynamically adjust the operating parameters of the refrigeration control system based on the predicted ice storage volume and ambient temperature.

[0065] Specifically, the refrigeration control system dynamically adjusts the operating parameters of the ice slurry circulation system based on the predicted ice storage volume and ambient temperature, combined with a preset fuzzy rule base, in order to optimize the refrigeration effect.

[0066] In another implementation, a fuzzy control algorithm is applied to the optimization of the cooling strategy. Based on fuzzy logic, this algorithm establishes a fuzzy rule base and generates corresponding control outputs based on input variables such as ice storage volume and ambient temperature. The fuzzy control algorithm can handle nonlinear and uncertain system behavior, making the cooling system's response more flexible and precise. For example, when low ice storage volume and rising ambient temperature are detected, the fuzzy controller will correspondingly increase the speed of the circulating pump, enhance the ice slurry circulation flow rate, and simultaneously improve the heat exchanger's heat exchange efficiency to maintain the cooling needs of the computer room. Furthermore, the fuzzy control algorithm can also combine the output of an ice volume prediction model to achieve proactive adjustments to the cooling strategy, thereby further improving the system's energy efficiency ratio and stability.

[0067] This disclosure provides a cooling control method that employs a target prediction algorithm to predict the amount of ice stored in an ice storage tank, obtains the ambient temperature, and dynamically adjusts the operating parameters of the cooling control system based on the predicted ice storage amount and ambient temperature using a fuzzy rule base. By utilizing natural cold energy in winter (i.e., using natural cooling) to make ice and storing it in an ice storage tank, summer cooling of the data center is achieved. This not only effectively reduces data center energy consumption but also reduces the use of traditional refrigerants and minimizes environmental impact.

[0068] In some implementations, when step 203 involves dynamically adjusting the operating parameters of the refrigeration control system based on the predicted ice storage volume and ambient temperature using a fuzzy rule base, this can be achieved, but is not limited to, the following: dynamically adjusting the operating parameters of the circulation system using a fuzzy rule base based on the predicted ice storage volume and ambient temperature. The operating parameters of the circulation system include the rotational speed of the circulation pump and / or the heat exchange efficiency of the heat exchanger. This allows for flexible adjustment of the ice slurry circulation system's operating state according to actual needs, improving refrigeration efficiency and reducing energy consumption.

[0069] In some feasible implementations, the predicted ice quantity and real-time cooling demand are fed into a fuzzy control algorithm, using the predicted ice storage volume and ambient temperature as inputs. This algorithm internally defines fuzzy sets and membership functions for ice quantity status (e.g., sufficient, moderate, insufficient) and cooling demand (e.g., high, medium, low), and pre-sets a series of IF-THEN fuzzy rules (e.g., IF sufficient ice quantity and high cooling demand, THEN increase ice slurry flow rate). Through fuzzy inference and defuzzification processes, the algorithm outputs dynamic adjustment commands for key operating parameters of the ice slurry circulation system.

[0070] In some implementations, the prediction accuracy of the ice volume prediction model is a key indicator, requiring its prediction error (e.g., root mean square error, RMSE) to be typically controlled within 5% to ensure decision reliability. Cooling demand is determined based on specific parameter thresholds. For example, high cooling demand can be identified when the temperature in the core area of ​​the computer room exceeds a set value (e.g., 25°C) or when the overall load rate of the server cluster consistently exceeds 80%. The output parameters of the fuzzy control algorithm directly correspond to the adjustable variables of the ice slurry circulation system, mainly including: the speed adjustment of the circulation pump (e.g., stepless adjustment within the range of 30%-100% of the rated speed), and the mixing ratio of ice slurry and cooling water controlled by the regulating valve (e.g., dynamically adjusted ice slurry mass ratio between 10% and 40%). The adjustment range and frequency of these parameters are determined by the algorithm in real time, and the specific values ​​are not limited.

[0071] The system operates continuously throughout the data center's cooling season. During normal summer operation, the system may continuously collect data, perform predictions and judgments, and fine-tune circulation strategies on a minute or ten-minute cycle. When a sudden surge in IT load (such as during peak business periods) causes a sharp increase in cooling demand, the system can respond quickly, increasing the ice slurry circulation flow and heat exchange intensity within seconds to prevent the data center from overheating. Conversely, at night or during off-peak periods, the system automatically reduces the ice slurry supply to conserve ice storage. In addition, if ice volume forecasts indicate that the remaining ice volume may not be sufficient until the end of the cooling season, the system will activate energy-saving control mode in advance, smoothly reducing the ice slurry consumption rate by appropriately increasing the upper limit of the data center's allowable temperature.

[0072] The dynamic adjustment of the circulation system's operating parameters enables a leap from extensive cooling to precise, on-demand cooling. Through forward-looking ice quantity prediction and real-time demand sensing, the system intelligently allocates limited natural cold sources (ice blocks), preventing premature ice depletion or the waste of large amounts of residual ice after the cooling season. This improves the utilization efficiency of underground ice storage, a natural cold source, and optimizes the refrigeration system's energy efficiency ratio to an even lower level. Simultaneously, dynamic adjustment ensures a stable thermal environment in the computer room while meeting equipment operating requirements, enhancing the system's adaptability to load fluctuations and overall reliability.

[0073] In some embodiments, the cooling control method can also be applied to the following scenarios: The underground ice storage tank is connected to the air conditioning systems of surrounding buildings (such as shopping malls and office buildings) to store natural cooling capacity in winter and supply cooling to multiple sources in summer, thus achieving cascaded energy utilization.

[0074] Agricultural cold chain: Combining the needs of major grain-producing areas, ice storage pools are used to provide a low-temperature environment for grain warehouses, inhibiting pests and mold, and reducing energy consumption by 70% compared to traditional mechanical refrigeration.

[0075] In some embodiments, integrated photovoltaic-storage-cooling system can also be adopted: photovoltaic panels are laid on the top of the ice-making pool to generate electricity for ice making in winter and to feed surplus electricity into the grid in summer, forming a closed loop of "power generation-storage-cooling".

[0076] Carbon capture synergy: Using the condensate generated during ice making for industrial carbon capture, achieving water resource recycling and negative carbon emissions.

[0077] Extreme Environment Adaptation For polar environmental research stations, the insulation structure of ice storage pools can be improved (e.g., by adding vacuum insulation panels) to achieve energy-free cooling throughout the year.

[0078] Applying ice-slurry phase change materials to satellite thermal control systems allows for natural ice formation at extremely low temperatures in space, solving the spacecraft's heat dissipation problem.

[0079] As an alternative approach, ice storage tanks can also employ vertical stratified ice storage: using a spiral underground ice storage tower, rotating blades achieve uniform distribution of ice layers, avoiding the "cold bridge" effect of traditional ice storage tanks, while also reducing the footprint.

[0080] The phase change material can also use low-cost PCM materials such as sodium acetate trihydrate to replace the polyurethane and bentonite composite structure described in the present invention, thereby increasing the latent heat of phase change by 20%.

[0081] In other implementations, the refrigeration system 3 is reconfigured through a refrigeration cycle and adopts a dual-cycle refrigeration system: an air direct evaporation cooling branch is added outside the main ice slurry cycle, and when the ambient temperature is below 10°C, it switches to air cooling to reduce ice slurry consumption.

[0082] In some other implementations, the ice slurry breaking unit 31 includes a magnetically levitated ice slurry pump: a frictionless magnetically levitated pump is used instead of a screw pump, reducing energy consumption by 35% and avoiding uneven ice particle breaking caused by mechanical wear.

[0083] The ice volume prediction model 41 can also be upgraded with intelligent control algorithms to achieve digital twin prediction: construct a three-dimensional digital model of the ice storage pool, combine meteorological big data and intelligent prediction, optimize the ice making / melting strategy 72 hours in advance, and improve the accuracy by 15% compared with traditional fuzzy control.

[0084] Edge computing distributed control: The control module is pushed down to each ice storage pool node, and decentralized collaboration is achieved through 5G communication, reducing the system response latency to less than 50ms.

[0085] In other implementations, the ice-making pool 14 employs a biomimetic ice surface treatment: a biomimetic superhydrophobic coating (such as polytetrafluoroethylene nanoparticles) is applied to the surface of the ice-making pool, reducing ice adhesion by 80% and reducing ice-breaking energy consumption.

[0086] In the ice-making process, microbial enhancement can also be used: adding ice-nucleating bacteria (such as Pseudomonas syringae) can accelerate the freezing process and increase ice-making efficiency by 40% at -5℃.

[0087] Corresponding to the above-described refrigeration control method, this invention also proposes a refrigeration control device. Since the device embodiments of this invention correspond to the above-described method embodiments, details not disclosed in the device embodiments can be referred to the above-described method embodiments, and will not be repeated here.

[0088] This disclosure also provides a refrigeration control device. Figure 4 This is a schematic diagram of the structure of a refrigeration control device provided in an embodiment of this disclosure. Figure 4 As shown, the refrigeration control device includes a first prediction unit 301, a first acquisition unit 302, and a first control unit 303.

[0089] The first prediction unit 301 is used to predict the amount of ice stored in the ice storage pool using a target prediction algorithm.

[0090] The first acquisition unit 302 is used to acquire the ambient temperature.

[0091] The first control unit 303 is used to dynamically adjust the operating parameters of the refrigeration control system based on the predicted ice storage volume and ambient temperature using a fuzzy rule base.

[0092] This disclosure provides a cooling control device that employs a target prediction algorithm to predict the amount of ice stored in an ice storage tank, obtains the ambient temperature, and dynamically adjusts the operating parameters of the cooling control system based on the predicted ice storage amount and ambient temperature using a fuzzy rule base. By utilizing natural cold energy in winter (i.e., using natural cooling) to make ice and storing it in the ice storage tank, summer cooling of the data center is achieved. This not only effectively reduces data center energy consumption but also reduces the use of traditional refrigerants and minimizes environmental impact.

[0093] In some implementations, the first prediction unit 301 is further configured to dynamically adjust the operating parameters of the circulation system based on the predicted ice storage quantity and the ambient temperature through the fuzzy rule base, wherein the operating parameters of the circulation system include the rotational speed of the circulation pump and / or the heat exchange efficiency of the heat exchanger.

[0094] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of the embodiments of this disclosure, and the principle is the same. Therefore, the embodiments of this disclosure are not limited thereto.

[0095] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0096] Figure 5 A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0097] like Figure 5As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory 402 or a computer program loaded from a storage unit 408 into a random access memory 403. The random access memory 403 may also store various programs and data required for the operation of the electronic device 400. The computing unit 401, the read-only memory 402, and the random access memory 403 are interconnected via a bus 404. An input / output interface 405 is also connected to the bus 404.

[0098] Multiple components in electronic device 400 are connected to input / output interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of monitors, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows electronic device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0099] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as cooling control methods. For example, in some embodiments, the cooling control method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via read-only memory 402 and / or communication unit 409. When the computer program is loaded into random access memory 403 and executed by the computing unit 401, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the aforementioned cooling control method by any other suitable means (e.g., by means of firmware).

[0100] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0101] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0102] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0103] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0104] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0105] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0106] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0107] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0108] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A refrigeration control system, characterized in that, It includes an ice-making module, an ice storage tank, a circulation system, and a control unit, among which: The ice-making module is used to prepare ice blocks by natural cooling and to transport the ice blocks to the ice storage tank for storage. The ice storage tank adopts a composite structure of insulation layer and phase change material for storing ice blocks; The circulation system is used to break the ice in the ice storage tank into ice slurry and control the circulation of the ice slurry. The control unit is used to dynamically adjust the operating parameters of the circulation system according to the amount of ice stored in the ice storage tank.

2. The refrigeration control system according to claim 1, characterized in that, The ice storage tank includes: an insulation layer and a phase change material; wherein... The insulation layer is made of polyurethane foam material; The phase change material is used to absorb and release heat, and the phase change temperature of the phase change material is related to the freezing point.

3. The refrigeration control system according to claim 1, characterized in that, The control unit includes: an ice volume prediction model and a control module; wherein... The ice quantity prediction model uses a support vector machine or neural network algorithm to predict the amount of ice stored in the ice storage pool. The control module dynamically adjusts the operating parameters of the circulation system based on the predicted ice storage volume and ambient temperature using a fuzzy rule base.

4. The refrigeration control system according to claim 3, characterized in that, The control module is also used for: Based on the predicted ice storage volume and the ambient temperature, the rotational speed of the circulating pump and / or the heat exchange efficiency of the heat exchanger in the circulation system are dynamically adjusted using the fuzzy rule base.

5. The refrigeration control system according to any one of claims 1-4, characterized in that, The ice-making module includes an ice-making unit, a temperature sensor, a cooling water circulation unit, an ice-making tank, and a temperature control unit. The cooling water circulation unit is used to control the water circulation flow in the ice-making tank; The temperature sensor is used to monitor the water temperature data in the ice-making tank and transmit the water temperature data to the temperature control unit; The ice-making unit is used to make ice by controlling the flow rate of cooling water and the water temperature data sent by the temperature control unit.

6. The refrigeration control system according to claim 1, characterized in that, The circulation system further includes: an ice slurry crushing unit, an ice slurry preparation unit, an ice slurry circulation unit, and an ice slurry recovery unit; wherein... The ice slurry crushing unit is used to crush the ice blocks in the ice storage tank into ice slurry; The ice slurry preparation unit is used to mix the ice slurry with cooling water through a mixer to form an ice slurry mixture; The ice slurry circulation unit is used to control the circulation of the ice slurry mixture, and the circulating ice slurry mixture removes heat to achieve a cooling effect. The ice slurry recovery unit is used to recycle and reuse the ice slurry mixture.

7. A refrigeration control method, characterized in that, The method is applied to a refrigeration control system as described in any one of claims 1-6, comprising: A target prediction algorithm is used to predict the amount of ice stored in the ice storage pool; Obtain the ambient temperature; The operating parameters of the refrigeration control system are dynamically adjusted based on the predicted ice storage volume and the ambient temperature using a fuzzy rule base.

8. The refrigeration control method according to claim 7, characterized in that, The step of dynamically adjusting the operating parameters of the refrigeration control system based on the predicted ice storage volume and the ambient temperature using a fuzzy rule base includes: Based on the predicted ice storage volume and the ambient temperature, the operating parameters of the circulation system are dynamically adjusted through the fuzzy rule base, wherein the operating parameters of the circulation system include the rotational speed of the circulation pump and / or the heat exchange efficiency of the heat exchanger.

9. A refrigeration control device, characterized in that, The device is applied in a refrigeration control system as described in any one of claims 1-6, comprising: The first prediction unit is used to predict the amount of ice stored in the ice storage pool using a target prediction algorithm. The first acquisition unit is used to acquire the ambient temperature; The first control unit is used to dynamically adjust the operating parameters of the refrigeration control system based on the predicted ice storage quantity and the ambient temperature using a fuzzy rule base.

10. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 7-8.

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