Data center three-working-condition efficient condensation and heat recovery system and method based on combined type heat exchanger

Abstract

The invention discloses a data center three-working-condition efficient condensation and heat recovery system and method based on a combined type heat exchanger, the core of the system is a single combined type heat exchanger, a fluorine path channel and a water path channel in the single combined type heat exchanger are thermally coupled through shared aluminum fins, and controllable distribution of heat of three media of Freon, water and air is achieved; a multi-working-condition cooperative control algorithm is built in the intelligent control unit, and the intelligent control unit comprises a working condition judgment module, an energy value evaluation module, a condensation pressure monitoring module, a heat recovery load calculation module, a fan intelligent cold supplementing module, a cooperative control module and a self-adaptive learning module. High integration of three functions of air cooling, water cooling and heat recovery is achieved, the number of devices is remarkably reduced, the occupied space of a machine room is remarkably reduced, the annual comprehensive energy efficiency ratio is greatly increased, the PUE of a data center is greatly reduced, the payback period is remarkably shortened, the annual carbon dioxide emission reduction effect is remarkable, and good economic benefits and environmental benefits are achieved.

Description

Technical Field

[0001] This invention relates to the field of data center thermal management and comprehensive energy utilization technology, and in particular to a high-efficiency condensation and heat recovery system and method for data centers under three operating conditions based on a composite heat exchanger. Background Technology

[0002] With the rapid development of the digital economy, data center energy consumption is becoming increasingly serious, with cooling systems accounting for a significant portion of energy consumption. Traditional air-cooled precision air conditioning systems suffer from three major technical bottlenecks: First, in high-temperature summer environments, condenser heat dissipation efficiency decreases, compressor discharge pressure increases, cooling efficiency drops significantly, and energy consumption surges. Second, a large amount of medium- and low-temperature waste heat generated during transitional seasons is directly discharged into the atmosphere, failing to achieve energy resource utilization. Third, winter heat recovery systems operate on a single mode, unable to dynamically adjust according to heat demand, resulting in insufficient recovery rates. Furthermore, existing technical solutions generally employ separate heat exchanger designs, leading to low system integration and high equipment redundancy. Traditional systems typically adopt a conservative "cooling-first" control strategy, failing to quantitatively assess the economic value of heat recovery and the cost of cooling power consumption, resulting in the waste of high-grade waste heat. Therefore, there is an urgent need in this field for a comprehensive solution with high integration, extreme energy efficiency, and intelligent energy value matching. Summary of the Invention

[0003] The purpose of this invention is to provide a high-efficiency condensation and heat recovery system and method for data centers under three operating conditions based on a composite heat exchanger, thereby solving the aforementioned problems existing in the prior art.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] A high-efficiency condensation and heat recovery system for data centers under three operating conditions based on a composite heat exchanger includes:

[0006] The composite heat exchanger has independent refrigerant channels and water channels that are thermally coupled through shared aluminum fins. The refrigerant channels are connected to the data center's cooling system, and the water channels can be selectively connected to a cooling tower, a first water source heat pump, or a second water source heat pump through an intelligent valve group.

[0007] A condenser fan is installed on the composite heat exchanger to provide air-cooled heat dissipation;

[0008] The intelligent valve assembly consists of three electric three-way valves, with both electrical and mechanical interlocks between each valve.

[0009] The intelligent control unit includes a controller and a sensor system. The input of the controller is electrically connected to an outdoor ambient temperature sensor, a condensing pressure sensor, and a water temperature sensor, and the output is electrically connected to an intelligent valve group, a condensing fan, and a water source heat pump.

[0010] The intelligent control unit incorporates a multi-condition collaborative control algorithm, which includes: a condition judgment module to determine the current condition range based on the outdoor temperature; an energy value assessment module to prioritize the transfer of condensing heat to the first water source heat pump for heat recovery during transitional seasons; a condensing pressure monitoring module to monitor the pressure in the refrigerant circuit in real time; and a fan intelligent cooling module to dynamically adjust the condensing fan speed when the condensing pressure exceeds a preset pressure threshold.

[0011] In some specific embodiments, the multi-condition cooperative control algorithm further includes:

[0012] The heat recovery load calculation module is used to calculate the heat recovery load of the water channel under transitional seasonal conditions.

[0013] The air-cooling compensation calculation module is used to calculate the required air-cooling load based on the condensing pressure deviation, which is the difference between the actual pressure value and the preset pressure threshold.

[0014] The coordination control module is used to generate switching commands for the intelligent valve group and speed control commands for the condenser fan based on the heat recovery load and the air cooling load.

[0015] In some specific embodiments, the fluorine channel uses an internally threaded copper tube and is designed to withstand higher pressure than the water channel. The water channel uses a smooth-walled copper tube, and the shared aluminum fins are hydrophilic aluminum foil windowed fins that are connected to the copper tube through expansion tubes.

[0016] In some specific embodiments, the first water source heat pump is a high-temperature water loop heat pump unit, used to raise the low-temperature waste heat to the domestic hot water temperature; the second water source heat pump is a ground source / water source heat pump unit, used to extract low-grade heat to provide heating hot water to the building; the intelligent valve group is equipped with a switching delay protection device, and the switching sequence is to first close the valve under the current operating condition, and after a delay to confirm that the valve is completely closed, then open the valve under the target operating condition.

[0017] In some specific embodiments, the intelligent control unit also integrates an adaptive learning module, which is configured as follows:

[0018] Record historical operating data, including outdoor ambient temperature, data center load rate, domestic hot water demand, and energy efficiency ratio of each water source heat pump;

[0019] Based on historical operating data, an optimized algorithm is used to dynamically adjust the preset pressure threshold and the operating condition switching temperature range.

[0020] Predict the demand curve for domestic hot water in the future and adjust the operation strategy of the primary water source heat pump in advance.

[0021] A method for a high-efficiency condensation and heat recovery system for a data center under three operating conditions based on a composite heat exchanger, based on the same concept, includes the following steps:

[0022] S1: Real-time acquisition of outdoor ambient temperature, refrigerant condensation pressure, water supply and return water temperature, and domestic hot water demand signals via sensor system;

[0023] S2: The operating condition judgment module judges the current operating condition based on the outdoor temperature: when the temperature is higher than the first temperature threshold, it enters the summer heat dissipation operating condition; when the temperature is lower than the second temperature threshold, it enters the winter heating operating condition; when the temperature is between the first and second temperature thresholds, it enters the transitional season heat recovery operating condition.

[0024] S3: During the transitional season heat recovery operation, the energy value assessment module prioritizes starting the first water source heat pump and shuts down the condenser fan;

[0025] S4: The condensing pressure monitoring module continuously monitors the pressure value. When the actual pressure exceeds the preset pressure threshold, the intelligent cooling module for the fan starts the condensing fan and performs speed control.

[0026] S5: When the working condition switching condition is triggered, the coordination control module controls the intelligent valve group to act according to the preset timing sequence: close the current valve → delay confirmation → open the target valve → start the corresponding equipment;

[0027] S6: The adaptive learning module periodically analyzes historical data, updates control parameters, and optimizes the hot water demand forecasting model.

[0028] In some specific embodiments, the speed control is specifically as follows:

[0029] When the actual pressure is within the first pressure range, the fan speed is PID regulated within the first speed range; when the actual pressure exceeds the second pressure threshold, the fan runs at full speed; when the actual pressure is below the third pressure threshold, the fan stops completely.

[0030] In some specific embodiments, under the heat recovery conditions during the transitional season, the energy value assessment module executes the following judgment logic:

[0031] When the demand for domestic hot water exceeds the first demand threshold, the condenser fan is kept off and all the heat from condensation is used for hot water preparation.

[0032] When the demand for domestic hot water is lower than the second demand threshold, the hot water demand is given priority, and excess heat is discharged into the atmosphere through the condenser fan.

[0033] When the condensing pressure is detected to exceed the safety threshold for a predetermined time, the condensing fan is forcibly started and the hot water preparation load is reduced.

[0034] In some specific embodiments, the adaptive learning module performs the following steps:

[0035] A system efficiency model is built based on historical data, which is linked to outdoor temperature, data center load and hot water demand;

[0036] The control parameters are adjusted through iterative optimization algorithms to minimize the deviation between the actual energy efficiency and the target energy efficiency.

[0037] By using time series analysis to predict future hot water demand, the temperature setpoint of the primary water source heat pump can be adjusted in advance.

[0038] In some specific embodiments, security protection steps are also included:

[0039] Set minimum operating time protection for the compressor and / or water pump;

[0040] When the water flow switch detects that the flow rate is lower than the preset flow rate threshold, the water source heat pump is prohibited from starting.

[0041] When the condensing pressure or refrigerant circuit temperature exceeds the safety threshold, an emergency shutdown will be initiated and an alarm will be triggered.

[0042] The beneficial effects of this invention are:

[0043] Integrated innovation and multi-purpose device: Through the original composite heat exchanger design, the three functions of air cooling, water cooling and heat recovery are highly integrated into a single physical carrier, which significantly reduces the number of devices, significantly reduces the space occupied in the computer room, and effectively saves the initial investment cost.

[0044] Energy cascade utilization and ultimate energy efficiency: The system's overall energy efficiency ratio is significantly improved throughout the year, far exceeding that of traditional systems. Heat recovery efficiency reaches a high level during the transition season, and deep heat recovery efficiency is further improved in winter, maximizing the utilization of low-grade waste heat.

[0045] Intelligent wind and water coordinated control and energy value ranking: The innovative multi-condition coordinated control algorithm has a built-in energy value assessment module. By comparing the heat recovery benefit per unit of heat with the energy consumption cost of the fan, it automatically executes the "heat recovery priority, air-cooled intelligent supplementary cooling" strategy. Under the premise of ensuring cooling safety, it maximizes the output of high-value hot water, significantly shortens the investment payback period, and is far superior to the industry average.

[0046] PUE significantly reduced: Real-world engineering cases show that after system upgrades, the average annual PUE of data centers is significantly reduced, resulting in substantial energy savings and considerable annual power reductions.

[0047] Significant carbon emission reduction and environmental benefits: Compared with traditional systems, the annual carbon dioxide emission reduction is greatly increased, the emission reduction rate is obvious, and it has good positive environmental externalities.

[0048] Adaptive learning and operating condition prediction: The adaptive learning module dynamically optimizes the control threshold based on historical operating data, predicts the domestic hot water demand curve and adjusts the strategy in advance. The system has the ability to self-evolve and has a low energy efficiency decay rate in long-term operation, which is significantly better than the fixed parameter system.

[0049] System stability and security: The system employs valve switching logic with both electrical and mechanical interlocks, along with multiple safety mechanisms such as compressor / pump minimum operating time protection and water flow interlock protection, achieving a high level of system availability and ensuring data center cooling safety. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the structure of the composite heat exchanger of the present invention;

[0051] Figure 2 This is a schematic diagram of the overall structure of the system and the principle of three working conditions of the present invention;

[0052] Figure 3 This is a diagram of the multi-condition collaborative control algorithm architecture of the intelligent control unit of this invention;

[0053] Figure 4 This is a flowchart of the three operating conditions of the present invention;

[0054] Figure 5 This is a logic block diagram of the energy value assessment module of the present invention;

[0055] Figure 6 This is a diagram of the multi-condition collaborative control algorithm architecture of the present invention;

[0056] Figure 7 This is a flowchart of the control method of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0058] Reference Figures 1 to 6 The aforementioned high-efficiency condensation and heat recovery system for data centers under three operating conditions based on a composite heat exchanger includes:

[0059] The composite heat exchanger has independent refrigerant channels and water channels that are thermally coupled through shared aluminum fins. The refrigerant channels are connected to the data center's cooling system, and the water channels can be selectively connected to a cooling tower, a first water source heat pump, or a second water source heat pump through an intelligent valve group.

[0060] A condenser fan is installed on the composite heat exchanger to provide air-cooled heat dissipation;

[0061] The intelligent valve assembly consists of three electric three-way valves, with both electrical and mechanical interlocks between each valve.

[0062] The intelligent control unit includes a controller and a sensor system. The input of the controller is electrically connected to an outdoor ambient temperature sensor, a condensing pressure sensor, and a water temperature sensor, and the output is electrically connected to an intelligent valve group, a condensing fan, and a water source heat pump.

[0063] The intelligent control unit incorporates a multi-condition collaborative control algorithm, which includes: a condition judgment module to determine the current condition range based on the outdoor temperature; an energy value assessment module to prioritize the transfer of condensing heat to the first water source heat pump for heat recovery during transitional seasons; a condensing pressure monitoring module to monitor the pressure in the refrigerant circuit in real time; and a fan intelligent cooling module to dynamically adjust the condensing fan speed when the condensing pressure exceeds a preset pressure threshold.

[0064] In this embodiment, the system includes the following core components:

[0065] I. High-efficiency condensation and heat recovery system under three operating conditions based on a composite heat exchanger

[0066] 1. Composite heat exchanger

[0067] The composite heat exchanger, as the core heat exchange unit of the system, has independent refrigerant and water channels that are thermally coupled through shared aluminum fins. The refrigerant channel connects to the data center's cooling system to circulate high-temperature, high-pressure gaseous refrigerant and release condensation heat. The water channel, via intelligent valve assemblies, can selectively connect to a cooling tower, a primary water source heat pump, or a secondary water source heat pump to circulate cooling water or heat recovery water, removing or recovering heat.

[0068] The refrigerant path uses internally threaded copper tubing, designed with a higher pressure resistance than the water path to meet the compressor's discharge pressure requirements under high-temperature conditions. The water path uses smooth-walled copper tubing. The shared aluminum fins are hydrophilic aluminum foil fins with a hydrophilic coating on the surface, connected to the copper tubing via an expansion process to ensure low contact thermal resistance. The refrigerant and water path copper tubing are arranged in a staggered pattern within the shared aluminum fins, forming a partially counter-current heat exchange structure to maximize the overall heat transfer temperature difference.

[0069] 2. Condensing fan

[0070] The condenser fan is mounted on the composite heat exchanger to provide forced air convection for air-cooled heat dissipation. The fan speed is dynamically adjusted according to the system operating conditions and condensing pressure. During transitional seasons, in heat recovery priority mode, it may stop completely to achieve zero-power cooling.

[0071] 3. Intelligent valve assembly

[0072] The intelligent valve group consists of three electrically operated three-way valves, corresponding to the cooling tower circuit, the first water source heat pump circuit, and the second water source heat pump circuit, respectively. Each valve is equipped with both electrical and mechanical interlocks to ensure that only one circuit is connected to the water channel at any given time, preventing cross-contamination or pressure conflicts between circuits operating under different conditions.

[0073] 4. First source heat pump and second source heat pump

[0074] The first water source heat pump is a high-temperature water-loop heat pump unit used to raise low-temperature waste heat to the temperature for domestic hot water, meeting the building's domestic hot water needs. The second water source heat pump is a ground source / water source heat pump unit used to extract low-grade heat to provide heating hot water to the building.

[0075] 5. Intelligent control unit

[0076] The intelligent control unit includes a controller and a sensor system. The controller's inputs are electrically connected to an outdoor ambient temperature sensor, a condensing pressure sensor, and a water circuit temperature sensor to collect system operating parameters in real time. Its outputs are electrically connected to the intelligent valve assembly, condenser fan, and water source heat pump to execute control commands.

[0077] The intelligent valve group is equipped with a switching delay protection device. The switching sequence is to first close the valve under the current operating condition, and after a delay to confirm that the valve is completely closed, open the valve under the target operating condition to ensure a smooth and shock-free switching process.

[0078] II. Multi-condition cooperative control algorithm

[0079] The intelligent control unit incorporates a multi-condition collaborative control algorithm. This algorithm adopts a modular architecture design, achieving efficient system operation and optimized control through the coordinated cooperation of various functional modules. The algorithm includes the following main functional modules:

[0080] 1. Operating Condition Judgment Module

[0081] The operating condition determination module is used to determine the current operating condition of the system. The input parameters for this module are the outdoor ambient temperature and the hysteresis temperature range. Internally, the module executes the following judgment logic: when the outdoor ambient temperature is greater than or equal to a preset summer switching threshold, the system is determined to enter summer heat dissipation mode; when the outdoor ambient temperature is less than or equal to a preset winter switching threshold, the system is determined to enter winter heating mode; when the outdoor ambient temperature is between the summer and winter switching thresholds, the system is determined to enter transitional season heat recovery mode. The module outputs the current operating condition status signal and the operating condition switching flag, providing a basis for subsequent control logic.

[0082] 2. Condensing pressure monitoring module

[0083] The condensing pressure monitoring module is used to monitor the system's condensing pressure status in real time. The module's input parameters include the real-time detection value from the condensing pressure sensor, a preset pressure threshold, and a pressure dead zone. The module performs digital filtering on the real-time detection value from the condensing pressure sensor, calculates the pressure deviation between it and the preset pressure threshold, and further calculates the pressure change rate. When the monitored pressure exceeds the upper or lower safety limit, the module outputs a pressure over-limit alarm signal, triggering the system's safety protection mechanism.

[0084] 3. Energy Value Assessment Module

[0085] The energy value assessment module evaluates the economic value of heat recovery and determines the priority of control strategies. Input parameters for this module include real-time domestic hot water demand, hot water economic value coefficient, real-time condenser fan power, electricity price coefficient, and the current energy efficiency ratio of the water source heat pump. The core logic of the module is to calculate the value of heat recovery revenue, which is calculated as the product of real-time domestic hot water demand, the hot water economic value coefficient, and the current energy efficiency ratio of the water source heat pump. Simultaneously, the module calculates the fan operating cost, which is calculated as the product of real-time condenser fan power and the electricity price coefficient. By comparing the value of heat recovery revenue with the fan operating cost, the module outputs a heat recovery priority strategy and a target heat recovery load. When the value of heat recovery revenue is greater than the fan operating cost, the module determines that a heat recovery priority strategy is adopted, and heat recovery operations are executed first.

[0086] 4. Heat recovery load calculation module

[0087] The heat recovery load calculation module is used to calculate the heat recovery load allocation under transitional season conditions. The module's input parameters include the current operating status, strategy mode, target heat recovery load, and real-time IT load of the data center. During the transitional season, when the strategy mode is set to heat recovery priority, the module calculates the waterway heat load demand, which is the smaller of the target heat recovery load and the real-time IT load of the data center multiplied by a proportional coefficient. The remaining heat load is borne by the airflow, and the module outputs the airflow heat load demand.

[0088] 5. Intelligent cooling supplement module for fans

[0089] The intelligent cooling supplement module for the condenser fan is used to dynamically adjust the fan speed to achieve intelligent cooling supplementation. The module's input parameters include pressure deviation, strategy mode, and pressure change rate. When the strategy mode is heat recovery priority and the pressure deviation is within the dead zone, the module forces the output fan speed command to zero, and the fan remains stopped. Otherwise, the module performs PID control calculations, outputs the fan speed command, and limits the calculation results to ensure the output value is between zero and full speed.

[0090] 6. Coordination and Control Module

[0091] The coordination control module generates control commands for each actuator and coordinates their timing. Input parameters for this module include the current operating status, water circuit heat load, fan speed command, and valve position feedback signal. The module performs operating condition switching timing control, adjusts the heat pump capacity output according to heat load demand, and generates valve opening commands, heat pump start / stop commands, and fan start / stop commands to ensure coordinated operation of all equipment.

[0092] 7. Adaptive Learning Module

[0093] The adaptive learning module is used for self-optimization of system operating parameters and demand forecasting. The module's input parameters include historical operating data (outdoor ambient temperature, data center load rate, domestic hot water demand, and energy efficiency ratios of each water source heat pump), actual PUE values, and target PUE values. The module adjusts control parameters through an iterative optimization algorithm to minimize the deviation between the actual and target PUE. It uses time series analysis to predict the domestic hot water demand curve for future periods and adjusts the operating strategy of the first water source heat pump in advance. The module outputs optimized preset pressure thresholds, temperature switching thresholds, and predicted hot water demand signals, which are fed back to the corresponding modules to achieve closed-loop optimization control.

[0094] In some specific embodiments, the multi-condition cooperative control algorithm further includes:

[0095] The heat recovery load calculation module is used to calculate the heat recovery load of the water channel under transitional seasonal conditions.

[0096] The air-cooling compensation calculation module is used to calculate the required air-cooling load based on the condensing pressure deviation, which is the difference between the actual pressure value and the preset pressure threshold.

[0097] The coordination control module is used to generate switching commands for the intelligent valve group and speed control commands for the condenser fan based on the heat recovery load and the air cooling load.

[0098] In some specific embodiments, to further improve the system's control accuracy and response capability, the multi-condition collaborative control algorithm also integrates a heat recovery load calculation module, an air-cooling compensation calculation module, and a coordination control module. These modules work together to achieve precise allocation and execution of heat recovery and cooling loads.

[0099] 1. Detailed configuration of the heat recovery load calculation module

[0100] The heat recovery load calculation module is specifically designed to accurately calculate the heat recovery load that the waterway should bear under transitional seasonal conditions. This module is configured to perform the following data processing flow:

[0101] The module receives the current operating status signal from the operating condition judgment module, the heat recovery priority strategy signal from the energy value assessment module, and the target heat recovery load value from the energy value assessment module. It also receives real-time IT load data from the data center as a basic reference. The module internally presets a proportionality coefficient, preferably 0.8, to ensure that the heat recovery load does not exceed 80% of the total condensation heat of the data center, thus reserving an appropriate safety margin to cope with load fluctuations.

[0102] When the operating condition judgment module determines that the current operating condition is in the transitional season and the energy value assessment module outputs a heat recovery priority strategy signal, the module initiates the load calculation logic: It calculates the product of the real-time IT load of the data center and a preset proportional coefficient to obtain the theoretical maximum heat recovery capacity of the water channel; it compares this theoretical maximum heat recovery capacity with the target heat recovery load value, taking the smaller value as the final demand heat load of the water channel; and it uses the difference between the total condensation heat of the data center and the demand heat load of the water channel as the compensation heat load of the air channel. The module outputs water channel demand heat load signals and air channel demand heat load signals, which are transmitted to the coordination control module and the intelligent fan cooling module, respectively, providing accurate load commands for subsequent execution components.

[0103] The technical benefits of this module are: it avoids the problems of excessive heat recovery (leading to excessively high condensing pressure) or insufficient heat recovery (causing energy waste) caused by inaccurate heat recovery load estimation in traditional systems, and achieves dynamic matching between heat recovery volume and real-time heat generation of the data center, significantly improving the system's economy and security.

[0104] 2. Detailed configuration of the air-cooling compensation calculation module

[0105] The air-cooling compensation calculation module is dedicated to dynamically calculating the required air-cooling compensation load based on the condensing pressure deviation. This module is configured to perform the following data processing flow:

[0106] The module receives the difference signal between the actual pressure value from the condensing pressure monitoring module and the preset pressure threshold, i.e., the condensing pressure deviation value. The module integrates a PID control algorithm, including proportional, integral, and derivative calculation units. The proportional calculation unit multiplies the condensing pressure deviation value by the proportional gain coefficient to generate a fast response component; the integral calculation unit performs time integration on the condensing pressure deviation value to generate a steady-state error-eliminating component; and the derivative calculation unit calculates the rate of change of the condensing pressure deviation value to generate an anti-adjustment component.

[0107] The outputs of the three computational components are weighted and summed to obtain the air-cooled load demand value. The module converts this air-cooled load demand value into a fan speed command and limits the command value to ensure that the output does not exceed the rated speed range of the fan. The air-cooled compensation calculation module and the heat recovery load calculation module work in parallel. When the condensing pressure continues to rise under the heat recovery priority strategy, the fan speed command output by the air-cooled compensation calculation module gradually increases, and is superimposed with the airflow load demand output by the heat recovery load calculation module to jointly constitute the final control command of the fan.

[0108] The technical benefits of this module are: it achieves closed-loop precise control of condensing pressure, and while ensuring priority for heat recovery, it maintains the condensing pressure within the optimal operating range through dynamic air-cooling compensation. This avoids the surge in compressor power consumption and safety hazards caused by excessive pressure, and also reduces the unnecessary running time of the fan, further reducing system energy consumption.

[0109] 3. Detailed configuration of the coordination and control module

[0110] The coordination control module, as the core of the execution layer of the multi-condition collaborative control algorithm, is responsible for converting the calculated load demand into specific action instructions for each actuator, and strictly controlling the timing of these actions to ensure safe system switching. This module is configured to execute the following coordination control logic:

[0111] The module receives the current operating status signal from the operating condition judgment module, the water circuit demand heat load signal and the air circuit demand heat load signal from the heat recovery load calculation module, the fan speed command signal from the fan intelligent cooling module, and the real-time position feedback signal of each three-way valve in the intelligent valve group.

[0112] When the operating condition judgment module outputs a switching flag, the coordination control module initiates the operating condition switching procedure: First, it sends a closing command to the currently operating three-way valve, with the command execution time set to 30 seconds, and the valve closes at a uniform speed; after the valve is fully closed, the module delays for 5 seconds and confirms that the valve has been completely shut off through a position feedback signal; then, it sends an opening command to the three-way valve of the target operating condition, with the opening time also set to 30 seconds, and the valve opens at a uniform speed to the fully open position; at the instant the valve switching is completed, the module sends a start command to the corresponding water source heat pump or cooling tower according to the new operating condition status, and at the same time adjusts the capacity output of the heat pump according to the water circuit demand heat load signal.

[0113] During normal operation, the coordination control module continuously maps the water circuit heat load demand signal to the capacity adjustment command of the first or second water source heat pump. It then superimposes the air circuit heat load demand signal with the fan speed command signal to generate the final condenser fan speed command, which is output to the frequency converter for execution. The module also monitors the position feedback of each three-way valve in real time. If an abnormal position signal or switching timeout is detected, a safety protection program is immediately triggered, locking the current operating condition and issuing an alarm signal.

[0114] The module's technical advantages lie in the following: through strict timing control and closed-loop feedback, it completely eliminates the risks of water and pressure leakage between different operating loops, avoids pressure shocks and airflow disturbances during switching processes, and ensures the continuous and stable operation of the data center cooling system. Simultaneously, the modular instruction generation logic enables fast system response and high control precision, achieving seamless integration and dynamic balance between heat recovery and cooling load, significantly improving overall energy efficiency.

[0115] In some specific embodiments, the fluorine channel uses an internally threaded copper tube and is designed to withstand higher pressure than the water channel. The water channel uses a smooth-walled copper tube, and the shared aluminum fins are hydrophilic aluminum foil windowed fins that are connected to the copper tube through expansion tubes.

[0116] In some specific embodiments, the first water source heat pump is a high-temperature water loop heat pump unit, used to raise the low-temperature waste heat to the domestic hot water temperature; the second water source heat pump is a ground source / water source heat pump unit, used to extract low-grade heat to provide heating hot water to the building; the intelligent valve group is equipped with a switching delay protection device, and the switching sequence is to first close the valve under the current operating condition, and after a delay to confirm that the valve is completely closed, then open the valve under the target operating condition.

[0117] In some specific embodiments, the intelligent control unit also integrates an adaptive learning module, which is configured as follows:

[0118] Record historical operating data, including outdoor ambient temperature, data center load rate, domestic hot water demand, and energy efficiency ratio of each water source heat pump;

[0119] Based on historical operating data, an optimized algorithm is used to dynamically adjust the preset pressure threshold and the operating condition switching temperature range.

[0120] Predict the demand curve for domestic hot water in the future and adjust the operation strategy of the primary water source heat pump in advance.

[0121] In some specific embodiments, to further enhance the system's intelligence level and long-term operating efficiency, the multi-condition collaborative control algorithm also integrates an adaptive learning module. This adaptive learning module, acting as the intelligent decision-making layer in the algorithm architecture, enables the system to adapt to changes in operating conditions, load variations, and environmental disturbances through continuous learning and self-optimization. The adaptive learning module is configured to perform the following three core functions:

[0122] 1. Historical operation data recording and storage function

[0123] The adaptive learning module is equipped with non-volatile storage media for long-term recording of the system's historical operating data. The recorded data covers multiple dimensions of key operating parameters: outdoor ambient temperature data, recorded every 5 minutes to form a continuous time-series temperature curve; data center load rate data, obtained in real-time from the environmental monitoring system to acquire IT equipment power consumption, recorded every minute; domestic hot water demand data, calculated from hot water flow meters and temperature sensors to determine actual heat load, recorded every 10 minutes; and energy efficiency ratio data for each water source heat pump, obtained by monitoring the ratio of heat pump capacity to power consumption, recorded every 30 minutes. In addition, the module also records key system control parameters, including condensing pressure threshold, operating condition switching temperature range, fan speed curve, and valve operation frequency.

[0124] Data records are timestamped, with each record including the collection time, parameter value, and data quality flag. The module is equipped with a data cleaning sub-function, automatically removing abnormal fluctuation values ​​and sensor malfunction data, and interpolating to complete missing data. Historical data is stored in the storage medium for at least a 90-day rolling period; data older than 90 days is automatically transferred to the cloud or external storage devices, providing a data foundation for subsequent big data analysis.

[0125] 2. Dynamic adjustment function of control parameters based on optimization algorithm

[0126] The adaptive learning module dynamically adjusts the system's core control parameters based on recorded historical operating data using optimization algorithms to achieve continuous energy efficiency optimization. The implementation process for this function includes:

[0127] First, the module extracts historical operational data from the storage medium over the past 30 days to construct a multidimensional dataset. The data preprocessing sub-function normalizes the dataset to eliminate dimensional differences and uses principal component analysis to extract key feature variables.

[0128] Secondly, the module constructs a system energy efficiency optimization objective function. This objective function aims to minimize the overall PUE value of the data center while maximizing domestic hot water production. The input variables of the objective function include adjustable parameters such as preset pressure thresholds, operating condition switching temperature ranges, and heat recovery priority weighting coefficients. The output value is the total system energy consumption calculated based on historical data simulations.

[0129] Furthermore, the module employs gradient descent as the core of its optimization algorithm. During the algorithm's iteration, using the current control parameters as initial values, the partial derivatives of the objective function with respect to each parameter are calculated, and the parameter values ​​are updated along the negative gradient direction. The learning rate is adaptively adjusted, using a larger learning rate in the early stages of iteration to accelerate convergence, and a smaller learning rate for finer searching when approaching the optimal solution. To prevent overfitting, the algorithm introduces a regularization term to limit the magnitude of parameter adjustment.

[0130] Specifically, the preset pressure threshold is adjusted within the range of 1.7-2.0 MPa. The algorithm finds the optimal pressure point that minimizes the sum of compressor power consumption and fan power consumption based on the historical correlation between condensing pressure and heat recovery. The operating condition switching temperature range is adjusted within the range of 24-28℃ for summer and 8-12℃ for winter. The algorithm automatically optimizes the temperature combination that minimizes switching frequency and ensures the most stable energy efficiency based on the system energy efficiency fluctuations during seasonal transitions.

[0131] After optimization, the module sends the new control parameters to the operating condition judgment module and the condensing pressure monitoring module, and records the parameter change log. Parameter updates employ a gradual strategy, with each adjustment not exceeding 5% of the original value to avoid drastic fluctuations in system operation. The module is also configured with A / B testing functionality, synchronously recording virtual operating data under the old parameters during the initial application of the new parameters to verify their effectiveness.

[0132] 3. Domestic hot water demand prediction and pre-adjustment function

[0133] The adaptive learning module uses historical hot water demand data and a time series analysis model to predict future domestic hot water demand curves, and adjusts the operation strategy of the primary water source heat pump in advance to achieve feedforward control for supply and demand matching. The implementation process of this function includes:

[0134] First, the module extracts historical data on domestic hot water demand from the past 60 days to construct a time-series dataset. Data preprocessing includes removing periodic fluctuations, correcting outliers during holidays, and imputing missing data.

[0135] Secondly, the module uses the ARIMA model as the core of its prediction algorithm. The model order is automatically determined through analysis of the autocorrelation function and partial autocorrelation function, typically choosing the ARIMA(5,1,0) model. During model training, a rolling prediction method is used to verify prediction accuracy; if the prediction error exceeds a preset threshold, the model is retrained. The trained model can output the predicted hot water demand for the next 24 hours, with a time resolution of 1 hour.

[0136] Furthermore, based on the prediction results, the module generates a pre-adjustment strategy for the first water source heat pump. When a significant increase in hot water demand is predicted in the next two hours, the module starts the first water source heat pump 30 minutes in advance and gradually increases its outlet water temperature setpoint, ensuring the heat pump reaches a stable and efficient operating state before the peak demand arrives. When a decrease in hot water demand is predicted, the module reduces the heat pump load in advance to avoid energy waste caused by excessive heating.

[0137] The pre-adjustment strategy also includes feedforward compensation for the heat recovery load calculation module. The module converts the predicted hot water demand curve into a predicted heat recovery load curve, integrates it with the real-time load calculation results, generates a smoother load command, reduces the frequent start-up and shutdown of the heat pump, and extends the service life of the equipment.

[0138] The technical benefits of this function are: by enabling demand forecasting, it shifts from passive response to proactive regulation, significantly reducing energy waste caused by mismatch between hot water supply and demand. Engineering practice shows that after introducing demand forecasting and pre-regulation, the energy efficiency ratio of the domestic hot water system increases by approximately 12%, the start-stop frequency of the primary water source heat pump decreases by approximately 40%, and the overall operational stability of the system is significantly improved.

[0139] Reference Figure 7 The method for a high-efficiency condensation and heat recovery system for a data center under three operating conditions based on a composite heat exchanger, as shown, includes the following steps:

[0140] S1: Real-time acquisition of outdoor ambient temperature, refrigerant condensation pressure, water supply and return water temperature, and domestic hot water demand signals via sensor system;

[0141] S2: The operating condition judgment module judges the current operating condition based on the outdoor temperature: when the temperature is higher than the first temperature threshold, it enters the summer heat dissipation operating condition; when the temperature is lower than the second temperature threshold, it enters the winter heating operating condition; when the temperature is between the first and second temperature thresholds, it enters the transitional season heat recovery operating condition.

[0142] S3: During the transitional season heat recovery operation, the energy value assessment module prioritizes starting the first water source heat pump and shuts down the condenser fan;

[0143] S4: The condensing pressure monitoring module continuously monitors the pressure value. When the actual pressure exceeds the preset pressure threshold, the intelligent cooling module for the fan starts the condensing fan and performs speed control.

[0144] S5: When the working condition switching condition is triggered, the coordination control module controls the intelligent valve group to act according to the preset timing sequence: close the current valve → delay confirmation → open the target valve → start the corresponding equipment;

[0145] S6: The adaptive learning module periodically analyzes historical data, updates control parameters, and optimizes the hot water demand forecasting model.

[0146] The system operates by collecting outdoor ambient temperature, refrigerant condensing pressure, water supply and return temperatures, and domestic hot water demand signals in real time via a sensor system. The operating condition judgment module determines the current operating condition based on the outdoor temperature. When the temperature is above a first temperature threshold, it enters summer heat dissipation mode; when the temperature is below a second temperature threshold, it enters winter heating mode; and when the temperature is between the first and second temperature thresholds, it enters transitional season heat recovery mode.

[0147] During the transitional season heat recovery operation, the energy value assessment module prioritizes starting the primary water source heat pump and shuts down the condenser fan. The condenser pressure monitoring module continuously monitors the pressure value. When the actual pressure exceeds the preset pressure threshold, the intelligent fan cooling module starts the condenser fan and executes speed control. Specifically, the speed control works as follows: when the actual pressure is within the first pressure range, the fan speed is PID-regulated within the first speed range; when the actual pressure exceeds the second pressure threshold, the fan runs at full speed; and when the actual pressure is below the third pressure threshold, the fan stops completely.

[0148] When the operating condition switching condition is triggered, the coordination control module controls the intelligent valve group to act according to the preset timing sequence: close the current valve, delay to confirm that the valve is completely closed, then open the target valve, and then start the corresponding equipment.

[0149] During the transitional season heat recovery operation, the energy value assessment module executes the following judgment logic: when the domestic hot water demand exceeds the first demand threshold, the condenser fan is kept off, and all condensation heat is used for hot water preparation; when the domestic hot water demand is lower than the second demand threshold, the hot water demand is prioritized, and excess heat is discharged into the atmosphere through the condenser fan; when the condensation pressure is detected to exceed the safety threshold for a predetermined time, the condenser fan is forcibly started and the hot water preparation load is reduced.

[0150] The adaptive learning module periodically analyzes historical data, uses optimization algorithms to dynamically adjust preset pressure thresholds and operating condition switching temperature ranges, and uses time series analysis to predict the domestic hot water demand curve for future periods, adjusting the operation strategy of the primary water source heat pump in advance.

[0151] The system also includes safety protection measures: setting minimum operating time protection for the compressor and / or water pump; prohibiting the start of the water source heat pump when the water flow switch detects that the flow rate is lower than the preset flow threshold; and performing an emergency shutdown and alarm when the condensing pressure or refrigerant temperature exceeds the safety threshold.

[0152] In some specific embodiments, the speed control is specifically as follows:

[0153] When the actual pressure is within the first pressure range, the fan speed is PID regulated within the first speed range; when the actual pressure exceeds the second pressure threshold, the fan runs at full speed; when the actual pressure is below the third pressure threshold, the fan stops completely.

[0154] In some specific embodiments, under the heat recovery conditions during the transitional season, the energy value assessment module executes the following judgment logic:

[0155] When the demand for domestic hot water exceeds the first demand threshold, the condenser fan is kept off and all the heat from condensation is used for hot water preparation.

[0156] When the demand for domestic hot water is lower than the second demand threshold, the hot water demand is given priority, and excess heat is discharged into the atmosphere through the condenser fan.

[0157] When the condensing pressure is detected to exceed the safety threshold for a predetermined time, the condensing fan is forcibly started and the hot water preparation load is reduced.

[0158] In some specific embodiments, the adaptive learning module performs the following steps:

[0159] A system efficiency model is built based on historical data, which is linked to outdoor temperature, data center load and hot water demand;

[0160] The control parameters are adjusted through iterative optimization algorithms to minimize the deviation between the actual energy efficiency and the target energy efficiency.

[0161] By using time series analysis to predict future hot water demand, the temperature setpoint of the primary water source heat pump can be adjusted in advance.

[0162] In some specific embodiments, security protection steps are also included:

[0163] Set minimum operating time protection for the compressor and / or water pump;

[0164] When the water flow switch detects that the flow rate is lower than the preset flow rate threshold, the water source heat pump is prohibited from starting.

[0165] When the condensing pressure or refrigerant circuit temperature exceeds the safety threshold, an emergency shutdown will be initiated and an alarm will be triggered.

[0166] Example 1: Large Data Center Application

[0167] A large data center with an IT load of 2MW was upgraded using the system of this invention. The composite heat exchanger has external dimensions of 2000mm×1500mm×300mm. The refrigerant system uses 6 loops of internally threaded copper tubing with an outer diameter of Φ9.52mm, each loop being 35m long, and designed to withstand a pressure of not less than 4.2MPa. The water system uses 4 loops of smooth-walled copper tubing with an outer diameter of Φ12.7mm, each loop being 30m long, and designed to withstand a pressure of not less than 1.6MPa. The fins are 0.12mm thick hydrophilic aluminum foil fins with a fin spacing of 2.0mm, and the contact thermal resistance is ensured to be no more than 0.0004 m²·K / W through secondary mechanical expansion. The refrigerant and water tubing are arranged in a 1:1 cross pattern, with a tube spacing of 25mm and a row spacing of 22mm.

[0168] The first water source heat pump is equipped with two high-temperature water-loop heat pump units, each with a heating capacity of 500kW. Under conditions of 30 / 35℃ inlet water, it can produce 55℃ domestic hot water, achieving a COP of 4.0-5.0. The second water source heat pump is equipped with one 800kW ground / water source heat pump unit to provide heating for adjacent office buildings. The intelligent valve group uses a three-channel electric three-way ball valve with a leakage rating of VI. The switching sequence is as follows: first, the current valve is closed (at a constant speed for 30 seconds), then after a 5-second delay to confirm the valve position feedback signal is stable, the target valve is opened (at a constant speed for 30 seconds).

[0169] The intelligent control unit uses a Siemens S7-1500 series PLC, with a CPU model of 1515-2 PN, and is equipped with a 32-point DI / DO module and an 8-channel AI / AO module. The condensing pressure sensor has a range of 0-5.0 MPa and an accuracy of ±0.5%FS, and is installed at the compressor exhaust end. The outdoor temperature sensor uses a PT100 platinum resistance thermometer with an accuracy of ±0.2℃, and is installed inside an outdoor radiation-proof ventilation hood. The water circuit temperature sensor group includes armored PT100 sensors, installed at locations such as the cooling tower supply and return water and the water source heat pump side supply and return water.

[0170] One year after the system was put into operation, it produced an average of 15 tons of 55℃ domestic hot water per day during the transitional season and provided 800kW of heating load for the office building in winter, saving approximately 1.2 × 10⁻⁶ kWh of electricity annually. 6 kWh, with an annual carbon dioxide emission reduction of approximately 950 tons.

[0171] The operational control logic includes: a condition judgment module collects the outdoor temperature every 5 seconds; when the temperature is ≥26℃, it is determined to be summer operation; ≤10℃, winter operation; and 10-25℃, transitional season operation. A 5℃ hysteresis range is set between each operation to prevent frequent switching. The energy value assessment module calculates heat recovery priority based on domestic hot water demand and electricity price. When the priority index is greater than 1.0, a heat recovery priority strategy is implemented, and the condenser fan completely stops. When the condensing pressure exceeds 1.8MPa, the intelligent cooling module for the fan starts PID control, adjusting the speed to 30%-70% within the 1.8-2.0MPa range, and running at full speed when it exceeds 2.0MPa. A timing protection system is set during the switching process, with a 30-second shutdown, a 5-second delay, and a 30-second on / off sequence.

[0172] Example 2: Modular Small Data Center

[0173] For a small data center with an IT load of 500kW, a modular design is adopted, reducing the size of the composite heat exchanger to 1200mm×1000mm×250mm. The refrigerant system uses 4 loops of internally threaded copper pipes with an outer diameter of Φ7.94mm, and the water system uses 3 loops of smooth-walled copper pipes with an outer diameter of Φ12.7mm. The first water source heat pump is configured with a single high-temperature water-loop heat pump unit with a heating capacity of 200kW. The remaining structure, process, and control logic are the same as in Example 1, and the system availability reaches over 99.9%.

[0174] By adopting the above-disclosed technical solution of this invention, the following beneficial effects are obtained:

[0175] Firstly, it integrates innovation and offers multiple uses. Through its unique composite heat exchanger design, it highly integrates air cooling, water cooling, and heat recovery functions into a single physical carrier, significantly reducing the number of devices, substantially decreasing the space occupied in the machine room, and effectively saving initial investment costs.

[0176] Secondly, energy cascade utilization and extreme energy efficiency. The system's overall energy efficiency ratio is significantly improved throughout the year, far exceeding that of traditional systems. Heat recovery efficiency reaches a high level during the transition season, and deep heat recovery efficiency is further improved in winter, maximizing the utilization of low-grade waste heat.

[0177] Thirdly, intelligent wind and water coordinated control and energy value ranking. The innovative multi-condition coordinated control algorithm has a built-in energy value assessment module. By comparing the heat recovery benefit per unit of heat with the energy consumption cost of the fan, it automatically executes the "heat recovery priority, intelligent air cooling supplement" strategy. Under the premise of ensuring cooling safety, it maximizes the output of high-value hot water, significantly shortens the investment payback period, and is far superior to the industry average.

[0178] Fourth, PUE is significantly reduced. Real-world engineering cases show that after system upgrades, the average annual PUE of data centers is significantly reduced, resulting in substantial energy savings and considerable annual electricity reductions.

[0179] Fifth, significant carbon emission reduction and environmental benefits. Compared with traditional systems, the annual carbon dioxide emission reduction is greatly increased, the emission reduction rate is obvious, and it has good positive environmental externalities.

[0180] Sixth, adaptive learning and operating condition prediction. The adaptive learning module dynamically optimizes the control threshold based on historical operating data, predicts the domestic hot water demand curve and adjusts the strategy in advance. The system has self-evolution capabilities and a low energy efficiency degradation rate over long-term operation, which is significantly better than that of a fixed parameter system.

[0181] Seventh, system stability and security. Employing a dual electrical and mechanical interlocking valve switching logic, coupled with multiple safety mechanisms such as compressor / pump minimum operating time protection and water flow interlock protection, the system achieves a high level of availability, ensuring data center cooling safety.

[0182] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A high-efficiency condensation and heat recovery system for a data center under three operating conditions based on a composite heat exchanger, characterized in that, include: The composite heat exchanger has an internal refrigerant channel and a water channel that are independent of each other and thermally coupled by sharing aluminum fins. The refrigerant channel is connected to the cooling system of the data center, and the water channel can be selectively connected to a cooling tower, a first water source heat pump or a second water source heat pump through an intelligent valve group. A condenser fan is installed on the composite heat exchanger to provide air-cooled heat dissipation; The intelligent valve group consists of three electric three-way valves, and each valve is equipped with both electrical and mechanical interlocks. The intelligent control unit includes a controller and a sensor system. The input terminal of the controller is electrically connected to an outdoor ambient temperature sensor, a condensing pressure sensor, and a water circuit temperature sensor, and the output terminal is electrically connected to the intelligent valve group, the condensing fan, and the water source heat pump. The intelligent control unit incorporates a multi-condition collaborative control algorithm, which includes: a condition judgment module for determining the current condition range based on the outdoor temperature; an energy value assessment module for prioritizing the transfer of condensing heat to the first water source heat pump for heat recovery during transitional seasons; a condensing pressure monitoring module for real-time monitoring of the refrigerant circuit pressure; and a fan intelligent cooling module for dynamically adjusting the condensing fan speed when the condensing pressure exceeds a preset pressure threshold.

2. The system according to claim 1, characterized in that, The multi-condition cooperative control algorithm also includes: The heat recovery load calculation module is used to calculate the heat recovery load of the water channel under transitional seasonal conditions. The air-cooling compensation calculation module is used to calculate the required air-cooling load based on the condensing pressure deviation, where the condensing pressure deviation is the difference between the actual pressure value and the preset pressure threshold. The coordination control module is used to generate switching commands for the intelligent valve group and speed control commands for the condenser fan based on the heat recovery load and the air cooling load.

3. The system according to claim 2, characterized in that, The fluorine channel uses an internally threaded copper tube and is designed to withstand higher pressure than the water channel. The water channel uses a smooth-walled copper tube. The shared aluminum fins are hydrophilic aluminum foil fins with open windows and are connected to the copper tubes via expansion tubes.

4. The system according to claim 3, characterized in that, The first water source heat pump is a high-temperature water-loop heat pump unit, used to raise low-temperature waste heat to the temperature of domestic hot water; the second water source heat pump is a ground source / water source heat pump unit, used to extract low-grade heat to provide heating hot water to buildings; the intelligent valve group is equipped with a switching delay protection device, and the switching sequence is to first close the valve under the current operating condition, and after a delay to confirm that the valve is completely closed, open the valve under the target operating condition.

5. The system according to claim 4, characterized in that, The intelligent control unit also integrates an adaptive learning module, which is configured as follows: Record historical operating data, including outdoor ambient temperature, data center load rate, domestic hot water demand, and energy efficiency ratio of each water source heat pump; Based on the historical operating data, an optimization algorithm is used to dynamically adjust the preset pressure threshold and the operating condition switching temperature range; Predict the domestic hot water demand curve for future periods and adjust the operation strategy of the first water source heat pump in advance.

6. A method for operating the system as described in any one of claims 1-5, characterized in that, Includes the following steps: S1: Real-time acquisition of outdoor ambient temperature, refrigerant condensation pressure, water supply and return water temperature, and domestic hot water demand signals via sensor system; S2: The operating condition judgment module judges the current operating condition based on the outdoor temperature: when the temperature is higher than the first temperature threshold, it enters the summer heat dissipation operating condition; when the temperature is lower than the second temperature threshold, it enters the winter heating operating condition; when the temperature is between the first and second temperature thresholds, it enters the transitional season heat recovery operating condition. S3: Under the heat recovery condition during the transition season, the energy value assessment module prioritizes starting the first water source heat pump and shuts down the condenser fan; S4: The condensing pressure monitoring module continuously monitors the pressure value. When the actual pressure exceeds the preset pressure threshold, the intelligent cooling module for the fan starts the condensing fan and performs speed control. S5: When the working condition switching condition is triggered, the coordination control module controls the intelligent valve group to act according to the preset timing sequence: close the current valve → delay confirmation → open the target valve → start the corresponding equipment; S6: The adaptive learning module periodically analyzes historical data, updates control parameters, and optimizes the hot water demand prediction model.

7. The method according to claim 6, characterized in that, The speed control specifically refers to: When the actual pressure is within the first pressure range, the fan speed is PID regulated within the first speed range; when the actual pressure exceeds the second pressure threshold, the fan runs at full speed; when the actual pressure is below the third pressure threshold, the fan stops completely.

8. The method according to claim 6, characterized in that, During the transitional season heat recovery operation, the energy value assessment module performs the following judgment logic: When the demand for domestic hot water exceeds the first demand threshold, the condenser fan is kept off and all the heat from condensation is used for hot water preparation. When the demand for domestic hot water is lower than the second demand threshold, the hot water demand is given priority, and excess heat is discharged into the atmosphere through the condenser fan. When the condensing pressure is detected to exceed the safety threshold for a predetermined time, the condensing fan is forcibly started and the hot water preparation load is reduced.

9. The method according to claim 6, characterized in that, The adaptive learning module performs the following steps: A system efficiency model is built based on historical data, which is linked to outdoor temperature, data center load and hot water demand; The control parameters are adjusted through iterative optimization algorithms to minimize the deviation between the actual energy efficiency and the target energy efficiency. By using time series analysis to predict future hot water demand, the temperature setpoint of the primary water source heat pump can be adjusted in advance.

10. The method according to claim 6, characterized in that, It also includes safety protection steps: Set minimum operating time protection for the compressor and / or water pump; When the water flow switch detects that the flow rate is lower than the preset flow rate threshold, the water source heat pump is prohibited from starting. When the condensing pressure or refrigerant circuit temperature exceeds the safety threshold, an emergency shutdown will be initiated and an alarm will be triggered.

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