Swimming pool waste heat recovery system based on phase change energy storage material

Abstract

The invention discloses a swimming pool waste heat recovery system based on a phase change energy storage material, and belongs to the technical field of phase change energy storage technologies. The system comprises a phase change energy storage module which realizes efficient energy storage in a wide temperature range through phase change materials with different melting points; the heat exchange module adopts a bionic fractal spiral structure and an advanced surface treatment technology, so that the heat exchange area is obviously increased, and the fluid resistance is reduced; the intelligent hierarchical control module is used for realizing comprehensive intelligent management from millisecond-level real-time control to long-term optimization; the sensing network module is combined with optical fiber sensing and wireless sensing technologies and is used for providing high-precision and high-spatial-resolution omnibearing monitoring; the energy conveying module achieves efficient, reliable and low-noise fluid conveying through the integrated design of a magnetic suspension bearing and a permanent magnet synchronous motor. The auxiliary heating module integrates a solar heat collector and a CO2 heat pump, uses a phase change material as a heat buffer, and provides a flexible and efficient supplementary heat source when stored energy is insufficient.

Description

Technical Field

[0001] This application relates to the field of phase change energy storage technology, and more specifically, to a swimming pool waste heat recovery system based on phase change energy storage materials. Background Technology

[0002] Traditional heat recovery systems face many challenges in swimming pool energy management, such as inefficient heat recovery, insufficient energy utilization, and high system operating costs. Waste heat in swimming pools is often wasted due to ineffective recovery, especially in summer when pool water temperatures are high. If the generated heat cannot be effectively utilized, it will directly impact energy economy and environmental protection.

[0003] Most existing swimming pool waste heat recovery systems rely on simple heat exchange devices, which cannot meet the needs of efficient energy storage and heat recovery over a wide temperature range. In addition, traditional systems often suffer from low heat exchange efficiency, poor control precision, and insufficient adaptability to environmental changes, resulting in heat recovery performance that is far from optimal.

[0004] Phase change materials (PCMs) are widely used in energy management systems due to their excellent heat storage and release properties. However, existing PCM applications still face some challenges, such as low heat transfer efficiency, limited heat storage capacity, and high system complexity. Combining advanced heat exchange technologies with intelligent control systems will effectively improve the application of PCMs in swimming pool waste heat recovery.

[0005] In summary, how to design a swimming pool waste heat recovery system that integrates advanced phase change energy storage materials, biomimetic fractal heat exchange technology, and intelligent hierarchical control to achieve efficient and intelligent heat recovery and utilization has become a key issue that urgently needs to be addressed in the current technological field. Summary of the Invention

[0006] To overcome a series of shortcomings in the existing technology, the purpose of this application is to provide a swimming pool waste heat recovery system based on phase change energy storage materials, comprising the following components:

[0007] Phase change energy storage modules achieve efficient energy storage over a wide temperature range by using phase change materials with different melting points;

[0008] The heat exchange module adopts a biomimetic fractal spiral structure and advanced surface treatment technology, which significantly increases the heat exchange area and reduces fluid resistance, thereby greatly improving the heat exchange efficiency.

[0009] The intelligent hierarchical control module integrates edge computing and cloud computing technologies to achieve comprehensive intelligent management, from millisecond-level real-time control to long-term optimization.

[0010] The sensor network module combines fiber optic sensing and wireless sensing technologies to provide high-precision, high-spatial-resolution all-around monitoring.

[0011] The energy delivery module, through the integrated design of magnetic levitation bearings and permanent magnet synchronous motors, achieves efficient, reliable, and low-noise fluid delivery while reducing maintenance costs.

[0012] The auxiliary heating module integrates a solar collector and a CO2 heat pump, and uses phase change materials as a thermal buffer to provide a flexible and efficient supplementary heat source when energy storage is insufficient, maximizing the utilization of renewable energy.

[0013] Furthermore, the phase change energy storage module includes the following components:

[0014] The PCM container uses a three-layer concentric cylindrical structure made of alloy material to achieve physical isolation and heat gradient transfer of the PCM.

[0015] The high-melting-point PCM layer, located in the inner layer of the PCM container, is filled with modified hydrated salts with a melting point of 38-42℃ for high-temperature heat storage and to improve peak energy storage capacity.

[0016] The medium-melting-point PCM layer, located in the middle layer of the PCM container, is filled with bio-based fatty acid esters with a melting point of 30-35℃, serving as a transition layer to enhance temperature adaptability and energy storage stability.

[0017] The low-melting-point PCM layer, located on the outer layer of the PCM container, is filled with nano-reinforced paraffin with a melting point of 24-28℃, used for low-temperature heat recovery and to improve energy utilization efficiency.

[0018] The heat conduction unit is enhanced by using a three-dimensional graphene heat conduction network that runs through three layers of PCM to improve the heat conduction rate inside the PCM and accelerate the charging and discharging process.

[0019] The phase change monitoring and control unit integrates temperature sensors and control valves to monitor the phase change status of each PCM layer in real time and optimize heat distribution and release strategies.

[0020] Furthermore, integrating temperature sensors and control valves allows for real-time monitoring of the phase transition state of each PCM layer, optimizing heat distribution and release strategies, including the following steps:

[0021] Temperature sensors are placed at the center, edge, and middle of each layer of the PCM container;

[0022] High-precision, fast-response electromagnetic control valves are installed at the heat exchange fluid inlet and between each layer;

[0023] Temperature data is collected in real time, filtered, and then calculated and analyzed to determine the phase transition state of the PCM, and the data is visualized and stored.

[0024] Based on thermodynamic principles and experimental data, a phase transition kinetic model is constructed for each layer to accurately describe the phase transition process of PCM under different temperature and heat flow conditions.

[0025] Based on real-time temperature data, phase change state, and predicted heat demand, the optimal heat distribution and release strategy is calculated.

[0026] Furthermore, the heat exchange module includes the following components:

[0027] The biomimetic fractal spiral body is made using 3D metal printing technology. Through a multi-level branch channel design, it significantly increases the heat exchange area and optimizes fluid distribution.

[0028] A superhydrophobic inner wall coating is applied to the inner wall of the biomimetic fractal spiral body to reduce fluid resistance and prevent scaling, thereby improving long-term heat exchange efficiency.

[0029] A graphene composite layer is coated on the outer wall of the biomimetic fractal helix body to enhance the efficiency of heat transfer from the fluid to the PCM.

[0030] The dynamic flow channel adjustment mechanism adopts a variable cross-section design driven by shape memory alloy, which dynamically adjusts the flow channel shape according to flow rate and temperature to optimize heat exchange performance under different operating conditions.

[0031] An integrated eddy current generator is integrated into a key position of the biomimetic fractal helix body. By generating controlled eddies, it breaks the boundary layer, enhances convective heat transfer, and further improves heat transfer efficiency.

[0032] The intelligent flow distribution unit, combined with a pressure sensor and an electric micro-valve, enables intelligent flow distribution control, ensuring balanced flow across multiple branch channels and maximizing heat exchange efficiency.

[0033] Furthermore, by combining pressure sensors and electric micro-valves, intelligent flow distribution control is achieved to ensure balanced flow across multiple branch channels and maximize heat exchange efficiency, including the following steps:

[0034] High-precision differential pressure sensors are installed at key locations on the biomimetic fractal spiral body, especially at the inlet and outlet of each branch channel, to monitor minute pressure changes in real time and provide accurate data support for flow control. At the same time, precision electric micro-valve is installed at the inlet of each branch channel to achieve fine adjustment of the flow rate of each branch channel.

[0035] Acquire pressure data at a high sampling rate and process it in real time to calculate pressure and estimate flow rate;

[0036] Taking into account the structure of each branch channel, fluid characteristics, and temperature change factors, a mathematical model describing the relationship between pressure, valve opening, and flow rate is constructed. The model parameters are then verified and optimized using computational fluid dynamics simulation to ensure the accuracy of flow distribution prediction.

[0037] Based on real-time pressure data and mathematical models, and considering both flow balance and heat exchange efficiency, the optimal valve control strategy is calculated and implemented.

[0038] Furthermore, the flow rate can be estimated using the following formula: Among them, Q i C is the flow of the i-th branch channel; d，i A is the flow coefficient of the i-th channel; i ΔP is the cross-sectional area of ​​the i-th branch channel; i ρ is the pressure difference of the i-th branch channel; ρ is the fluid density, and the fluid is water.

[0039] Furthermore, the optimal valve control strategy is expressed by the following formula: Among them, Opt j It is the optimal opening degree of the j-th valve; η i P is the heat transfer efficiency coefficient of the i-th channel; i Q is the power consumption of the i-th channel; i，min and Q i，max Q represents the flow range of the i-th channel; total The overall target traffic; N is the number of branch channels.

[0040] Furthermore, the intelligent hierarchical control module includes the following components:

[0041] The real-time control unit uses a combination of low-power FPGA and ARM Cortex-M7 MCU to achieve millisecond-level real-time data processing and fast control decisions, ensuring the system's instantaneous performance.

[0042] Edge computing nodes integrate embedded AI accelerators to perform short-term predictions and local optimizations, balancing real-time performance with complex computing needs;

[0043] The cloud-based analytics server, based on a containerized microservice architecture, is responsible for big data analysis and generating long-term optimization strategies to continuously improve the overall system performance.

[0044] A multi-level communication gateway integrates multiple communication protocols to ensure stable, secure, and efficient data exchange between control levels.

[0045] An adaptive learning engine, which integrates reinforcement learning and transfer learning algorithms, continuously optimizes control strategies and improves system adaptability based on historical operating data and environmental changes.

[0046] The human-computer interaction interface provides web and mobile application interfaces, supports system monitoring, parameter adjustment and remote control, and enhances the operability and user-friendliness of the system.

[0047] Furthermore, the auxiliary heating module includes the following components:

[0048] The solar thermal collector unit employs a vacuum tube design with a selective absorption coating to maximize solar energy absorption efficiency and provide the primary renewable energy heat source.

[0049] The heat pump unit uses environmentally friendly refrigerant CO2 and achieves efficient heating through a transcritical cycle, serving as a supplementary heat source on cloudy days or at night.

[0050] The thermal buffer energy storage unit uses high-temperature phase change materials to smooth heat source output fluctuations and improve system stability and energy utilization efficiency.

[0051] The intelligent heat source dispatch controller optimizes the coordinated operation strategy of different heat sources based on weather forecasts, energy prices, and user needs.

[0052] Multi-stage heat exchanger units, including solar-PCM heat exchangers, heat pump-PCM heat exchangers, and PCM-user-end heat exchangers, enable efficient heat transfer and distribution.

[0053] The variable flow circulating pump adopts a high-efficiency variable frequency water pump, which adjusts the flow rate according to the real-time heat load demand to ensure optimal performance under different operating conditions.

[0054] Furthermore, based on weather forecasts, energy prices, and user demand, optimize the coordinated operation strategy of different heat sources, including the following steps:

[0055] Real-time collection of weather data, energy market prices, and user demand information; and performance of data cleaning, standardization, and feature extraction.

[0056] Based on historical data and machine learning algorithms, a user demand prediction model is constructed that takes into account weather, date and seasonal factors.

[0057] Detailed performance models are established for each heat source to describe its efficiency and output characteristics under different operating conditions, so as to calculate the operating costs and benefits of each heat source under current and predicted future conditions.

[0058] By balancing energy costs, system efficiency, user comfort, and environmental impact objectives, a series of Pareto optimal solutions are generated, providing multiple options for decision-making. At the same time, a constraint handling mechanism is implemented to ensure that the generated solutions meet equipment operating limitations and safety requirements.

[0059] Based on the Pareto optimal solution, the operating parameters of each heat source are dynamically adjusted according to the current state and short-term forecasts;

[0060] Collect actual operational data and compare it with the prediction results, use Bayesian optimization to fine-tune parameters, set performance indicators, and generate reports regularly.

[0061] Compared with the prior art, the beneficial effects of this application are as follows:

[0062] This application utilizes multilayer phase change energy storage materials and biomimetic fractal heat exchange modules, combined with intelligent hierarchical control and sensor networks, to achieve efficient and intelligent heat storage and utilization. Furthermore, by integrating solar collectors and CO2 heat pumps, it optimizes heat source scheduling, maximizes the use of renewable energy, and significantly improves energy efficiency and system stability. Attached Figure Description

[0063] Figure 1 This is a schematic diagram of a swimming pool waste heat recovery system based on phase change energy storage material disclosed in an embodiment of this application. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some embodiments of this invention, but not all embodiments.

[0065] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0067] like Figure 1 As shown, a swimming pool waste heat recovery system based on phase change energy storage materials includes the following components:

[0068] Phase change energy storage modules achieve efficient energy storage over a wide temperature range by using phase change materials with different melting points;

[0069] The heat exchange module adopts a biomimetic fractal spiral structure and advanced surface treatment technology, which significantly increases the heat exchange area and reduces fluid resistance, thereby greatly improving the heat exchange efficiency.

[0070] The intelligent hierarchical control module integrates edge computing and cloud computing technologies to achieve comprehensive intelligent management, from millisecond-level real-time control to long-term optimization.

[0071] The sensor network module combines fiber optic sensing and wireless sensing technologies to provide high-precision, high-spatial-resolution all-around monitoring.

[0072] The energy delivery module, through the integrated design of magnetic levitation bearings and permanent magnet synchronous motors, achieves efficient, reliable, and low-noise fluid delivery while reducing maintenance costs.

[0073] The auxiliary heating module integrates a solar collector and a CO2 heat pump, and uses phase change materials as a thermal buffer to provide a flexible and efficient supplementary heat source when energy storage is insufficient, maximizing the utilization of renewable energy.

[0074] In this embodiment, the phase change energy storage module achieves efficient thermal energy storage and release over a wide temperature range by using phase change materials (PCMs) with different melting points. These materials can absorb or release a large amount of latent heat during the phase change process, thereby maintaining the thermal stability of the system when the ambient temperature changes. The technical advantage of the phase change energy storage module lies in its ability to store heat at low temperatures and release it at high temperatures, providing a stable heat supply. This module can effectively regulate the water temperature of a swimming pool, reduce temperature fluctuations, and lower energy consumption. By rationally selecting phase change materials with different melting points, energy demands under different seasons and climatic conditions can be met, improving the system's adaptability and efficiency.

[0075] In this embodiment, the heat exchange module employs a biomimetic fractal spiral structure and advanced surface treatment technology, significantly increasing the surface area for heat exchange while reducing fluid flow resistance. This design enables the heat exchange module to achieve higher heat exchange efficiency within a smaller volume. The fractal spiral structure mimics natural heat exchange mechanisms, such as the vein structure of plant leaves, increasing the heat exchange rate by increasing the contact area and optimizing the fluid flow path. Furthermore, advanced surface treatment technologies, such as microstructure coatings, further reduce thermal resistance and improve heat conduction efficiency. The result is a more efficient heat transfer in the swimming pool system, reduced energy waste, and an improved overall system energy efficiency ratio.

[0076] In this embodiment, the intelligent hierarchical control module integrates edge computing and cloud computing technologies, achieving comprehensive intelligent management from millisecond-level real-time control to long-term optimization. Edge computing processes data near its generation point, providing a fast response time to ensure real-time control and adjustment of the system. Cloud computing, on the other hand, handles more complex data analysis and long-term optimization, using big data processing and artificial intelligence algorithms to continuously improve system performance. This hierarchical control mode enhances the system's flexibility and intelligence, enabling the swimming pool waste heat recovery system to adapt to changing environmental conditions and achieve efficient and precise thermal energy management.

[0077] In this embodiment, the sensor network module combines fiber optic sensing and wireless sensing technologies, providing comprehensive monitoring capabilities with high precision and high spatial resolution. Fiber optic sensing technology enables accurate data acquisition over long distances and in high-temperature environments, while wireless sensing technology offers flexible deployment and easy maintenance. Through the combination of these two technologies, the sensor network can monitor key parameters of the system such as temperature, pressure, and flow rate in real time and transmit the data to the control system. High-precision and high-resolution monitoring data can help identify problems promptly, optimize system performance, and enhance the system's reliability and stability.

[0078] In this embodiment, the energy delivery module achieves efficient, reliable, and low-noise fluid transport through the integrated design of magnetic levitation bearings and a permanent magnet synchronous motor. The magnetic levitation bearings reduce frictional losses, improve operating efficiency, and extend the equipment's lifespan. The permanent magnet synchronous motor provides efficient power output and precise speed control. The overall design reduces maintenance costs and improves system stability and operational quality. The low-noise characteristic not only improves the operating environment but also reduces noise impact on the surrounding environment, making the system more environmentally friendly and user-friendly.

[0079] In this embodiment, the auxiliary heating module integrates a solar collector and a CO2 heat pump, and utilizes a phase change material as a thermal buffer to provide a flexible and efficient supplementary heat source. The solar collector can utilize solar energy for preheating, reducing energy consumption, while the CO2 heat pump can operate efficiently even at low temperatures. The thermal buffering function of the phase change material ensures that additional heat can be quickly provided when energy storage is insufficient. This design maximizes the utilization of renewable energy while improving the overall efficiency and economy of the system.

[0080] In summary, the swimming pool waste heat recovery system based on phase change energy storage materials disclosed in this application achieves optimized utilization of swimming pool thermal energy through efficient energy storage, excellent heat exchange, intelligent control, precise monitoring, advanced energy transfer, and flexible auxiliary heating modules. The design and technical effects of each module complement each other, jointly improving the system's energy efficiency, stability, and reliability. Overall, this system not only effectively reduces energy consumption and maintenance costs but also enhances the system's intelligence level and user experience, representing a highly integrated and optimized waste heat recovery solution.

[0081] Furthermore, the phase change energy storage module includes the following components:

[0082] The PCM container uses a three-layer concentric cylindrical structure made of alloy material to achieve physical isolation and heat gradient transfer of the PCM.

[0083] The high-melting-point PCM layer, located in the inner layer of the PCM container, is filled with modified hydrated salts with a melting point of 38-42℃ for high-temperature heat storage and to improve peak energy storage capacity.

[0084] The medium-melting-point PCM layer, located in the middle layer of the PCM container, is filled with bio-based fatty acid esters with a melting point of 30-35℃, serving as a transition layer to enhance temperature adaptability and energy storage stability.

[0085] The low-melting-point PCM layer, located on the outer layer of the PCM container, is filled with nano-reinforced paraffin with a melting point of 24-28℃, used for low-temperature heat recovery and to improve energy utilization efficiency.

[0086] The heat conduction unit is enhanced by using a three-dimensional graphene heat conduction network that runs through three layers of PCM to improve the heat conduction rate inside the PCM and accelerate the charging and discharging process.

[0087] The phase change monitoring and control unit integrates temperature sensors and control valves to monitor the phase change status of each PCM layer in real time and optimize heat distribution and release strategies.

[0088] In this embodiment, the PCM container adopts a three-layer concentric cylindrical structure made of alloy material. Its main function is to achieve physical isolation of the phase change material (PCM) and gradient heat transfer. This design not only effectively prevents the mixing of different layers of PCM but also enables good heat transfer across different temperature ranges. The selection of alloy material ensures the structural strength and durability of the container under high-temperature conditions. Simultaneously, the three-layer concentric cylindrical structure, through optimized interlayer isolation and temperature gradient design, significantly improves the efficiency of thermal energy storage and release. The PCM container provides a stable and reliable thermal energy storage environment, thereby enhancing the stability and adaptability of the entire system in the face of temperature fluctuations.

[0089] In this embodiment, a high-melting-point PCM layer is located within the PCM container and uses modified hydrated salt with a melting point of 38-42°C. The main function of this layer is to store heat from high-temperature zones, improving peak energy storage capacity under high-temperature conditions. The modified hydrated salt has a high melting point and good thermal stability, enabling it to store heat at higher temperatures and release energy to maintain a stable swimming pool temperature. The high-melting-point PCM layer effectively handles heat from high-temperature sources and provides stable thermal support during peak demand periods, thereby optimizing thermal management and reducing energy waste.

[0090] In this embodiment, the medium-melting-point PCM layer serves as a transition layer, primarily used to enhance temperature adaptability and energy storage stability. Bio-based fatty acid esters not only possess excellent phase transition properties but also exhibit high environmental friendliness. This medium-melting-point PCM layer enables effective heat storage and release, alleviating system stress during temperature fluctuations and ensuring the entire system maintains good thermal management even under changing environmental conditions.

[0091] In this embodiment, the low-melting-point PCM layer is specifically designed for heat recovery in the low-temperature range to improve energy utilization efficiency. Nano-reinforced paraffin has a low melting point and excellent thermal conductivity, enabling it to effectively store and release heat at lower temperatures. The low-melting-point PCM layer allows for rapid response under low-temperature conditions, improving the system's energy recovery efficiency and providing additional heat support when cooling demands are high, further optimizing the overall system's thermal energy utilization.

[0092] In this embodiment, the enhanced thermal conductivity unit employs a three-dimensional graphene thermal conductivity network that penetrates three layers of PCM, significantly improving the heat conduction rate within the PCM. Graphene, with its excellent thermal conductivity and high strength, can form efficient heat conduction paths within the PCM, accelerating the heat charging and dissipation process. By enhancing the thermal conductivity unit, the thermal conductivity efficiency of the phase change material can be significantly improved, the heat transfer time shortened, and the response speed of heat storage and release enhanced, thereby improving the overall thermal energy utilization efficiency of the system.

[0093] In this embodiment, the phase change monitoring and control unit integrates a temperature sensor and control valves to monitor the phase change state of each PCM layer in real time and optimize heat distribution and release strategies. The temperature sensor accurately measures the temperature changes of the PCM and provides timely feedback to the control mechanism; the control valves adjust the flow and release of heat according to actual needs. Through precise monitoring and flexible control, the phase change state of each PCM layer can be dynamically adjusted, thereby optimizing the heat storage and release strategies and improving the overall performance and efficiency of the system.

[0094] In summary, the phase change energy storage module achieves efficient thermal energy storage and release by integrating a PCM container, high-melting-point PCM layers, medium-melting-point PCM layers, low-melting-point PCM layers, enhanced thermal conductivity units, and a phase change monitoring and control unit. The design and function of each component complement each other, collectively improving thermal energy utilization efficiency, response speed, and stability. Through multi-layered PCM configuration and advanced thermal conductivity technology, the module provides optimized thermal energy management across different temperature ranges, while precise monitoring and control mechanisms ensure efficient heat distribution. This comprehensive technical solution not only enhances overall performance but also provides a strong guarantee for the efficient operation of swimming pool waste heat recovery systems.

[0095] Furthermore, integrating temperature sensors and control valves allows for real-time monitoring of the phase transition state of each PCM layer, optimizing heat distribution and release strategies, including the following steps:

[0096] Temperature sensors are placed at the center, edge, and middle of each layer of the PCM container. The sensor at the center provides the average temperature data for that layer of PCM, while the sensors at the edges and middle help detect local temperature changes. This arrangement provides comprehensive temperature distribution information, ensuring accurate monitoring of temperature changes in different areas. The high accuracy and good time response characteristics of the sensors enable real-time data acquisition and processing, which is crucial for adjusting and optimizing thermal management strategies.

[0097] Installing high-precision, fast-response electromagnetic control valves at the inlet of the heat exchange fluid and between each layer effectively controls fluid flow and heat distribution. These valves can precisely adjust fluid flow based on real-time data, rapidly responding to changes in system requirements. This step enables precise control during heat exchange, improving heat transfer efficiency and preventing excessive heat waste or unnecessary heat shortages. This rapid response capability ensures the stability and efficiency of the system under various operating conditions.

[0098] Real-time temperature data is acquired, filtered, and then calculated and analyzed to determine the phase transition state of the PCM, enabling data visualization and storage. Filtering removes noise and improves data accuracy, while calculation and analysis determine the current phase transition state of each PCM layer, with the results visualized and stored. Data visualization provides a clear view of temperature changes and phase transition processes across different PCM layers, allowing operators and control systems to make immediate adjustments. This data processing workflow improves the monitoring accuracy and ease of operation of the PCM phase transition process, providing reliable data support for optimizing thermal management strategies.

[0099] Based on thermodynamic principles and experimental data, a phase transition kinetic model is constructed for each layer to accurately describe the phase transition process of the PCM under different temperature and heat flux conditions. The phase transition kinetic model considers parameters such as the phase transition temperature, heat capacity, and thermal conductivity of the PCM, and can simulate its thermal behavior in actual operation. The technical advantage of this model lies in its ability to accurately predict the thermal response of the PCM, helping to formulate scientific and reasonable heat distribution strategies. Furthermore, through continuous adjustment and optimization of the model, the thermal management performance of the entire system can be improved.

[0100] Based on real-time temperature data, phase change state, and predicted heat demand, the optimal heat distribution and release strategy is calculated. By comprehensively analyzing the state of each PCM layer and the predicted heat demand, the system can formulate the most suitable heat distribution scheme for the current conditions. This strategy optimization ensures efficient heat utilization, meets heat demand at different times, reduces energy waste, and improves the overall efficiency of the system. The technical effect lies in achieving dynamic adjustment and optimization, making thermal energy management more flexible and intelligent, and adaptable to changing needs under different operating environments.

[0101] In summary, the phase change monitoring and control unit of the phase change energy storage module (PCM) achieves real-time monitoring and precise control of each PCM layer by integrating temperature sensors and high-precision electromagnetic control valves. The placement and data processing technology of the temperature sensors, the precise adjustment of the electromagnetic control valves, and the construction of the phase change kinetic model collectively enhance the system's ability to optimize heat distribution and release. This system not only improves the accuracy and efficiency of thermal energy management but also achieves dynamic adjustment and efficient utilization through real-time data analysis and strategy optimization. Overall, this technical solution provides powerful intelligent control and optimization capabilities for phase change energy storage systems, ensuring effective heat management and stable system operation.

[0102] Furthermore, the heat exchange module includes the following components:

[0103] The biomimetic fractal spiral body is made using 3D metal printing technology. Through a multi-level branch channel design, it significantly increases the heat exchange area and optimizes fluid distribution.

[0104] A superhydrophobic inner wall coating is applied to the inner wall of the biomimetic fractal spiral body to reduce fluid resistance and prevent scaling, thereby improving long-term heat exchange efficiency.

[0105] A graphene composite layer is coated on the outer wall of the biomimetic fractal helix body to enhance the efficiency of heat transfer from the fluid to the PCM.

[0106] The dynamic flow channel adjustment mechanism adopts a variable cross-section design driven by shape memory alloy, which dynamically adjusts the flow channel shape according to flow rate and temperature to optimize heat exchange performance under different operating conditions.

[0107] An integrated eddy current generator is integrated into a key position of the biomimetic fractal helix body. By generating controlled eddies, it breaks the boundary layer, enhances convective heat transfer, and further improves heat transfer efficiency.

[0108] The intelligent flow distribution unit, combined with a pressure sensor and an electric micro-valve, enables intelligent flow distribution control, ensuring balanced flow across multiple branch channels and maximizing heat exchange efficiency.

[0109] In this embodiment, the biomimetic fractal helix body mimics fractal structures found in nature, such as the veins or vascular system of plants, effectively increasing the contact area for heat exchange and the flow path of fluids through hierarchical branching. This multi-level branching structure enables a large heat exchange area within a small volume, improving heat transfer efficiency. The application of 3D printing technology also allows for the high-precision manufacture of this complex structure, achieving a combination of optimized design and efficient heat exchange. The biomimetic fractal helix body significantly improves heat exchange efficiency, reduces the system's volume and weight, and optimizes fluid distribution and flow, enhancing overall heat exchange performance.

[0110] In this embodiment, a superhydrophobic inner wall coating is applied to the inner wall of the biomimetic fractal spiral body, effectively reducing fluid resistance and preventing fouling, thereby improving long-term heat exchange efficiency. This coating, using advanced materials and coating processes, forms a superhydrophobic surface that reduces fluid contact with the inner wall as it passes through the heat exchange module, thus lowering friction and flow resistance. Furthermore, the superhydrophobic surface prevents fouling and deposits, keeping the inner wall clean and avoiding a decrease in heat exchange efficiency due to deposits. The superhydrophobic inner wall coating provides a long-term stable and efficient heat exchange environment, enhancing the overall performance and durability of the heat exchange module.

[0111] In this embodiment, a graphene composite layer is coated on the outer wall of the biomimetic fractal helix body to enhance the efficiency of heat transfer from the fluid to the PCM. Graphene is renowned for its excellent thermal conductivity; by coating the outer wall with this material, the speed and efficiency of heat conduction can be significantly improved. While enhancing thermal conductivity, the graphene composite layer also enhances the material's mechanical strength and corrosion resistance to a certain extent. This graphene composite layer enables highly efficient heat transfer between the fluid and the PCM, improving the overall heat exchange performance of the heat exchange module and ensuring stability and reliability during long-term operation.

[0112] In this embodiment, the dynamic flow channel adjustment mechanism employs a shape memory alloy-driven variable cross-section design, enabling dynamic adjustment of the flow channel shape based on flow rate and temperature, thereby optimizing heat exchange performance under different operating conditions. The shape memory alloy material can recover its preset shape at a specific temperature, allowing the flow channel cross-section to be adjusted according to actual needs. This dynamic flow channel adjustment mechanism flexibly adapts to different flow rate and temperature conditions, optimizing fluid distribution and heat exchange efficiency during the heat exchange process. This design not only improves the system's adaptability under various operating conditions but also enhances the overall performance and energy efficiency of the heat exchange module.

[0113] In this embodiment, an integrated vortex generator is integrated into a key position of the biomimetic fractal helix body. By generating controlled vortices, it breaks the boundary layer of the fluid, enhancing convective heat transfer. The function of the vortex generator is to break the boundary layer in the fluid flow, increasing the contact area between the fluid and the heat exchange surface, thereby improving the efficiency of convective heat transfer. The integrated vortex generator can significantly improve the convective heat transfer capability of the heat exchange module, making heat transfer more efficient and further improving the overall heat exchange performance of the heat exchange module.

[0114] In this embodiment, the intelligent flow distribution unit combines a pressure sensor and an electric microvalve to achieve intelligent flow distribution control, ensuring balanced flow across multiple branch channels. The pressure sensor monitors the flow and pressure data of each branch channel in real time, while the electric microvalve adjusts the flow rate based on this data to ensure uniform fluid distribution within the system. This intelligent flow distribution unit achieves intelligent management and balanced distribution of flow, thereby maximizing the heat exchange effect of the heat exchange module and improving the overall energy efficiency and stability of the system.

[0115] In summary, the heat exchange module achieves efficient heat exchange and optimized fluid management by employing advanced technologies such as a biomimetic fractal spiral body, a superhydrophobic inner wall coating, a graphene composite layer, a dynamic flow channel adjustment mechanism, an integrated vortex generator, and an intelligent flow distribution unit. The design of each component plays a crucial role in improving heat exchange efficiency, reducing resistance, enhancing heat transfer, and optimizing flow distribution. By combining these technologies, the heat exchange module can provide stable and reliable heat exchange performance under various operating conditions, significantly improving the energy efficiency and operational stability of the entire system.

[0116] Furthermore, by combining pressure sensors and electric micro-valves, intelligent flow distribution control is achieved to ensure balanced flow across multiple branch channels and maximize heat exchange efficiency, including the following steps:

[0117] High-precision differential pressure sensors are installed at key locations on the biomimetic fractal spiral body, especially at the inlet and outlet of each branch channel, to monitor minute pressure changes in real time and provide accurate data support for flow control. At the same time, precision electric micro-valve is installed at the inlet of each branch channel to achieve fine adjustment of the flow rate of each branch channel.

[0118] Acquire pressure data at a high sampling rate and process it in real time to calculate pressure and estimate flow rate;

[0119] Taking into account the structure of each branch channel, fluid characteristics, and temperature change factors, a mathematical model describing the relationship between pressure, valve opening, and flow rate is constructed. The model parameters are then verified and optimized using computational fluid dynamics simulation to ensure the accuracy of flow distribution prediction.

[0120] Based on real-time pressure data and mathematical models, and considering both flow balance and heat exchange efficiency, the optimal valve control strategy is calculated and implemented.

[0121] In summary, intelligent flow distribution control achieves precise regulation of flow in multi-branch channels by installing high-precision differential pressure sensors and precision electric micro-valvees on the biomimetic fractal spiral body. High-sampling-rate pressure data acquisition and processing, combined with the construction and simulation optimization of mathematical models, provide accurate data for flow control. Ultimately, through the calculation and implementation of the optimal valve control strategy, flow balance and maximum heat exchange efficiency can be achieved. This comprehensive control scheme improves the performance of the heat exchange module and ensures high efficiency and stability under various operating conditions.

[0122] Furthermore, the flow rate can be estimated using the following formula: Among them, Q i C is the flow of the i-th branch channel; d，i A is the flow coefficient of the i-th channel; i ΔP is the cross-sectional area of ​​the i-th branch channel; i ρ is the pressure difference of the i-th branch channel; ρ is the fluid density, and the fluid is water.

[0123] Furthermore, the optimal valve control strategy is expressed by the following formula: Among them, Opt j It is the optimal opening degree of the j-th valve; η i P is the heat transfer efficiency coefficient of the i-th channel; i Q is the power consumption of the i-th channel; i，min and Q i，max Q represents the flow range of the i-th channel; total The overall target traffic; N is the number of branch channels.

[0124] Furthermore, the intelligent hierarchical control module includes the following components:

[0125] The real-time control unit uses a combination of low-power FPGA and ARM Cortex-M7 MCU to achieve millisecond-level real-time data processing and fast control decisions, ensuring the system's instantaneous performance.

[0126] Edge computing nodes integrate embedded AI accelerators to perform short-term predictions and local optimizations, balancing real-time performance with complex computing needs;

[0127] The cloud-based analytics server, based on a containerized microservice architecture, is responsible for big data analysis and generating long-term optimization strategies to continuously improve the overall system performance.

[0128] A multi-level communication gateway integrates multiple communication protocols to ensure stable, secure, and efficient data exchange between control levels.

[0129] An adaptive learning engine, which integrates reinforcement learning and transfer learning algorithms, continuously optimizes control strategies and improves system adaptability based on historical operating data and environmental changes.

[0130] The human-computer interaction interface provides web and mobile application interfaces, supports system monitoring, parameter adjustment and remote control, and enhances the operability and user-friendliness of the system.

[0131] In this embodiment, the real-time control unit employs a combination of a low-power FPGA (Field-Programmable Gate Array) and an ARM Cortex-M7 MCU (Microcontroller), aiming to achieve millisecond-level real-time data processing and rapid control decisions. The FPGA's advantages lie in its highly parallel processing capabilities and flexible hardware programming, enabling it to efficiently handle complex real-time data streams and control tasks. The ARM Cortex-M7 MCU offers high processing speed and low power consumption, making it suitable for executing more complex control algorithms and data processing. This combination ensures the system responds quickly in dynamic environments, adjusts control strategies in real time, and guarantees the system's immediate performance and stability. The technical benefits are a significant reduction in system response latency, improved control accuracy, and enhanced real-time performance.

[0132] In this embodiment, the edge computing node integrates an embedded AI accelerator for performing short-term predictions and local optimizations, balancing real-time performance with complex computational requirements. The edge computing node performs computation and processing near the data generation source, reducing data transmission latency and bandwidth requirements. The embedded AI accelerator enables the node to handle complex computational tasks, such as short-term predictions and local optimizations, ensuring a balance between real-time performance and computational demands. By improving data processing speed and efficiency through edge computing nodes, the control system can adapt more quickly to changes in the local environment, enabling effective short-term adjustments and optimizations.

[0133] In this embodiment, the cloud analytics server, based on a containerized microservice architecture, is responsible for big data analysis and long-term optimization strategy generation. Containerization enables efficient scaling and management of cloud services, while the microservice architecture provides flexible service deployment and maintenance methods. The cloud analytics server processes massive amounts of data from the system, generating long-term optimization strategies to improve overall system performance through advanced analytics and modeling. The cloud analytics server's ability to process and analyze massive amounts of data provides in-depth insights and optimization suggestions, driving continuous system improvement and performance enhancement.

[0134] In this embodiment, the multi-level communication gateway integrates multiple communication protocols to ensure stable, secure, and efficient data exchange between control levels. The gateway's design allows for efficient data transmission between different levels and components, supporting compatibility and conversion between different protocols. This integration not only improves data exchange efficiency but also enhances system security and stability. The multi-level communication gateway enables seamless data flow between different system levels, ensuring the overall collaborative operation and high performance of the system.

[0135] In this embodiment, the adaptive learning engine integrates reinforcement learning and transfer learning algorithms. Based on historical operating data and environmental changes, it continuously optimizes the control strategy, improving system adaptability. Reinforcement learning algorithms, through simulation and reward mechanisms, enable the system to optimize its control strategy through continuous trial and error. Transfer learning leverages existing knowledge and experience to accelerate the learning process in new environments. The adaptive learning engine can self-adjust based on historical data and real-time environmental changes, continuously improving the effectiveness of the control strategy and enhancing the system's adaptability and intelligence.

[0136] In this embodiment, the human-computer interaction interface provides web and mobile application interfaces, supporting system monitoring, parameter adjustment, and remote control, thus enhancing the system's operability and user-friendliness. Users can conveniently access the system's real-time status, set parameters, and perform remote operations via the web or mobile application. This interface design makes system management and control more convenient and improves the user experience. The enhanced operability through the human-computer interaction interface allows users to interact with the system more intuitively and conveniently, thereby better managing and optimizing system performance.

[0137] In summary, the intelligent hierarchical control module achieves efficient, intelligent, and flexible control by integrating a real-time control unit, edge computing nodes, a cloud analytics server, a multi-level communication gateway, an adaptive learning engine, and a human-machine interface. Each component plays a crucial role in enhancing the system's real-time performance, computing power, data analysis, and optimization. The real-time control unit and edge computing nodes ensure rapid system response and local optimization; the cloud analytics server provides long-term strategies and big data analysis; the multi-level communication gateway guarantees efficient data exchange; the adaptive learning engine continuously optimizes control strategies; and the human-machine interface improves the system's operability.

[0138] Furthermore, the auxiliary heating module includes the following components:

[0139] The solar thermal collector unit employs a vacuum tube design with a selective absorption coating to maximize solar energy absorption efficiency and provide the primary renewable energy heat source.

[0140] The heat pump unit uses environmentally friendly refrigerant CO2 and achieves efficient heating through a transcritical cycle, serving as a supplementary heat source on cloudy days or at night.

[0141] The thermal buffer energy storage unit uses high-temperature phase change materials to smooth heat source output fluctuations and improve system stability and energy utilization efficiency.

[0142] The intelligent heat source dispatch controller optimizes the coordinated operation strategy of different heat sources based on weather forecasts, energy prices, and user needs.

[0143] Multi-stage heat exchanger units, including solar-PCM heat exchangers, heat pump-PCM heat exchangers, and PCM-user-end heat exchangers, enable efficient heat transfer and distribution.

[0144] The variable flow circulating pump adopts a high-efficiency variable frequency water pump, which adjusts the flow rate according to the real-time heat load demand to ensure optimal performance under different operating conditions.

[0145] In this embodiment, the solar thermal collector unit employs a vacuum tube design with a selective absorption coating to maximize solar energy absorption efficiency. This design enhances solar energy absorption capacity through the use of a selective absorption coating while reducing heat radiation loss. The vacuum tube design creates a vacuum environment, reducing heat conduction and convection losses, thereby further improving solar energy utilization efficiency. The solar thermal collector unit significantly improves solar energy absorption and conversion efficiency, providing a stable and efficient renewable energy heat source and serving as the primary source of heat energy. This design enables the effective collection and utilization of solar energy under conditions of ample sunlight, reducing reliance on non-renewable energy sources.

[0146] In this embodiment, the heat pump unit uses the environmentally friendly refrigerant CO2, achieving efficient heating through a transcritical cycle, serving as a supplementary heat source on cloudy days or at night. CO2, as a refrigerant, possesses excellent thermal conductivity and environmentally friendly properties, enabling a highly efficient heating process through a transcritical cycle. The heat pump can effectively extract heat from the surrounding environment in low-temperature conditions and transfer it to the system. The heat pump unit provides a reliable supplementary heat source, ensuring the necessary heat can still be provided when solar energy is insufficient, thus improving adaptability and continuity.

[0147] In this embodiment, the thermal buffer energy storage unit employs a high-temperature phase change material (PCM) to smooth fluctuations in heat source output, thereby improving system stability and energy utilization efficiency. High-temperature PCM can store and release a large amount of heat during phase change, a characteristic that effectively smooths heat source fluctuations and reduces heat supply instability. The thermal buffer energy storage unit improves stability, reduces the impact of uneven heat source output, and enhances energy utilization efficiency, enabling the system to operate more smoothly.

[0148] In this embodiment, the intelligent heat source scheduling controller integrates multiple data sources, such as weather forecast data, energy market prices, and real-time user demand, and calculates the optimal heat source usage strategy through intelligent algorithms. The intelligent heat source scheduling controller improves the system's operating efficiency and economy, ensuring optimal performance under different environmental and economic conditions by optimizing the usage ratio of different heat sources. This intelligent scheduling not only reduces energy costs but also improves the overall system efficiency.

[0149] In this embodiment, the multi-stage heat exchanger group includes a solar-PCM heat exchanger, a heat pump-PCM heat exchanger, and a PCM-user-end heat exchanger, designed to achieve efficient heat transfer and distribution. The solar-PCM heat exchanger transfers solar heat to the PCM energy storage unit, the heat pump-PCM heat exchanger transfers heat generated by the heat pump to the PCM, and the PCM-user-end heat exchanger ultimately transfers the heat stored in the PCM to the user end. Through the multi-stage heat exchanger design, efficient heat transfer and distribution are achieved, optimizing the overall heat exchange efficiency of the system and ensuring a stable heat supply to the user end.

[0150] In this embodiment, the variable flow circulating pump employs a high-efficiency variable frequency water pump, adjusting the flow rate according to real-time heat load requirements to ensure optimal performance under different operating conditions. The variable frequency water pump can adjust according to actual heat load demands, thereby achieving precise flow control. This design allows the system to dynamically adjust the pump's operating state according to different needs, optimizing fluid circulation and heat transfer. The variable flow circulating pump improves the system's operating efficiency and energy saving, ensuring optimal performance under various operating conditions while reducing energy consumption and operating costs.

[0151] In summary, the auxiliary heating module integrates a solar thermal collector, a heat pump unit, a thermal buffer energy storage unit, an intelligent heat source scheduling controller, a multi-stage heat exchanger assembly, and a variable flow circulating pump, achieving efficient, stable, and intelligent heat replenishment and distribution. The solar thermal collector and heat pump units provide primary and supplementary heat sources, the thermal buffer energy storage unit smooths out heat source output fluctuations, the intelligent heat source scheduling controller optimizes the coordinated operation strategy of the heat sources, the multi-stage heat exchanger assembly ensures efficient heat transfer, and the variable flow circulating pump improves the system's fluid control accuracy. Combining the technical effects of these components, the entire system provides an efficient, stable, and intelligent auxiliary heating solution, significantly improving energy utilization efficiency and overall system performance.

[0152] Furthermore, based on weather forecasts, energy prices, and user demand, optimize the coordinated operation strategy of different heat sources, including the following steps:

[0153] The system collects real-time weather data, energy market prices, and user demand information, and performs data cleaning, standardization, and feature extraction. Data acquisition must be conducted frequently and accurately in real time, while data cleaning, standardization, and feature extraction are performed simultaneously. Data cleaning removes noise and errors, and standardization converts the data into a uniform format for further analysis. Feature extraction extracts useful features from the raw data to facilitate model training and prediction. This process provides a clean and reliable data source for subsequent prediction and optimization, ensuring the system can make optimization decisions based on accurate information.

[0154] Based on historical data and machine learning algorithms, a user demand prediction model is constructed that takes into account weather, date and seasonal factors.

[0155] Detailed performance models are established for each heat source to describe its efficiency and output characteristics under different operating conditions, so as to calculate the operating costs and benefits of each heat source under current and predicted future conditions.

[0156] By balancing energy costs, system efficiency, user comfort, and environmental impact objectives, a series of Pareto optimal solutions are generated, providing multiple options for decision-making. At the same time, a constraint handling mechanism is implemented to ensure that the generated solutions meet equipment operating limitations and safety requirements.

[0157] Based on the Pareto optimal solution, the operating parameters of each heat source are dynamically adjusted according to the current state and short-term forecasts;

[0158] Collect actual operational data and compare it with the prediction results, use Bayesian optimization to fine-tune parameters, set performance indicators, and generate reports regularly.

[0159] In summary, optimizing the coordinated operation strategy of different heat sources achieves efficient and intelligent heat source management by integrating steps such as real-time data acquisition, user demand forecasting, heat source performance modeling, Pareto optimal solution generation, dynamic adjustment, and actual operation data analysis. Real-time data acquisition and processing provide the foundational data for optimization decisions, while user demand forecasting models and heat source performance models support the formulation of precise operation strategies. Pareto optimal solutions offer multiple optimization options, dynamic adjustment ensures the system's flexibility and adaptability, and the collection of actual operation data and parameter tuning drive continuous improvement in system performance. These combined technical measures improve the efficiency of heat source utilization, reduce energy costs, and enhance the overall system performance and user satisfaction.

Claims

1. A swimming pool waste heat recovery system based on phase change energy storage materials, It is characterized in that Includes the following components: Phase change energy storage module, which uses phase change materials with different melting points to achieve efficient energy storage in a wide temperature range; The heat exchange module adopts a bionic fractal spiral structure and advanced surface treatment technology to significantly increase the heat exchange area and reduce fluid resistance, thereby greatly improving the heat exchange efficiency; Intelligent hierarchical control module integrates edge computing and cloud computing technologies to achieve all-round intelligent management from millisecond-level real-time control to long-term optimization; The sensor network module combines fiber optic sensing and wireless sensing technology to provide all-round monitoring with high precision and high spatial resolution; Energy delivery module, through the integrated design of magnetic bearing and permanent magnet synchronous motor, realizes efficient, reliable and low-noise fluid delivery while reducing maintenance costs; Auxiliary heating module with integrated solar collectors and CO 2 Heat pumps use phase change materials as thermal buffers to provide a flexible and efficient supplementary heat source when energy storage is insufficient, maximizing the use of renewable energy.

2. A swimming pool waste heat recovery system based on phase change energy storage materials according to claim 1, It is characterized in that The phase change energy storage module includes the following components: The PCM container adopts a three-layer concentric cylindrical structure made of alloy material to achieve physical isolation and heat gradient transfer of PCM; The high melting point PCM layer is located in the inner layer of the PCM container and is filled with modified hydrated salt with a melting point of 38-42°C. It is used for heat storage in the high temperature section and improves the peak energy storage capacity. The medium melting point PCM layer is located in the middle layer of the PCM container and is filled with bio-based fatty acid esters with a melting point of 30-35°C. It serves as a transition layer to enhance temperature adaptability and energy storage stability. The low melting point PCM layer is located on the outer layer of the PCM container and is filled with nano-enhanced paraffin with a melting point of 24-28°C, which is used for heat recovery in the low temperature section to improve energy utilization efficiency; Enhanced heat conduction unit, using a three-dimensional graphene heat conduction network that runs through three layers of PCM to increase the heat conduction rate inside the PCM and speed up the charging and dissipating process; The phase change monitoring and control unit integrates temperature sensors and control valves to monitor the phase change state of each layer of PCM in real time and optimize heat distribution and release strategies.

3. A swimming pool waste heat recovery system based on phase change energy storage material according to claim 2, It is characterized in that Integrate temperature sensors and control valves to monitor the phase change state of each layer of PCM in real time and optimize heat distribution and release strategies, including the following steps: Arrange temperature sensors at the center, edge and middle of each layer of the PCM container; Install high-precision, fast-response electromagnetic control valves at the heat exchange fluid inlet and between each layer; Real-time temperature data collection, calculation and analysis after filtering to determine the phase change state of PCM, and data visualization and storage; Based on thermodynamic principles and experimental data, a phase change kinetic model is constructed for each layer to accurately describe the phase change process of PCM under different temperature and heat flow conditions; The optimal heat allocation and release strategy is calculated based on real-time temperature data, phase change state and predicted heat demand.

4. A swimming pool waste heat recovery system based on phase change energy storage material according to claim 1, It is characterized in that The heat exchange module includes the following components: The bionic fractal spiral body is made of 3D metal printing technology. Through the multi-level branch channel design, the heat exchange area is significantly increased while optimizing fluid distribution; Super hydrophobic inner wall coating, applied on the inner wall of the bionic fractal spiral body, reduces fluid resistance and prevents scaling, improving long-term heat exchange efficiency; A graphene composite layer, coated on the outer wall of the bionic fractal spiral body, enhances the efficiency of heat transfer from the fluid to the PCM; Dynamic flow channel adjustment mechanism, using a variable cross-section design driven by shape memory alloy, dynamically adjusts the flow channel shape according to flow and temperature to optimize heat exchange performance under different working conditions; Integrated vortex generator, integrated in the key position of the bionic fractal spiral body, generates controlled vortex, breaks the boundary layer, strengthens convective heat transfer, and further improves heat transfer efficiency; The intelligent flow distribution unit, combined with the pressure sensor and electric micro valve, realizes intelligent flow distribution control, ensures the flow balance among multiple branch channels, and maximizes the heat exchange effect.

5. A swimming pool waste heat recovery system based on phase change energy storage material according to claim 4, It is characterized in that Combined with pressure sensors and electric micro valves, intelligent flow distribution control is realized to ensure flow balance among multi-branch channels and maximize heat exchange effect, including the following steps: High-precision differential pressure sensors are installed at key locations of the bionic fractal spiral body, especially at the inlet and outlet of each branch channel, to monitor tiny pressure changes in real time and provide accurate data support for flow control. At the same time, precision electric microvalves are installed at the inlet of each branch channel to achieve fine adjustment of the flow of each branch channel. Acquire pressure data at a high sampling rate and process it in real time to calculate pressure and estimate flow; Taking into account the structure of each branch channel, fluid characteristics and temperature change factors, a mathematical model describing the relationship between pressure, valve opening and flow is constructed, and computational fluid dynamics simulation is used to verify and optimize model parameters to ensure the accuracy of flow distribution prediction; Based on real-time pressure data and mathematical models, the optimal valve control strategy is calculated and implemented taking into account flow balance and heat transfer efficiency.

6. A swimming pool waste heat recovery system based on phase change energy storage materials according to claim 5, It is characterized in that The flow rate is estimated using the following formula: Among them, Q i is the flow rate of the i-th branch channel; C d，i is the flow coefficient of the ith channel; A i is the cross-sectional area of ​​the i-th branch channel; ΔP i is the pressure difference of the i-th branch channel; ρ is the fluid density, and water is selected as the fluid.

7. A swimming pool waste heat recovery system based on phase change energy storage material according to claim 5, It is characterized in that The optimal valve control strategy is expressed by the following formula: Among them, Opt j is the optimal opening of the jth valve; η i is the heat transfer efficiency coefficient of the ith channel; P i is the power consumption of the ith channel; Q i，min and Q i，max is the flow range of the ith channel; Q total The overall target flow rate; N is the number of branch channels.

8. A swimming pool waste heat recovery system based on phase change energy storage materials according to claim 1, It is characterized in that The intelligent tiered control module includes the following components: Real-time control unit, using a combination of low-power FPGA and ARM Cortex-M7 MCU, to achieve real-time data processing and fast control decision-making with millisecond response, ensuring the instant performance of the system; Edge computing nodes, with integrated embedded AI accelerators, perform short-term prediction and local optimization, balancing real-time and complex computing requirements; The cloud-based analysis server, based on a microservice architecture deployed in containers, is responsible for big data analysis and long-term optimization strategy generation to continuously improve the overall performance of the system; Multi-level communication gateway integrates multiple communication protocols to ensure stable, secure and efficient data exchange between various levels of control; Adaptive learning engine, integrating reinforcement learning and transfer learning algorithms, continuously optimizes control strategies and improves system adaptability based on historical operating data and environmental changes; Human-computer interaction interface provides web and mobile application interfaces, supports system monitoring, parameter adjustment and remote control, and enhances the operability and user-friendliness of the system.

9. A swimming pool waste heat recovery system based on phase change energy storage materials according to claim 1, It is characterized in that The auxiliary heating module consists of the following components: Solar thermal collection units, using a vacuum tube design with a selective absorption coating to maximize solar absorption efficiency, providing the primary renewable energy heat source; Heat pump unit, using environmentally friendly refrigerant CO 2 , achieving efficient heating through transcritical cycle, serving as a supplementary heat source on cloudy days or at night; Thermal buffer energy storage unit, using high-temperature phase change materials to smooth heat source output fluctuations, improve system stability and energy efficiency; Intelligent heat source dispatch controller, which optimizes the coordinated operation strategy of different heat sources according to weather forecasts, energy prices and user needs; Multi-stage heat exchanger group, including solar-PCM heat exchanger, heat pump-PCM heat exchanger and PCM-user end heat exchanger, to achieve efficient heat transfer and distribution; The variable flow circulation pump adopts a high-efficiency variable frequency water pump to adjust the flow according to the real-time heat load demand to ensure the best performance under different working conditions.

10. A swimming pool waste heat recovery system based on phase change energy storage material according to claim 9, It is characterized in that According to weather forecast, energy price and user demand, the coordinated operation strategy of different heat sources is optimized, including the following steps: Collect weather data, energy market prices and user demand information in real time and perform data cleaning, standardization and feature extraction; Based on historical data and machine learning algorithms, a user demand forecasting model that takes into account weather, date, and seasonal factors is built; Develop detailed performance models for each heat source, describing its efficiency and output characteristics under different operating conditions, in order to calculate the operating costs and benefits of each heat source under current and predicted future conditions; By balancing energy cost, system efficiency, user comfort and environmental impact goals, a series of Pareto optimal solutions are generated to provide multiple options for decision-making, while implementing constraint processing mechanisms to ensure that the generated solutions meet equipment operation constraints and safety requirements; Based on the Pareto optimal solution, the operating parameters of each heat source are dynamically adjusted according to the current status and short-term forecast; Collect actual operation data and compare it with the predicted results, use Bayesian optimization to tune parameters, set performance indicators and generate reports regularly.

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