High-efficiency thermoelectric decoupling system based on phase change energy storage

Abstract

The invention relates to the technical field of energy and power engineering, and discloses an efficient thermoelectric decoupling system based on phase change energy storage, which comprises an energy storage unit module, a regulation and control algorithm module and a scheduling optimization module. The energy storage unit module adopts a nanotechnology novel phase change material, so that the energy storage density and stability are improved; the regulation and control algorithm module realizes accurate control of charging and discharging through a multi-stage regulation and control strategy; and the scheduling optimization module predicts energy demand and production by using a machine learning algorithm, and dynamically adjusts an energy distribution strategy. The problems that a traditional heat storage material is low in energy storage density and poor in stability and lacks an intelligent scheduling mechanism can be solved, the energy utilization efficiency and operation reliability of a thermoelectric system are improved, and the energy cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of energy and power engineering technology, and in particular to a high-efficiency thermoelectric decoupling system based on phase change energy storage. Background Art

[0002] With the continuous growth of energy demand, cogeneration systems play a vital role in energy supply. However, in traditional thermoelectric systems, thermal decoupling faces numerous challenges: Limited energy storage density: Traditional heat storage materials, such as hot water and molten salt, have low heat storage densities, making it difficult to meet the demand for large-scale, long-term thermal energy storage. This limitation results in insufficient storage and efficient utilization of thermal energy during the cogeneration process, which has a certain impact on energy efficiency. Insufficient energy storage stability: Existing energy storage systems experience significant temperature fluctuations during charging and discharging, and their energy storage stability has certain shortcomings. This situation may not only affect the normal operation of thermoelectric equipment but also reduce system reliability and service life. Lack of intelligent scheduling mechanisms: Traditional thermoelectric systems lack flexible intelligent scheduling mechanisms, making it difficult to optimize the allocation of heat and electricity based on dynamic changes in energy supply and demand. This deficiency can lead to energy waste and increased operating costs.

[0003] Problems with existing technologies also include: the low energy storage density of traditional heat storage materials results in insufficient thermal energy storage. During the cogeneration process, excess thermal energy is often directly discharged, resulting in a certain degree of energy waste; the unstable operation of the energy storage system can easily lead to frequent start and stop of thermal power equipment, shortening equipment life and increasing maintenance costs; in addition, the lack of an intelligent scheduling mechanism makes the thermal power system blind in energy distribution, making it difficult to flexibly adjust according to changes in energy market prices and user demand, thereby increasing energy costs. Summary of the Invention

[0004] The purpose of the present invention is to provide a highly efficient thermoelectric decoupling system based on phase change energy storage, aiming to address the existing issues of low energy storage density, poor energy storage stability, and a lack of intelligent scheduling mechanisms in traditional thermal storage materials. These issues lead to insufficient thermal energy storage, severe energy waste, frequent equipment startups and shutdowns, and irrational energy distribution during the cogeneration process.

[0005] The embodiment of the present invention is implemented as follows: on the one hand, a high-efficiency thermoelectric decoupling system based on phase change energy storage includes:

[0006] Energy storage unit module, which uses a new phase change material based on nanotechnology as the core energy storage medium, and uses special packaging technology to encapsulate the phase change material into an independent energy storage unit. The energy storage unit module includes at least one or more groups of energy storage units, and each group of energy storage units is connected to the thermoelectric system through a heat conduction pipe;

[0007] A control algorithm module is used to perform multi-level control on the energy storage unit module based on real-time monitored temperature data, wherein the control algorithm module includes at least a charge control submodule and a discharge control submodule, each of which is used to adjust the temperature change of the energy storage unit module during the charging and discharging process;

[0008] The scheduling optimization module is used to collect the operating parameters and external information of the thermal power system, generate an energy demand prediction model and an energy production prediction model through a machine learning algorithm, and formulate an energy allocation strategy based on the prediction results, wherein the scheduling optimization module includes at least a data acquisition unit, a prediction model unit and a strategy execution unit.

[0009] As a further solution of the present invention, the energy storage unit module specifically includes:

[0010] The Materials Research and Development Unit is responsible for developing new phase change materials based on nanotechnology, such as nanocomposite phase change materials or nanostructured phase change materials, and improving their phase change latent heat and thermal conductivity through the design and optimization of the material's microstructure;

[0011] The packaging technology unit is used to: encapsulate new phase change materials using a multi-layer composite packaging structure. The inner layer uses high thermal conductivity materials to enhance heat transfer efficiency, and the outer layer uses materials with excellent thermal insulation properties to reduce heat loss, while improving the corrosion resistance and aging resistance of the packaging material.

[0012] As a further solution of the present invention, the control algorithm module specifically includes:

[0013] The temperature monitoring unit is used to: install a high-precision temperature sensor to monitor the temperature changes of the phase change material in the energy storage unit module in real time and transmit the temperature data to the control system;

[0014] The multi-stage control unit is used to: adopt a multi-stage control strategy based on temperature monitoring data, in which the phase change material undergoes phase change according to a predetermined temperature curve by adjusting the heating power and heating time during the charging process, and maintains the stability of the output thermal energy by adjusting the heat dissipation rate during the discharge process.

[0015] As a further solution of the present invention, the scheduling optimization module further includes:

[0016] The data acquisition unit is used to collect the operating parameters of the thermoelectric system through sensors, including power generation, heat supply, energy consumption, etc., and to obtain external information such as energy market prices and meteorological data;

[0017] The forecasting model unit is used to: use machine learning algorithms such as neural networks and support vector machines to establish energy demand forecasting models and energy production forecasting models, and combine historical data and real-time data to improve forecasting accuracy;

[0018] The strategy execution unit is used to formulate an energy allocation strategy based on energy demand forecast results and real-time energy production conditions, wherein the thermal energy in the energy storage unit module is released first during peak energy demand periods, and the excess electrical energy is used to charge the energy storage unit module during low energy demand periods.

[0019] As a further solution of the present invention, the scheduling optimization module further includes a dynamic optimization scheduling unit, and the dynamic optimization scheduling unit specifically includes:

[0020] Multi-source data fusion unit is used to integrate multi-source information such as energy production equipment data, energy consumption data, meteorological data, energy market data, and user behavior data, and use deep learning algorithms to conduct in-depth mining to extract valuable information;

[0021] The dynamic adjustment unit is used to: adopt the model predictive control algorithm to dynamically adjust the energy allocation strategy according to the real-time energy supply and demand situation and the prediction results. For example, when the energy market price fluctuates greatly, the power generation and heating strategies are adjusted in time to reduce energy costs. At the same time, the safety and reliability constraints of the energy system are taken into consideration to ensure the stable operation of the energy system.

[0022] As a further solution of the present invention, the energy storage unit module further includes an adaptive control unit, and the adaptive control unit specifically includes:

[0023] Environmental monitoring unit, used to: monitor the external environment temperature changes in real time and transmit the monitoring data to the control system;

[0024] The adaptive adjustment unit is used to automatically adjust the heating and cooling strategies according to changes in the external ambient temperature to ensure the stable operation of the energy storage unit module under different working conditions.

[0025] As a further solution of the present invention, the energy storage unit module further includes a multi-physics field coupling analysis unit, and the multi-physics field coupling analysis unit specifically includes:

[0026] Mathematical modeling unit, used to: establish mathematical models of multi-physics field coupling such as heat transfer, mass transfer and phase change, and use numerical simulation methods to analyze the phase change process and temperature distribution of phase change materials under different working conditions;

[0027] Theoretical support unit is used to provide a theoretical basis for the multi-level control algorithm module and ensure the accuracy of the control strategy.

[0028] As a further solution of the present invention, the scheduling optimization module further includes a user interaction unit, and the user interaction unit specifically includes:

[0029] An information prompt unit is used to: when it is detected that the user leaves a certain area, or does not enter a certain area within a set time, issue a prompt message indicating whether there is additional heating or cooling demand;

[0030] The authority management unit is used to obtain the user's authorization information and determine whether the user has left the current area when the user terminal displays the navigation route and moves towards a certain location.

[0031] As a further solution of the present invention, the scheduling optimization module further includes a recovery control unit, which specifically includes:

[0032] A return detection unit, configured to trigger a recovery control program when the user terminal moves away from a certain location along the navigation route and to a preset distance;

[0033] The control option unit is used to: display new control information execution options, control the working status of the thermoelectric equipment in the relevant area or keep it as it is based on the user's selection, where the control information execution options include the estimated time of the user's return and the temperature information of the current area.

[0034] As a further solution of the present invention, the energy storage unit module and the scheduling optimization module are connected via a communication interface, and the communication interface adopts an industrial standard protocol to ensure the stability and real-time performance of data transmission.

[0035] This invention provides a highly efficient thermoelectric decoupling system based on phase change energy storage. It offers the following benefits: By employing a novel nanophase change material as the energy storage medium, it improves energy storage density and stability; a multi-level control algorithm module enables precise control of the charging and discharging processes of the energy storage unit module; and a scheduling optimization module enables accurate prediction and dynamic allocation of energy demand and production. These technical approaches work together to address the low energy storage density, poor energy storage stability, and lack of intelligent scheduling mechanisms associated with traditional thermal storage materials, improving the energy utilization efficiency and operational reliability of the thermoelectric system and reducing energy costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Schematic diagram of the overall structure of the system of the present invention;

[0037] Figure 2 Schematic diagram of the structure of the energy storage unit module of the present invention;

[0038] Figure 3 This is a workflow diagram of the control algorithm module of the present invention;

[0039] Figure 4 This is a structural block diagram of the scheduling optimization module of the present invention;

[0040] Figure 5This is a working principle diagram of the dynamic optimization scheduling unit of the present invention;

[0041] Figure 6 Schematic diagram of the working of the adaptive control unit of the present invention;

[0042] Figure 7 This is a mathematical modeling flow chart of the multi-physics field coupling analysis unit of the present invention;

[0043] Figure 8 Schematic diagram of the functions of the user interaction unit and the recovery control unit of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] The embodiment of the present invention provides a high-efficiency thermoelectric decoupling system based on phase change energy storage, the overall structure of which is as follows: Figure 1 As shown, it includes an energy storage unit module, a control algorithm module, and a scheduling optimization module. These modules are connected and exchange data through heat conduction pipes and communication interfaces. The energy storage unit module is a core component. It uses a new phase change material based on nanotechnology as the energy storage medium. The phase change material is encapsulated into an independent energy storage unit through special packaging technology. Each group of energy storage units is connected to the thermoelectric system through a heat conduction pipe, thereby realizing the storage and release of thermal energy. The control algorithm module is responsible for multi-level control of the energy storage unit module, including a charging control submodule and a discharging control submodule, which are used to adjust the temperature changes during the charging and discharging process respectively. The scheduling optimization module generates energy demand forecast models and energy production forecast models by collecting the operating parameters and external information of the thermoelectric system and combining machine learning algorithms, and formulates energy allocation strategies.

[0046] The specific structure of the energy storage unit module is as follows Figure 2As shown, it houses the Materials R&D Unit and the Packaging Technology Unit. The Materials R&D Unit develops new phase-change materials based on nanotechnology, such as nanocomposite or nanostructured phase-change materials. By designing and optimizing the materials' microstructures, they enhance their latent heat and thermal conductivity. The Packaging Technology Unit encapsulates the new phase-change materials using a multi-layer composite packaging structure. The inner layer uses a highly thermally conductive material to enhance heat transfer efficiency, while the outer layer uses a material with excellent thermal insulation properties to reduce heat loss and improve the corrosion resistance and aging resistance of the packaging material. The encapsulated energy storage unit module is connected to the heat exchanger of the thermoelectric system via a thermal pipe. Heat energy is transferred between the energy storage unit module and the thermoelectric system through the thermal pipe. One end of the thermal pipe connects to the bottom of the energy storage unit's packaging shell, and the other end connects to the inlet of the thermoelectric system's heat exchanger, ensuring efficient heat transfer.

[0047] The workflow of the control algorithm module is as follows Figure 3 As shown, it internally contains a temperature monitoring unit and a multi-level control unit. The temperature monitoring unit is equipped with a high-precision temperature sensor to monitor the temperature changes of the phase change material in the energy storage unit module in real time and transmit the temperature data to the control system. The multi-level control unit adopts a multi-level control strategy based on the temperature monitoring data. During the charging process, it adjusts the heating power and heating time to make the phase change material undergo a phase change according to the predetermined temperature curve. During the discharge process, it adjusts the heat dissipation rate to maintain the stability of the output thermal energy. The control algorithm module is connected to the energy storage unit module and the scheduling optimization module via a communication interface. It receives temperature data from the energy storage unit module and provides feedback on control instructions. At the same time, it provides charging and discharging status information to the scheduling optimization module to optimize the energy allocation strategy.

[0048] The structure of the scheduling optimization module is as follows Figure 4 As shown in Figure 1, it internally comprises a data acquisition unit, a prediction model unit, and a strategy execution unit. The data acquisition unit uses sensors to collect operating parameters of the thermal power system, including power generation, heat supply, and energy consumption. It also obtains external information such as energy market prices and meteorological data. The prediction model unit uses machine learning algorithms such as neural networks and support vector machines to establish energy demand and energy production forecast models, combining historical and real-time data to improve forecast accuracy. The strategy execution unit formulates energy allocation strategies based on energy demand forecasts and real-time energy production. For example, it prioritizes the release of thermal energy from the energy storage module during peak energy demand and utilizes excess power to charge the energy storage module during low energy demand periods. The scheduling optimization module connects to the energy storage module and the control algorithm module via a communication interface, receiving operating status information from the energy storage module and sending control instructions to the control algorithm module.

[0049] The working principle of the dynamic optimization scheduling unit is as follows Figure 5As shown in Figure 1, it internally includes a multi-source data fusion unit and a dynamic adjustment unit. The multi-source data fusion unit integrates multiple sources of information, including energy production equipment data, energy consumption data, meteorological data, energy market data, and user behavior data, and uses deep learning algorithms to conduct in-depth mining to extract valuable information. The dynamic adjustment unit uses a model predictive control algorithm to dynamically adjust energy allocation strategies based on real-time energy supply and demand conditions and forecast results. For example, when energy market prices fluctuate significantly, it can promptly adjust power generation and heating strategies to reduce energy costs. At the same time, it considers the safety and reliability constraints of the energy system to ensure its stable operation. The dynamic optimization scheduling unit connects to the scheduling optimization module via a communication interface, receives predictive model data from the scheduling optimization module, and sends optimization instructions to the strategy execution unit.

[0050] The working diagram of the adaptive control unit is as follows: Figure 6 As shown, it internally includes an environmental monitoring unit and an adaptive adjustment unit. The environmental monitoring unit monitors ambient temperature changes in real time and transmits this data to the control system. The adaptive adjustment unit automatically adjusts heating and cooling strategies based on ambient temperature changes, ensuring stable operation of the energy storage module under different operating conditions. The adaptive control unit connects to the energy storage module and the control algorithm module via a communication interface, receiving temperature data from the environmental monitoring unit and sending adjustment instructions to the control algorithm module.

[0051] The mathematical modeling process of the multi-physics coupling analysis unit is as follows: Figure 7 As shown in Figure 1, it comprises a mathematical modeling unit and a theoretical support unit. The mathematical modeling unit establishes a mathematical model for the coupled effects of multiple physical fields, such as heat transfer, mass transfer, and phase change, and uses numerical simulation methods to analyze the phase change process and temperature distribution of the phase change material under different operating conditions. The theoretical support unit provides a theoretical basis for the multi-level control algorithm module, ensuring the accuracy of the control strategy. The multi-physics coupling analysis unit connects to the control algorithm module via a communication interface, receives control data from the control algorithm module, and sends theoretical support information to the multi-level control unit.

[0052] The functional diagram of the user interaction unit and the recovery control unit is as follows Figure 8As shown, it internally contains an information prompt unit, an authority management unit, a return detection unit and a control option unit. When the information prompt unit detects that the user has left a certain area or has not entered a certain area within the time set by the user, it issues a prompt message indicating whether there is additional heating or cooling demand. The authority management unit obtains the user's authorization information, and determines that the user has left the current area when the user terminal displays the navigation route and moves towards a certain location. The return detection unit triggers the recovery control program when the user terminal moves away from a certain location along the navigation route and moves to a preset distance. The control option unit displays a new control information execution option, and controls the working status of the thermoelectric equipment in the relevant area or keeps it as it is based on the user's selection, wherein the control information execution option includes the estimated time of the user's return and the temperature information of the current area. The user interaction unit and the recovery control unit are connected to the scheduling optimization module through the communication interface, receive user behavior data from the scheduling optimization module and send control instructions to the strategy execution unit.

[0053] The energy storage unit module and the scheduling optimization module are connected via a communication interface that uses an industry-standard protocol to ensure stable and real-time data transmission. The energy storage unit module is connected to the thermoelectric system via a heat transfer pipe, through which heat energy is transferred between the two systems. The control algorithm module connects to the energy storage unit module and the scheduling optimization module via the communication interface, receiving temperature data from the energy storage unit module and providing feedback on control instructions. It also provides charge and discharge status information to the scheduling optimization module to optimize the energy allocation strategy. The scheduling optimization module connects to the energy storage unit module and the control algorithm module via the communication interface, receiving operating status information from the energy storage unit module and sending control instructions to the control algorithm module. The dynamic optimization scheduling unit connects to the scheduling optimization module via the communication interface, receiving prediction model data from the scheduling optimization module and sending optimization instructions to the strategy execution unit. The adaptive control unit connects to the energy storage unit module and the control algorithm module via the communication interface, receiving temperature data from the environmental monitoring unit and sending adjustment instructions to the control algorithm module. The multi-physics coupling analysis unit connects to the control algorithm module via the communication interface, receiving control data from the control algorithm module and sending theoretical support information to the multi-level control unit. The user interaction unit and the recovery control unit are connected to the scheduling optimization module through a communication interface, receive user behavior data from the scheduling optimization module and send control instructions to the strategy execution unit.

[0054] In practical applications, this system can be applied to district heating and power supply systems. For example, during winter heating, the energy storage unit stores heat during nighttime off-peak electricity prices and releases it during daytime peak electricity demand to meet heating demand. The control algorithm module monitors the temperature changes of the energy storage unit in real time and dynamically adjusts the charging and discharging strategies based on the operating parameters of the thermoelectric system. The scheduling optimization module collects meteorological data and energy market price information to predict energy demand and production for the next day and formulate the optimal energy allocation strategy. The dynamic optimization scheduling unit dynamically adjusts the energy allocation strategy based on real-time energy supply and demand and forecast results. For example, it can adjust power generation and heating strategies to reduce energy costs when energy market prices fluctuate significantly. The adaptive control unit automatically adjusts heating and cooling strategies based on changes in ambient temperature, ensuring stable operation of the energy storage unit under different operating conditions. The multi-physics coupling analysis unit uses mathematical modeling to analyze the phase change process and temperature distribution of phase change materials under different operating conditions, providing theoretical support for the control algorithm module. The user interaction unit and recovery control unit adjust the operating state of thermoelectric equipment in the relevant area based on user behavior data to ensure that user comfort needs are met at different time periods.

[0055] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the specific implementation principle of the present invention is further supplemented below in combination with a specific application scenario.

[0056] During the winter heating period, the energy storage unit module uses the nighttime off-peak electricity price period to store thermal energy and releases the thermal energy during the daytime peak electricity consumption period to meet the heating demand. First, the nano-composite phase change material developed by the material research and development unit is encapsulated in a multi-layer composite structure. The inner layer uses a high thermal conductivity material to enhance heat transfer efficiency, and the outer layer uses a material with excellent thermal insulation properties to reduce heat loss. The encapsulated energy storage unit is connected to the heat exchanger of the thermoelectric system via a heat conduction pipe to ensure that the heat energy remains efficient during the transmission process. One end of the heat conduction pipe is connected to the bottom of the encapsulation shell of the energy storage unit module, and the other end is connected to the heat exchanger inlet of the thermoelectric system.

[0057] When the system enters the nighttime off-peak electricity price period, the temperature monitoring unit in the control algorithm module activates a high-precision temperature sensor to monitor the temperature changes of the phase change material in the energy storage unit in real time. The multi-stage control unit adjusts the heating power and time based on this temperature data, causing the phase change material to undergo a phase change according to a predetermined temperature curve. This process feeds charging and discharging status information to the scheduling optimization module via a communication interface to optimize energy allocation strategies.

[0058] The data acquisition unit in the scheduling optimization module uses sensors to collect operational parameters such as power generation, heat supply, and energy consumption, while also acquiring external information such as meteorological data and energy market prices. The forecasting model unit uses neural networks and support vector machines to establish energy demand and production forecasting models, integrating historical and real-time data to improve forecast accuracy. The strategy execution unit formulates an energy allocation strategy based on the forecast results, prioritizing the release of thermal energy from the energy storage modules during peak energy demand periods and utilizing excess electricity to recharge them during low energy demand periods.

[0059] The multi-source data fusion unit within the dynamic optimization and scheduling unit integrates production equipment data, energy consumption data, meteorological data, market data, and user behavior data, extracting valuable information using deep learning algorithms. The dynamic adjustment unit employs a model predictive control algorithm to dynamically adjust power generation and heating strategies based on real-time energy supply and demand and forecast results, reducing energy costs and ensuring stable operation of the energy system.

[0060] The environmental monitoring unit within the adaptive control unit monitors ambient temperature changes in real time. The adaptive adjustment unit automatically adjusts heating and cooling strategies based on this monitoring data to ensure stable operation of the energy storage module under varying operating conditions. The mathematical modeling unit within the multi-physics coupling analysis unit establishes mathematical models for heat transfer, mass transfer, and phase change. Using numerical simulation methods, it analyzes the phase change process and temperature distribution of the phase change material under varying operating conditions, providing theoretical support for the multi-stage control unit.

[0061] When the information prompt unit in the user interaction unit detects that a user has left an area or has not entered an area on time, it issues a prompt indicating whether additional heating or cooling is required. The permission management unit obtains user authorization information and determines whether the user has left the current area. The return detection unit in the recovery control unit triggers the recovery control program when the user terminal moves a preset distance away from a location along the navigation route. The control option unit displays new control information execution options, and based on the user's selection, controls the operating status of the thermoelectric equipment in the relevant area or maintains it as is.

[0062] Through the above steps, the energy storage unit module can efficiently store thermal energy during low-price nighttime electricity prices and release thermal energy during peak daytime electricity consumption, thereby significantly improving energy utilization efficiency. The control algorithm module and the scheduling optimization module work together to ensure that the operating parameters of the thermal power system are always optimal, reducing energy waste and extending equipment life. The dynamic optimization scheduling unit and the adaptive control unit further enhance the flexibility and stability of the system, ensuring efficient operation even in complex environmental conditions. The theoretical support provided by the multi-physics coupling analysis unit makes the control strategy more precise, while the user interaction unit and the recovery control unit ensure that the user's comfort needs are responded to in a timely manner.

[0063] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A high-efficiency thermoelectric decoupling system based on phase change energy storage, characterized in that: include: Energy storage unit module, which uses a new phase change material based on nanotechnology as the core energy storage medium and encapsulates the phase change material into independent energy storage units through special packaging technology. Each group of energy storage units is connected to the thermoelectric system through a heat conduction pipe; A control algorithm module is used to perform multi-level control of the energy storage unit module based on real-time monitored temperature data, including a charge control submodule and a discharge control submodule, each used to adjust the temperature change of the energy storage unit module during the charging and discharging process; The scheduling optimization module is used to collect the operating parameters and external information of the thermal power system, generate energy demand forecast models and energy production forecast models through machine learning algorithms, and formulate energy allocation strategies based on the forecast results.

2. A high-efficiency thermoelectric decoupling system based on phase change energy storage according to claim 1, characterized in that: The energy storage unit module specifically includes: The Materials Research and Development Unit is responsible for developing new phase change materials based on nanotechnology, and improving their phase change latent heat and thermal conductivity through microstructural design and optimization of the materials; The packaging technology unit is used to encapsulate new phase change materials using a multi-layer composite packaging structure. The inner layer uses high thermal conductivity materials to enhance heat transfer efficiency, and the outer layer uses materials with excellent thermal insulation properties to reduce heat loss, while improving the corrosion resistance and aging resistance of the packaging material.

3. The high-efficiency thermoelectric decoupling system based on phase change energy storage according to claim 1, characterized in that: The control algorithm module specifically includes: The temperature monitoring unit is used to install a high-precision temperature sensor to monitor the temperature changes of the phase change material in the energy storage unit module in real time and transmit the temperature data to the control system; The multi-level control unit is used to adopt a multi-level control strategy based on temperature monitoring data. During the charging process, the heating power and heating time are adjusted to make the phase change material undergo phase change according to a predetermined temperature curve. During the discharge process, the heat dissipation rate is adjusted to maintain the stability of the output thermal energy.

4. The high-efficiency thermoelectric decoupling system based on phase change energy storage according to claim 1, characterized in that: The scheduling optimization module specifically includes: A data acquisition unit, which is used to collect the operating parameters of the thermoelectric system through sensors, including power generation, heat supply, and energy consumption, and to obtain external information, including energy market prices and meteorological data; A prediction model unit, used to establish an energy demand prediction model and an energy production prediction model using a neural network and a support vector machine algorithm; The strategy execution unit is used to formulate energy allocation strategies based on energy demand forecast results and real-time energy production conditions.

5. The high-efficiency thermoelectric decoupling system based on phase change energy storage according to claim 1, characterized in that: The scheduling optimization module further includes a dynamic optimization scheduling unit, which specifically includes: Multi-source data fusion unit, used to integrate energy production equipment data, energy consumption data, meteorological data, energy market data, and user behavior data, and extract valuable information using deep learning algorithms; The dynamic adjustment unit is used to adopt the model predictive control algorithm to dynamically adjust the energy allocation strategy according to the real-time energy supply and demand situation and the prediction results.

6. The high-efficiency thermoelectric decoupling system based on phase change energy storage according to claim 1, characterized in that: The energy storage unit module further includes an adaptive control unit, which specifically includes: Environmental monitoring unit, used to monitor the temperature changes of the external environment in real time and transmit the monitoring data to the control system; The adaptive adjustment unit is used to automatically adjust the heating and cooling strategies according to changes in the external environment temperature.

7. The high-efficiency thermoelectric decoupling system based on phase change energy storage according to claim 1, characterized in that: The energy storage unit module further includes a multi-physics field coupling analysis unit, which specifically includes: Mathematical modeling unit, used to establish mathematical models of multi-physics field coupling such as heat transfer, mass transfer and phase change, and use numerical simulation methods to analyze the phase change process and temperature distribution of phase change materials under different working conditions; Theoretical support unit is used to provide a theoretical basis for the multi-level control algorithm module.

8. The high-efficiency thermoelectric decoupling system based on phase change energy storage according to claim 1, characterized in that: The scheduling optimization module further includes a user interaction unit, which specifically includes: An information prompt unit is used to issue a prompt message indicating whether there is additional heating or cooling demand when it detects that the user has left a certain area or has not entered a certain area within a set time; The authority management unit is used to obtain the user's authorization information and determine whether the user has left the current area when the user terminal displays the navigation route and moves towards a certain location.

9. The high-efficiency thermoelectric decoupling system based on phase change energy storage according to claim 1, characterized in that: The scheduling optimization module further includes a recovery control unit, which specifically includes: a return detection unit, configured to trigger a recovery control program when the user terminal moves away from a certain location along the navigation route and to a preset distance; The control option unit is used to display new control information execution options and control the working status of the thermoelectric equipment in the relevant area or keep it as it is based on the user's selection.

10. The high-efficiency thermoelectric decoupling system based on phase change energy storage according to claim 1, characterized in that: The energy storage unit module and the scheduling optimization module are connected via a communication interface, and the communication interface adopts an industrial standard protocol to ensure the stability and real-time performance of data transmission.

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