Green temperature control device and method integrating photovoltaic smart sleepers and shallow geothermal energy

Abstract

This invention discloses a green temperature control device and method integrating photovoltaic smart sleepers and shallow geothermal energy, belonging to the field of railway engineering technology. The photovoltaic smart sleeper in the green temperature control device integrates solar panels, energy storage modules, and sensor arrays. Combined with a high-frequency vibration damping layer and flexible pipelines in a coordinated stress-bearing design, it achieves on-site self-sufficiency in energy supply and equipment protection under high-frequency vibration. The intelligent control system incorporates a model predictive control algorithm based on multi-constraint rolling optimization, solves for the optimal control sequence based on sensor data, and controls the actuators to perform corresponding operations. The shallow geothermal exchange system, according to control commands, circulates the heat transfer medium between the buried pipe heat exchanger and the track bed, achieving active cooling at high temperatures and active heating at low temperatures for the track bed. This invention enables all-weather, adaptive, and low-carbon intelligent temperature control of the track bed, improving the long-term stability of the track structure and the safety of train operation.

Description

Technical Field

[0001] This invention belongs to the field of railway engineering technology, specifically relating to a green temperature control device and method that integrates photovoltaic smart sleepers and shallow geothermal energy. Background Technology

[0002] The statements herein provide only background information in relation to this invention and do not necessarily constitute prior art.

[0003] As the foundation of the track structure, the stability of the railway ballast bed directly affects traffic safety. Under extreme weather conditions, freeze-thaw cycles can easily trigger frost heave and mud pumping, seriously jeopardizing traffic safety. Existing conventional temperature control measures, such as passive heat pipes, active electric heating, and insulation materials, generally suffer from significant limitations, including limited efficiency, high energy consumption, reliance on external power grids, or poor vibration resistance and durability.

[0004] In recent years, photovoltaic railway sleepers have been able to provide clean electricity on-site, while shallow geothermal energy can provide a constant natural source of heat and cold. The combination of the two offers a new approach to green temperature control of railway tracks. However, directly and rigidly integrating these two technologies faces extremely high structural engineering barriers. When trains pass at high speeds, railway sleepers must withstand massive transient dynamic loads of tens of tons and continuous high-frequency vibrations. Traditional rigid connection and encapsulation methods have fatal flaws: on the one hand, the fragile solar panels are prone to microcracks or breakage under rigid impact; on the other hand, if the heat exchange pipes inside the track bed cannot achieve stress decoupling and flexible coordination with the sleepers, they are prone to rupture and leakage under long-term strong vibrations, and the leakage will further aggravate the track bed's condition. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a green temperature control device and method that integrates photovoltaic smart sleepers and shallow geothermal energy.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: In a first aspect, the technical solution of the present invention provides a green temperature control device that integrates photovoltaic smart sleepers and shallow geothermal energy, comprising: a photovoltaic smart sleeper, an intelligent control system, and a shallow geothermal exchange system; wherein, the photovoltaic smart sleeper includes a sleeper body, a first mounting groove is formed on its upper surface, a solar panel is installed in the first mounting groove and connected to an energy storage module inside the sleeper body; a high-frequency shock-absorbing buffer layer is embedded between the solar panel and the first mounting groove; a cleaning system is installed on the side of the sleeper body and is powered by the energy storage module; A second mounting slot is provided below the first mounting slot. A sensor group is installed in the second mounting slot and is communicatively connected to the intelligent control system. The intelligent control system has a built-in model predictive control algorithm based on multi-constraint rolling optimization. It is configured to solve for the optimal control sequence based on the sensor group data and control the actuator to perform the corresponding operation. The shallow geothermal exchange system includes a buried pipe heat exchanger, a circulating water pump, and a heat pump unit, which are used to exchange heat with the track bed. Among them, when the buried pipe heat exchanger and the heat exchange pipeline connecting to the track bed are combined with the sleeper body or pass through the core area of ​​strong vibration of the track bed, a high-strength flexible corrugated pipe transition design is adopted, and a pressure-resistant and shock-absorbing sleeve is installed on the outside of the pipeline.

[0007] In at least one embodiment, the surface of the solar panel is covered with an anti-reflective coating and a transparent wear-resistant cover; the high-frequency shock-absorbing buffer layer is specifically a polymer damping rubber layer with a thickness of 5-15 mm.

[0008] In at least one embodiment, the second mounting slot is connected to the first mounting slot, and a mounting bracket is installed inside the second mounting slot, on which the sensor assembly is mounted.

[0009] In at least one embodiment, the cleaning system includes retractable bristles and a water sprayer, powered by an energy storage module; a condensate collection tank is provided inside or at the bottom of the sleeper body, and the water sprayer is connected to the condensate collection tank.

[0010] In at least one embodiment, the energy storage module is externally wrapped with a thermally conductive silicone pad and is tightly fitted with the heat exchange pipeline of the shallow geothermal exchange system.

[0011] In at least one embodiment, the shallow geothermal exchange system further includes a water collector and a water distributor; wherein the water collector distributes a main pipe from the heat pump unit to various underground pipes; and the water distributor collects the branch pipe fluid returning from multiple underground pipe loops into a main pipe and then sends it back to the heat pump unit.

[0012] In at least one embodiment, the intelligent control system includes a data acquisition unit, a controller, and an actuator. The data acquisition unit is connected to a sensor array of the photovoltaic intelligent sleeper and communicates with the controller. The controller incorporates a model predictive control algorithm based on multi-constraint rolling optimization. This algorithm constructs a comprehensive performance index function based on the deviation and rate of change between the real-time track bed temperature and a preset temperature threshold. It then performs weighted optimization on overshoot, settling time, steady-state error, and energy consumption to obtain the optimal control sequence. Based on this optimal control sequence, it outputs control commands to control the start / stop / speed of the circulating water pump and adjust the operating state of the heat pump unit. The actuator includes control components for the circulating water pump and the heat pump unit, and performs corresponding operations according to the control commands output by the controller.

[0013] Secondly, the technical solution of the present invention also provides a green temperature control method that integrates photovoltaic smart sleepers and shallow geothermal energy, comprising: Acquire sensor data and weather forecast data for the future prediction period; Based on sensor data and meteorological forecast data for the future prediction period, a model predictive control algorithm based on multi-constraint rolling optimization is used to establish a thermodynamic state-space prediction model for the track bed. According to the preset track bed safety temperature reference trajectory, rolling optimization calculation is performed in the prediction time domain to solve the optimal control command sequence that satisfies the hard constraints of system equipment capacity and temperature safety. Based on the optimal control command sequence, the speed of the circulating water pump and the cooling or heating capacity of the heat pump unit are precisely adjusted to achieve active cooling or heating and meet quantitative index constraints.

[0014] In at least one embodiment, the thermodynamic state-space prediction model for the track bed is specifically expressed as follows:

[0015]

[0016] In the formula, The current moment; The number of steps to predict the future; For system state variables; The control variables include the circulating water pump speed and the heat pump unit operating frequency; For measurable external disturbances, including predicted values ​​of ambient temperature and photovoltaic radiation; For the predicted critical temperature of the track bed; This is the system state matrix.

[0017] In at least one embodiment, based on an optimal control command sequence, the circulating water pump speed and the cooling or heating capacity of the heat pump unit are precisely adjusted, specifically including: When cooling is required, the circulating water pump is started based on the optimal control command sequence, and the pump speed is dynamically adjusted according to the optimized parameters to drive the heat transfer medium to circulate in the closed loop formed by the track bed heat exchange circuit and the buried pipe heat exchanger; the heat pump unit is set to run in cooling mode, and its working power is optimized in real time according to the algorithm. When heating is required, the circulating water pump is started based on the optimal control command sequence and its operating frequency is optimized to drive the heat transfer medium to circulate; at the same time, the heat pump unit is set to operate in heating mode and its capacity is adjusted in real time.

[0018] The beneficial effects of the above-described technical solution of the present invention are as follows: 1) The green temperature control device of the present invention, which integrates photovoltaic smart sleepers and shallow geothermal energy, breaks through the limitations of existing railway track temperature control relying on traditional energy or grid thermal power by constructing a "photovoltaic-geothermal" dual renewable energy synergistic energy supply system. It realizes solar power generation through photovoltaic smart sleepers and forms a "power generation-storage-power supply" closed loop with energy storage modules. Combined with the bidirectional temperature control capability of the shallow geothermal exchange system of "heat extraction in winter and heat dissipation in summer", it forms a "photothermal complementary" self-sufficient energy system with no energy consumption throughout the temperature control cycle, effectively reducing carbon emissions.

[0019] 2) This invention utilizes a built-in sensor array in the sleepers and a model predictive control algorithm embedded in the intelligent control system to incorporate weather forecasts as feedforward information, enabling proactive responses to extreme weather changes. Simultaneously, the algorithm directly performs rolling optimization under hard constraints, effectively overcoming the significant hysteresis and inertia of the geothermal heat exchange system and the track bed medium, thus significantly improving the accuracy of global temperature control and the overall energy efficiency ratio of the system.

[0020] 3) This invention addresses the harsh operating conditions of high-frequency vibration during train operation by introducing a high-frequency damping buffer layer and a flexible pipeline collaborative force-bearing structure. This design effectively isolates the rigid body of the sleeper from the fragile photovoltaic panels and geothermal circulating water pipes, solving engineering pain points such as component breakage and pipeline leakage that are easily caused by cross-integration, and significantly improving the structural reliability and long-term service life of the system in harsh railway environments.

[0021] 4) The green temperature control device of the present invention, which integrates photovoltaic smart sleepers and shallow geothermal energy, can get rid of dependence on the main power grid, and is especially suitable for energy-scarce areas along railways such as plateaus and deserts, ensuring the continuity of temperature control in extreme environments; it can be applied to railway lines under various climatic conditions, and can play a good temperature control effect in both high-temperature and cold regions, reducing the impact of extreme climate on track conditions and providing a guarantee for the safe operation of trains. Attached Figure Description

[0022] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] Figure 1 This is a schematic diagram of the green temperature control device that integrates photovoltaic smart sleepers and shallow geothermal energy, as disclosed in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the photovoltaic smart rail sleeper structure disclosed in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the green temperature control method that integrates photovoltaic smart sleepers and shallow geothermal energy, as disclosed in Embodiment 1 of the present invention.

[0024] In the diagram: 1. Sleeper body; 2. First mounting slot; 3. Solar panel; 4. Cleaning system; 5. Energy storage module; 6. Second mounting slot; 7. Mounting frame; 8. Sensor group; 9. Data acquisition unit; 10. Controller; 11. Underground pipe heat exchanger; 12. Circulating water pump; 13. Heat pump unit; 14. Water collector; 15. Water distributor; 16. Photovoltaic smart sleeper; 17. Track bed. Detailed Implementation

[0025] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0026] Example 1 In a typical embodiment of the present invention, such as Figure 1 and Figure 2 As shown, this embodiment discloses a green temperature control device that integrates photovoltaic smart sleeper 16 and shallow geothermal energy. It includes photovoltaic smart sleeper 16, intelligent control system and shallow geothermal exchange system. Through the synergy of dual energy sources, the sleeper temperature is automatically regulated, which can not only ensure the long-term stability of the track structure, but also reduce the dependence on traditional energy and reduce maintenance costs.

[0027] like Figure 2 As shown, the photovoltaic smart sleeper 16 includes a sleeper body 1. A first mounting groove 2 is formed on the upper surface of the sleeper body 1, and a solar panel 3 is installed in the first mounting groove 2. The solar panel 3 is connected to an energy storage module 5 inside the sleeper body 1, enabling it to convert solar energy into electrical energy and store it in the energy storage module 5. As a further embodiment, the surface of the solar panel 3 is covered with an anti-reflective coating and a transparent wear-resistant cover. The anti-reflective coating effectively increases light transmittance and reduces incident angle loss, thereby improving the power generation efficiency of the solar panel 3. The transparent wear-resistant cover not only provides physical protection for the solar panel 3 but also ensures the light transmittance of the cover and effectively isolates the solar panel 3 from the corrosion caused by moisture, dust, and chemicals in the track bed 17 environment, extending the insulation life of the module. By setting the anti-reflective coating and the transparent wear-resistant cover, the high efficiency and long-term reliability of photovoltaic power generation can be guaranteed under the harsh conditions of high dust, high vibration, and open-air unprotected operation of the railway track bed 17. Considering that the interaction between the wheels and rails when a train passes will generate transient dynamic loads and broadband vibrations of up to tens of tons, in this embodiment, a high-frequency damping buffer layer is embedded between the base and surrounding sides of the solar panel 3 and the first mounting groove 2. When the dynamic load of the train is transmitted downwards, the high-frequency damping buffer layer undergoes elastic deformation to absorb the excitation energy, cutting off the transmission path of high-frequency vibration to the silicon wafer or thin film inside the photovoltaic system, thus avoiding structural damage. As a further embodiment, the high-frequency damping buffer layer is a polymer damping rubber layer with a thickness of 5-15 mm, which has good damping performance and low-temperature resistance. It can maintain a stable elastic modulus in an ambient temperature range of -40°C to 60°C, effectively preventing the high-frequency damping buffer layer from hardening and failing in extremely cold environments.

[0028] In this embodiment, a cleaning system 4 is installed on the side of the sleeper body 1. The cleaning system 4 includes retractable bristles and a water sprayer, and is driven by photovoltaic power stored in the energy storage module 5. As a further embodiment, a condensate collection tank is provided inside or at the bottom of the sleeper body 1. The water sprayer is connected to the condensate collection tank, and the condensate generated by the diurnal temperature difference or natural precipitation is used as the cleaning water source to achieve self-maintaining cleaning without manual water replenishment.

[0029] As a further implementation, the energy storage module 5, which is located inside the sleeper body 1, is wrapped with a thermally conductive silicone pad and is tightly attached to the heat exchange pipeline of the shallow geothermal exchange system. The cooling capacity of the geothermal circulation is used to provide auxiliary passive heat dissipation for the energy storage module 5, preventing the energy storage module 5 from overheating and failing due to high temperature exposure in summer, and extending the service life of the energy storage battery.

[0030] In this embodiment, a second mounting groove 6 is provided on the lower side of the first mounting groove 2. The second mounting groove 6 communicates with the first mounting groove 2, and a mounting bracket 7 is installed in the second mounting groove 6. A sensor group 8 is provided on the mounting bracket 7. The sensor group 8 is communicatively connected to the intelligent control system and can transmit the real-time collected sensor data to the intelligent control system for processing. As a further embodiment, the sensor group 8 includes sensors such as temperature sensors, displacement sensors, and strain sensors, which are used to collect data such as temperature, displacement, and stress of the sleeper body 1 in real time.

[0031] In this embodiment, the shallow geothermal exchange system includes a buried pipe heat exchanger 11, a circulating water pump 12, and a heat pump unit 13, which are interconnected through fluid pipelines for heat exchange with the track bed 17. The buried pipe heat exchanger 11 is a key component of the shallow geothermal exchange system and typically consists of a series of pipes buried underground. In winter, the buried pipe heat exchanger 11 absorbs heat from the soil and supplies it to the heat pump unit 13 for heating the track bed 17; in summer, the buried pipe heat exchanger 11 transfers the residual heat from the track bed 17 to the underground soil through a heat transfer medium for dissipation.

[0032] To prevent damage to the closed-loop pipeline under the pressure of crushed stone and train vibration on the track bed 17, the buried pipe heat exchanger 11 and the heat exchange pipeline connecting to the track bed 17 in this embodiment adopt a high-strength flexible corrugated pipe transition design when entering and exiting the sleeper body 1 and passing through the strong vibration core area of ​​the track bed 17 (i.e., buried in the area of ​​track bed 17 where stress is concentrated). A pressure-resistant and shock-absorbing sleeve is also installed outside the pipeline. This achieves deformation coordination and synergistic stress distribution between the flexible pipeline and the rigid body of the sleeper, allowing the pipeline to adaptively and synergistically displace with the slight settlement and vibration of the sleeper and track bed 17. This eliminates the hidden dangers of fatigue fracture and leakage failure caused by the dynamic load of trains and long-term high-frequency vibration in traditional rigid pipe connections. As a further implementation, the high-strength flexible corrugated pipe can be a high-pressure resistant flexible metal hose or a cross-linked polyethylene (PE-X) pipe, with a pressure-resistant sleeve with shock-absorbing ring ribs wrapped around the outside of the pipeline.

[0033] The core function of the circulating water pump 12 is to overcome the resistance of the heat transfer medium in the closed pipeline, provide circulation power for the heat transfer medium, and ensure the continuous progress of the heat transfer process. As a further implementation, the heat transfer medium can be selected as water, or a mixture of water and antifreeze, depending on the ambient temperature.

[0034] The heat pump unit 13 is the core equipment for energy conversion, including components such as a compressor, evaporator, condenser, and expansion valve, and includes two modes: heating and cooling. In heating mode, the heat pump unit 13 absorbs heat from the heat transfer medium in the buried pipe heat exchanger 11 through the evaporator, and after being heated by the compressor, releases the high-temperature heat to the track bed 17 through the condenser. In cooling mode, the heat pump unit 13 operates in reverse, transferring the heat in the track bed 17 to the buried pipe heat exchanger 11, and then dissipating it through the underground soil.

[0035] As a further implementation, the shallow geothermal exchange system also includes a water collector 14 and a water distributor 15. The water collector 14 distributes the main pipe from the heat pump unit 13 to the various underground pipes, while the water distributor 15 collects the fluid from the branch pipes returning from the multiple underground pipe loops into the main pipe and then sends it back to the heat pump unit 13.

[0036] In this embodiment, the intelligent control system includes a data acquisition unit 9, a controller 10, and an actuator. The data acquisition unit 9 is connected to the sensor group 8 of the photovoltaic intelligent sleeper 16 and communicates with the controller 10. It is used to acquire sensor data in real time, perform preliminary filtering, outlier detection, and data fusion on the acquired raw data to ensure data accuracy and reliability, calculate the deviation and rate of change between the real-time track bed 17 temperature and a preset temperature threshold based on the processed data, and transmit the calculation results to the controller 10.

[0037] The controller 10 has a built-in model predictive control algorithm based on multi-constraint rolling optimization. It can receive data transmitted by the data acquisition unit 9, construct a comprehensive performance index function based on the deviation and rate of change between the real-time track bed 17 temperature and the preset temperature threshold, and perform weighted optimization on overshoot, settling time, steady-state error and energy consumption to obtain the optimal control sequence. Based on the optimal control sequence, it outputs control commands in real time to control the start / stop / speed of the circulating water pump 12 and adjust the working state of the heat pump unit 13, so as to achieve adaptive, robust and precise temperature control.

[0038] The actuator includes the control components of the circulating water pump 12 and the heat pump unit 13, which execute corresponding operations according to the control commands calculated by the controller 10 through the model predictive control algorithm, to ensure that the system operates at the optimal energy efficiency ratio.

[0039] As a further implementation, the model predictive control algorithm based on multi-constraint rolling optimization built into the controller 10 is based on the thermodynamic dynamic model of the track bed 17. It combines the predicted values ​​of ambient temperature and solar radiation in the future time period. Under the hard constraints of the upper and lower limits of heat pump power, the speed constraints of circulating water pump 12 and the safe temperature range of track bed 17, it solves the constrained optimization problem online and outputs the optimal control sequence of heat pump unit 13 and circulating water pump 12 in the future control time domain. This achieves accurate temperature prediction and minimum energy consumption control under large lag and strong disturbance environment.

[0040] Specifically, controller 10 receives the current real-time temperature data. The model predictive control algorithm based on multi-constraint rolling optimization is called to solve the rolling optimization problem using meteorological forecast data (such as ambient temperature and light intensity) for the future forecast period.

[0041] First, a thermodynamic state-space prediction model for the track bed 17, integrating photovoltaic heat input, geothermal exchange, and environmental heat dissipation, is established based on the current moment. State variables and future control sequences, predicting the future The temperature change trajectory of track bed 17 at each sampling time (prediction time domain) is specifically represented as follows:

[0042]

[0043] In the formula, The number of steps to predict the future, for example, when the sampling period is 5 minutes. This means predicting the state 15 minutes after the current moment; For system state variables; The control variables include the speed of the circulating water pump 12 and the operating frequency of the heat pump unit 13; For measurable external disturbances, including ambient temperature And the predicted value of photovoltaic radiation; The predicted temperature of 17 key points in the track bed; This is the system state matrix. In specific implementation, the state matrix... The specific heat capacity and thermal conductivity parameters of the track bed material 17 can be used to identify and obtain the matrix. The physical work efficiency of the associated circulating water pump 12 and the heat pump unit 13 is represented by the matrix. This integrates external heat transfer coefficients such as soil thermal resistance and ambient wind speed, thereby ensuring a high degree of consistency between the prediction model and the actual physical heat transfer process. Matrix Used to determine from system state variables Which temperature information should be extracted as the prediction output? Specifically, when the temperature of the 17 key temperature points of the track bed is already included in the system state variables... When in the middle, the output matrix Used from Select the corresponding temperature state; when the temperature of the 17 key temperature points of the track bed needs to be represented by multiple temperature states, output the matrix. The coefficients in the table represent the magnitude of the influence of different temperature conditions on the critical temperature point.

[0044] Then, a quadratic cost function is constructed that balances temperature tracking accuracy and system energy consumption. In the control time domain The internal online optimization is specifically expressed as follows:

[0045] In the formula, This serves as a reference trajectory for the safe temperature of track bed 17. This is the error weight matrix; To control the incremental weight matrix, which is used to smooth out the energy consumption of the actuator; To control the increment.

[0046] Next, multiple hard constraints are constructed, which must satisfy physical and safety boundary constraints during the solution process. These multiple hard constraints include control quantity constraints, control increment constraints, and state safety constraints. Among them, the control quantity constraints require the heat pump and water pump to operate within the range between the upper and lower power limits, specifically expressed as follows:

[0047] In the formula, This is the upper limit of power; This is the lower limit of power.

[0048] Controlling incremental constraints is to prevent drastic movement of the equipment, specifically expressed as:

[0049] In the formula, To control the upper limit of increment; To control the lower limit of the increment.

[0050] The condition safety constraint refers to the requirement that the temperature of the key point of the track bed 17 must be within the range of the anti-freezing heave temperature and the anti-softening temperature of the track bed 17, specifically expressed as follows:

[0051] In the formula, The track bed is designed to withstand frost heave at temperature 17. The temperature for preventing softening of the track bed is 17.

[0052] Finally, the optimal control increment sequence is obtained by solving the optimization problem, and the first term is extracted. It operates on the current system. At the next sampling time, the latest sensor measurement data is introduced to correct the prediction error and optimize the forward scrolling of the view window. In specific implementation, the state matrix... The specific heat capacity and thermal conductivity parameters of the track bed material 17 can be used to identify and obtain the matrix. The physical work efficiency of the associated circulating water pump 12 and the heat pump unit 13 is represented by the matrix. It integrates external heat transfer coefficients such as soil thermal resistance and ambient wind speed to ensure that the prediction model closely matches the actual physical heat transfer process.

[0053] The controller 10 precisely adjusts the speed of the circulating water pump 12 and the cooling / heating capacity of the heat pump unit 13 based on the optimal control sequence obtained by the rolling solution of the model predictive control algorithm based on multi-constraint rolling optimization, so as to achieve active cooling / heating and meet the quantitative index constraints.

[0054] When cooling is required, the controller 10 controls the actuator to start the circulating water pump 12 and dynamically adjusts the pump speed according to the optimized parameters output by the algorithm, driving the heat transfer medium to circulate in the closed loop formed by the heat exchange circuit of the track bed 17 and the buried pipe heat exchanger 11. Simultaneously, the controller 10 sets the heat pump unit 13 to operate in cooling mode and can optimize its operating power in real time according to the algorithm. At this time, excess heat in the track bed 17 is absorbed by the flowing heat transfer medium and transported to the evaporator side of the heat pump unit 13. The heat pump unit 13, through a reverse Carnot cycle, transfers the heat absorbed by the evaporator to the condenser side, and finally releases it through the condenser to the heat transfer medium flowing to the buried pipe heat exchanger 11. The heat-carrying heat transfer medium flows through the buried pipe heat exchanger 11, exchanging heat to the underground soil for dissipation, thereby achieving active cooling of the track bed 17.

[0055] When heating is required, the controller 10, based on the optimal sequence output by the model predictive control algorithm based on multi-constraint rolling optimization, controls the actuator to start the circulating water pump 12 and optimize its operating frequency to drive the circulation of the heat transfer medium. Simultaneously, the controller 10 sets the heat pump unit 13 to operate in heating mode and adjusts its capacity in real time. At this time, the heat transfer medium flows through the buried pipe heat exchanger 11, absorbing heat from the relatively high-temperature underground soil. This heat is transferred to the evaporator of the heat pump unit 13 and absorbed. After the compressor performs work to improve its quality, high-temperature heat is released at the condenser to heat the heat transfer medium flowing to the track bed 17. The heated heat transfer medium flows through the heat exchange pipes in the track bed 17 area, heating the track bed 17 and preventing freezing.

[0056] Example 2 In a typical embodiment of the present invention, such as Figure 3 As shown, this embodiment discloses a green temperature control method integrating photovoltaic smart sleeper 16 and shallow geothermal energy, specifically including the following steps: S1. Acquire sensor data and weather forecast data for the future prediction period.

[0057] In this step, sensor array 8 integrated into the photovoltaic smart sleeper 16 collects real-time temperature, stress, and displacement data of key points on the track bed 17. The collected data is then transmitted to data acquisition unit 9 for preliminary filtering, outlier detection, and data fusion to ensure data accuracy and reliability. Data acquisition unit 9 calculates the deviation and rate of change between the real-time temperature of the track bed 17 and a preset temperature threshold based on the processed data, and transmits the calculation results to controller 10. Simultaneously, controller 10 receives the current real-time temperature data collected by the temperature sensor. And weather forecast data for the future prediction period, including ambient temperature and light intensity.

[0058] S2. Based on sensor data and meteorological forecast data for the future prediction period, a model predictive control algorithm based on multi-constraint rolling optimization is adopted to establish a thermodynamic state space prediction model for track bed 17; according to the preset safe temperature reference trajectory of track bed 17, rolling optimization calculation is performed in the prediction time domain to solve the optimal control command sequence that satisfies the hard constraints of system equipment capacity and temperature safety.

[0059] In this step, firstly, a thermodynamic state-space prediction model for the track bed 17 is established, integrating photovoltaic heat input, geothermal exchange, and environmental heat dissipation, based on the current moment. State variables and future control sequences, predicting the future The temperature change trajectory of track bed 17 at each sampling time (prediction time domain) is specifically represented as follows:

[0060]

[0061] In the formula, The number of steps to predict the future, for example, when the sampling period is 5 minutes. This means predicting the state 15 minutes after the current moment; For system state variables; The control variables include the speed of the circulating water pump 12 and the operating frequency of the heat pump unit 13; For measurable external disturbances, including predicted values ​​of ambient temperature and photovoltaic radiation; The predicted temperature of 17 key points in the track bed; This is the system state matrix. In specific implementation, the state matrix... The specific heat capacity and thermal conductivity parameters of the track bed material 17 can be used to identify and obtain the matrix. The physical work efficiency of the associated circulating water pump 12 and the heat pump unit 13 is represented by the matrix. This integrates external heat transfer coefficients such as soil thermal resistance and ambient wind speed, thereby ensuring a high degree of consistency between the prediction model and the actual physical heat transfer process. Matrix Used to determine from system state variables Which temperature information should be extracted as the prediction output? Specifically, when the temperature of the 17 key temperature points of the track bed is already included in the system state variables... When in the middle, the output matrix Used from Select the corresponding temperature state; when the temperature of the 17 key temperature points of the track bed needs to be represented by multiple temperature states, output the matrix. The coefficients in the table represent the magnitude of the influence of different temperature conditions on the critical temperature point.

[0062] Then, based on the thermodynamic state-space prediction model of track bed 17, a quadratic cost function that balances temperature tracking accuracy and system energy consumption is constructed. In the control time domain The internal online optimization is specifically expressed as follows:

[0063] In the formula, This serves as a reference trajectory for the safe temperature of track bed 17. This is the error weight matrix; To control the incremental weight matrix, which is used to smooth out the energy consumption of the actuator; To control the increment.

[0064] Next, multiple hard constraints are constructed, which must satisfy physical and safety boundary constraints during the solution process. These multiple hard constraints include control quantity constraints, control increment constraints, and state safety constraints. Among them, the control quantity constraints require the heat pump and water pump to operate within the range between the upper and lower power limits, specifically expressed as follows:

[0065] In the formula, This is the upper limit of power; This is the lower limit of power.

[0066] Controlling incremental constraints is to prevent drastic movement of the equipment, specifically expressed as:

[0067] In the formula, To control the upper limit of increment; To control the lower limit of the increment.

[0068] The condition safety constraint refers to the requirement that the temperature of the key point of the track bed 17 must be within the range of the anti-freezing heave temperature and the anti-softening temperature of the track bed 17, specifically expressed as follows:

[0069] In the formula, The track bed is designed to withstand frost heave at temperature 17. The temperature for preventing softening of the track bed is 17.

[0070] Finally, the optimal control increment sequence is obtained by solving the optimization problem, and the first term is extracted. It operates on the current system. At the next sampling time, the latest sensor measurement data is introduced to correct the prediction error and optimize the forward scrolling of the view window. In specific implementation, the state matrix... The specific heat capacity and thermal conductivity parameters of the track bed material 17 can be used to identify and obtain the matrix. The physical work efficiency of the associated circulating water pump 12 and the heat pump unit 13 is represented by the matrix. It integrates external heat transfer coefficients such as soil thermal resistance and ambient wind speed to ensure that the prediction model closely matches the actual physical heat transfer process.

[0071] S3. Based on the optimal control command sequence, the speed of the circulating water pump 12 and the cooling or heating capacity of the heat pump unit 13 are precisely adjusted to achieve active cooling or heating and meet the quantitative index constraints.

[0072] In this step, when cooling is required, the controller 10 controls the actuator to start the circulating water pump 12 based on the optimal control command sequence, and dynamically adjusts the pump speed according to the optimized parameters output by the algorithm, driving the heat transfer medium to circulate in the closed loop formed by the heat exchange circuit of the track bed 17 and the buried pipe heat exchanger 11. Simultaneously, the controller 10 sets the heat pump unit 13 to operate in cooling mode and can optimize its operating power in real time according to the algorithm. At this time, excess heat in the track bed 17 is absorbed by the flowing heat transfer medium and transported to the evaporator side of the heat pump unit 13. The heat pump unit 13, through a reverse Carnot cycle, transfers the heat absorbed by the evaporator to the condenser side, and finally releases it through the condenser to the heat transfer medium flowing to the buried pipe heat exchanger 11. The heat-carrying heat transfer medium flows through the buried pipe heat exchanger 11, exchanging heat to the underground soil for dissipation, thereby achieving active cooling of the track bed 17.

[0073] When heating is required, the controller 10 also controls the actuator to start the circulating water pump 12 and optimize its operating frequency according to the optimal control command sequence, driving the heat transfer medium to circulate. Simultaneously, the controller 10 sets the heat pump unit 13 to operate in heating mode and adjusts its capacity in real time. At this time, the heat transfer medium flows through the buried pipe heat exchanger 11, absorbing heat from the relatively high-temperature underground soil. This heat is transferred to the evaporator of the heat pump unit 13 and absorbed. After the compressor performs work to improve its quality, high-temperature heat is released at the condenser to heat the heat transfer medium flowing to the track bed 17. The heated heat transfer medium flows through the heat exchange pipes in the track bed 17 area, heating the track bed 17 and preventing freezing.

[0074] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A green temperature control device integrating photovoltaic smart sleepers and shallow geothermal energy, characterized in that, include: The system includes a photovoltaic smart sleeper, a smart control system, and a shallow geothermal exchange system. The photovoltaic smart sleeper includes a sleeper body with a first mounting groove on its upper surface. A solar panel is installed in the first mounting groove and connected to an energy storage module inside the sleeper body. A high-frequency shock-absorbing buffer layer is embedded between the solar panel and the first mounting groove. A cleaning system, powered by the energy storage module, is installed on the side of the sleeper body. A second mounting slot is provided below the first mounting slot. A sensor group is installed in the second mounting slot and is communicatively connected to the intelligent control system. The intelligent control system has a built-in model predictive control algorithm based on multi-constraint rolling optimization. It is configured to solve for the optimal control sequence based on the sensor group data and control the actuator to perform the corresponding operation. The shallow geothermal exchange system includes a buried pipe heat exchanger, a circulating water pump, and a heat pump unit, which are used to exchange heat with the track bed. Among them, when the buried pipe heat exchanger and the heat exchange pipeline connecting to the track bed are combined with the sleeper body or pass through the core area of ​​strong vibration of the track bed, a high-strength flexible corrugated pipe transition design is adopted, and a pressure-resistant and shock-absorbing sleeve is installed on the outside of the pipeline.

2. The green temperature control device integrating photovoltaic smart sleepers and shallow geothermal energy as described in claim 1, characterized in that, The surface of the solar panel is covered with an anti-reflective coating and a transparent wear-resistant cover; the high-frequency shock absorption buffer layer is specifically a polymer damping rubber layer with a thickness of 5-15 mm.

3. The green temperature control device integrating photovoltaic smart sleepers and shallow geothermal energy as described in claim 1, characterized in that, The second mounting slot is connected to the first mounting slot, and a mounting bracket is installed inside it, on which the sensor assembly is mounted.

4. The green temperature control device integrating photovoltaic smart sleepers and shallow geothermal energy as described in claim 1, characterized in that, The cleaning system includes retractable bristles and a water sprayer, powered by an energy storage module; a condensate collection tank is installed inside or on the bottom of the sleeper body, and the water sprayer is connected to the condensate collection tank.

5. The green temperature control device integrating photovoltaic smart sleepers and shallow geothermal energy as described in claim 1, characterized in that, The energy storage module is wrapped with a thermally conductive silicone pad and is tightly fitted to the heat exchange pipeline of the shallow geothermal exchange system.

6. The green temperature control device integrating photovoltaic smart sleepers and shallow geothermal energy as described in claim 1, characterized in that, The shallow geothermal exchange system also includes a water collector and a water distributor; the water collector distributes the main pipe from the heat pump unit to each buried pipe; the water distributor collects the fluid from the branch pipes returning from multiple buried pipe loops into a main pipe, and then sends it back to the heat pump unit.

7. The green temperature control device integrating photovoltaic smart sleepers and shallow geothermal energy as described in claim 1, characterized in that, The intelligent control system includes a data acquisition unit, a controller, and actuators. The data acquisition unit is connected to the sensor array of the photovoltaic smart sleeper and communicates with the controller. The controller incorporates a model predictive control algorithm based on multi-constraint rolling optimization. This algorithm constructs a comprehensive performance index function based on the deviation and rate of change between the real-time track bed temperature and the preset temperature threshold. It then performs weighted optimization on overshoot, settling time, steady-state error, and energy consumption to obtain the optimal control sequence. Based on this optimal control sequence, it outputs control commands to control the start / stop / speed of the circulating water pump and adjust the operating status of the heat pump unit. The actuators include control components for the circulating water pump and the heat pump unit, which execute corresponding operations according to the control commands output by the controller.

8. A green temperature control method integrating photovoltaic smart sleepers and shallow geothermal energy, characterized in that, include: Acquire sensor data and weather forecast data for the future prediction period; Based on sensor data and meteorological forecast data for the future prediction period, a model predictive control algorithm based on multi-constraint rolling optimization is used to establish a thermodynamic state-space prediction model for the track bed. According to the preset track bed safety temperature reference trajectory, rolling optimization calculation is performed in the prediction time domain to solve the optimal control command sequence that satisfies the hard constraints of system equipment capacity and temperature safety. Based on the optimal control command sequence, the speed of the circulating water pump and the cooling or heating capacity of the heat pump unit are precisely adjusted to achieve active cooling or heating and meet quantitative index constraints.

9. The green temperature control method integrating photovoltaic smart sleepers and shallow geothermal energy as described in claim 8, characterized in that, The thermodynamic state-space prediction model for track bed is specifically expressed as follows: In the formula, The current moment; The number of steps to predict the future; For system state variables; The control variables include the circulating water pump speed and the heat pump unit operating frequency; For measurable external disturbances, including predicted values ​​of ambient temperature and photovoltaic radiation; For the predicted critical temperature of the track bed; This is the system state matrix.

10. The green temperature control method integrating photovoltaic smart sleepers and shallow geothermal energy as described in claim 9, characterized in that, Based on the optimal control command sequence, the circulating water pump speed and the cooling or heating capacity of the heat pump unit are precisely adjusted, specifically including: When cooling is required, the circulating water pump is started based on the optimal control command sequence, and the pump speed is dynamically adjusted according to the optimized parameters to drive the heat transfer medium to circulate in the closed loop formed by the track bed heat exchange circuit and the buried pipe heat exchanger; the heat pump unit is set to run in cooling mode, and its working power is optimized in real time according to the algorithm. When heating is required, the circulating water pump is started based on the optimal control command sequence and its operating frequency is optimized to drive the heat transfer medium to circulate; at the same time, the heat pump unit is set to operate in heating mode and its capacity is adjusted in real time.

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