A dry-wet combined cooling tower anti-freezing protection device for compressed air energy storage

Abstract

This invention relates to the field of compressed air energy storage technology and provides an anti-freezing protection device for a combined dry and wet cooling tower used in compressed air energy storage. The device includes a cooling tower body and further comprises: a temperature control module configured to construct a heater temperature control model based on a comprehensive risk index, the current heating temperature, and the remaining lifespan of the heat conduction control layer, and output a target heating temperature; and a judgment module configured to activate the temperature control module to control the heater group to heat at the target heating temperature when the freezing risk index exceeds its sub-item threshold or the comprehensive risk index exceeds its preset threshold. This invention enables independent temperature control in different zones and efficient heat conduction, optimizing energy consumption and anti-freezing uniformity: the heater group adopts a serpentine arrangement or segmented independent layout, with each segment equipped with an independent temperature sensor and power adjustment module, achieving independent temperature control in easily frozen areas.

Description

Technical Field

[0001] This invention belongs to the field of compressed air energy storage technology, and particularly relates to an antifreeze protection device for a dry and wet combined cooling tower for compressed air energy storage. Background Technology

[0002] Compressed air energy storage systems, as a key technology for large-scale energy storage, play a central role in grid load regulation and renewable energy integration. Cooling towers in these systems perform the core function of heat dissipation, maintaining system efficiency by reducing the temperature of the compressed air. Combined dry and wet cooling towers integrate the water-saving advantages of dry cooling with the high efficiency of wet cooling, dynamically adjusting their operating mode according to environmental conditions to achieve water conservation and optimized cooling performance. However, in low-temperature climates, this type of cooling tower faces multiple anti-freezing challenges.

[0003] In wet cooling systems, the water medium is highly susceptible to phase change when the ambient temperature approaches or falls below the freezing point, forming ice crystals that gradually accumulate. This leads to blockage of the packing channels, pipe structure rupture, and a significant decrease in heat dissipation efficiency, potentially causing overall equipment failure. Simultaneously, the switching between wet and dry modes is accompanied by rapid temperature fluctuations, causing the tower material to undergo rapid thermal expansion and contraction. This results in a non-uniform distribution of thermal stress within the tower. The accumulation of this stress over time can induce the propagation of microcracks, ultimately leading to structural fatigue damage or even macroscopic cracking.

[0004] Existing antifreeze solutions mainly rely on fixed-power electric heating devices or manual intervention, which have significant technical shortcomings. The control logic is limited to a single temperature threshold trigger mechanism, failing to dynamically capture early signs of icing. Heating is often initiated only after ice has formed, resulting in a severe lag in the antifreeze response. The risk assessment system lacks a systematic approach, failing to integrate multiple dimensions such as the icing dynamics, the cumulative effect of thermal stress, the real-time matching of heat source supply and antifreeze demand, and the transient impact of switching between dry and wet conditions. This leads to a lack of theoretical support for heating strategy formulation.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this invention is to provide an antifreeze protection device for a combined dry and wet cooling tower for compressed air energy storage, in order to solve the above-mentioned problems.

[0007] This invention is implemented as follows: a dry-wet combined cooling tower antifreeze protection device for compressed air energy storage, comprising a cooling tower body, and further comprising:

[0008] A heater assembly is installed in the freezing zone of the cooling tower body to heat the freezing zone.

[0009] A heat-conducting control layer is disposed between the heater group and the cooling tower body for heat transfer;

[0010] A controller, disposed within the cooling tower body, comprises:

[0011] The freezing dynamics assessment module is configured to build a freezing dynamics assessment model based on ambient temperature, water temperature, and duration of low temperature, and output a freezing risk index.

[0012] The thermal stress damage assessment module is configured to construct a thermal stress damage assessment model based on the temperature change rate, structural temperature difference, and vibration amplitude, and output the thermal stress damage index.

[0013] The heat supply and demand imbalance assessment module is configured to construct a heat supply and demand imbalance assessment model based on the available heat source temperature difference, heating response hysteresis and heat transfer temperature difference, and output the supply and demand imbalance index.

[0014] The dry-wet switching impact assessment module is configured to build a dry-wet switching impact assessment model based on the switching temperature difference and switching frequency, and output the dry-wet switching impact index.

[0015] The comprehensive risk assessment module is configured to construct a comprehensive risk assessment model based on the icing risk index, thermal stress damage index, supply and demand imbalance index, and dry-wet switching impact index, and output a comprehensive risk index.

[0016] The temperature control module is configured to construct a heater temperature control model based on the comprehensive risk index, the current heating temperature, and the remaining lifespan of the thermally conductive control layer, and output the target heating temperature.

[0017] The judgment module is configured to activate the temperature control module to control the heater group to heat at the target heating temperature when the freezing risk index exceeds its sub-item threshold or the comprehensive risk index exceeds its preset threshold.

[0018] In a further technical solution, the heater group includes at least one set of electric heating elements. The electric heating elements are arranged in a serpentine pattern or in separate segments along the easily frozen area of ​​the cooling tower body. Each segment of the electric heating element is equipped with an independent temperature sensor and a power adjustment module for independent temperature control of the easily frozen area.

[0019] In a further technical solution, the thermal conductivity control layer is a graphene thermal conductive film layer, a thermal conductive silicone pad layer, or a metal thermal conductive plate layer. The graphene thermal conductive film layer, thermal conductive silicone pad layer, or metal thermal conductive plate layer is attached between the heater group and the easily frozen area of ​​the cooling tower body, and is used to uniformly conduct the heat generated by the heater group to the easily frozen area.

[0020] In a further technical solution, the icing risk index in the icing dynamics assessment module is obtained as follows: The product of the low-temperature duration index and the preset cumulative icing time coefficient is calculated, and then the negative exponent of this product is taken. The result of the negative exponent calculation is subtracted from 1 to obtain the time accumulation factor; the difference between the freezing point temperature index and the ambient temperature index is calculated and divided by the difference between the freezing point temperature index and the minimum temperature value to obtain the temperature difference factor; the time accumulation factor and the temperature difference factor are multiplied to obtain the icing risk index; wherein, each index is calculated by substituting the measured values ​​into the maximum-minimum value normalization formula.

[0021] A further technical solution involves obtaining the thermal stress damage index in the thermal stress damage assessment module as follows: The square of the temperature change rate index is calculated and multiplied by a preset thermal stress sensitivity coefficient to obtain the basic thermal stress term; the vibration amplification factor is obtained by calculating (1 plus the product of the vibration amplification factor and the vibration amplitude index); the basic thermal stress term is multiplied by the vibration amplification factor, and then multiplied by the structural temperature difference index to obtain the comprehensive thermal stress value; the negative exponent of this comprehensive thermal stress value is calculated, and then the result of the negative exponent calculation is subtracted from 1 to obtain the thermal stress damage index; each index is calculated by substituting the measured value into the maximum-minimum value normalization formula.

[0022] A further technical solution is a heat flow supply-demand imbalance assessment module, in which the supply-demand imbalance index is obtained through the following methods:

[0023] When the available heat source temperature index is lower than the antifreeze target temperature index, the supply-demand imbalance index is directly set to 1.

[0024] Otherwise, calculate the difference between the available heat source temperature index and the antifreeze target temperature index, divide it by the maximum possible temperature difference index, and obtain the temperature difference deviation factor.

[0025] Calculate the product of the heating response lag time exponent and the preset lag sensitivity coefficient, then take the negative exponent of the product result, and subtract the negative exponent result from 1 to obtain the lag response factor;

[0026] Calculate (1 plus the product of the heat loss amplification factor and the heat transfer temperature difference index) to obtain the heat loss amplification factor;

[0027] Multiply the temperature difference deviation factor, the hysteresis response factor, and the heat loss amplification factor to obtain the supply and demand imbalance index.

[0028] Each index is calculated by substituting the measured values ​​into the maximum-minimum normalization formula.

[0029] In a further technical solution, the dry-wet switching impact assessment module obtains the dry-wet switching impact index in the following way:

[0030] Calculate the product of the switching frequency index and the preset frequency sensitivity coefficient, then take the negative exponent of the product result, and subtract the negative exponent result from 1 to obtain the frequency impulse factor.

[0031] Multiply the frequency impact factor by the switching temperature difference index to obtain the dry-wet switching impact index.

[0032] Each index is calculated by substituting the measured values ​​into the maximum-minimum normalization formula.

[0033] In a further technical solution, the comprehensive risk index in the comprehensive risk assessment module is obtained through the following methods:

[0034] The maximum value among the icing risk index, thermal stress damage index, supply and demand imbalance index, and dry-wet switching impact index is selected as the maximum risk index.

[0035] Calculate the arithmetic mean of the four risk indices mentioned above, and use it as the average risk index;

[0036] Multiply the maximum risk index by the preset extreme value weight coefficient, multiply the average risk index by (1 minus the extreme value weight coefficient), and then sum the two to obtain the comprehensive risk index.

[0037] In a further technical solution, the target heating temperature in the temperature control module is obtained in the following way:

[0038] Calculate the product of the comprehensive risk index and the preset risk sensitivity coefficient, then take the negative exponent of the product result, and subtract the negative exponent result from 1 to obtain the risk response factor;

[0039] Calculate (1 minus the product of the lifetime loss coefficient and the thermal conductivity layer lifetime loss rate) to obtain the lifetime loss factor;

[0040] Multiply the maximum compensated temperature difference by the risk response factor, and then multiply by the life loss factor to obtain the compensated temperature difference; add the base heating temperature to the compensated temperature difference to obtain the target heating temperature.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0042] Multi-dimensional comprehensive risk assessment improves the accuracy of antifreeze response: By setting up modules for icing dynamics assessment, thermal stress damage assessment, heat flow supply and demand imbalance assessment, and dry-wet switching impact assessment, respectively, icing risk index, thermal stress damage index, supply and demand imbalance index, and dry-wet switching impact index are output. Furthermore, a comprehensive risk index is constructed, realizing a systematic quantitative assessment of multiple risks such as icing evolution process, thermal stress accumulation, heat source supply and demand matching, and mode switching impact. This overcomes the lag defect of single temperature threshold triggering and provides a more scientific and timely theoretical basis for heating strategies.

[0043] Dynamic intelligent temperature control balances antifreeze performance and equipment lifespan: The temperature control module constructs a heater temperature control model based on the comprehensive risk index, current heating temperature, and remaining lifespan of the heat conduction control layer, outputting the target heating temperature. This model can dynamically adjust the heating intensity according to the risk level and appropriately reduce the compensation temperature difference when the lifespan of the heat conduction layer is high. This ensures effective antifreeze in low-temperature environments while avoiding accelerated aging and damage to the heat conduction control layer and tower body caused by overheating, thus extending the overall service life of the equipment.

[0044] Independent temperature control and efficient heat conduction optimize energy consumption and freeze protection uniformity: The heater group adopts a serpentine arrangement or segmented independent layout, with each segment equipped with an independent temperature sensor and power regulation module to achieve independent temperature control for easily frozen areas. Simultaneously, a graphene thermally conductive film layer, a thermally conductive silicone pad layer, or a metal thermally conductive plate layer is placed between the heater group and the tower body as a heat conduction regulation layer to evenly conduct heat to easily frozen areas. This combined design significantly improves the utilization efficiency of heating energy, avoids localized overheating or heating blind spots, and enhances freeze protection uniformity and overall energy efficiency.

[0045] A multi-threshold joint triggering mechanism ensures the timeliness and robustness of antifreeze protection: the judgment module is configured to activate the temperature control module for heating when the icing risk index exceeds its sub-item threshold or the comprehensive risk index exceeds a preset threshold. This dual-trigger logic can respond quickly to the direct risk of icing and implement comprehensive protection based on overall risk assessment. It effectively prevents missed judgments that may occur when a single risk indicator fails to reach the threshold but the comprehensive risk is already high, thus enhancing the timeliness and operational robustness of the antifreeze protection device. Attached Figure Description

[0046] Figure 1 A schematic diagram of a freeze protection device for a combined dry and wet cooling tower used for compressed air energy storage;

[0047] Figure 2 This is a schematic diagram of the controller module in this invention.

[0048] Reference numerals: 1. Cooling tower body; 2. Heater assembly; 3. Heat conduction control layer. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0050] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0051] like Figures 1-2As shown, an anti-freezing protection device for a combined dry and wet cooling tower for compressed air energy storage, according to an embodiment of the present invention, includes a cooling tower body 1, and further includes:

[0052] Heater group 2 is installed in the freezing zone of the cooling tower body 1 and is used to heat the freezing zone;

[0053] A heat-conducting control layer 3 is disposed between the heater group 2 and the cooling tower body 1 for heat transfer;

[0054] The controller is installed in the cooling tower body 1 and includes: an icing kinetics assessment module, a thermal stress damage assessment module, a heat flow supply and demand imbalance assessment module, a dry-wet switching impact assessment module, a comprehensive risk assessment module, a temperature control module, and a judgment module.

[0055] The cooling tower body 1 is the main structural component of the dry-wet combined cooling tower in the compressed air energy storage system. Its function is to provide support and containment space for the cooling medium and to realize heat exchange with the ambient air.

[0056] Heater assembly 2 refers to a collection of devices used to generate heat and heat specific areas of the cooling tower body 1. Its main function is to provide the necessary antifreeze heat source in low-temperature environments to prevent ice formation inside the cooling tower.

[0057] The thermal conductivity control layer 3 is a material layer disposed between the heater group 2 and the cooling tower body 1. Its function is to effectively transfer the heat generated by the heater group 2 to the easily frozen area of ​​the cooling tower body 1, and may regulate the heat distribution to achieve uniform heating.

[0058] The controller is the core intelligent unit of the entire antifreeze protection device, integrating multiple evaluation and control modules. The controller is responsible for receiving data from various sensors, conducting risk assessments, and issuing heating commands based on the assessment results to achieve precise control of heater group 2.

[0059] The icing kinetics assessment module is a component of the controller. Its function is to construct an icing kinetics assessment model based on parameters such as ambient temperature, water temperature, and duration of low temperature, thereby outputting a quantified icing risk index. This module aims to predict the likelihood and severity of icing.

[0060] The thermal stress damage assessment module is a component of the controller. Its function is to construct a thermal stress damage assessment model based on parameters such as temperature change rate, structural temperature difference, and vibration amplitude, thereby outputting a quantified thermal stress damage index. This module aims to assess the risk of damage to the cooling tower structure caused by thermal stress due to temperature changes.

[0061] The heat supply and demand imbalance assessment module is a component of the controller. Its function is to construct a heat supply and demand imbalance assessment model based on parameters such as the available heat source temperature difference, heating response hysteresis, and heat transfer temperature difference, thereby outputting a quantitative supply and demand imbalance index. This module aims to assess the degree of matching between the heat required for freeze protection and the actual available heat.

[0062] The dry-wet switching impact assessment module is a component of the controller. Its function is to construct a dry-wet switching impact assessment model based on parameters such as switching temperature difference and switching frequency, thereby outputting a quantified dry-wet switching impact index. This module aims to assess the potential impact of switching between dry and wet cooling modes on the cooling tower structure.

[0063] The comprehensive risk assessment module is a component of the controller. Its function is to construct a comprehensive risk assessment model based on the icing risk index, thermal stress damage index, supply and demand imbalance index, and dry-wet switching impact index, thereby outputting a comprehensive risk index. This module aims to provide an overall assessment of the risk situation.

[0064] The temperature control module is a component of the controller. Its function is to construct a heater temperature control model based on parameters such as the comprehensive risk index, the current heating temperature, and the remaining lifespan of the thermal conductivity control layer, thereby outputting a target heating temperature. This module aims to determine the optimal heating strategy.

[0065] The judgment module is a component of the controller. Its function is to determine whether to activate the temperature control module and control the heater group 2 to heat at the target heating temperature based on whether the icing risk index exceeds its sub-item threshold or whether the comprehensive risk index exceeds its preset threshold. This module is the trigger mechanism for antifreeze protection measures.

[0066] Frozen zones refer to parts of the cooling tower body 1 that are particularly prone to freezing in low-temperature environments, such as the water pan, spray pipes, and the bottom of the packing. Targeted heating of these areas is key to freeze protection.

[0067] In a preferred embodiment of the present invention, the heater group 2 includes at least one set of electric heating elements. The electric heating elements are arranged in a serpentine pattern or in segments along the easily frozen area of ​​the cooling tower body 1. Each segment of the electric heating element is equipped with an independent temperature sensor and a power adjustment module for independent temperature control of the easily frozen area.

[0068] In this embodiment, the electric heating element is a device that generates Joule heat by passing an electric current through a resistance wire. Its function is to serve as a heat source, providing the heating capacity required for antifreeze. This electric heating element can specifically take the form of a resistance wire, a PTC heating element, an electric heating tube, etc., or it can be a flexible heating film, a ceramic heater, etc.

[0069] The electric heating elements are arranged in a serpentine pattern or in segments along the easily frozen area of ​​the cooling tower body 1. This describes the physical layout of the electric heating elements in the easily frozen area of ​​the cooling tower body 1, which aims to optimize heat distribution, avoid local overheating or heating blind spots, and improve antifreeze efficiency and uniformity.

[0070] The serpentine arrangement refers to the electric heating elements covering the target area in a continuous, curved path to ensure uniform heat distribution; the segmented independent arrangement refers to dividing the easily frozen area into multiple sub-areas, with each sub-area equipped with an independent electric heating element.

[0071] The independent temperature sensor is used to monitor the temperature of each section of the electric heating element or its corresponding area in real time, providing accurate temperature feedback data, which is the basis for realizing independent temperature control of each zone. This temperature sensor can be a contact sensor such as a thermistor, thermocouple, or platinum resistance thermometer, or a non-contact sensor such as an infrared temperature sensor.

[0072] The power regulation module dynamically adjusts the heating power of the corresponding electric heating element based on feedback data from the temperature sensor, achieving precise power control of each section of the electric heating element and ensuring that the temperature of the target area is maintained within the set range. This power regulation module can use a solid-state relay in conjunction with a PID controller to adjust the heating power through pulse width modulation or phase shift control, or it can employ a silicon controlled rectifier (SCR) voltage regulator module to directly adjust the effective value of the AC voltage to control the heating power.

[0073] Ultimately, through the above configuration, independent temperature control for easily frozen areas is achieved. This means that heating is independently controlled according to the actual temperature requirements of different areas, addressing the differentiated antifreeze needs that may exist in different areas and improving the accuracy and energy efficiency of antifreeze.

[0074] In a preferred embodiment of the present invention, the thermal conductivity control layer 3 is a graphene thermal conductive film layer, a thermal conductive silicone pad layer, or a metal thermal conductive plate layer. The graphene thermal conductive film layer, thermal conductive silicone pad layer, or metal thermal conductive plate layer is attached between the heater group 2 and the easily frozen area of ​​the cooling tower body 1, and is used to uniformly conduct the heat generated by the heater group to the easily frozen area.

[0075] In this embodiment, the thermal conductivity control layer 3 is a material layer used to transfer heat between different media. Its core function is to optimize the heat transfer path and distribution, ensuring that heat can be efficiently and uniformly transferred from the heat source to the target area. This layer can be made of materials with different thermal conductivity, flexibility, or mechanical strength, depending on the actual application requirements.

[0076] Graphene thermally conductive films are thin films made using the excellent thermal conductivity of graphene. They are characterized by extremely high thermal conductivity, thinness, and good flexibility, allowing them to adapt to irregular surfaces and providing efficient lateral heat dissipation. Thermally conductive silicone pads are flexible pads made with silicone as the base material and added thermally conductive fillers. They are characterized by good thermal conductivity, electrical insulation, compressibility, and surface wettability, effectively filling tiny gaps, reducing thermal resistance, and absorbing some vibration. Metal thermally conductive plates are typically made of highly thermally conductive metals such as aluminum and copper. They are characterized by stable thermal conductivity, high mechanical strength, and a large heat dissipation area, enabling rapid heat conduction and dissipation, making them suitable for applications requiring mechanical stress or large-area uniform heat dissipation.

[0077] The application of the heat transfer control layer 3 between the heater assembly 2 and the cooling tower body 1 in the frost-prone area refers to the tight installation of the heat transfer control layer 3 between the surface of the heater assembly 2 and the frost-prone area of ​​the cooling tower body 1, ensuring good thermal contact between the two. This application method aims to maximize the efficiency of heat transfer from the heater assembly 2 to the cooling tower body 1, reduce heat loss, and ensure that heat can directly act on the area most in need of frost protection.

[0078] The purpose of the heat control layer 3 is to uniformly transfer the heat generated by the heater group 2 to the frost-prone area. It guides the heat control layer 3 to transfer the heat energy generated by the heater group 2 to the frost-prone area of ​​the cooling tower body 1 in a uniform manner through its own thermal conductivity and structural design, so as to avoid local overheating or insufficient heating, thereby achieving efficient and balanced antifreeze protection.

[0079] In a preferred embodiment of the present invention, the icing risk index in the icing dynamics assessment module is calculated as follows:

[0080]

[0081] in This is the cumulative coefficient for freezing time. , This is an index representing the duration of low temperature. The ambient temperature index. The freezing point temperature index. This refers to the icing risk index.

[0082] , as well as The method for obtaining the value is as follows: the actual low temperature duration, ambient temperature, and water temperature are successively substituted into the maximum-minimum normalization formula for calculation. This formula is applicable to conventional freezing conditions. For special conditions such as supercooled water, it is necessary to combine other parameters for comprehensive judgment.

[0083] In this embodiment, the icing dynamics assessment module is a functional unit within the controller. Its core function is to quantitatively assess the icing risk in the easily frozen areas of the cooling tower body 1. It collects and processes environmental and operational parameters related to icing to construct a mathematical model to predict the likelihood and severity of icing. This module can be implemented in software, such as as a specific algorithm program running within the controller, or in a combination of hardware and software.

[0084] Part One This reflects the cumulative effect of the duration of low temperature on the risk of icing. This is the cumulative freezing time coefficient, with a value ranging from 2 to 5, used to adjust the low-temperature duration index. The impact on the rate of risk accumulation. Significant. The value indicates that the risk of icing accumulates more rapidly with increasing duration of low temperatures. The low temperature duration index is obtained by normalizing the actual low temperature duration and represents the length of time the low temperature environment lasts. It is an empirical coefficient set manually based on the cooling tower structure, operating environment and antifreeze requirements. It can also be calibrated through data fitting, simulation or on-site debugging. The default recommended value can be set to 3 or 3.5 as the starting value.

[0085] The second part of the formula This reflects the relative difference between the current ambient temperature and the freezing point temperature. The freezing point temperature index is obtained by normalizing the actual water temperature (or freezing point temperature). The ambient temperature index is obtained by normalizing the actual ambient temperature. This represents a preset minimum temperature used to normalize the denominator, ensuring that the value of this term approaches 1 when the ambient temperature is close to the freezing point, thus accurately reflecting the urgency of freezing. The icing risk index is a value between 0 and 1, with a higher value indicating a higher risk of icing.

[0086] , as well as The value is obtained by substituting the actual measured duration of low temperature, ambient temperature, and water temperature into the maximum-minimum normalization formula. This normalization process eliminates differences between different physical dimensions, mapping them uniformly to the range of 0 to 1, facilitating comprehensive calculations and comparisons by the model. It is important to note that this calculation formula is primarily applicable to conventional icing conditions, i.e., the icing process under normal atmospheric pressure and water quality conditions. For special conditions such as supercooled water, due to their more complex icing mechanisms, it may be necessary to combine other additional parameters or more advanced physical models for comprehensive judgment to ensure the accuracy of the assessment.

[0087] Specifically, when the cooling tower body 1 is in a low-temperature environment, the icing kinetics evaluation module in the controller continuously receives real-time data from sensors (such as temperature sensors and time recorders), including the current ambient temperature, the actual water temperature, and the duration of the low-temperature event. This raw data is first processed by max-min normalization, converting it into a dimensionless exponential form, namely the low-temperature duration exponent. Ambient temperature index Freezing point temperature index This normalization process ensures that the weights and influences of different physical quantities in the model can be reasonably compared and calculated. Subsequently, these normalized exponents are substituted into the formula for calculating the icing risk index. This formula cleverly incorporates the cumulative effect of the duration of low temperature on icing risk (through an exponential decay term). (Reflected) and the degree of closeness between the current temperature conditions and the freezing point (via the temperature difference ratio). (This should be combined with)

[0088] Among them, the cumulative coefficient of freezing time The system is allowed to adjust the rate at which the duration of low temperature affects risk accumulation based on actual conditions. In this way, the icing kinetics assessment module can output a dynamically changing icing risk index. This index not only reflects instantaneous temperature conditions, but more importantly, it considers the duration of the low-temperature environment, thus enabling earlier detection of potential icing risks. When this icing risk index... When the preset sub-threshold is exceeded, the judgment module in the controller will activate the temperature control module, thereby controlling heater group 2 to heat. This evaluation method based on multi-parameter, dynamic model, together with other evaluation modules in the controller (such as thermal stress damage evaluation module, heat flow supply and demand imbalance evaluation module, and dry-wet switching impact evaluation module), provides key icing risk input for the comprehensive risk assessment module.

[0089] In a preferred embodiment of the present invention, the thermal stress damage index in the thermal stress damage assessment module is calculated as follows:

[0090]

[0091] in The thermal stress sensitivity coefficient, , This is the vibration amplification factor. , It is the temperature change rate index. The vibration amplitude index, For structural temperature difference index, The thermal stress damage index;

[0092] , , The method for obtaining the value is as follows: the actual temperature change rate, vibration amplitude, and structural temperature difference are successively substituted into the maximum-minimum normalization formula for calculation.

[0093] In this embodiment, the thermal stress damage assessment module is a functional unit within the controller. Its core function is to quantitatively assess the thermal stress damage that the cooling tower body 1 may experience during operation. This module can be implemented as a software algorithm, running on the microprocessor or dedicated computing chip within the controller. The calculation method of the thermal stress damage index aims to provide a quantitative indicator to characterize the degree of thermal stress damage experienced by the cooling tower body 1 structure. This calculation method can be implemented as an algorithm function within the controller, receiving data from sensors as input and outputting the calculation results.

[0094] The thermal stress sensitivity coefficient characterizes the sensitivity of the cooling tower body 1 material to thermal stress, and its value ranges from [value range missing]. This indicates that it is an adjustable parameter that can be calibrated according to the actual material properties during system debugging or operation. This coefficient can be stored in the controller's internal non-volatile memory and recalled during calculations, for example, through online adjustment and optimization via an external interface.

[0095] This is the vibration amplification factor, which is used to quantify the amplification effect of vibration on thermal stress damage. Its value range is... It allows adjustment based on actual vibration characteristics. This coefficient can also be stored internally in the controller and invoked during calculations, for example, by determining its initial value through experimental testing or finite element analysis.

[0096] The temperature change rate index reflects how quickly the temperature of the cooling tower body 1 changes locally or as a whole over time. It is obtained by normalizing the actual temperature change rate to a value between 0 and 1 for easy model calculation. Its implementation can include, but is not limited to: real-time temperature data acquisition via temperature sensors, differential calculation of temperature data at continuous time points by the controller, and then normalization processing.

[0097] The vibration amplitude index characterizes the intensity of vibration experienced by the cooling tower body 1. It is obtained by normalizing the actual vibration amplitude. The implementation methods may include, but are not limited to: collecting vibration data through an accelerometer or displacement sensor, processing the collected vibration signals with a controller, and then normalizing them.

[0098] The structural temperature difference index reflects the temperature difference between different locations inside the cooling tower body 1. It is obtained by normalizing the actual structural temperature difference. Its implementation can include, but is not limited to: arranging multiple temperature sensors at different key locations in the cooling tower body 1, with the controller monitoring the sensor readings in real time, calculating the maximum temperature difference, and then normalizing it.

[0099] The thermal stress damage index is the final output of the thermal stress damage assessment module. It is a quantitative and comprehensive indicator used to indicate the level of thermal stress damage risk currently experienced by the cooling tower body 1. A higher index value indicates a greater risk of thermal stress damage. Its implementation can include, but is not limited to, using it as a variable within the controller, with its value updated in real time according to the formula described above.

[0100] , , The acquisition of these values ​​all relies on converting actual physical quantities using the maximum-minimum normalization formula. Its function is to map physical quantities of different dimensions and ranges to a unified interval of 0 to 1, so as to facilitate unified processing and comparison in mathematical models.

[0101] In the formula, the temperature change rate exponent The squared term highlights the nonlinear amplification effect of rapid temperature changes on thermal stress; that is, the faster the temperature change, the exponentially greater its contribution to thermal stress damage. Vibration amplitude exponent. By comparing with vibration amplification factor This analysis further corrected for thermal stress damage, demonstrating the accelerating effect of vibration on structural fatigue. (Structural temperature difference index) This directly reflects the stress generated by the uneven temperature distribution within the structure. Thermal stress sensitivity coefficient This serves as an overall adjustment factor, adjusting the model's overall sensitivity to thermal stress damage based on the specific material and structural characteristics of cooling tower body 1. In this way, the thermal stress damage assessment module can organically integrate multi-source, multi-dimensional environmental and operational parameters, outputting a comprehensive thermal stress damage index. The value of this index can intuitively reflect the level of thermal stress damage risk faced by the current cooling tower body 1.

[0102] In a preferred embodiment of the present invention, the heat flow supply and demand imbalance assessment module calculates the supply and demand imbalance index as follows:

[0103]

[0104] when season This means that the supply-demand imbalance has reached its maximum.

[0105] in The hysteresis sensitivity coefficient, , This is the heat loss amplification factor. , The temperature index of the available heat source. The target temperature index for frost protection. The maximum possible temperature difference index. The heating response hysteresis time index. The heat transfer temperature difference index, This is an index of supply and demand imbalance;

[0106] , , , as well as The method for obtaining the value is as follows: the actual available heat source temperature, the antifreeze target temperature, the maximum possible temperature difference, the heating response lag time, and the heat transfer temperature difference are successively substituted into the maximum-minimum normalization formula for calculation.

[0107] In this embodiment, the heat supply and demand imbalance assessment module is an important component of the controller, and its function is to assess the balance between heat supply and demand during the antifreeze heating process. This module can run as an independent software program segment on the controller's main processor, or as a dedicated computing unit responsible for performing complex mathematical operations. The microcontroller receives data from multiple thermocouples or platinum resistance temperature sensors via an analog-to-digital converter interface. These sensors are respectively arranged on the surface of the heater group 2, the inner and outer sides of the thermal conductivity control layer 3, and the frost-prone area of ​​the cooling tower body 1. In addition, the system can also monitor the time difference between the start-up time of the heater group 2 and the time it takes for the temperature of the frost-prone area to reach the target value through a timer module to obtain the heating response lag time.

[0108] Supply and demand imbalance index This is the core output of the module; it's a dimensionless numerical value used to quantify the degree of imbalance between heat supply and demand. A larger value indicates a higher risk of imbalance. The index is calculated using a multi-factor product model, where the `max(0,...)` function ensures the non-negativity of the temperature difference term between the heat source and demand, avoiding deviations in the physical meaning of the calculation results. When the available heat source temperature index... Below the target temperature index for frost protection At that time, the system will immediately update the supply and demand imbalance index. Setting it to the maximum value of 1 is a rapid response mechanism for severe heat source insufficiency, ensuring that the highest level of antifreeze protection can be triggered in time under extreme conditions.

[0109] Lag Sensitivity Coefficient and heat loss amplification factor It is a key adjustment parameter in the model. Hysteresis sensitivity coefficient An exponent used to characterize the hysteresis time of a system's response to heating. The sensitivity of the coefficient, ranging from [3,5], can be determined through system dynamic response testing or simulation optimization to accurately reflect the thermal inertia of different cooling tower systems. Heat loss amplification factor. This is used to amplify the heat transfer temperature difference index. The impact on supply and demand imbalance has a value range of [0.2, 0.8], which can be calibrated based on the thermal insulation performance of the cooling tower body 1 and the heat transfer efficiency of the thermal conductivity control layer 3.

[0110] Available heat source temperature index This represents the heat level that heater group 2 can provide, and can be, for example, a normalized value of the surface temperature of the heating element or the outlet temperature of the heating medium. Antifreeze target temperature index. This is a normalized representation of the minimum safe temperature that the easily frozen zone of the cooling tower body 1 needs to be maintained, typically slightly higher than the freezing point of water. Maximum Possible Temperature Difference Index Used to standardize the temperature difference between the heat source and the antifreeze target. Heating response hysteresis time index. It measures the time required from the issuance of a heating command to the attainment of the expected temperature in the easily frozen area, reflecting the dynamic response characteristics of the system. Heat transfer temperature difference index This quantifies the temperature gradient or loss of heat during the transfer from heater group 2 through thermally conductive control layer 3 to the easily frozen area of ​​cooling tower body 1. All these indices are obtained by substituting actual measured values ​​into the maximum-minimum normalization formula, thereby unifying different physical quantities to the same dimension, facilitating comprehensive evaluation of the model.

[0111] The solution proposed in this application, through the aforementioned heat supply and demand imbalance assessment module, can accurately quantify the risk of heat supply and demand imbalance. This module receives real-time data from various monitoring points on the cooling tower body 1, including ambient temperature, water temperature, the operating status of the heater group 2, and the heat transfer efficiency of the thermal conductivity control layer 3, and converts these actual physical quantities into a unified index form. These indices are then input into the calculation formula for the supply and demand imbalance index, which cleverly combines the sufficiency of the heat source, the timeliness of the heating response, and the effectiveness of heat transfer. Through this multi-dimensional and dynamic assessment mechanism, the module can accurately capture potential problems such as insufficient heat supply, heating lag, or excessive heat loss, and quantify them as a supply and demand imbalance index. This index, as an important input to the comprehensive risk assessment module in the controller, works in conjunction with other risk indices, enabling the controller to make a comprehensive and detailed judgment on the antifreeze risk of the dry-wet combined cooling tower for compressed air energy storage. This collaborative working method allows the antifreeze protection device to shift from passive response to proactive prediction and prevention, thereby optimizing the operating strategy of the heater group 2 and ensuring the safe and stable operation of the cooling tower in low-temperature environments.

[0112] In a preferred embodiment of the present invention, the dry-wet switching impact index in the dry-wet switching impact assessment module is calculated as follows:

[0113]

[0114] in For frequency sensitivity coefficient, To switch frequency index, To switch the temperature difference index, The impact index for switching between dry and wet conditions;

[0115] , The method for obtaining this value is as follows: the actual switching frequency and the actual temperature difference between dry and wet switching are successively substituted into the maximum-minimum normalization formula for calculation.

[0116] In this embodiment, the dry-wet switching impact assessment module is a functional unit within the controller. Its function is to assess the impact or potential risks that the combined dry and wet cooling tower may cause to the equipment structure and operational stability during the dry-wet mode switching process. This module can be implemented on a general-purpose processor using software algorithms, such as running specific program code in a microcontroller or industrial control computer.

[0117] Impact index of switching between dry and wet This is a quantitative indicator used to comprehensively reflect the impact of switching between dry and wet cooling on the cooling tower system. The higher the value, the greater the risk of switching impact. The calculation results of this index can be stored in the controller's memory for use by the subsequent comprehensive risk assessment module; or displayed in real time on the operation interface for reference by operation and maintenance personnel.

[0118] Frequency sensitivity coefficient The weighting used to adjust the impact of switching frequency on the dry-wet switching impact index reflects the system's sensitivity to changes in switching frequency. This coefficient can be preset in the controller's parameter configuration and calibrated based on the specific model, material properties, and operating experience of the cooling tower; or it can be adaptively optimized and adjusted based on historical data through machine learning algorithms during system operation.

[0119] Switching frequency index It is a normalized indicator representing the number of times a wet-dry combined cooling tower switches modes within a certain time period. The actual switching frequency can be obtained by monitoring sensor data of the cooling tower's operating mode, such as by detecting the start / stop status of the wet cooling water pump or the operating status of the dry fan to count the number of switching times; then the counted actual switching frequency is substituted into the maximum-minimum normalization formula to ensure that it is within a standardized range between 0 and 1.

[0120] Switch temperature difference index This is a normalized index representing the temperature change in key components of the cooling tower before and after switching between dry and wet modes. The actual temperature difference during the dry-wet switching process can be measured in real time by temperature sensors placed at key locations on the cooling tower body, such as on the surface of the dry-wet heat exchanger or inside the tower structure. The measured actual temperature difference is then processed using a maximum-minimum normalization formula to ensure it falls within a standardized range of 0 to 1. The maximum-minimum normalization formula is a commonly used data processing method to linearly transform raw data to a specific range so that data of different dimensions can be compared and calculated. This formula can be implemented in the controller's software algorithm as part of the data preprocessing.

[0121] The dry-wet switching impact assessment module of this application receives real-time data from the cooling tower's operating status, including the actual switching frequency and the actual temperature difference during dry-wet switching. This raw data is first processed using a maximum-minimum normalization method to convert it into a dimensionless switching frequency exponent. and switching temperature difference index Subsequently, the module uses a specific mathematical model to calculate the impact index of dry-wet switching. In this model, the exponential function It cleverly captures part of the cumulative effect of switching frequency; that is, as the switching frequency increases, the impact risk does not increase linearly, but rather shows a gradual saturation trend, in which the frequency sensitivity coefficient... The system can adjust this sensitivity based on the specific characteristics of the cooling tower. Simultaneously, it can switch the temperature difference index. This directly reflects the severity of temperature changes during each switch. By multiplying these two key factors, the model can comprehensively consider both the frequency and severity of each switch, thus outputting a comprehensive and accurate dry-wet switching shock index. As a key input to the comprehensive risk assessment module, it works in conjunction with the icing risk index, thermal stress damage index, and supply-demand imbalance index to construct the comprehensive risk assessment model and output a comprehensive risk index. When the comprehensive risk index exceeds a preset threshold, the judgment module will activate the temperature control module to control heater group 2 to heat at the target heating temperature. This mechanism enables the antifreeze protection device to adjust its heating strategy in a timely manner based on a precise quantitative assessment of the impact of switching between dry and wet conditions, thereby effectively avoiding structural fatigue and equipment damage caused by frequent or drastic switching, and improving the intelligence and response speed of the entire antifreeze protection device.

[0122] In a preferred embodiment of the present invention, the comprehensive risk index in the comprehensive risk assessment module is calculated as follows:

[0123]

[0124] ;

[0125] in These are the extreme value weighting coefficients. , This is the icing risk index. The thermal stress damage index, This is a supply and demand imbalance index. The impact index for switching between dry and wet conditions. The highest risk index, This is the average risk index. This is a comprehensive risk index.

[0126] In this embodiment, the comprehensive risk assessment module is a core component of the antifreeze protection device. Its main function is to comprehensively and quantitatively assess the various risks that the cooling tower may face in low-temperature environments. This module can be implemented using hardware platforms such as embedded processors, microcontrollers, or industrial control computers, and its assessment logic can be implemented through software algorithms. Comprehensive Risk Index This is the final risk quantification value output by the module, which represents the overall risk level currently faced by the cooling tower and provides a key basis for subsequent antifreeze strategy decisions.

[0127] Extreme value weight coefficient This is a configurable parameter, ranging from 0.5 to 0.8, used to adjust the maximum risk index in the comprehensive risk assessment. and average risk index The degree of emphasis. This coefficient can be set based on actual operating experience, equipment sensitivity, or specific operating conditions to accommodate different risk preferences. Maximum Risk Index Used to identify and highlight the most severe risk among all current assessments, ensuring the system remains highly vigilant against the most pressing threats. Average Risk Index This provides an overall average level for all assessed risks, reflecting the general risk situation faced by cooling towers. Icing Risk Index Thermal stress damage index Supply and demand imbalance index and the impact index of switching between dry and wet conditions These are the fundamental inputs that constitute the comprehensive risk assessment. They quantify risks related to icing, structural thermal stress, the matching of heat supply and demand, and the impact of switching between dry and wet modes. These indices are calculated independently by their respective assessment modules and provided to the comprehensive risk assessment module.

[0128] The proposed solution effectively addresses the challenge of balancing extreme and average risks in multi-dimensional risk assessment by constructing a weighted combined comprehensive risk assessment model. Specifically, the comprehensive risk assessment module first receives risk indices from the icing kinetics assessment module, thermal stress damage assessment module, heat flow supply-demand imbalance assessment module, and dry-wet switching impact assessment module, i.e., icing risk indices. Thermal stress damage index Supply and demand imbalance index and the impact index of switching between dry and wet conditions Based on this, the module calculates the maximum risk index. This is the maximum value among the four independent risk indices, enabling the rapid identification of the most prominent single risk point. Simultaneously, this module also calculates the average risk index. This is the arithmetic mean of the four independent risk indices, used to comprehensively reflect the overall risk situation faced by the cooling tower. Subsequently, the comprehensive risk assessment module utilizes extreme value weighting coefficients... Will and Weighted combinations are then used to generate the final comprehensive risk index. Extreme value weighting coefficient The set range (0.5 to 0.8) allows the system to, to some extent, prioritize extreme risks based on actual needs, avoiding the possibility of high-risk events being diluted by the average, while also taking into account the overall risk level. This assessment mechanism ensures the comprehensive risk index... This approach allows for both sensitivity to sudden, high-risk events and comprehensive consideration of persistent, widespread risks, providing a more accurate and robust risk quantification basis for the antifreeze protection device's judgment module. In this way, the antifreeze protection device can more intelligently determine when to activate the temperature control module for heating, avoiding misjudgments or delayed responses that may result from relying solely on a single threshold or simple average value.

[0129] In a preferred embodiment of the present invention, the target heating temperature in the temperature control module is calculated as follows:

[0130]

[0131] in For risk sensitivity coefficient, , This is the lifespan loss coefficient. , As the reference heating temperature, To maximize compensation for temperature differences, As a comprehensive risk index, This refers to the lifespan loss rate of the heat-conducting layer. The target heating temperature.

[0132] In this embodiment, the target heating temperature This refers to the ideal temperature that heater group 2 needs to reach to effectively prevent icing in the easily frozen areas of cooling tower body 1, while also considering energy consumption and equipment lifespan. Reference heating temperature This is the minimum guaranteed temperature for freeze protection, usually set slightly above the freezing point of water or a safe temperature determined based on historical data. It can be a fixed value or preset and adjusted according to the season or operating mode. Maximum compensated temperature difference. Defines the heating temperature relative to the reference heating temperature under the most extreme risk conditions. The maximum increase should be determined by considering the heating capacity of heater group 2, the material resistance of cooling tower body 1, and the actual requirements for freeze protection. For example, it can be determined through experimental testing or simulation analysis. Risk sensitivity coefficient. It is an adjustment parameter used to control the overall risk index. Target heating temperature The degree of influence, and its range of values. This ensures that the heating temperature can respond and increase rapidly when risks increase, but will not increase indefinitely, thus avoiding overheating. Lifespan loss factor. It is another adjustment parameter used to control the remaining lifespan of the thermal regulation layer. Target heating temperature The degree of influence, and its range of values. This allows for a moderate reduction in heating intensity when the lifespan of the thermal conductivity control layer 3 is high, thereby extending its service life. (Comprehensive Risk Index) Outputted by the comprehensive risk assessment module, it quantifies risks from multiple aspects, including icing, thermal stress, heat supply and demand imbalance, and the impact of switching between dry and wet conditions, providing a comprehensive basis for heating strategies. (Thermal conductivity layer lifespan loss rate is also mentioned.) It reflects the health status of the thermal conductivity control layer 3. For example, it can be evaluated by monitoring the material aging degree, thermal conductivity degradation, or cumulative working time of the thermal conductivity control layer 3.

[0133] This application's solution addresses the problem of precisely balancing antifreeze effect, energy consumption, and lifespan loss in temperature control by providing a specific calculation formula. This formula uses a reference heating temperature... Based on, combined with the maximum compensated temperature difference Comprehensive Risk Index and thermal conductive layer lifespan loss rate Dynamically adjust the target heating temperature This ensures that energy use and equipment lifespan are optimized while preventing freezing. Specifically, As a reference heating temperature, it provides basic antifreeze protection; Set the maximum compensable temperature difference to provide an upper limit for temperature adjustment; Partially based on the comprehensive risk index The exponential decay function is used to make the compensation temperature difference increase with the increase of risk but tend to saturate, so as to avoid overheating when the risk is low. Partially considering the lifespan loss rate of the thermal conductive layer Reduce compensation when the loss rate is high to protect material life; risk sensitivity coefficient. and life loss coefficient The specific range ensures that the calculations are within a reasonable range, optimizing control accuracy. This calculation method is closely integrated with the comprehensive risk assessment module in the controller, enabling the heating strategy to make intelligent decisions based on multi-dimensional risk factors, rather than simply judging temperature thresholds. Simultaneously, considering the lifespan of the thermal conductivity control layer 3, it avoids accelerating its aging due to overheating, thereby extending the overall lifespan of the equipment. This dynamic and intelligent temperature control mechanism makes antifreeze protection more precise and efficient.

[0134] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A freeze protection device for a combined dry and wet cooling tower for compressed air energy storage, comprising a cooling tower body (1), characterized in that, Also includes: Heater assembly (2) is installed in the freezing zone of the cooling tower body (1) for heating the freezing zone; A heat-conducting control layer (3) is disposed between the heater group (2) and the cooling tower body (1) for transferring heat; A controller is disposed in the cooling tower body (1), the controller comprising: The freezing dynamics assessment module is configured to build a freezing dynamics assessment model based on ambient temperature, water temperature, and duration of low temperature, and output a freezing risk index. The thermal stress damage assessment module is configured to construct a thermal stress damage assessment model based on the temperature change rate, structural temperature difference, and vibration amplitude, and output the thermal stress damage index. The heat supply and demand imbalance assessment module is configured to construct a heat supply and demand imbalance assessment model based on the available heat source temperature difference, heating response hysteresis and heat transfer temperature difference, and output the supply and demand imbalance index. The dry-wet switching impact assessment module is configured to build a dry-wet switching impact assessment model based on the switching temperature difference and switching frequency, and output the dry-wet switching impact index. The comprehensive risk assessment module is configured to construct a comprehensive risk assessment model based on the icing risk index, thermal stress damage index, supply and demand imbalance index, and dry-wet switching impact index, and output a comprehensive risk index. The temperature control module is configured to construct a heater temperature control model based on the comprehensive risk index, the current heating temperature, and the remaining lifespan of the thermally conductive control layer, and output the target heating temperature. The judgment module is configured to activate the temperature control module to control the heater group to heat at the target heating temperature when the freezing risk index exceeds its sub-item threshold or the comprehensive risk index exceeds its preset threshold.

2. The antifreeze protection device for a combined dry and wet cooling tower for compressed air energy storage according to claim 1, characterized in that, The heater group (2) includes at least one set of electric heating elements. The electric heating elements are arranged in a serpentine pattern or in separate segments along the easily frozen area of ​​the cooling tower body (1). Each segment of the electric heating element is equipped with an independent temperature sensor and power adjustment module for independent temperature control of the easily frozen area.

3. The antifreeze protection device for a combined dry and wet cooling tower for compressed air energy storage according to claim 1, characterized in that, The thermal conductivity control layer (3) is a graphene thermal conductivity film layer, a thermal conductivity silicone pad layer, or a metal thermal conductivity plate layer. The graphene thermal conductivity film layer, thermal conductivity silicone pad layer, or metal thermal conductivity plate layer is attached between the heater group (2) and the easily frozen area of ​​the cooling tower body (1) to uniformly conduct the heat generated by the heater group to the easily frozen area.

4. The antifreeze protection device for a combined dry and wet cooling tower for compressed air energy storage according to claim 1, characterized in that, In the icing dynamics assessment module, the icing risk index is obtained by multiplying the low temperature duration index by the preset icing time cumulative coefficient, then taking the negative exponent of the product result, and subtracting the negative exponent result from 1 to obtain the time cumulative factor. The difference between the freezing point temperature index and the ambient temperature index is calculated and divided by the difference between the freezing point temperature index and the minimum temperature value to obtain the temperature difference factor. The time accumulation factor is multiplied by the temperature difference factor to obtain the freezing risk index. Each index is calculated by substituting the measured values ​​into the maximum-minimum normalization formula.

5. The antifreeze protection device for a combined dry and wet cooling tower for compressed air energy storage according to claim 1, characterized in that, In the thermal stress damage assessment module, the thermal stress damage index is obtained as follows: the square of the temperature change rate index is calculated and multiplied by the preset thermal stress sensitivity coefficient to obtain the basic thermal stress term; the vibration amplification factor is obtained by calculating (1 plus the product of the vibration amplification factor and the vibration amplitude index); the basic thermal stress term is multiplied by the vibration amplification factor and then by the structural temperature difference index to obtain the comprehensive thermal stress value; the negative exponent of this comprehensive thermal stress value is calculated, and then the result of the negative exponent calculation is subtracted from 1 to obtain the thermal stress damage index; each index is calculated by substituting the measured value into the maximum-minimum value normalization formula.

6. The antifreeze protection device for a combined dry and wet cooling tower for compressed air energy storage according to claim 1, characterized in that, The heat supply and demand imbalance assessment module obtains the supply and demand imbalance index through the following methods: When the available heat source temperature index is lower than the antifreeze target temperature index, the supply-demand imbalance index is directly set to 1. Otherwise, calculate the difference between the available heat source temperature index and the antifreeze target temperature index, divide it by the maximum possible temperature difference index, and obtain the temperature difference deviation factor. Calculate the product of the heating response lag time exponent and the preset lag sensitivity coefficient, then take the negative exponent of the product result, and subtract the negative exponent result from 1 to obtain the lag response factor; Calculate (1 plus the product of the heat loss amplification factor and the heat transfer temperature difference index) to obtain the heat loss amplification factor; Multiply the temperature difference deviation factor, the hysteresis response factor, and the heat loss amplification factor to obtain the supply and demand imbalance index. Each index is calculated by substituting the measured values ​​into the maximum-minimum normalization formula.

7. The antifreeze protection device for a combined dry and wet cooling tower for compressed air energy storage according to claim 1, characterized in that, In the dry-wet switching impact assessment module, the dry-wet switching impact index is obtained in the following way: Calculate the product of the switching frequency index and the preset frequency sensitivity coefficient, then take the negative exponent of the product result, and subtract the negative exponent result from 1 to obtain the frequency impulse factor. Multiply the frequency impact factor by the switching temperature difference index to obtain the dry-wet switching impact index. Each index is calculated by substituting the measured values ​​into the maximum-minimum normalization formula.

8. The antifreeze protection device for a combined dry and wet cooling tower for compressed air energy storage according to claim 1, characterized in that, In the comprehensive risk assessment module, the comprehensive risk index is obtained through the following methods: The maximum value among the icing risk index, thermal stress damage index, supply and demand imbalance index, and dry-wet switching impact index is selected as the maximum risk index. Calculate the arithmetic mean of the four risk indices mentioned above, and use it as the average risk index; Multiply the maximum risk index by the preset extreme value weight coefficient, multiply the average risk index by (1 minus the extreme value weight coefficient), and then sum the two to obtain the comprehensive risk index.

9. The antifreeze protection device for a combined dry and wet cooling tower for compressed air energy storage according to claim 1, characterized in that, In the temperature control module, the target heating temperature is obtained in the following way: Calculate the product of the comprehensive risk index and the preset risk sensitivity coefficient, then take the negative exponent of the product result, and subtract the negative exponent result from 1 to obtain the risk response factor; Calculate (1 minus the product of the lifetime loss coefficient and the thermal conductivity layer lifetime loss rate) to obtain the lifetime loss factor; Multiply the maximum compensated temperature difference by the risk response factor, and then multiply by the life loss factor to obtain the compensated temperature difference; add the base heating temperature to the compensated temperature difference to obtain the target heating temperature.

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