A heat recovery control method and device for an air conditioning system

Abstract

This invention discloses a heat recovery control method and apparatus for air conditioning systems. The heat recovery control method includes the following steps: acquiring external environmental parameters and the current operating parameters of the air conditioning system; preprocessing the environmental and operating parameters to extract multi-dimensional feature vectors; inputting the multi-dimensional feature vectors into a preset model to output a predicted probability of frost formation in the air conditioning system; comparing the predicted probability with a preset risk threshold to obtain the current frost risk of the air conditioning system; based on the external environmental parameters and the current frost risk of the air conditioning system, outputting the current operating mode of the air conditioning system through a preset mode switching logic; recalculating the predicted probability of frost formation in the air conditioning system at preset time intervals, and switching the current operating mode of the air conditioning system in real time in conjunction with the external environmental parameters. Using this invention, the air conditioning system can switch between multiple operating modes according to different operating conditions, improving the overall heat recovery efficiency.

Description

Technical Field

[0001] This invention relates to a heat recovery control method for an air conditioning system, and also to a corresponding heat recovery control device, belonging to the field of air conditioning technology. Background Technology

[0002] With the continuous improvement of building energy efficiency standards, the requirement for heat recovery efficiency has increased from the original 50% to over 75%. However, actual operation data shows that, affected by environmental factors, the annual average heat recovery efficiency of existing equipment is generally below 70%, especially in the winter in northern regions where the efficiency degradation problem is more serious.

[0003] In addressing the frosting problem, the industry currently employs three main solutions: The first is timed counter-current defrosting, which melts the frost layer by periodically switching the airflow direction, but this method results in approximately 30% heat loss; the second is electric heating defrosting, which uses resistance wires installed on the heat exchanger surface for heating defrosting, which is effective but energy-intensive and poses safety hazards; the third is hot gas bypass, which introduces some high-temperature exhaust gas into the evaporator to melt the frost layer, but this reduces the system's air conditioning energy efficiency ratio (Coefficient of Performance).

[0004] Existing defrosting methods generally suffer from three main drawbacks: First, they have a slow response time, making it impossible to predict frost formation trends in advance, and their control dimensions are limited, with 82% of devices still relying solely on temperature as a single parameter for judgment. Second, they consume too much energy, exhibiting an "energy efficiency black hole" phenomenon, where defrosting energy consumption accounts for 8% to 12% of the total energy consumption of the air conditioning system, yet it can only solve about 35% of actual frost problems. Third, their control strategies are crude, using fixed thresholds for defrosting, which can easily lead to "over-defrosting" or "under-defrosting," affecting the stability and energy efficiency of the air conditioning system. Summary of the Invention

[0005] The primary technical problem to be solved by this invention is to provide a heat recovery control method for air conditioning systems.

[0006] Another technical problem to be solved by the present invention is to provide a heat recovery control device for an air conditioning system.

[0007] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution:

[0008] According to a first aspect of the present invention, a heat recovery control method for an air conditioning system is provided, comprising the following steps:

[0009] Acquire external environmental parameters and the current operating parameters of the air conditioning system;

[0010] The environmental and operational parameters are preprocessed to extract multidimensional feature vectors; wherein the multidimensional feature vectors include at least the evaporator surface temperature change rate, ambient humidity gradient, airflow velocity, evaporator surface temperature, historical frost matching degree, and device operating time.

[0011] The multidimensional feature vector is input into a preset model to output the predicted probability P of frost formation in the air conditioning system.

[0012] The predicted probability P is compared with a preset risk threshold to determine the current frost risk of the air conditioning system;

[0013] Based on the external environmental parameters and the current frosting risk of the air conditioning system, the current operating mode of the air conditioning system is determined according to a preset mode switching logic; wherein, the current operating mode is one of rotary mode, heat pipe mode, and hybrid mode; in rotary mode, the air conditioning system recovers latent heat and sensible heat through the operation of the rotary heat exchanger; in heat pipe mode, the air conditioning system recovers sensible heat through the operation of the heat pipe heat exchanger; in hybrid mode, the air conditioning system recovers latent heat and sensible heat through the combined operation of the rotary heat exchanger and the heat pipe heat exchanger;

[0014] At preset intervals, the predicted probability P of frost formation on the air conditioning system is recalculated, and the current operating mode of the air conditioning system is switched in real time in conjunction with external environmental parameters.

[0015] Preferably, the preset mode switching logic specifically includes:

[0016] If the predicted probability P is less than the first risk threshold and the outside temperature is within the first temperature range, the air conditioning system is controlled by the PLC controller to execute the rotary mode.

[0017] If the predicted probability P is less than the first risk threshold and the outside temperature is in the second temperature range, the air conditioning system is controlled by the PLC controller to execute an optimized hybrid mode with the rotary mode as the main mode and the heat pipe mode as the auxiliary mode.

[0018] If the predicted probability P is not less than the first risk threshold and not greater than the second risk threshold, and the outside temperature is in the third temperature range, then the air conditioning system is controlled by the PLC controller to execute a stable hybrid mode with the rotary mode as the auxiliary mode and the heat pipe mode as the main mode.

[0019] If the predicted probability P is greater than the second risk threshold or the outside temperature is in the fourth temperature range, the air conditioning system is controlled by the PLC controller to execute the heat pipe mode.

[0020] Preferably, in the optimized hybrid mode where the rotary mode is primary and the heat pipe mode is secondary, the rotary heat exchanger and the heat pipe heat exchanger of the air conditioning system distribute energy in the following manner:

[0021] Q total＝ α·Q 热管 + (1-α)·Q 转轮 ;

[0022] Where α=β P and β are weighting factors based on the external temperature, and the value of β ranges from 0.2 to 0.4.

[0023] Preferably, in the stable hybrid mode where the rotary heat exchanger is secondary and the heat pipe mode is primary, the rotary heat exchanger and the heat pipe heat exchanger of the air conditioning system distribute energy in the following manner:

[0024] Q total＝ α·Q 热管 + (1-α)·Q 转轮 ;

[0025] Where α=γ P and γ are heat pipe load adjustment coefficients, which are positively correlated with the value of P.

[0026] Preferably, in the rotary mode, 90% of the fresh air from the air conditioning system enters the rotary heat exchanger for sensible and latent heat exchange; and 10% of the fresh air from the air conditioning system enters the bypass channel for pressure balancing.

[0027] The rotor speed of the rotary heat exchanger is positively correlated with the relative humidity. When the relative humidity is lower than a preset humidity threshold, the rotor speed is reduced to the minimum energy-saving speed. When the relative humidity is not lower than the preset humidity threshold, the rotor speed is based on the base speed, and the rotor speed is increased by 0.5 rpm for every 10% increase in relative humidity, until it is increased to the maximum speed.

[0028] Preferably, in the heat pipe mode, 100% of the fresh air from the air conditioning system enters the heat pipe heat exchanger for sensible heat exchange; and the set power of the heat pipe heat exchanger is negatively correlated with the ambient temperature.

[0029] The heat pipe heat exchanger periodically switches between hot and cold ends based on the predicted probability P and the ambient temperature.

[0030] Preferably, if frost is detected, the zoned heating wires are activated as needed based on the frost thickness and distribution calculated by the MPDT algorithm.

[0031] The power density of each zone heating wire is dynamically set based on the predicted probability P and the rate of change of the predicted probability P, ΔP / Δt, and the power density ranges from 2 to 4 W / cm². 2 .

[0032] Preferably, if the predicted probability P is greater than the second risk threshold, an alarm procedure is triggered to send alarm information to maintenance personnel and display the alarm information through a human-machine interface.

[0033] According to a second aspect of the present invention, a heat recovery control device for an air conditioning system is provided, comprising:

[0034] The data acquisition unit is used to acquire external environmental parameters and the current operating parameters of the air conditioning system.

[0035] An edge computing unit is connected to the data acquisition unit and has a built-in preset model for preprocessing the environmental parameters and operating parameters to extract multi-dimensional feature vectors; and inputs the multi-dimensional feature vectors into the preset model to output the predicted probability P of frost formation in the air conditioning system.

[0036] The logic control unit is connected to the edge computing unit and the data acquisition unit to output the current operating mode of the air conditioning system based on the external environmental parameters and the predicted probability P of the air conditioning system frosting through a preset mode switching logic.

[0037] An execution unit, connected to the logic control unit, controls the air conditioning system to execute the current operating mode via a PLC controller based on the control commands output by the logic control unit.

[0038] According to a third aspect of the present invention, another heat recovery control device for an air conditioning system is provided, comprising a processor and a memory, wherein the processor reads a computer program in the memory for implementing the above-described heat recovery control method.

[0039] Compared with the prior art, the present invention has the following technical effects:

[0040] (1) Using this invention, the air conditioning system can switch between multiple operating modes according to different operating conditions, thereby performing different degrees of sensible heat recovery and latent heat recovery. Thus, the contradiction between the traditional single heat recovery mode and the latent heat recovery is difficult to balance, and the overall heat recovery efficiency of the air conditioning system is improved, the frosting rate is reduced, and the maintenance cost is reduced.

[0041] (2) It can control the air conditioning system to operate in the most energy-efficient mode according to changes in the environment, thereby solving the problems of lag and high energy consumption of traditional defrosting control strategies.

[0042] (3) It can monitor the frosting of the air conditioning system in real time, thereby enabling early prediction and zone diagnosis of frosting, and avoiding the problem of sudden efficiency drop caused by heat exchanger frosting in low temperature and high humidity environment. Attached Figure Description

[0043] Figure 1 This is an overall flowchart of a heat recovery control method for an air conditioning system provided in the first embodiment of the present invention;

[0044] Figure 2 This is a flowchart illustrating the logic for determining the current operating mode of the air conditioning system in the first embodiment of the present invention.

[0045] Figure 3 This is a structural diagram of a heat recovery control device for an air conditioning system provided in the second embodiment of the present invention;

[0046] Figure 4 This is a structural diagram of a heat recovery control device for an air conditioning system provided in the third embodiment of the present invention. Detailed Implementation

[0047] The technical content of the present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0048] The technical concept of this invention lies in predicting the probability P of frosting in the air conditioning system and, in conjunction with external environmental parameters, using a PLC controller to switch the air conditioning system between multiple operating modes based on preset mode switching logic. This allows the air conditioning system to perform different degrees of sensible heat recovery and latent heat recovery according to different operating conditions. Consequently, the heat recovery efficiency of the air conditioning system is significantly improved, the frosting rate is reduced, and maintenance costs are decreased.

[0049] First Embodiment

[0050] like Figure 1 As shown, the first embodiment of the present invention provides a heat recovery control method for an air conditioning system, which includes at least the following steps:

[0051] S10: Data Acquisition.

[0052] S20: Data processing, outputting the current operating mode of the air conditioning system.

[0053] S30: Controls the air conditioning system to execute the current operating mode.

[0054] In step S30, after the air conditioning system executes the current operating mode and continues for a preset duration, it returns to step S10 to re-acquire data, thereby forming a complete closed-loop control.

[0055] The following is a detailed explanation of each step:

[0056] S10: Data Acquisition.

[0057] In this embodiment, it is necessary to acquire external environmental parameters and the current operating parameters of the air conditioning system. Specifically, various parameters are collected by deploying a multi-source sensor network. Among them, environmental parameters include at least: temperature and humidity, PM2.5 concentration, etc.; operating parameters include at least: equipment status information, duct pressure difference, heat exchanger surface temperature gradient, etc., thereby providing raw input for subsequent data processing.

[0058] The following table shows the various sensors used in this embodiment:

[0059] Sensor type parameter accuracy Function Temperature sensor temperature ±0.3℃ Monitor indoor and outdoor air temperature Capacitive humidity sensor humidity ±2%RH Measuring relative humidity Micro differential pressure transmitter pressure difference 0~500Pa This reflects changes in duct resistance and helps determine the degree of filter clogging. PM2.5 / VOC sensor air quality ---- Detection of particulate matter and volatile organic compound concentrations

[0060] In one embodiment of the present invention, all sensors support the RS485-Modbus RTU protocol, facilitating access to the main control unit or edge nodes and enabling high-speed and stable data transmission. These parameters constitute the basic dataset for the operation of the air conditioning system, used for subsequent frosting warning, energy efficiency optimization, and mode switching decisions.

[0061] S20: Data processing, outputting the current operating mode of the air conditioning system.

[0062] After obtaining the external environmental parameters and the operating parameters of the air conditioning system through the above step S10, the collected data needs to be preprocessed and transmitted to the edge computing unit. The built-in preset model performs real-time analysis to determine the current operating mode of the air conditioning system and generate corresponding control commands to drive the lower-level devices to perform corresponding operations.

[0063] In this embodiment, the edge computing unit, acting as an embedded computing device (such as an NVIDIA Jetson or Raspberry Pi with an AI acceleration chip), is responsible for preprocessing, feature extraction, and model inference of the raw sensor data, thereby reducing cloud computing latency and improving response speed. Furthermore, the preset model employs an LSTM (Long Short-Term Memory) model, trained using a training set comprised of historical temperature and humidity, pressure differential, and fan frequency data, to output the predicted probability P of the air conditioning system frosting within the next 1-2 hours.

[0064] Specifically, step S20 includes the following steps:

[0065] S21: Data preprocessing.

[0066] By preprocessing the aforementioned environmental and operational parameters, a multidimensional feature vector is extracted. This multidimensional feature vector includes:

[0067] ① Evaporator surface temperature change rate, used to reflect the frosting trend;

[0068] ② Ambient humidity gradient, used to reflect the rate of humidity change;

[0069] ③ Airflow velocity, used to reflect the degree of influence on the frosting rate;

[0070] ④ Evaporator surface temperature, which is directly related to the risk of frosting;

[0071] ⑤ Historical frost pattern matching degree, used to make predictions based on past data;

[0072] ⑥ Air conditioning system operating time, used to determine the likelihood of frost formation.

[0073] S22: Output the predicted probability P of frost formation on the air conditioning system.

[0074] By inputting the aforementioned multidimensional feature vectors into a pre-built LSTM model, the frost probability P can be accurately predicted, with a value between 0 and 1. It is understood that this LSTM model excels at processing time-series data, can capture long-term dependencies, and is suitable for predicting frost trends.

[0075] S23: Outputs the current operating mode of the air conditioning system.

[0076] Specifically, after outputting the predicted probability P of frost formation in the air conditioning system through step S22, the external environmental parameters collected in step S10 (specifically, the external temperature T) are combined with the data from the previous step. out The current operating mode of the air conditioning system is determined by a preset mode switching logic.

[0077] In this embodiment, the current operating mode of the air conditioning system is one of the following: rotary heat exchanger mode, heat pipe mode, and hybrid mode. In rotary heat exchanger mode, the air conditioning system recovers latent and sensible heat through the operation of the rotary heat exchanger. In heat pipe mode, the air conditioning system recovers sensible heat through the operation of the heat pipe heat exchanger. In hybrid mode, the air conditioning system recovers latent and sensible heat through the combined operation of the rotary heat exchanger and the heat pipe heat exchanger.

[0078] like Figure 2 As shown below, the method for determining the current operating mode of an air conditioning system is explained in detail:

[0079] (1) Rotary mode.

[0080] If the predicted probability P is less than the first risk threshold (0.3 in this embodiment) and the outside temperature is within the first temperature range, the air conditioning system is controlled by the PLC controller to execute the rotary mode.

[0081] Specifically, when the predicted probability P < 0.3, it indicates a low risk of frosting in the air conditioning system. In this case, optimal energy efficiency should be the preferred operating condition. Therefore, when the outside temperature T... out At ≥3℃ (safety margin), the rotary heat exchanger operates at full load, thereby maximizing total heat recovery (sensible heat + latent heat).

[0082] In rotary heat exchanger mode, 90% of the fresh air from the air conditioning system enters the rotary heat exchanger for sensible and latent heat exchange; and 10% of the fresh air enters the bypass channel for pressure balancing. Furthermore, the rotor speed of the rotary heat exchanger is positively correlated with relative humidity. When the relative humidity is below a preset humidity threshold (e.g., 60%), the rotor speed decreases to the minimum energy-saving speed (e.g., 8 rpm); when the relative humidity is not below the preset humidity threshold, the rotor speed is set to the base speed (e.g., 10 rpm) and adjusted according to the relative humidity: for every 10% increase in relative humidity, the rotor speed increases by 0.5 rpm until it reaches the maximum speed (e.g., 15 rpm).

[0083] Understandably, in rotary mode, the heat pipe heat exchanger of the air conditioning system is in a closed or minimum power standby state, thus making the sensible heat efficiency of the entire air conditioning system >78%.

[0084] (2) Optimize the hybrid mode

[0085] If the predicted probability P is less than the first risk threshold and the outside temperature is in the second temperature range, the PLC controller will control the air conditioning system to execute an optimized hybrid mode with the rotary mode as the main mode and the heat pipe mode as the auxiliary mode.

[0086] Specifically, when the air conditioning system has a low risk of frosting, if -3℃ ≤ T out <3℃, then due to the outside temperature T out The temperature is 3°C below the safety margin. Therefore, while maximizing energy efficiency, it is also necessary to ensure basic sensible heat protection. This allows the air conditioning system to operate in an optimized hybrid mode, with the rotary wheel mode as the main mode and the heat pipe mode as the auxiliary mode (i.e., optimized hybrid mode). This prioritizes the efficient total heat recovery capability of the rotary wheel and significantly reduces the operating energy consumption of the air conditioning system.

[0087] In this optimized hybrid mode, the rotary heat exchanger and heat pipe heat exchanger of the air conditioning system distribute energy according to the following formula:

[0088] Q total＝ α·Q 热管 + (1-α)·Q 转轮 ;

[0089] Where α=β P and β are weighting factors based on the external temperature, and the value of β ranges from 0.2 to 0.4. For example: T out At 0℃, β takes the value of 0.2, T out At -3℃, β is 0.4. Therefore, under low frost risk, a smaller β (usually 0.2 to 0.4) is ensured, allowing the rotor to bear more latent heat load.

[0090] Furthermore, in the optimized mixing mode, the rotor speed is reduced to 6-8 rpm, and the P-value is checked every 15 minutes to dynamically adjust the energy. In addition, the heat pipe heat exchanger provides approximately 500-800W of heat recovery as a basic sensible heat guarantee.

[0091] (3) Stable mixing mode

[0092] If the predicted probability P is not less than the first risk threshold and not greater than the second risk threshold (0.7 in this embodiment), and the outside temperature is in the third temperature range, then the air conditioning system is controlled by the PLC controller to execute a stable mixed mode (i.e., temperature mixed mode) with the rotary mode as the auxiliary mode and the heat pipe mode as the main mode.

[0093] Specifically, if 0.3 ≤ P ≤ 0.7, and -10℃ < T out If the temperature is below 5°C, the risk of frost formation in the air conditioning system is considered moderate. Therefore, when there is a potential risk of frost formation, the heat pipes provide a stable sensible heat recovery base and share most of the cooling load, while the rotor handles latent heat to a limited extent, achieving the best balance between safety and efficiency.

[0094] In this stable mixing mode, the rotary heat exchanger and heat pipe heat exchanger of the air conditioning system distribute energy in the following manner:

[0095] Q total =α·Q 热管 + (1-α)·Q 转轮 ;

[0096] Where α=γ P and γ are the heat pipe load adjustment coefficients, which are positively correlated with the value of P. For example, when P = 0.5, α ≈ 0.6, and when P = 0.7, α ≈ 0.75. The higher the value of P, the greater the load on the heat pipe. It can be understood that in a stable mixing mode, the heat pipe acts as the main sensible heat recovery unit and dynamically adjusts the load according to α.

[0097] Furthermore, preferably, based on α, it can be finely adjusted according to the external temperature Tout. When Tout < 0℃, based on γ... Based on the α calculated from P, α is slightly increased by 0.05 for every degree Celsius increase. In addition, the rotor speed is reduced to 4-6 rpm, and the P value is checked every 10 minutes. When RH rises but P does not reach a high risk level, the dehumidification speed can be temporarily increased (for example, the rotor speed is slightly increased by 0.2 rpm for every 10% increase in relative humidity RH).

[0098] (4) Heat pipe mode

[0099] If the predicted probability P is greater than the second risk threshold or the outside temperature is in the fourth temperature range, the air conditioning system will be controlled by the PLC controller to execute the heat pipe mode.

[0100] Specifically, if P > 0.7 (high-risk frosting) or Tout ≤ -10℃ (extreme cold hard protection), it indicates that the air conditioning system has a high risk of frosting or is in a high-risk frosting environment. Therefore, it is necessary to immediately avoid frosting on the heat pipe and do everything possible to ensure the safe and efficient operation of the heat pipe to prevent system performance failure. Among these measures, predictive switching with P > 0.7 is the key to solving the lag in traditional control.

[0101] In this heat pipe mode, 100% of the fresh air from the air conditioning system enters the heat pipe heat exchanger for sensible heat exchange; and the set power of the heat pipe heat exchanger is negatively correlated with the ambient temperature. For example: the set power based on the ambient temperature is: T out >-5℃, 3W / m;-10℃<T out ≤-5℃, 5W / m; T out ≤-10℃, 8W / m.

[0102] Furthermore, this heat pipe heat exchanger periodically switches between hot and cold ends based on a predicted probability P and the ambient temperature. Specifically, if P > 0.8 or T < 0.8, the heat pipe heat exchanger will switch between hot and cold ends periodically. out If the temperature is less than -8℃, the hot and cold ends should be switched periodically every 60 minutes; otherwise, the hot and cold ends should be switched periodically every 120 minutes.

[0103] Furthermore, if frost is detected, the zoned heating wires are activated as needed based on the frost thickness and distribution calculated using the MPDT (Matrix Phase Detection Thermal Imaging) algorithm. The power density of each zoned heating wire is dynamically set based on the predicted probability P and the rate of change of the predicted probability P, ΔP / Δt, and the power density ranges from 2 to 4 W / cm². 2 .

[0104] It is understood that in this embodiment, steps S21 to S23 described above can adapt to environmental changes in real time, improve the adaptability of the air conditioning system, reduce the risk of frosting, and extend the service life of the equipment. Furthermore, it can balance sensible and latent heat recovery according to different operating conditions, maximizing energy recovery efficiency and reducing operating costs.

[0105] S30: Controls the air conditioning system to execute the current operating mode.

[0106] In one embodiment of the present invention, after the current operating mode of the air conditioning system is output through step S20, the PLC controller needs to execute different strategies based on the P value. Specifically:

[0107] ① High risk (P>0.7): Switch to antifreeze mode. Specific measures include switching the heat pipe unit to reverse heat circulation to prevent condensate from freezing, and heating key areas of the evaporator (such as areas with thick frost) according to a power density of 2W / cm², while reducing the heating power of other areas proportionally (e.g., 0.5~1.5W / cm²) based on the predicted probability P and the frost distribution.

[0108] ②Low risk (P < 0.3): Improve operating efficiency. Run the rotary unit at full speed (such as a heat recovery rotary unit) to improve fresh air exchange efficiency and reduce energy consumption.

[0109] ③Medium risk (0.3 ≤ P ≤ 0.7): Maintain the current status, but continue to monitor in case of emergencies.

[0110] Furthermore, after the air conditioning system has been running in the current operating mode for a preset duration (e.g., 2 hours), it returns to step S10 above to collect data for a new round, thereby completing the switching of the operating mode and forming a complete closed-loop control.

[0111] Furthermore, in the above embodiments, preferably, the following steps are also included:

[0112] S40: Alarm notification.

[0113] Specifically, if the predicted probability P is greater than or equal to 0.7, the air conditioning system will trigger an alarm procedure to notify maintenance personnel via SMS, email, or other means. At the same time, a warning message will be displayed on the human-machine interface, and historical data will be recorded for subsequent analysis.

[0114] Understandably, this alarm function ensures that operators can intervene when necessary to prevent air conditioning system malfunctions or performance degradation caused by frost.

[0115] Second Embodiment

[0116] like Figure 3 As shown, based on the first embodiment described above, the second embodiment of the present invention also provides a heat recovery control device for an air conditioning system, including a data acquisition unit 1, an edge computing unit 2, a logic control unit 3, and an execution unit 4.

[0117] The data acquisition unit 1 includes the aforementioned sensors, which are used to acquire external environmental parameters and the current operating parameters of the air conditioning system, thereby serving as input for the preset model.

[0118] Edge computing unit 2 is connected to data acquisition unit 1 and has a built-in preset model for preprocessing environmental and operating parameters to extract multidimensional feature vectors; and inputs the multidimensional feature vectors into the preset model to output the predicted probability P of frost formation in the air conditioning system.

[0119] The logic control unit 3 is connected to the edge computing unit 2 and the data acquisition unit 1 to output the current operating mode of the air conditioning system based on external environmental parameters and the predicted probability P of frost formation in the air conditioning system, through a preset mode switching logic. It is understood that the logic control process of this logic control unit 3 is the same as step S23 described above, and will not be repeated here.

[0120] The execution unit 4 is connected to the logic control unit 3 to control the air conditioning system to execute the current operating mode via the PLC controller based on the control instructions output by the logic control unit. Specifically, when the PLC controller receives a control instruction, it divides the control instruction into a rotor control instruction and a heat pipe control instruction, thereby independently controlling the rotor and heat pipe respectively, and thus controlling the air conditioning system to execute the current operating mode.

[0121] It is understood that the functions and connections of the above-mentioned modules are only one specific implementation of the heat recovery method in the first embodiment. In other embodiments, the functions and connections of the modules can be adapted as needed, and no specific limitations are made here.

[0122] Third Embodiment

[0123] like Figure 4 As shown, based on the above-described heat recovery control method for air conditioning systems, the third embodiment of the present invention further provides a heat recovery control device for air conditioning systems. This heat recovery control device includes one or more processors and a memory. The memory is coupled to the processor and is used to store one or more programs. When the program is executed by the processor, the processor implements the heat recovery control method for air conditioning systems described in the above embodiments.

[0124] The processor controls the overall operation of the heat recovery control device to complete all or part of the steps of the heat recovery control method for the air conditioning system described above. The processor can be a central processing unit (CPU), graphics processing unit (GPU), field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), digital signal processing (DSP) chip, etc. The memory stores various types of data to support the operation of the heat recovery control device. This data may include, for example, instructions for any application or method operating on the heat recovery control device, and application-related data. The memory can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, etc.

[0125] In one exemplary embodiment, the heat recovery control device may be implemented by a computer chip or physical entity, or by a product with certain functions, to implement the aforementioned heat recovery control method for an air conditioning system and achieve the same technical effect as the method described above. A typical embodiment is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interface device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0126] In another exemplary embodiment, the present invention also provides a computer-readable storage medium including program instructions that, when executed by a processor, implement the steps of the heat recovery control method for an air conditioning system in any of the above embodiments. For example, the computer-readable storage medium may be the memory including the program instructions described above, which can be executed by a processor of a heat recovery control device to complete the heat recovery control method for an air conditioning system described above and achieve the same technical effects as the method described above.

[0127] In summary, the heat recovery control method and apparatus for air conditioning systems provided by the embodiments of the present invention have the following beneficial effects:

[0128] (1) Using this invention, the air conditioning system can switch between multiple operating modes according to different operating conditions, thereby performing different degrees of sensible heat recovery and latent heat recovery. Thus, the contradiction between the traditional single heat recovery mode and the latent heat recovery is difficult to balance, and the overall heat recovery efficiency of the air conditioning system is improved, the frosting rate is reduced, and the maintenance cost is reduced.

[0129] (2) It can control the air conditioning system to operate in the most energy-efficient mode according to changes in the environment, thereby solving the problems of lag and high energy consumption of traditional defrosting control strategies.

[0130] (3) It can monitor the frosting of the air conditioning system in real time, thereby enabling early prediction and zone diagnosis of frosting, and avoiding the problem of sudden efficiency drop caused by heat exchanger frosting in low temperature and high humidity environment.

[0131] It should be noted that the above embodiments are merely illustrative examples. The technical solutions of each embodiment can be combined, and all are within the protection scope of this invention.

[0132] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0133] The above provides a detailed description of the heat recovery control method and apparatus for air conditioning systems provided by this invention. Any obvious modifications made by those skilled in the art without departing from the essence of this invention will constitute an infringement of the patent rights of this invention and will incur corresponding legal liability.

Claims

1. A heat recovery control method for an air conditioning system, characterized in that... Includes the following steps: Acquire external environmental parameters and the current operating parameters of the air conditioning system; The environmental and operational parameters are preprocessed to extract multidimensional feature vectors; wherein the multidimensional feature vectors include at least the evaporator surface temperature change rate, ambient humidity gradient, airflow velocity, evaporator surface temperature, historical frost matching degree, and device operating time. The multidimensional feature vector is input into a preset model to output the predicted probability P of frost formation in the air conditioning system. The predicted probability P is compared with a preset risk threshold to determine the current frost risk of the air conditioning system; Based on the external environmental parameters and the current frosting risk of the air conditioning system, the current operating mode of the air conditioning system is determined according to a preset mode switching logic; wherein, the current operating mode is one of rotary mode, heat pipe mode, and hybrid mode; in rotary mode, the air conditioning system recovers latent heat and sensible heat through the operation of the rotary heat exchanger; in heat pipe mode, the air conditioning system recovers sensible heat through the operation of the heat pipe heat exchanger; in hybrid mode, the air conditioning system recovers latent heat and sensible heat through the combined operation of the rotary heat exchanger and the heat pipe heat exchanger; At preset intervals, the predicted probability P of frost formation on the air conditioning system is recalculated, and the current operating mode of the air conditioning system is switched in real time in conjunction with external environmental parameters.

2. The heat recovery control method as described in claim 1, characterized in that... The preset mode switching logic specifically includes: If the predicted probability P is less than the first risk threshold and the outside temperature is within the first temperature range, the air conditioning system is controlled by the PLC controller to execute the rotary mode. If the predicted probability P is less than the first risk threshold and the outside temperature is in the second temperature range, the air conditioning system is controlled by the PLC controller to execute an optimized hybrid mode with the rotary mode as the main mode and the heat pipe mode as the auxiliary mode. If the predicted probability P is not less than the first risk threshold and not greater than the second risk threshold, and the outside temperature is in the third temperature range, then the air conditioning system is controlled by the PLC controller to execute a stable hybrid mode with the rotary mode as the auxiliary mode and the heat pipe mode as the main mode. If the predicted probability P is greater than the second risk threshold or the outside temperature is in the fourth temperature range, the air conditioning system is controlled by the PLC controller to execute the heat pipe mode.

3. The heat recovery control method as described in claim 2, characterized in that: In the optimized hybrid mode where the rotary mode is primary and the heat pipe mode is secondary, the rotary heat exchanger and the heat pipe heat exchanger of the air conditioning system distribute energy in the following manner: Q total＝ α·Q 热管 + (1-α)·Q 转轮 ; Where α=β P and β are weighting factors based on the external temperature, and the value of β ranges from 0.2 to 0.

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4. The heat recovery control method as described in claim 2, characterized in that: In the stable hybrid mode where the rotary mode is secondary and the heat pipe mode is primary, the rotary heat exchanger and the heat pipe heat exchanger of the air conditioning system distribute energy in the following manner: Q total＝ α·Q 热管 + (1-α)·Q 转轮 ; Where α=γ P and γ are heat pipe load adjustment coefficients, which are positively correlated with the value of P.

5. The heat recovery control method as described in claim 2, characterized in that: In the rotary mode, 90% of the fresh air from the air conditioning system enters the rotary heat exchanger for sensible and latent heat exchange; and 10% of the fresh air from the air conditioning system enters the bypass channel for pressure balancing. The rotor speed of the rotary heat exchanger is positively correlated with the relative humidity. When the relative humidity is lower than a preset humidity threshold, the rotor speed is reduced to the minimum energy-saving speed. When the relative humidity is not lower than the preset humidity threshold, the rotor speed is based on the base speed, and the rotor speed is increased by 0.5 rpm for every 10% increase in relative humidity, until it is increased to the maximum speed.

6. The heat recovery control method as described in claim 2, characterized in that: In the heat pipe mode, 100% of the fresh air from the air conditioning system enters the heat pipe heat exchanger for sensible heat exchange; and the set power of the heat pipe heat exchanger is negatively correlated with the ambient temperature. The heat pipe heat exchanger periodically switches between hot and cold ends based on the predicted probability P and the ambient temperature.

7. The heat recovery control method as described in claim 6, characterized in that: If frost is detected, the zoned heating wires are activated as needed based on the frost thickness and distribution calculated using the MPDT algorithm. The power density of each zone heating wire is dynamically set based on the predicted probability P and the rate of change of the predicted probability P, ΔP / Δt, and the power density ranges from 2 to 4 W / cm². 2 .

8. The heat recovery control method as described in claim 1, characterized in that: If the predicted probability P is greater than the second risk threshold, an alarm procedure is triggered to send alarm information to maintenance personnel and display the alarm information through a human-machine interface.

9. A heat recovery control device for an air conditioning system, implementing the heat recovery control method according to any one of claims 1 to 8, characterized in that... include: The data acquisition unit is used to acquire external environmental parameters and the current operating parameters of the air conditioning system. An edge computing unit is connected to the data acquisition unit and has a built-in preset model for preprocessing the environmental parameters and operating parameters to extract multi-dimensional feature vectors; and inputs the multi-dimensional feature vectors into the preset model to output the predicted probability P of frost formation in the air conditioning system. The logic control unit is connected to the edge computing unit and the data acquisition unit to output the current operating mode of the air conditioning system based on the external environmental parameters and the predicted probability P of the air conditioning system frosting through a preset mode switching logic. An execution unit, connected to the logic control unit, controls the air conditioning system to execute the current operating mode via a PLC controller based on the control commands output by the logic control unit.

10. A heat recovery control device for an air conditioning system, characterized in that... It includes a processor and a memory, wherein the processor reads a computer program from the memory for implementing the heat recovery control method according to any one of claims 1 to 8.

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