Heat recovery control method and device for air conditioning system

Abstract

The invention discloses a heat recovery control method and device for an air conditioning system. The heat recovery control method comprises the following steps that external environment parameters and current operation parameters of the air conditioning system are obtained; the environment parameters and the operation parameters are preprocessed to extract multi-dimensional feature vectors; inputting the multi-dimensional feature vector into a preset model so as to output a prediction probability of frosting of the air conditioning system; the prediction probability is compared with a preset risk threshold value, so that the current frosting risk of the air conditioning system is obtained; based on the external environment parameters and the current frosting risk of the air conditioning system, the current operation mode of the air conditioning system is output through preset mode switching logic; and the prediction probability of frosting of the air conditioning system is recalculated every preset time interval, and the current operation mode of the air conditioning system is switched in real time in combination with the external environment parameters. By means of the air conditioning system, the air conditioning system can be switched among multiple operation modes according to different working conditions, and the overall heat recovery efficiency is improved.

Description

Technical Field

[0001] The present 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 technical field of air conditioning. Background Art

[0002] As building energy-saving standards continue to improve, the requirement for heat recovery efficiency has increased from 50% to over 75%. However, actual operational data shows that due to environmental factors, the average annual heat recovery efficiency of existing equipment is generally below 70%. This efficiency degradation is particularly severe in northern regions during winter.

[0003] To address the problem of frost, the industry currently uses three main solutions: the first is timed countercurrent defrosting, which melts the frost layer by periodically switching the airflow direction, but this method causes approximately 30% heat loss; the second is electric heating defrosting, which uses resistance wire installed on the surface of the heat exchanger for heating and defrosting. Although effective, it consumes a lot of energy and poses safety risks; 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 (EEP).

[0004] Existing defrosting methods generally have three major defects: first, the response is delayed, and the frosting trend cannot be predicted in advance. The control dimension is single, and currently 82% of the equipment still relies only on the single parameter of temperature for judgment; second, the energy consumption is too high, and there is an "energy efficiency black hole" phenomenon, that is, the defrosting energy consumption accounts for 8% to 12% of the total energy consumption of the air-conditioning system, but can only solve about 35% of the actual frosting problems; third, the control strategy is extensive, and a fixed threshold is used for defrosting, which can easily cause "over-defrosting" or "insufficient defrosting", affecting the stability and energy efficiency performance of the air-conditioning system. Summary of the Invention

[0005] The primary technical problem to be solved by the present invention is to provide a heat recovery control method for an air-conditioning system.

[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] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

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

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

[0010] Preprocessing the environmental parameters and operating parameters to extract a multidimensional feature vector; wherein the multidimensional feature vector includes at least the evaporator surface temperature change rate, the ambient humidity gradient, the airflow velocity, the evaporator surface temperature, the historical frost matching degree, and the device operation time;

[0011] Inputting the multidimensional feature vector into a preset model to output a predicted probability P of frost formation in the air-conditioning system;

[0012] Comparing the predicted probability P 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 frost 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 a rotary mode, a heat pipe mode, and a hybrid mode; in the rotary mode, the air-conditioning system recovers system latent heat and sensible heat through the operation of a rotary heat exchanger; in the heat pipe mode, the air-conditioning system recovers system sensible heat through the operation of a heat pipe heat exchanger; in the hybrid mode, the air-conditioning system recovers system latent heat and sensible heat through the combined operation of a rotary heat exchanger and a heat pipe heat exchanger;

[0014] The predicted probability P of frost formation in the air-conditioning system is recalculated at every preset time interval, and the current operating mode of the air-conditioning system is switched in real time in combination with external environmental parameters.

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

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

[0017] If the predicted probability P is less than the first risk threshold and the outside temperature is within the second temperature range, the PLC controller controls 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;

[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 within the third temperature range, the PLC controller controls the air conditioning system 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 in which 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, β is a weight factor based on the external temperature, and the value range of β is 0.2 to 0.4.

[0023] Preferably, in a stable hybrid mode in which the rotary mode is auxiliary 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, γ is the heat pipe load adjustment coefficient, which is positively correlated with the size of P value.

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

[0027] Among them, the rotor speed of the rotary heat exchanger is positively correlated with the relative humidity; when the relative humidity is lower than the 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 basic speed, and the rotor speed increases by 0.5 rpm for every 10% increase in relative humidity until it reaches the maximum speed.

[0028] Preferably, in the heat pipe mode, 100% of the fresh air of 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 outside temperature;

[0029] The heat pipe heat exchanger periodically switches between the hot and cold ends according to the predicted probability P and the external temperature.

[0030] Preferably, if a frost layer is detected, the zone heating wires are activated as needed based on the frost layer 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 change rate of the predicted probability P ΔP / Δt, and the power density range is 2 to 4 W / cm 2 .

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

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

[0034] Data acquisition unit, used to obtain external environmental parameters and current operating parameters of the air-conditioning system;

[0035] an edge computing unit, connected to the data acquisition unit and having a built-in preset model, for preprocessing the environmental parameters and operating parameters to extract a multidimensional feature vector; and inputting the multidimensional feature vector into the preset model to output a predicted probability P of frost formation in the air-conditioning system;

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

[0037] An execution unit is connected to the logic control unit to control the air-conditioning system to execute a current operation mode through a PLC controller based on a control instruction output by the logic control unit.

[0038] According to a third aspect of an embodiment 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 to implement the above-mentioned heat recovery control method.

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

[0040] (1) The present invention enables the air conditioning system to switch between multiple operating modes according to different operating conditions, thereby recovering sensible heat and latent heat to varying degrees. This solves the problem of the traditional single heat recovery mode being unable to recover both sensible and latent heat, improves the overall heat recovery efficiency of the air conditioning system, reduces the frost rate, and reduces maintenance costs.

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

[0042] (3) It can monitor the frosting condition of the air-conditioning system in real time, thereby realizing early prediction and zoning diagnosis of frosting, and avoiding the efficiency drop caused by frosting of the heat exchanger in low temperature and high humidity environment. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0044] Figure 2 This is a logic flow chart for determining the current operating mode of the air-conditioning system in the first embodiment of the present invention;

[0045] Figure 3 A structural diagram of a heat recovery control device for an air-conditioning system provided by a 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 DESCRIPTION

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

[0048] The technical concept of this embodiment of the invention is to predict the probability P of frost formation in the air conditioning system, combine it with external environmental parameters, and use a PLC controller to control the air conditioning system to switch between multiple operating modes based on preset mode switching logic. This allows the air conditioning system to recover sensible and latent heat to varying degrees according to different operating conditions. This significantly improves the heat recovery efficiency of the air conditioning system, reduces the frost formation rate, and reduces maintenance costs.

[0049] First embodiment

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

[0051] S10: Data collection.

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

[0053] S30: Control the air conditioning system to execute the current operation mode.

[0054] In step S30 , after the air-conditioning system executes the current operation mode for a preset period of time, the system returns to step S10 to re-collect data, thereby forming a complete closed-loop control.

[0055] Below, each step is described in detail:

[0056] S10: Data collection.

[0057] In this embodiment, it is necessary to obtain external environmental parameters and the current operating parameters of the air conditioning system. Specifically, a multi-source sensor network is deployed to collect these parameters. Environmental parameters include at least temperature, humidity, and PM2.5 concentration, while operating parameters include at least device status information, air duct pressure differential, and heat exchanger surface temperature gradient, providing raw input for subsequent data processing.

[0058] As shown in the table below, the sensors in this embodiment are as follows:

[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 of air Micro differential pressure transmitter pressure difference 0~500Pa Reflect the change of air duct resistance and judge the degree of filter blockage 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 connection to a master control unit or edge node, enabling high-speed and stable data transmission. These parameters form the basic data set for air conditioning system operation, which is used for subsequent frost warnings, 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 pre-processed and transmitted to the edge computing unit, and its built-in preset model will perform real-time analysis to determine the current operating mode of the air-conditioning system and generate corresponding control instructions to drive the lower-level equipment to perform corresponding operations.

[0063] In this embodiment, the edge computing unit, an embedded computing device (e.g., an NVIDIA Jetson or Raspberry Pi with an AI accelerator chip), is responsible for preprocessing raw sensor data, extracting features, and performing model inference, thereby reducing cloud computing latency and improving response speed. Furthermore, the pre-set model uses an LSTM (Long Short-Term Memory) model, trained using a training set consisting of historical data on temperature and humidity, pressure differentials, and fan frequency. This model outputs a predicted probability P of frost formation in the air conditioning system within the next 1-2 hours.

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

[0065] S21: Data preprocessing.

[0066] By preprocessing the above environmental parameters and operating parameters, a multi-dimensional feature vector is extracted. The multi-dimensional 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 Air flow velocity, used to reflect the impact of frosting rate;

[0070] ④ Evaporator surface temperature, used to directly correlate with frost risk;

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

[0072] ⑥ The operating time of the air conditioning system is used to determine the possibility of frost.

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

[0074] The multidimensional feature vectors are input into a pre-built LSTM model to accurately predict the frost probability P, with a value between 0 and 1. As can be seen, the LSTM model excels at processing time series data and can capture long-term dependencies, making it suitable for predicting frost trends.

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

[0076] Specifically, after the predicted probability P of frost formation in the air conditioning system is outputted in step S22, the external environmental parameters (specifically the external temperature T out ), the current operating mode of the air-conditioning system is determined through the preset mode switching logic.

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

[0078] like Figure 2 As shown, the following details how to determine the current operating mode of the air conditioning system:

[0079] (1) Wheel mode.

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

[0081] Specifically, when the predicted probability P is less than 0.3, it means that the air conditioning system has a low risk of frost formation. At this time, the optimal energy efficiency is the preferred operating condition. out When the temperature is ≥3℃ (safety margin), the rotary heat exchanger operates at full load, thus maximizing the total heat recovery (sensible heat + latent heat).

[0082] In rotary mode, 90% of the air conditioning system's fresh air enters the rotary heat exchanger for sensible and latent heat exchange; 10% of the air conditioning system's fresh air enters the bypass channel for pressure balancing. Furthermore, the rotary heat exchanger's rotor speed is positively correlated with relative humidity. When the relative humidity falls below a preset threshold (e.g., 60%), the rotor speed is reduced to a minimum energy-saving speed (e.g., 8 rpm). When the relative humidity is at or above the preset threshold, the rotor speed is set to a base speed (e.g., 10 rpm) and then adjusted based on 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] It is understandable that in the wheel mode, the heat pipe heat exchanger of the air-conditioning system is in a closed or minimum power standby state, so that the sensible heat efficiency of the entire air-conditioning system is greater than 78%.

[0084] (2) Optimize the mixing 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 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.

[0086] Specifically, when the air conditioning system is at low frost risk, if -3℃≤T out <3℃, then due to the external temperature T out It is 3°C lower than the safety margin. Therefore, while maximizing the energy efficiency, it is also necessary to take into account the basic sensible heat guarantee, so that the air-conditioning system executes the optimized hybrid mode with the rotor mode as the main and the heat pipe mode as the auxiliary (i.e., the optimized hybrid mode), thereby giving priority to the efficient full heat recovery capability of the rotor and significantly reducing the operating energy consumption of the air-conditioning system.

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

[0088] Q total＝ α·Q 热管 + (1-α)·Q 转轮 ;

[0089] Where α = β P, β is a weighting factor based on the external temperature, and the value range of β is 0.2 to 0.4. For example: T out =0℃, β is 0.2, T out = -3°C, β is set to 0.4. Therefore, under low frost risk, β is kept small (usually 0.2 to 0.4), allowing the runner to bear more latent heat load.

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

[0091] (3) Stable hybrid 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, the air-conditioning system is controlled by the PLC controller to execute a stable hybrid mode (i.e., temperature hybrid 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 When the temperature is less than 5°C, the air conditioning system has a moderate risk of frost. Therefore, when there is a potential risk of frost, the heat pipe provides a stable sensible heat recovery foundation and shares most of the cooling load, while the rotor processes latent heat to a limited extent, achieving the best balance between safety and efficiency.

[0094] In this stable hybrid mode, the energy distribution between the rotary heat exchanger and the heat pipe heat exchanger of the air conditioning system is as follows:

[0095] Q total =α·Q 热管 + (1-α)·Q 转轮 ;

[0096] Where α = γ P, γ is the heat pipe load adjustment coefficient, which is positively correlated with the P value. For example, when P = 0.5, α ≈ 0.6, and when P = 0.7, α ≈ 0.75. The higher the P value, the greater the load borne by the heat pipe. Understandably, in stable hybrid mode, the heat pipe serves as the primary sensible heat recovery unit, dynamically adjusting its load based on α.

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

[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 is controlled by the PLC controller to execute the heat pipe mode.

[0100] Specifically, if P > 0.7 (high risk of frost) or Tout ≤ -10°C (extreme cold hard protection), the air conditioning system is at a high risk of frost or is in a high-risk environment. Therefore, rotor frost must be immediately avoided to ensure safe and efficient operation of the heat pipes and prevent system performance degradation. Predictive switching when P > 0.7 is key to addressing the lag inherent in traditional control.

[0101] In this heat pipe mode, 100% of the fresh air of 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 outside temperature. For example: set the power according to the outside temperature: T out >-5℃, 3W / m;-10℃<T out ≤-5℃,5W / m2;T out ≤-10℃,8W / m.

[0102] Furthermore, the heat pipe heat exchanger periodically switches the hot and cold ends according to the predicted probability P and the outside temperature. Specifically, if P>0.8 or T out If the temperature is less than -8℃, the hot and cold ends are switched periodically every 60 minutes; in other cases, the hot and cold ends are switched periodically every 120 minutes.

[0103] In addition, if frost is detected, the MPDT (Matrix Phase Detection Thermography) algorithm calculates the thickness and distribution of the frost layer and activates the heating wires in each zone as needed. 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 range is 2 to 4W / cm 2 .

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

[0105] S30: Control the air conditioning system to execute the current operation mode.

[0106] In one embodiment of the present invention, after the current operating mode of the air-conditioning system is outputted in step S20, different strategies need to be executed according to the P value by the PLC controller. Specifically, the strategies are as follows:

[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) at a power density of 2 W / cm². Heating power in other areas is proportionally reduced (for example, 0.5-1.5 W / cm²) based on the predicted probability P and frost distribution.

[0108] ② Low risk (P < 0.3): Improve operational efficiency. Run rotor units (such as heat recovery rotors) at full speed to increase fresh air exchange efficiency and reduce energy consumption.

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

[0110] Furthermore, when the air conditioning system executes the current operation mode for a preset time (for example, 2 hours), it returns to the above step S10 to perform a new round of data collection, thereby completing the switching of the operation mode to form a complete closed-loop control.

[0111] In addition, in the above embodiment, preferably, the following steps are further included:

[0112] S40: Alarm prompt.

[0113] Specifically, if the predicted probability P is greater than >0.7, the air conditioning system will trigger an alarm program to notify the operation and maintenance personnel through text messages, emails, etc., while displaying a warning message on the human-machine interface and recording historical data for subsequent analysis.

[0114] It is understandable that this alarm prompt function ensures that the operator can intervene when necessary to avoid air conditioning system failure or performance degradation due to frost.

[0115] Second embodiment

[0116] like Figure 3 As shown, based on the above-mentioned first embodiment, the second embodiment of the present invention further 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 above-mentioned multiple sensors, which are used to obtain external environmental parameters and current operating parameters of the air-conditioning system, so as to serve as inputs of the preset model.

[0118] The edge computing unit 2 is connected to the data acquisition unit 1 and has a built-in preset model for preprocessing environmental parameters and operating parameters to extract a multidimensional feature vector; and the multidimensional feature vector is input into the preset model to output the predicted probability P of frost 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 through a preset mode switching logic based on external environmental parameters and the predicted probability P of frost formation in the air conditioning system. It is understood that the logic control process of the logic control unit 3 is the same as that of step S23 above and will not be repeated here.

[0120] Execution unit 4 is connected to logic control unit 3 and controls 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, upon receiving the control instructions, the PLC controller divides the control instructions into rotor control instructions and heat pipe control instructions, thereby independently controlling the rotor and heat pipes, thereby controlling the air conditioning system to execute the current operating mode.

[0121] It can be understood that the functions and connection relationships of the above-mentioned module units are only a specific implementation method for realizing the heat recovery method in the above-mentioned first embodiment. In other embodiments, the functions and connection relationships of the module units can be adaptively adjusted as needed, and no specific limitation is made here.

[0122] Third embodiment

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

[0124] The processor is used to control the overall operation of the heat recovery control device to complete all or part of the steps of the heat recovery control method for an air conditioning system. The processor may be a central processing unit (CPU), a graphics processing unit (GPU), a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a digital signal processing (DSP) chip, etc. The memory is used to store 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, as well as application-related data. The memory may be implemented by 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 an exemplary embodiment, the heat recovery control device can be implemented as a computer chip or entity, or as a product with certain functions, to implement the aforementioned heat recovery control method for an air conditioning system and achieve the same technical effects as the aforementioned method. A typical embodiment is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, an in-vehicle human-computer interaction 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 a combination of any of these devices.

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

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

[0128] (1) The present invention enables the air conditioning system to switch between multiple operating modes according to different operating conditions, thereby recovering sensible heat and latent heat to varying degrees. This solves the problem of the traditional single heat recovery mode being unable to recover both sensible and latent heat, improves the overall heat recovery efficiency of the air conditioning system, reduces the frost rate, and reduces maintenance costs.

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

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

[0131] It should be noted that the above embodiments are merely examples, and the technical solutions of the various embodiments may be combined and are all within the scope of protection of the present 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 the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0133] The above describes in detail the heat recovery control method and device for an air conditioning system provided by the present invention. Any obvious modification to the present invention without departing from its essence would constitute an infringement of the present invention's patent rights and would incur corresponding legal liability.

Claims

1. A heat recovery control method for an air conditioning system, characterized in that The steps include: Obtain external environmental parameters and current operating parameters of the air-conditioning system; Preprocessing the environmental parameters and operating parameters to extract a multidimensional feature vector; wherein the multidimensional feature vector includes at least the evaporator surface temperature change rate, the ambient humidity gradient, the airflow velocity, the evaporator surface temperature, the historical frost matching degree, and the device operation time; Inputting the multidimensional feature vector into a preset model to output a predicted probability P of frost formation in the air-conditioning system; Comparing the predicted probability P 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 frost 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 a rotary mode, a heat pipe mode, and a hybrid mode; in the rotary mode, the air-conditioning system recovers system latent heat and sensible heat through the operation of a rotary heat exchanger; in the heat pipe mode, the air-conditioning system recovers system sensible heat through the operation of a heat pipe heat exchanger; in the hybrid mode, the air-conditioning system recovers system latent heat and sensible heat through the combined operation of a rotary heat exchanger and a heat pipe heat exchanger; The predicted probability P of frost formation in the air-conditioning system is recalculated at every preset time interval, and the current operating mode of the air-conditioning system is switched in real time in combination with external environmental parameters.

2. The heat recovery control method according to claim 1, characterized in that The preset mode switching logic specifically includes: If the predicted probability P is less than a first risk threshold and the outside temperature is within a first temperature range, controlling the air conditioning system to execute a rotary mode through a PLC controller; If the predicted probability P is less than the first risk threshold and the outside temperature is within the second temperature range, the PLC controller controls 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; 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 within the third temperature range, the PLC controller controls the air conditioning system 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 according to claim 2, wherein: In the optimized hybrid mode with the rotary mode as the main mode and the heat pipe mode as the auxiliary mode, 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, β is a weight factor based on the external temperature, and the value range of β is 0.2 to 0.

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4. The heat recovery control method according to claim 2, wherein: In the stable hybrid mode with the rotary mode as auxiliary and the heat pipe mode as main, 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, γ is the heat pipe load adjustment coefficient, which is positively correlated with the size of P value.

5. The heat recovery control method according to claim 2, wherein: In the rotary mode, 90% of the fresh air of the air conditioning system enters the rotary heat exchanger for sensible heat and latent heat exchange; and 10% of the fresh air of the air conditioning system enters the bypass channel for pressure balance; Among them, the rotor speed of the rotary heat exchanger is positively correlated with the relative humidity; when the relative humidity is lower than the 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 basic speed, and the rotor speed increases by 0.5 rpm for every 10% increase in relative humidity until it reaches the maximum speed.

6. The heat recovery control method according to claim 2, wherein: In the heat pipe mode, 100% of the fresh air of 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 outside temperature; The heat pipe heat exchanger periodically switches between the hot and cold ends according to the predicted probability P and the external temperature.

7. The heat recovery control method according to claim 6, wherein: If frost is detected, the zone heating wires are activated as needed based on the frost thickness and distribution calculated by the MPDT algorithm; The power density of each zone heating wire is dynamically set based on the predicted probability P and the change rate of the predicted probability P ΔP / Δt, and the power density range is 2 to 4 W / cm 2 .

8. The heat recovery control method according to claim 1, wherein: If the predicted probability P is greater than the second risk threshold, an alarm program is triggered to send an alarm message to maintenance personnel, and the alarm message is displayed 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: Data acquisition unit, used to obtain external environmental parameters and current operating parameters of the air-conditioning system; an edge computing unit, connected to the data acquisition unit and having a built-in preset model, for preprocessing the environmental parameters and operating parameters to extract a multidimensional feature vector; and inputting the multidimensional feature vector into the preset model to output a predicted probability P of frost formation in the air-conditioning system; a logic control unit connected to the edge computing unit and the data acquisition unit to output the current operating mode of the air-conditioning system through a preset mode switching logic based on the external environmental parameters and the predicted probability P of frost on the air-conditioning system; An execution unit is connected to the logic control unit to control the air-conditioning system to execute a current operation mode through a PLC controller based on a control instruction output by the logic control unit.

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

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