Air conditioner control method during failure of room temperature sensor, electronic equipment and air conditioner

Abstract

The embodiment of the invention discloses an air conditioner control method during failure of a room temperature sensor, electronic equipment and an air conditioner, and belongs to the technical field of air conditioner control. The air conditioner control method during failure of the room temperature sensor comprises the steps that when the room temperature sensor fails, the current set temperature of an air conditioner and pre-stored historical steady-state operation parameters corresponding to the set temperature are obtained, and the historical steady-state operation parameters at least comprise historical steady-state loads and historical steady-state inner pipe temperatures; the set temperature, the historical steady-state operation parameters and current operation parameters of the air conditioner are input into a pre-trained performance prediction model, and the target frequency of a compressor is obtained; controlling the compressor to operate according to the target frequency; and the inner pipe temperature of the air conditioner is obtained in real time, and the working state of the compressor is adjusted based on the inner pipe temperature.

Description

Technical Field

[0001] This application relates to the field of air conditioning control technology, and more specifically, to an air conditioning control method, electronic device, and air conditioner when a room temperature sensor fails. Background Technology

[0002] With the continuous development of air conditioning technology, users have placed higher demands on the comfort and energy efficiency of the equipment. During the operation of an air conditioning system, key control modules such as compressor frequency, electronic expansion valve opening, and performance prediction models rely on real-time data input from indoor ambient temperature sensors. However, when the room temperature sensor is damaged or malfunctions, the detected data may be inaccurate or even completely lost, leading to problems such as room temperature deviating from the set value, inaccurate compressor frequency control, increased energy consumption, and decreased system stability, affecting user experience and equipment reliability.

[0003] In existing technologies, a set temperature value is typically used instead of the actual detected value for control. For example, the system uses the set temperature as the default room temperature input and adjusts parameters such as compressor frequency and fan speed. However, this approach has certain limitations: on the one hand, it cannot obtain the real-time trend of indoor temperature changes, making it difficult to determine whether the room temperature is stabilizing; on the other hand, in the initial stage of air conditioning operation or under conditions of drastic environmental changes, there may be a significant deviation between the set temperature and the actual room temperature, leading to improper compressor frequency adjustment and consequently affecting cooling / heating efficiency and energy consumption.

[0004] Furthermore, the performance prediction model in an air conditioning system relies on multiple input variables for prediction and correction. If the room temperature input value deviates, the model prediction results may be affected, causing parameters such as compressor frequency to deviate from the target value, and even reducing the system's energy-saving and comfort performance. Therefore, existing technologies require a control method that can maintain stable system operation and closely approximate real-world environmental conditions when the room temperature sensor malfunctions.

[0005] Existing technologies also attempt to indirectly estimate room temperature through other sensors or use model predictions to replace sensor inputs, but these methods are limited by data accuracy, model generalization ability, and the real-time response requirements of the system, and there are still certain challenges in achieving stable and reliable control under different operating conditions. Summary of the Invention

[0006] This application provides an air conditioning control method, electronic device, and air conditioner when a room temperature sensor fails, in order to at least solve the above-mentioned technical problems in the related art.

[0007] According to a first aspect of the embodiments of this application, an air conditioning control method is provided when a room temperature sensor fails, including: When the room temperature sensor fails, the current set temperature of the air conditioner and the historical steady-state operating parameters corresponding to the pre-stored set temperature are obtained. The historical steady-state operating parameters include at least the historical steady-state load and the historical steady-state inner pipe temperature. The set temperature, the historical steady-state operating parameters, and the current operating parameters of the air conditioner are input into a pre-trained performance prediction model to obtain the target frequency of the compressor. Control the compressor to operate at the target frequency; The system acquires the internal pipe temperature of the air conditioner in real time and adjusts the compressor's operating status based on the internal pipe temperature.

[0008] The above solution replaces the traditional single-set-temperature control method by using historical steady-state operating parameters, set temperature, and air conditioning operating parameters as input data, combined with a performance prediction model, in the event of room temperature sensor failure. This approach not only maintains the stable operation of the air conditioning system but also enables dynamic adjustments through real-time feedback of the internal pipe temperature, ensuring the system's adaptability and accuracy under different operating conditions. Furthermore, by introducing a performance prediction model, it effectively reduces energy consumption increases and comfort degradation caused by sensor malfunctions, thereby improving the intelligence level of the air conditioning system and the user experience.

[0009] In conjunction with the first aspect, in an optional implementation of this application embodiment, the operating state includes a dynamic temperature-changing stage. Therefore, adjusting the compressor's operating state based on the inner tube temperature includes: In cooling mode, if the inner tube temperature is higher than the historical steady-state inner tube temperature, the working state is determined to be the dynamic temperature change stage. During the dynamic temperature change phase, the compressor operating frequency is increased based on the difference between the inner tube temperature and the historical steady-state inner tube temperature.

[0010] In conjunction with the first aspect, in an optional implementation of this application embodiment, the operating state includes a dynamic temperature-changing stage. Therefore, adjusting the compressor's operating state based on the inner tube temperature includes: In heating mode, if the inner tube temperature is lower than the historical steady-state inner tube temperature, the working state is determined to be the dynamic temperature change stage. During the dynamic temperature change phase, the compressor operating frequency is increased based on the difference between the inner tube temperature and the historical steady-state inner tube temperature.

[0011] The above scheme achieves the following: In cooling mode, when the inner pipe temperature is higher than the historical steady-state value, it indicates insufficient cooling effect. In this case, the cooling capacity is increased by increasing the compressor operating frequency. In heating mode, when the inner pipe temperature is lower than the historical steady-state value, it indicates insufficient heating effect. Similarly, the heating capacity is enhanced by increasing the compressor operating frequency. This method, by comparing the inner pipe temperature with the historical steady-state value, achieves precise adjustment of the compressor's operating state, avoiding over- or under-adjustment caused by relying on the set temperature in traditional control methods. This significantly improves the energy efficiency and comfort of the air conditioning system.

[0012] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, the working state includes a steady-state temperature control stage. Therefore, the working state of the compressor is determined based on the inner tube temperature, including: After the compressor has run for the target cycle of operating time, shut it down. When the compressor stops, it is restarted based on the difference between the internal pipe temperature and the set temperature.

[0013] Through the above scheme, during the steady-state temperature control phase, by setting the target cycle operating time of the compressor, the system ensures that it meets basic operational requirements while reducing energy consumption. When the compressor's operating time reaches the preset value, the system automatically enters a shutdown state. During the shutdown period, the difference between the internal pipe temperature and the set temperature is used to determine whether the compressor needs to be restarted. This process, through precise control of the compressor's operating time, avoids energy waste and equipment damage caused by frequent start-stop cycles, while ensuring that the indoor temperature remains within the set range, thus improving system stability and energy-saving performance.

[0014] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, controlling the compressor to restart based on the difference between the inner tube temperature and the set temperature includes: In cooling mode, when the difference between the inner tube temperature and the set temperature exceeds the first preset threshold, the compressor is restarted. In heating mode, the compressor is restarted when the difference between the inner tube temperature and the set temperature is less than or equal to the second preset threshold.

[0015] Through the above scheme, in cooling mode, when the difference between the inner pipe temperature and the set temperature exceeds the first preset threshold, it indicates that the indoor temperature has deviated from the set value. At this time, the compressor is restarted to restore the cooling effect. In heating mode, when the difference between the inner pipe temperature and the set temperature is less than or equal to the second preset threshold, it indicates that the indoor temperature has approached the set value. At this time, the compressor is restarted to maintain the heating effect. This method achieves intelligent start-stop control of the compressor by accurately judging the difference between the inner pipe temperature and the set temperature, which not only ensures the stability of the indoor temperature but also reduces the system's energy consumption.

[0016] In conjunction with the first aspect, in an optional implementation of this application embodiment, the historical steady-state operating parameters further include the historical cycle shutdown duration of the compressor. Therefore, the method during the steady-state temperature control phase further includes: Record the actual downtime of the compressor in the current cycle; Based on the actual downtime and the historical downtime, adjust the target cycle start-up time for the next operating cycle.

[0017] The above scheme, during the steady-state temperature control phase, records the actual downtime of the current cycle and compares it with the downtime of historical cycles to assess whether the current system's operating state deviates from expectations. If there is a significant deviation between the actual downtime and the historical downtime, the target cycle startup time for the next operating cycle is adjusted to optimize system efficiency. This process, through dynamic adjustment of downtime, further enhances the system's adaptability and energy-saving effect.

[0018] In conjunction with the first aspect, in an optional implementation of the embodiments of this application, the method further includes: Obtain the steady-state operating temperature of the inner tube during the current cycle; Based on the steady-state operating temperature of the inner tube and the historical steady-state temperature of the inner tube, adjust the steady-state load for the next operating cycle.

[0019] The above scheme, during the steady-state temperature control phase, acquires the current cycle's steady-state start-up inner tube temperature and compares it with historical steady-state inner tube temperatures to assess whether the system's heat exchange capacity has changed. If there is a deviation between the steady-state start-up inner tube temperature and the historical steady-state value, the steady-state load for the next operating cycle is adjusted to ensure the system is always in optimal operating condition. This method, through dynamic optimization of the steady-state load, further improves the system's stability and energy efficiency ratio.

[0020] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, The performance prediction model is a neural network model. The input parameters of the performance prediction model include at least historical steady-state operating parameters and air conditioning operating parameters. The air conditioning operating parameters include at least one of the following: compressor frequency, indoor fan speed, outdoor fan speed, valve opening, and indoor pipe temperature. The output parameter is the target compressor frequency. The performance prediction model is trained using the following method: Obtain the training dataset, which contains multiple sets of target input parameters, each set of target input parameters is labeled with the corresponding compressor frequency; Input the target input parameters into the initialized performance prediction model to obtain the target frequency output by the performance prediction model; The target frequency is compared with the corresponding labeled information, and the loss is calculated. Based on the loss, the performance prediction model is adjusted using the backpropagation algorithm until the performance prediction model converges, thus obtaining the trained performance prediction model.

[0021] The above approach utilizes a neural network model to construct a performance prediction model, which is then trained using a training dataset. During training, the model parameters are input into the target operating parameters, and the loss between the output target frequency and the labeled frequency is calculated. The backpropagation algorithm is then used to adjust the model parameters until the model converges. This method, through learning from a large amount of historical data, significantly improves the accuracy and generalization ability of the performance prediction model. This allows it to generate a compressor target frequency that meets actual needs even when the room temperature sensor fails, ensuring stable system operation and high energy efficiency.

[0022] According to a second aspect of the embodiments of this application, the present invention provides an air conditioning control device for use when a room temperature sensor fails, comprising: The acquisition unit is used to acquire the current set temperature of the air conditioner and the historical steady-state operating parameters corresponding to the pre-stored set temperature when the room temperature sensor fails. The historical steady-state operating parameters include at least the historical steady-state load and the historical steady-state inner pipe temperature. The processing unit is used to input the set temperature, the historical steady-state operating parameters, and the current operating parameters of the air conditioner into a pre-trained performance prediction model to obtain the target frequency of the compressor. The first control unit is used to control the compressor to operate at the target frequency; The second control unit is used to obtain the internal pipe temperature of the air conditioner in real time and adjust the working status of the compressor based on the internal pipe temperature.

[0023] According to a third aspect of the embodiments of this application, the present invention provides an electronic device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the air conditioning control method for the failure of the room temperature sensor described in the first aspect or any corresponding embodiment.

[0024] According to a fourth aspect of the embodiments of this application, this specification provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement an air conditioning control method as described above when a room temperature sensor fails.

[0025] According to a fifth aspect of the embodiments of this application, the present specification provides a computer program product or a computer program, the computer program product including a computer program stored in a computer-readable storage medium; a processor of a computer device reads the computer program from the computer-readable storage medium, and when the processor executes the computer program, it implements the air conditioning control method for the failure of the room temperature sensor as described above.

[0026] According to a sixth aspect of the embodiments of this application, the present specification provides an air conditioner that employs an air conditioner control method for when the room temperature sensor fails, as in the first aspect, or includes an air conditioner control device for when the room temperature sensor fails, as in the second aspect, or includes an electronic device, as in the third aspect.

[0027] The technical effects achieved by the second to fifth aspects mentioned above are similar to those achieved by the corresponding technical means in the first aspect, and will not be repeated here. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating the air conditioning control method for when the room temperature sensor fails, as provided in an embodiment of this application. Figure 2 This is a schematic diagram of the air conditioning control device when the room temperature sensor fails, as provided in the embodiments of this application. Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be understood that "multiple" as mentioned herein refers to two or more. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. In addition, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first," "second," etc., are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and the terms "first," "second," etc., do not necessarily imply that they are different.

[0031] In this disclosure, sensor failure refers to damage or malfunction of the room temperature sensor, resulting in deviations or loss of temperature detection results.

[0032] In this publication, neural network refers to a machine learning model used for performance prediction, such as a backpropagation (BP) neural network.

[0033] In this published text, room temperature control refers to the process of maintaining an indoor temperature within a set range through an air conditioning system.

[0034] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0035] As mentioned in the background section, with the continuous development of air conditioning technology, users have placed higher demands on the comfort and energy efficiency of the equipment. During the operation of an air conditioning system, key control modules such as compressor frequency, electronic expansion valve opening, and performance prediction models rely on real-time data input from indoor ambient temperature sensors. However, when the room temperature sensor is damaged or malfunctions, the detected data may be deviated or even completely lost, leading to problems such as room temperature deviating from the set value, inaccurate compressor frequency control, increased energy consumption, and decreased system stability, affecting user experience and equipment reliability.

[0036] In existing technologies, a set temperature value is typically used instead of the actual detected value for control. For example, the system uses the set temperature as the default room temperature input and adjusts parameters such as compressor frequency and fan speed. However, this approach has certain limitations: on the one hand, it cannot obtain the real-time trend of indoor temperature changes, making it difficult to determine whether the room temperature is stabilizing; on the other hand, in the initial stage of air conditioning operation or under conditions of drastic environmental changes, there may be a significant deviation between the set temperature and the actual room temperature, leading to improper compressor frequency adjustment and consequently affecting cooling / heating efficiency and energy consumption.

[0037] Furthermore, the performance prediction model in an air conditioning system relies on multiple input variables for prediction and correction. If the room temperature input value deviates, the model prediction results may be affected, causing parameters such as compressor frequency to deviate from the target value, and even reducing the system's energy-saving and comfort performance. Therefore, existing technologies require a control method that can maintain stable system operation and closely approximate real-world environmental conditions when the room temperature sensor malfunctions.

[0038] Existing technologies also attempt to indirectly estimate room temperature through other sensors or use model predictions to replace sensor inputs, but these methods are limited by data accuracy, model generalization ability, and the real-time response requirements of the system, and there are still certain challenges in achieving stable and reliable control under different operating conditions.

[0039] Based on this, this application provides an air conditioning control method when the room temperature sensor fails. Its core lies in achieving precise control of the compressor's target frequency through historical steady-state operating parameters and a performance prediction model, and combining this with real-time feedback of the inner pipe temperature to ensure stable system operation under different operating conditions. Figure 1 The method shown includes the following processing steps.

[0040] S101: When the room temperature sensor fails, obtain the current set temperature of the air conditioner and the historical steady-state operating parameters corresponding to the pre-stored set temperature.

[0041] In practice, historical steady-state operating parameters include at least historical steady-state load and historical steady-state inner pipe temperature. When a room temperature sensor failure is detected, the system automatically triggers backup control logic. At this time, the air conditioning controller retrieves the set temperature and pre-stored historical steady-state operating parameters from the storage unit. These historical steady-state operating parameters include, but are not limited to, historical steady-state load, historical steady-state inner pipe temperature, and historical cycle downtime. These data are obtained through recording and analyzing the long-term operating status of the air conditioner and have high reference value. For example, in cooling mode, if the historical steady-state inner pipe temperature is 10 degrees Celsius and the historical steady-state load is 2 kilowatts, these data will serve as important input conditions for subsequent calculations. The room temperature sensor failure detection method includes: determining whether the room temperature data exceeds the normal range, whether the sensor current signal is abnormal, or whether the functional module is receiving data normally. If abnormal, a room temperature abnormality flag is sent to the main unit, but the air conditioner does not stop and instead uses historical steady-state operating parameters.

[0042] In the air conditioning control method when the room temperature sensor fails, the key parameters involved and their definitions are as follows: Ta: User-set temperature, which serves as an equivalent control input to the indoor ambient temperature Tn when the room temperature sensor fails. It is typically set by the user via remote control or panel. Tn: Indoor ambient temperature, replaced by the set temperature Ta when the room temperature sensor fails. Tw: Outdoor ambient temperature (outdoor dry-bulb temperature). Ts: Real-time detected air conditioner inner pipe temperature. Ts1: Historical steady-state inner pipe temperature, representing the average inner pipe temperature when the air conditioner reaches a stable operating state under specific conditions. Ts2: Steady-state operating inner pipe temperature in the current cycle. Qs1: Historical steady-state load, representing the average capacity output of the air conditioner when it reaches a stable operating state under specific conditions. Qs2: Steady-state load in the current cycle. F: Compressor target frequency. F_old: Compressor current operating frequency. F_new: Corrected compressor operating frequency. F_corr: Compressor frequency correction amount, calculated as F_corr = k × ΔTs, where k is the correction coefficient. ΔTs: Difference between the inner pipe temperature and the historical steady-state inner pipe temperature, ΔTs = Ts -Ts1; ΔT: The difference between the inner tube temperature and the set temperature, ΔT = Ts - Ta; T1: Preset threshold, used to determine the relationship between the difference between the inner tube temperature and the set temperature; t1: The compressor's historical cycle start-up time; t2: Historical cycle downtime of the compressor; t22: Actual downtime of the current cycle; Ni: Internal fan speed; No: External fan speed; B: Throttling valve (electronic expansion valve) opening; k: Correction coefficient, preset according to system characteristics, typical value is 5Hz / ℃.

[0043] The above parameters are recorded and updated by the intelligent control module during normal operation of the air conditioner, forming a historical steady-state operating parameter database. When the room temperature sensor fails, the system calls the corresponding historical parameters and combines them with the real-time detected inner pipe temperature Ts to achieve precise control of the compressor frequency through the performance prediction model and working state logic.

[0044] S102: Input the set temperature, historical steady-state operating parameters, and current operating parameters of the air conditioner into the pre-trained performance prediction model to obtain the target frequency of the compressor.

[0045] In practice, the set temperature and historical steady-state operating parameters are input into a pre-trained performance prediction model to generate the compressor's target frequency. The performance prediction model is constructed using a neural network; its input parameters include at least the air conditioning load, the inner pipe temperature, and the set temperature, with the output parameter being the compressor's target frequency. The training process of the performance prediction model is as follows: First, a training dataset is obtained. This dataset contains multiple sets of target operating parameters, each labeled with a corresponding compressor frequency. For example, a set of target operating parameters might be an air conditioning load of 3 kW, an inner pipe temperature of 12 degrees Celsius, and a set temperature of 26 degrees Celsius, corresponding to a labeled compressor frequency of 50 Hz. Then, the target operating parameters are input into the initialized performance prediction model to obtain the model's output target frequency. The target frequency is compared with the labeled information, the loss value is calculated, and the model parameters are adjusted using a backpropagation algorithm based on the loss value until the model converges. In this way, the performance prediction model can accurately predict the compressor's target frequency based on the input parameters, thus replacing the traditional control method that relies on a room temperature sensor.

[0046] In one embodiment, the performance prediction model is a three-layer BP neural network model. The model input parameters include air conditioning operating parameters (compressor frequency, indoor fan speed, outdoor fan speed, valve opening, and indoor pipe temperature) and operating condition parameters (outdoor ambient temperature, indoor ambient temperature, and indoor relative humidity). The output parameters include real-time cooling / heating capacity and real-time power. During training, tens of thousands of sets of performance data from 50 typical laboratory operating conditions are obtained as the training dataset. The mean squared error loss function is used, and the model converges through backpropagation and gradient descent algorithms.

[0047] When the model is used for frequency control optimization, the input parameters are adjusted to air conditioning load, indoor pipe temperature, indoor fan speed, outdoor fan speed, throttle valve opening, outdoor dry bulb temperature, and indoor ambient temperature (with the set temperature as a substitute). The output parameter is the compressor frequency, and the optimization target is the energy saving rate or minimum operating power.

[0048] S103: Controls the compressor to operate at the target frequency.

[0049] S104: Real-time acquisition of the internal pipe temperature of the air conditioner, and adjustment of the compressor's operating status based on the internal pipe temperature.

[0050] In practice, after obtaining the target frequency of the compressor, the air conditioning controller will control the compressor's operation according to that frequency. Simultaneously, the system enters a working state to achieve real-time dynamic adjustment. The working state is divided into a dynamic temperature change stage and a steady-state temperature control stage. In the dynamic temperature change stage, the system will collect the air conditioner's inner pipe temperature in real time and compare it with the historical steady-state inner pipe temperature. For example, in cooling mode, if the current inner pipe temperature is 15 degrees Celsius, which is 10 degrees Celsius higher than the historical steady-state inner pipe temperature, the system will increase the compressor's operating frequency based on the difference. Specifically, if the difference is 5 degrees Celsius, the compressor's operating frequency may increase by 10 Hz to improve the cooling effect. Similarly, in heating mode, if the current inner pipe temperature is 8 degrees Celsius, which is 12 degrees Celsius lower than the historical steady-state inner pipe temperature, the system will also increase the compressor's operating frequency based on the difference. In this way, the system can dynamically adjust the compressor's operating state according to changes in the inner pipe temperature, thereby avoiding system fluctuations caused by the failure of the room temperature sensor.

[0051] During the steady-state temperature control phase, the system precisely controls the compressor's operating time. For example, when the compressor reaches the target cycle operating time, such as 10 minutes, the system will shut down the compressor and enter a shutdown state. During shutdown, the system determines whether to restart the compressor based on the difference between the internal pipe temperature and the set temperature. For example, in cooling mode, if the difference between the internal pipe temperature and the set temperature is greater than the first preset threshold, such as 3 degrees Celsius, the system will restart the compressor to restore the cooling effect. In heating mode, if the difference between the internal pipe temperature and the set temperature is less than or equal to the second preset threshold, such as 2 degrees Celsius, the system will also restart the compressor to maintain the heating effect. In this way, the system can avoid energy waste and equipment wear caused by frequent start-stop cycles while ensuring that the indoor temperature is always maintained within the set range.

[0052] In addition, during the steady-state temperature control phase, the system records the actual downtime of the current cycle and compares it with the downtime of historical cycles. For example, if the historical downtime is 5 minutes and the actual downtime of the current cycle is 8 minutes, the system will assess whether the current operating state deviates from expectations. If there is a significant deviation between the actual downtime and the historical downtime, the system will adjust the target cycle start-up time for the next operating cycle. For example, if the actual downtime is long, the target cycle start-up time for the next cycle may be shortened to optimize system operating efficiency. Simultaneously, the system also acquires the steady-state start-up internal pipe temperature of the current cycle and compares it with the historical steady-state internal pipe temperature. For example, if the steady-state start-up internal pipe temperature of the current cycle is 11 degrees Celsius and the historical steady-state internal pipe temperature is 10 degrees Celsius, the system will adjust the steady-state load for the next operating cycle to ensure the system is always in optimal operating condition.

[0053] Throughout the control process, the air conditioning controller continuously monitors changes in the inner pipe temperature and dynamically adjusts the compressor's operating status based on actual conditions. For example, in cooling mode, if the inner pipe temperature continues to rise, the system gradually increases the compressor's operating frequency until the inner pipe temperature approaches its historical steady-state value. Similarly, in heating mode, if the inner pipe temperature continues to fall, the system gradually increases the compressor's operating frequency to improve heating efficiency. In this way, the system can achieve precise adjustment of the compressor's operating status, avoiding over- or under-adjustment issues caused by relying on the set temperature in traditional control methods.

[0054] Furthermore, the introduction of a performance prediction model enables the system to maintain efficient operation even when the room temperature sensor fails. For example, in a real-world application scenario, the user sets the temperature to 26 degrees Celsius, but the outdoor ambient temperature is higher, leading to an increased air conditioning load. In this case, the system generates a target compressor frequency based on historical steady-state operating parameters and the set temperature, and dynamically adjusts the operating status through real-time feedback of the internal pipe temperature. Even in the event of a room temperature sensor failure, the system can ensure that the indoor temperature remains within the set range while reducing energy consumption through the performance prediction model and adjustments to the internal pipe temperature.

[0055] In practical applications, this method can be widely used in residential air conditioning, commercial air conditioning, and central air conditioning systems. For example, in a home setting, a user may be unable to use the air conditioner normally due to a malfunctioning room temperature sensor, but with this method, the system can automatically switch to backup control logic and achieve stable operation based on historical data and real-time feedback. In commercial settings, due to the large scale of air conditioning systems and the complex operating environment, this method can significantly improve the system's adaptability and energy-saving effect, thereby reducing operating costs.

[0056] During implementation, it is crucial to ensure that the accuracy of historical steady-state operating parameters directly impacts the system's control performance. Therefore, during normal air conditioning operation, historical steady-state operating parameters should be updated regularly to ensure they reflect the current system's actual operating status. For example, when seasonal changes or changes in the operating environment occur, the system should re-record and update historical steady-state parameters such as pipe temperature and load to improve control accuracy. Simultaneously, the training dataset for the performance prediction model should cover as many operating conditions as possible to enhance the model's generalization ability. For instance, the training process should include data from various scenarios such as different loads, ambient temperatures, and setpoint temperatures to ensure the model can adapt to diverse practical application needs.

[0057] In terms of hardware implementation, the air conditioner controller needs sufficient computing power and storage space to support the operation of performance prediction models and the storage of historical data. For example, the controller can use a high-performance microprocessor and be equipped with a large-capacity memory to meet data processing and storage requirements. Simultaneously, the system also needs a high-precision inner tube temperature sensor to ensure the accuracy of the collected data. For example, the measurement accuracy of the inner tube temperature sensor should reach ±0.5 degrees Celsius to meet the requirements of the operating conditions.

[0058] In summary, this invention achieves stable air conditioning operation even in the event of room temperature sensor failure by combining a performance prediction model based on historical steady-state operating parameters with real-time feedback of the inner pipe temperature. This method not only effectively solves the problem of system instability caused by sensor malfunctions but also improves the system's energy efficiency and comfort through dynamic adjustment and intelligent control, thereby meeting diverse user needs.

[0059] In one example, such as in a home scenario, when the room temperature sensor malfunctions due to accidental damage or signal abnormality during air conditioner operation, the system automatically detects this state and triggers backup control logic. At this time, the air conditioner controller retrieves the user-set temperature and historical steady-state operating parameters from its storage unit. For example, in cooling mode, if the user-set temperature is 26 degrees Celsius, and the historical steady-state inner pipe temperature is 10 degrees Celsius and the historical steady-state load is 2 kilowatts, these data will serve as important input conditions for subsequent calculations. The air conditioner controller inputs these parameters into a pre-trained performance prediction model to generate the compressor's target frequency. The performance prediction model is built on a neural network, and its input parameters include the air conditioner load, inner pipe temperature, and set temperature; the output parameter is the compressor's target frequency. Through this model, the system can generate a compressor operating frequency that meets actual needs based on the current operating conditions, thus replacing the traditional control method that relies on a room temperature sensor.

[0060] After obtaining the target frequency of the compressor, the air conditioner controller starts the compressor to run according to this frequency and adjusts the working state at the same time. Specifically, in the dynamic temperature change stage, the system collects the inner pipe temperature in real time and compares it with the historical steady-state inner pipe temperature. In the dynamic temperature change stage, according to the difference ΔTs = Ts - Ts1 between the inner pipe temperature (Ts) and the historical steady-state inner pipe temperature (Ts1), the compressor frequency is dynamically adjusted according to a preset correction function. The correction function is: F_corr = k * ΔTs, where k is the correction coefficient (for example, k = 5Hz / ℃). Specifically: in the cooling mode, if Ts > Ts1, increase the compressor frequency: F_new = F_old + F_corr; if Ts ≤ Ts1, enter the steady-state temperature control stage. In the heating mode, if Ts < Ts1, increase the compressor frequency: F_new = F_old + F_corr; if Ts ≥ Ts1, enter the steady-state temperature control stage. After running for a certain period of time, repeat the above comparison and correction until entering the steady-state temperature control stage.

[0061] For example, in the cooling mode, if the current inner pipe temperature is 15 degrees Celsius, which is higher than the historical steady-state inner pipe temperature of 10 degrees Celsius, the system will increase the compressor operating frequency according to the difference between the two. Specifically, if the difference is 5 degrees Celsius, the compressor operating frequency may increase by 10 Hertz to improve the cooling effect. Similarly, in the heating mode, if the current inner pipe temperature is 8 degrees Celsius, which is lower than the historical steady-state inner pipe temperature of 12 degrees Celsius, the system will also increase the compressor operating frequency according to the difference. In this way, the system can dynamically adjust the operating state of the compressor according to the change of the inner pipe temperature, avoiding system fluctuations caused by the failure of the room temperature sensor.

[0062] In the steady-state temperature control stage, the system will precisely control the running duration of the compressor. For example, when the running duration of the compressor reaches the target cycle startup duration (such as 10 minutes), the system will turn off the compressor and enter the shutdown state. During the shutdown period, the system judges whether to restart the compressor based on the difference between the inner pipe temperature and the set temperature. For example, in the cooling mode, if the difference between the inner pipe temperature and the set temperature is greater than the first preset threshold (such as 3 degrees Celsius), the system will restart the compressor to restore the cooling effect; in the heating mode, if the difference between the inner pipe temperature and the set temperature is less than or equal to the second preset threshold (such as 2 degrees Celsius), the system will also restart the compressor to maintain the heating effect. In this way, the system can avoid energy waste and equipment wear caused by frequent start and stop, while ensuring that the indoor temperature always remains within the set range.

[0063] In addition, during the steady-state temperature control phase, the system also records the actual downtime duration of the current cycle and compares it with the downtime duration of historical cycles. For example, if the downtime duration of a historical cycle is 5 minutes and the actual downtime duration of the current cycle is 8 minutes, the system will evaluate whether the current operating state deviates from the expectation. If there is a significant deviation between the actual downtime duration and the downtime duration of historical cycles, the system will adjust the target cycle startup duration for the next operating cycle. For example, if the actual downtime duration is long, the target cycle startup duration for the next cycle may be shortened to optimize the operating efficiency of the system. At the same time, the system also obtains the steady-state startup running inner tube temperature of the current cycle and compares it with the historical steady-state inner tube temperature.

[0064] During the steady-state temperature control phase, the compressor operates at the current steady-state load Qs2. After the compressor runs for the historical cycle startup duration t1, it shuts down, and the actual downtime duration t22 of the current cycle and the steady-state startup running inner tube temperature Ts2 (the average inner tube temperature during operation) are recorded. Then, during the compressor downtime, ΔT = Ts – Ta is calculated. In the cooling mode: If ΔT > T1 (for example, T1 = 3°C), it means the indoor temperature is too high, and the compressor is restarted. In the heating mode: If ΔT ≤ T1 (for example, T1 = 2°C), it means the indoor temperature is too low, and the compressor is restarted. Then, t22 is compared with the historical cycle downtime duration t2: If t22 ≥ t2, the current steady-state load Qs2 and the target cycle startup duration t1 remain unchanged.

[0065] If t22 < t2, then Ts2 is further compared with the historical steady-state inner tube temperature Ts1: In the cooling mode, if Ts2 ≥ Ts1, the steady-state load Qs2 is increased and the target cycle startup duration t1 is increased; if Ts2 < Ts1, Qs2 remains unchanged but t1 is increased.

[0066] In the heating mode, if Ts2 ≥ Ts1, Qs2 remains unchanged but t1 is increased; if Ts2 < Ts1, Qs2 is increased and t1 is increased.

[0067] In one example, in the cooling mode, the historical cycle downtime duration t2 = 5 minutes, the current actual downtime duration t22 = 3 minutes (t22 < t2), and Ts2 = 11°C, Ts1 = 10°C (Ts2 > Ts1), then the system will increase Qs2 and t1 to enhance the cooling capacity.

[0068] Through the above adaptive adjustment, it is ensured that the system adapts to the change of user load.

[0069] For example, if the steady-state operating temperature of the inner tube in the current cycle is 11 degrees Celsius, while the historical steady-state temperature of the inner tube is 10 degrees Celsius, the system will adjust the steady-state load for the next operating cycle to ensure that the system is always in the best operating condition.

[0070] Throughout the control process, the air conditioning controller continuously monitors changes in the inner pipe temperature and dynamically adjusts the compressor's operating status based on actual conditions. For example, in cooling mode, if the inner pipe temperature continues to rise, the system gradually increases the compressor's operating frequency until the inner pipe temperature approaches its historical steady-state value. Similarly, in heating mode, if the inner pipe temperature continues to fall, the system gradually increases the compressor's operating frequency to improve heating efficiency. In this way, the system achieves precise regulation of the compressor's operating status, avoiding over- or under-adjustment issues caused by reliance on set temperatures in traditional control methods.

[0071] In terms of hardware implementation, the air conditioning controller needs sufficient computing power and storage space to support the operation of performance prediction models and the storage of historical data. For example, the controller can use a high-performance microprocessor and be equipped with a large-capacity memory to meet data processing and storage requirements. Simultaneously, the system also needs a high-precision inner pipe temperature sensor to ensure the accuracy of the collected data. For example, the measurement accuracy of the inner pipe temperature sensor should reach ±0.5 degrees Celsius to meet adjustment requirements.

[0072] In summary, this invention achieves stable air conditioning operation even in the event of room temperature sensor failure by combining historical steady-state operating parameters, performance prediction models, and real-time feedback of the inner pipe temperature. This method not only effectively solves the problem of system instability caused by sensor malfunctions but also improves the system's energy efficiency and comfort through dynamic adjustment and intelligent control, thereby meeting diverse user needs.

[0073] like Figure 2 As shown in the figure, this application embodiment also provides an air conditioning control device 600 when the room temperature sensor fails, including: The acquisition unit 601 is used to acquire the current set temperature of the air conditioner and the historical steady-state operating parameters corresponding to the pre-stored set temperature when the room temperature sensor fails. The historical steady-state operating parameters include at least the historical steady-state load and the historical steady-state inner pipe temperature. Processing unit 602 is used to input the set temperature, the historical steady-state operating parameters and the current operating parameters of the air conditioner into a pre-trained performance prediction model to obtain the target frequency of the compressor; The first control unit 603 is used to control the compressor to operate at the target frequency; The second control unit 604 is used to obtain the internal pipe temperature of the air conditioner in real time and adjust the working status of the compressor based on the internal pipe temperature.

[0074] In one optional implementation of this application embodiment, the working state includes a dynamic temperature change stage, then the second control unit 604 is specifically used for: In cooling mode, if the inner tube temperature is higher than the historical steady-state inner tube temperature, the working state is determined to be the dynamic temperature change stage. During the dynamic temperature change phase, the compressor operating frequency is increased based on the difference between the inner tube temperature and the historical steady-state inner tube temperature.

[0075] In one optional implementation of this application embodiment, the working state includes a dynamic temperature change stage, then the second control unit 604 is specifically used for: In heating mode, if the inner tube temperature is lower than the historical steady-state inner tube temperature, the working state is determined to be the dynamic temperature change stage. During the dynamic temperature change phase, the compressor operating frequency is increased based on the difference between the inner tube temperature and the historical steady-state inner tube temperature.

[0076] In one optional implementation of this application embodiment, the working state includes a steady-state temperature control stage, then the second control unit 604 is specifically used for: After the compressor has run for the preset cycle duration, the compressor is turned off. When the compressor stops, it is restarted based on the difference between the internal pipe temperature and the set temperature.

[0077] In one optional implementation of this application embodiment, the second control unit 604 is specifically used for: In cooling mode, when the difference between the inner tube temperature and the set temperature exceeds the first preset threshold, the compressor is restarted. In heating mode, the compressor is restarted when the difference between the inner tube temperature and the set temperature is less than or equal to the second preset threshold.

[0078] In one optional implementation of this application embodiment, the historical steady-state operating parameters further include the historical cycle downtime of the compressor. In this case, the second control unit 604 is specifically used for: Record the actual downtime of the compressor in the current cycle; Adjust the cycle startup duration for the next operating cycle based on the actual downtime and the historical cycle downtime.

[0079] In one optional implementation of this application embodiment, the second control unit 604 is specifically used for: Obtain the steady-state operating temperature of the inner tube during the current cycle; Based on the steady-state operating temperature of the inner tube and the historical steady-state temperature of the inner tube, adjust the steady-state load for the next operating cycle.

[0080] In one optional implementation of this application embodiment, the performance prediction model is a neural network model. The input parameters of the performance prediction model include at least: air conditioning load, inner pipe temperature, and set temperature, and the output parameter is the target frequency of the compressor. Then, the second control unit 604 is specifically used to train the performance prediction model according to the following method: Obtain the training dataset, which contains multiple sets of target operating parameters, each set of target operating parameters is labeled with the corresponding compressor frequency; Input the target operating parameters into the initialized performance prediction model to obtain the target frequency output by the performance prediction model; The target frequency is compared with the corresponding labeled information, and the loss is calculated. Based on the loss, the performance prediction model is adjusted using the backpropagation algorithm until the performance prediction model converges, thus obtaining the trained performance prediction model.

[0081] This application also provides a computer program product, which includes computer program instructions that, when executed by a processor, cause the processor to perform the steps in the air conditioning control method for a room temperature sensor that fails, as described in the "Exemplary Methods" section above, according to various embodiments of this specification.

[0082] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this specification. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages.

[0083] This application also provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor in the steps of the air conditioning control method for a room temperature sensor failure as described in the "Exemplary Methods" section above, according to various embodiments of this specification.

[0084] This application also provides an electronic device, including a memory and a processor. The memory stores an air conditioning control method for when a room temperature sensor fails. The processor is used to employ the air conditioning control method for when a room temperature sensor fails when executing the air conditioning control method for when a room temperature sensor fails.

[0085] Specifically, such as Figure 3As shown, the electronic device includes a processor 100, at least one communication bus 200, a user interface 300, at least one external communication interface 400, and a memory 500. The communication bus 200 is configured to enable communication between these components. The user interface 300 may include a display screen, and the external communication interface 400 may include standard wired and wireless interfaces. The memory 500 stores an air conditioning control method for when a room temperature sensor fails. The processor 100 uses the aforementioned method when executing the air conditioning control method for when a room temperature sensor fails, stored in the memory 500.

[0086] The descriptions of the above computer program products, computer-readable storage media, and electronic devices are similar to those of the above method embodiments, and have similar beneficial effects. For any technical details not disclosed in the computer program products, computer-readable storage media, and electronic devices of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0087] The sequence numbers or order of description of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0089] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0090] Furthermore, the functional units in the various embodiments of this application can be integrated into a second control unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0091] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital versatile disc (DVD)), or a semiconductor medium (e.g., solid state disk (SSD)). It is worth noting that the computer-readable storage medium mentioned in the embodiments of this application can be a non-volatile storage medium; in other words, it can be a non-transient storage medium. It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in the embodiments of this application are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the scene data of the current frame in the 3D virtual scene involved in the embodiments of this application, the client's device information, and the scene interaction information are all obtained with full authorization.

[0092] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An air conditioning control method when a room temperature sensor fails, characterized in that, include: When the room temperature sensor fails, the current set temperature of the air conditioner and the pre-stored historical steady-state operating parameters corresponding to the set temperature are obtained. The historical steady-state operating parameters include at least the historical steady-state load and the historical steady-state inner pipe temperature. The set temperature, the historical steady-state operating parameters, and the current operating parameters of the air conditioner are input into a pre-trained performance prediction model to obtain the target frequency of the compressor. Control the compressor to operate at the target frequency; The internal pipe temperature of the air conditioner is acquired in real time, and the operating status of the compressor is adjusted based on the internal pipe temperature.

2. The method according to claim 1, characterized in that, The operating state includes a dynamic temperature change phase, so adjusting the operating state of the compressor based on the inner tube temperature includes: In cooling mode, if the inner tube temperature is greater than the historical steady-state inner tube temperature, then the working state is determined to be a dynamic temperature change stage. During the dynamic temperature change phase, the compressor operating frequency is increased based on the difference between the inner tube temperature and the historical steady-state inner tube temperature.

3. The method according to claim 1, characterized in that, The operating state includes a dynamic temperature change phase, so adjusting the operating state of the compressor based on the inner tube temperature includes: In heating mode, if the inner tube temperature is lower than the historical steady-state inner tube temperature, then the working state is determined to be a dynamic temperature change stage. During the dynamic temperature change phase, the compressor operating frequency is increased based on the difference between the inner tube temperature and the historical steady-state inner tube temperature.

4. The method according to claim 1, characterized in that, The operating state includes a steady-state temperature control phase, then the step of regulating the compressor's operating state based on the inner tube temperature includes: After the compressor has run for the target cycle of operating time, the compressor is shut down. When the compressor stops, the compressor is restarted based on the difference between the inner tube temperature and the set temperature.

5. The method according to claim 4, characterized in that, The method of controlling the compressor to restart based on the difference between the inner tube temperature and the set temperature includes: In cooling mode, when the difference between the inner tube temperature and the set temperature is greater than a first preset threshold, the compressor is restarted. In heating mode, when the difference between the inner tube temperature and the set temperature is less than or equal to the second preset threshold, the compressor is restarted.

6. The method according to claim 4, characterized in that, The historical steady-state operating parameters also include the historical cycle shutdown duration of the compressor. Therefore, during the steady-state temperature control phase, the method further includes: Record the actual downtime of the compressor in the current cycle; Based on the actual downtime and the historical cycle downtime, the target cycle startup time for the next operating cycle is adjusted.

7. The method according to claim 6, characterized in that, The method further includes: Obtain the steady-state operating temperature of the inner tube during the current cycle; Based on the steady-state start-up temperature of the inner tube and the historical steady-state temperature of the inner tube, the steady-state load for the next operating cycle is adjusted.

8. The method according to claim 1, characterized in that, The performance prediction model is a neural network model. The input parameters of the performance prediction model include at least historical steady-state operating parameters and air conditioning operating parameters. The air conditioning operating parameters include at least one of the following: compressor frequency, indoor fan speed, outdoor fan speed, valve opening, and indoor pipe temperature. The output parameter is the target frequency of the compressor. The performance prediction model is trained according to the following method: Obtain a training dataset, which contains multiple sets of target input parameters, each set of target input parameters being labeled with a corresponding compressor frequency; The target input parameters are input into the initialized performance prediction model to obtain the target frequency output by the performance prediction model. The target frequency is compared with the corresponding annotation information, and the loss is calculated. Based on the loss, the performance prediction model is adjusted using the backpropagation algorithm until the performance prediction model converges, thus obtaining the trained performance prediction model.

9. An air conditioning control device for use when a room temperature sensor fails, characterized in that, include: The acquisition unit is used to acquire the current set temperature of the air conditioner and the pre-stored historical steady-state operating parameters corresponding to the set temperature when the room temperature sensor fails. The historical steady-state operating parameters include at least the historical steady-state load and the historical steady-state inner pipe temperature. The processing unit is used to input the set temperature, the historical steady-state operating parameters, and the current operating parameters of the air conditioner into a pre-trained performance prediction model to obtain the target frequency of the compressor. A first control unit is used to control the compressor to operate at the target frequency; The second control unit is used to acquire the internal pipe temperature of the air conditioner in real time and adjust the working state of the compressor based on the internal pipe temperature.

10. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the air conditioning control method for a room temperature sensor failure as described in any one of claims 1 to 9.

11. An air conditioner, characterized in that, The air conditioning control method for when the room temperature sensor fails, as described in any one of claims 1-8, or includes the air conditioning control device for when the room temperature sensor fails, as described in claim 9, or includes the electronic device as described in claim 10.

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