Intelligent household electrical appliance control method and system, intelligent household electrical appliance, medium and program product

Abstract

The invention provides a control method and system of an intelligent household electrical appliance, the intelligent household electrical appliance, a medium and a program product. The control method comprises the steps that the gas temperature of the intelligent household electrical appliance is acquired; based on the comparison result of the gas temperature and the preset temperature, a combustion pressure signal of the intelligent household electrical appliance is obtained; the combustion resonance risk of the intelligent household electrical appliance is judged based on the combustion pressure signal, and a combustion resonance risk result is obtained; and controlling the intelligent household electrical appliance based on the combustion resonance risk result to optimize the combustion resonance of the intelligent household electrical appliance. According to the method, the combustion resonance risk of the intelligent household electrical appliance is judged through the combination of the gas temperature of the intelligent household electrical appliance and the combustion pressure signal, and the intelligent household electrical appliance is controlled according to the combustion resonance risk result, so that the combustion resonance of the intelligent household electrical appliance is optimized, and the situation that the combustion resonance easily occurs when the temperature of the intelligent household electrical appliance is too low is avoided.

Description

Technical Field

[0001] This disclosure relates to the field of smart home appliance technology, and in particular to a control method, system, smart home appliance, medium, and program product for a smart home appliance. Background Technology

[0002] As people's living standards improve and technologies such as the internet, big data, artificial intelligence, and voice interaction become more widespread, traditional lifestyles are gradually changing, and the use of home appliances is increasingly moving towards intelligentization. While bringing greater convenience to users, the functions of various home appliances are also becoming more diversified.

[0003] Gas water heaters heat water by producing high-temperature flue gas from combustion. If the gas used for combustion is too cold, combustion resonance can easily occur, especially in cold northern regions where this unstable condition is more likely to occur. Summary of the Invention

[0004] The technical problem to be solved by this disclosure is to overcome the defect in the prior art that smart home appliances are prone to combustion resonance when the temperature is too low, and to provide a control method, system, smart home appliance, medium and program product for smart home appliances.

[0005] This disclosure solves the above-mentioned technical problems through the following technical solution:

[0006] The first aspect of this disclosure provides a control method for a smart home appliance, the control method comprising:

[0007] Obtain the gas temperature of smart home appliances;

[0008] Based on the comparison between the gas temperature and the preset temperature, the combustion pressure signal of the smart home appliance is obtained.

[0009] The combustion resonance risk of smart home appliances is determined based on the combustion pressure signal, and the combustion resonance risk result is obtained.

[0010] The smart home appliance is controlled based on the combustion resonance risk results to optimize the combustion resonance of the smart home appliance.

[0011] Preferably, the step of obtaining the combustion pressure signal of the smart home appliance based on the comparison result of the gas temperature and the preset temperature includes:

[0012] In response to the gas temperature not exceeding a preset temperature, the gas preheating device is controlled to start, and the combustion pressure signal, combustion load, and electric auxiliary heating load of the smart home appliance are acquired.

[0013] Preferably, the step of determining the combustion resonance risk of smart home appliances based on the combustion pressure signal and obtaining the combustion resonance risk result includes:

[0014] The combustion pressure signal was subjected to spectral analysis to obtain a spectrum diagram;

[0015] Obtain the frequency peak values ​​in the spectrum and the preset frequency range of the smart home appliances;

[0016] In response to the frequency peak being within the preset frequency range and the amplitude corresponding to the frequency peak being less than or equal to the first preset amplitude, the combustion resonance risk result is determined to be low-risk resonance, and the gas temperature and combustion pressure signals of the smart home appliance are continuously monitored.

[0017] Preferably, the step of determining the combustion resonance risk of smart home appliances based on the combustion pressure signal and obtaining the combustion resonance risk result further includes:

[0018] In response to the fact that the peak frequency is within the preset frequency range, and the amplitude corresponding to the peak frequency is less than the first preset amplitude and less than the second preset amplitude, the combustion resonance risk result is determined to be medium-risk resonance, and the air-fuel ratio of the smart home appliance is adjusted.

[0019] Wherein, the first preset amplitude is smaller than the second preset amplitude.

[0020] Preferably, the step of determining the combustion resonance risk of smart home appliances based on the combustion pressure signal and obtaining the combustion resonance risk result further includes:

[0021] In response to the frequency peak being within the preset frequency range, and the amplitude corresponding to the frequency peak being greater than or equal to the second preset amplitude, the combustion resonance risk result is determined to be a high-risk resonance, and the combustion load of the smart home appliance is reduced while the electric auxiliary heating load is increased.

[0022] Preferably, the step of obtaining the combustion pressure signal of the smart home appliance based on the comparison result of the gas temperature and the preset temperature includes:

[0023] In response to the gas temperature being greater than the preset temperature, the gas preheating device is not activated, the smart home appliance ignites and burns normally, and the combustion pressure signal of the smart home appliance is acquired.

[0024] A second aspect of this disclosure provides a control system for a smart home appliance, the control system comprising:

[0025] The first acquisition module is used to acquire the gas temperature of smart home appliances;

[0026] The second acquisition module is used to acquire the combustion pressure signal of the smart home appliance based on the comparison result between the gas temperature and the preset temperature.

[0027] The third acquisition module is used to determine the combustion resonance risk of smart home appliances based on the combustion pressure signal and obtain the combustion resonance risk result.

[0028] A control module is used to control the smart home appliance based on the combustion resonance risk results, so as to optimize the combustion resonance of the smart home appliance.

[0029] Preferably, the second acquisition module is used to control the gas preheating device to start in response to the gas temperature not being greater than a preset temperature, and to acquire the combustion pressure signal, combustion load and electric auxiliary heating load of the smart home appliance.

[0030] Preferably, the third acquisition module includes:

[0031] The analysis unit is used to perform spectral analysis on the combustion pressure signal to obtain a spectrum diagram;

[0032] The acquisition unit is used to acquire the frequency peaks in the spectrum and the preset frequency range of the smart home appliances;

[0033] The first determining unit is configured to determine the combustion resonance risk result as low-risk resonance in response to the frequency peak being within the preset frequency range and the amplitude corresponding to the frequency peak being less than or equal to the first preset amplitude, and to continuously monitor the gas temperature and combustion pressure signals of the smart home appliance.

[0034] Preferably, the third acquisition module further includes:

[0035] The second determining unit is configured to determine the combustion resonance risk result as medium-risk resonance in response to the fact that the frequency peak is within the preset frequency range and the amplitude corresponding to the frequency peak is less than the first preset amplitude and less than the second preset amplitude, and adjust the air-fuel ratio of the smart home appliance.

[0036] Wherein, the first preset amplitude is smaller than the second preset amplitude.

[0037] Preferably, the third acquisition module further includes:

[0038] The third determining unit is used to determine the combustion resonance risk result as high-risk resonance in response to the fact that the frequency peak is within the preset frequency range and the amplitude corresponding to the frequency peak is greater than or equal to the second preset amplitude, and to reduce the combustion load of the smart home appliance and increase the electric auxiliary heating load.

[0039] Preferably, the second acquisition module is used to respond to the gas temperature being greater than a preset temperature, not to activate the gas preheating device, and for the smart home appliance to ignite and burn normally, and to acquire the combustion pressure signal of the smart home appliance.

[0040] A third aspect of this disclosure provides a smart home appliance, including a memory, a processor, and a computer program stored in the memory and for running on the processor, wherein the processor executes the computer program to implement the control method of the smart home appliance described in the first aspect.

[0041] The fourth aspect of this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the control method for smart home appliances described in the first aspect.

[0042] The fifth aspect of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the control method for smart home appliances as described in the first aspect.

[0043] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this disclosure.

[0044] The positive and progressive effects of this disclosure are as follows:

[0045] This disclosure uses the gas temperature and combustion pressure signal of smart home appliances to determine the risk of combustion resonance, and controls the smart home appliances according to the combustion resonance risk results to optimize the combustion resonance of smart home appliances and avoid the situation where combustion resonance easily occurs when the temperature of smart home appliances is too low. Attached Figure Description

[0046] Figure 1 A flowchart of a control method for a smart home appliance provided in Embodiment 1 of this disclosure.

[0047] Figure 2 This is a schematic diagram of the control system of a smart home appliance provided in Embodiment 2 of this disclosure.

[0048] Figure 3 This is a schematic diagram of the structure of an electronic device for implementing the control method of smart home appliances according to Embodiment 3 of this disclosure. Detailed Implementation

[0049] The present disclosure is further illustrated below by way of embodiments, but the present disclosure is not limited to the scope of the embodiments described herein.

[0050] The prefixes such as "first" and "second" used in this disclosure are merely for distinguishing different descriptive objects and do not limit the position, order, priority, quantity, or content of the described objects. The use of ordinal numbers and other prefixes used to distinguish descriptive objects in this disclosure does not constitute a limitation on the described objects. The description of the described objects is given in the claims or the context of the embodiments, and should not be construed as an unnecessary limitation. Furthermore, in the description of this embodiment, unless otherwise stated, "multiple" means two or more.

[0051] In this embodiment of the disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information comply with relevant laws and regulations and do not violate public order and good morals.

[0052] In this embodiment of the disclosure, the smart home appliance can be a gas water heater. By comparing the gas temperature of the smart home appliance with a preset temperature, the combustion pressure signal of the smart home appliance is obtained. Based on the combustion pressure signal, the combustion resonance risk of the smart home appliance is determined, and the combustion resonance risk result is obtained. Based on the combustion resonance risk result, the smart home appliance is controlled to optimize the combustion resonance of the smart home appliance.

[0053] Furthermore, the smart home appliance can be controlled using a voice module, which is equipped with a controller, a voice receiving module, and a voice parsing module. The voice receiving module receives user commands, and the voice parsing module parses the commands. Based on the parsed commands, the controller controls the smart home appliance to perform corresponding operations, thereby realizing intelligent control of the smart home appliance and improving the user experience.

[0054] It should be noted that the voice module, controller, voice receiving module, voice parsing module, and voice operation logic mentioned in this embodiment are all existing modules and logic in the prior art. This embodiment has not improved them, and will not elaborate further here.

[0055] Example 1

[0056] Figure 1 A flowchart of a control method for a smart home appliance provided in this disclosure embodiment is shown below. Figure 1 As shown, the control method includes:

[0057] S1. Obtain the gas temperature of smart home appliances;

[0058] S2. Based on the comparison between the gas temperature and the preset temperature, obtain the combustion pressure signal of the smart home appliance;

[0059] In this embodiment, the preset temperature is set according to the actual situation, and no specific limitation is made here.

[0060] S3. Determine the combustion resonance risk of smart home appliances based on combustion pressure signals and obtain the combustion resonance risk results;

[0061] S4. Control smart home appliances based on combustion resonance risk results to optimize combustion resonance of smart home appliances.

[0062] In this embodiment, the user load demand Q is first input. Before the smart home appliance (e.g., gas water heater) is run, it will first determine the relationship between the gas temperature Ta and the preset temperature T1. The preset temperature T1 is a preset parameter of the gas temperature. When the gas temperature Ta is lower than the preset temperature T1, combustion resonance is likely to occur.

[0063] In an optional embodiment, S2 includes:

[0064] In response to the gas temperature not exceeding the preset temperature, the gas preheating device is activated to acquire the combustion pressure signal, combustion load, and electric auxiliary heating load of the smart home appliance.

[0065] In this embodiment, if Ta ≤ T1, the gas preheating device is turned on. Specifically, the condition for turning on the gas preheating device is that Ta ≤ T1. During the operation of smart home appliances (e.g., gas water heaters), monitoring is also continuous. However, the gas temperature is low, and combustion resonance may not necessarily occur. Therefore, it is necessary to simultaneously monitor the combustion pressure signal. After processing the combustion pressure signal, if it is determined that resonance will not occur, the gas preheating device will also be turned off. (Resonance is influenced by many factors, such as changes in system resistance at low gas temperatures, long-term use leading to flue blockage, longer flue pipes resulting in greater resistance, and changes in the gas source).

[0066] It should be noted that the electric auxiliary heating load Q2 = Q - Q1, where Q represents the user's load demand and Q1 represents the combustion load.

[0067] In addition, during ignition and combustion, the combustion load Q1 gradually increases from a small value.

[0068] In an optional embodiment, S3 includes:

[0069] Spectral analysis of the combustion pressure signal was performed to obtain a spectrum diagram;

[0070] Obtain the frequency peaks in the spectrum and the preset frequency range of smart home appliances;

[0071] In response to the frequency peak being within the preset frequency range and the amplitude corresponding to the frequency peak being less than or equal to the first preset amplitude, the combustion resonance risk result is determined to be low-risk resonance, and the gas temperature and combustion pressure signals of the smart home appliance are continuously monitored.

[0072] In this embodiment, regardless of the relationship between the gas temperature Ta and the preset temperature T1, a sensor continuously acquires combustion pressure signals during combustion. These signals are transmitted to a processor for signal processing and spectral analysis. The processed pressure signal is then combined with the gas temperature for further analysis. If the gas temperature Ta ≤ T1, the gas preheating device is kept on for heating time t1. If vibration persists after t1, the air-fuel ratio is further adjusted. If the gas temperature Ta > T1, but vibration still exists, the air-fuel ratio is adjusted to correct the current combustion conditions. If adjusting the gas intake temperature and air-fuel ratio fails to alleviate the vibration, the combustion load and electric heating load are allocated, and the user is notified that the equipment requires maintenance.

[0073] In an optional embodiment, S3 includes:

[0074] In response to the fact that the frequency peak is within the preset frequency range and the amplitude corresponding to the frequency peak is less than the first preset amplitude and less than the second preset amplitude, the combustion resonance risk result is determined to be medium-risk resonance, and the air-fuel ratio of the smart home appliance is adjusted.

[0075] The first preset amplitude is smaller than the second preset amplitude.

[0076] In an optional embodiment, S3 includes:

[0077] In response to the frequency peak being within a preset frequency range and the amplitude corresponding to the frequency peak being greater than or equal to a second preset amplitude, the combustion resonance risk result is determined to be high-risk resonance, and the combustion load of the smart home appliance is reduced while the electric auxiliary heating load is increased.

[0078] In this embodiment, the first preset amplitude and the second preset amplitude are both set according to the actual situation, and no specific limitation is made here.

[0079] In an optional embodiment, S2 includes:

[0080] In response to the gas temperature exceeding the preset temperature, the gas preheating device is not activated, the smart home appliance ignites and burns normally, and the combustion pressure signal of the smart home appliance is acquired.

[0081] In this embodiment, if Ta > T1 (i.e., the gas temperature is greater than the preset temperature), the smart home appliance will start combustion at a low load while the gas preheating device is working, and then gradually increase the load until the user's heat load requirement Q is met. This is because when the gas temperature is low, high-load combustion is more prone to resonance. As the load gradually increases and the temperature inside the appliance rises, the gas heating device will shut off (Ta > T1). If the user's actual load requirement is greater than the current combustion load, the electric auxiliary heating device will be activated to heat the water, ensuring that the user's heat load requirement is met even when the initial combustion load is low.

[0082] In practice, a high-frequency dynamic pressure sensor installed near the combustion chamber can be used to capture rapid pressure fluctuations during the combustion process.

[0083] The collected combustion pressure signal contains noise and needs to be preprocessed. Specifically, low-frequency flow fluctuation noise and high-frequency electronic noise are filtered out, and slowly changing DC components or linear trends in the signal are removed, so that the combustion pressure signal fluctuates around the zero line.

[0084] Fast Fourier Transform (FFT): The FFT algorithm can decompose a time-domain signal (the curve of pressure changing with time) into a series of sine waves with different frequencies, amplitudes and phases, i.e., a spectrum. The X-axis is the frequency (Hz) and the Y-axis is the amplitude. Each "peak" on the spectrum represents a periodic fluctuation of that frequency in the original signal.

[0085] Obtaining the natural frequency of a smart home appliance (e.g., a gas water heater): The combustion chamber and the connected flue form an acoustic cavity with its own acoustic modal frequencies. These natural frequencies can be measured in the laboratory. However, due to differences in flue installation methods and lengths, the resistance varies from home to home. Therefore, after the smart home appliance is installed, a wideband excitation can be applied using a vibrator when the appliance is cold (not ignited). The pressure response spectrum can then be measured, and the frequency peak corresponding to the natural frequency is the natural frequency. This step can be performed by the after-sales installation technician. Alternatively, a vibrator can be configured in the smart home appliance (e.g., a gas water heater) to automatically complete the process after the appliance is installed and powered on.

[0086] For example, suppose that the combustion system of this type of smart home appliance (e.g., a gas water heater) has two main inherent acoustic modes around 125Hz and 350Hz.

[0087] a. Determine if the frequency peak is near the inherent frequency (e.g., within a preset frequency range). (For example, if the frequency peak spikes within the range of 125Hz ± 5Hz, it indicates that it falls near the inherent frequency. ± 5Hz can also be set to other values. A larger value results in a larger risk identification range, requiring more data processing; a smaller value results in a smaller risk identification range, requiring less data processing but potentially missing some combustion frequencies. If no explicit number is needed, letters can be used, such as F for inherent frequency and F±x for range.)

[0088] b. Determine whether the peak value in the real-time spectrum falls within the preset frequency range. If so, obtain the amplitude A corresponding to that peak value (amplitude means energy level). If the amplitude A exceeds the second preset amplitude and continues to increase, it means that positive feedback has been formed, combustion oscillation is being amplified, and the risk of resonance is extremely high. (The second preset amplitude needs to be determined based on a large amount of experimental data, with a certain safety margin. If a range is to be specified, several parameters can be assumed. If the amplitude at the peak is set as A, then the safety threshold can be set as the first preset amplitude A1 and the second preset amplitude A2, where A1 < A2).

[0089] c. If a is true, and the amplitude of b is small (A < A1), then the risk of resonance is low, no action is needed, and monitoring continues.

[0090] If a is true, and the amplitude of b is large (A1≤A≤A2), then it is judged to be in resonance risk. Adjusting the air-fuel ratio changes the combustion frequency, causing it to deviate from the natural frequency. Small adjustments to the air-fuel ratio are made (the process involves adjusting the fan current of the smart appliance and the current of the gas proportional valve. The adjustment strategy can be to decrease the proportional valve current and fan current, or to increase the proportional valve current and fan current. Small simultaneous increases / decreases in both currents ensure that the user's load demand remains unchanged).

[0091] If a is true, the amplitude of b is very large (A>A2), then it is judged that the resonance risk is high. The combustion load Q1 is immediately reduced (i.e., the combustion load is reduced to make the resonance point separate quickly), and the electric auxiliary heating load Q2 is increased (to ensure that Q1+Q2 meets the user's load demand Q). However, because the judgment of high resonance risk means that there is a problem with the smart home appliance, although the user's demand can be met by electric auxiliary heating in a short period of time, a fault code still needs to be reported to remind the user that the smart home appliance needs to be repaired.

[0092] Furthermore, the process of improving combustion resonance by adjusting the ratio of combustion load Q1 and electric auxiliary heating load Q2 is as follows:

[0093] If a high risk of resonance is identified, the combustion load Q1 is reduced while the electric auxiliary heating load Q2 is increased. This reduces the combustion load, causing the combustion frequency to deviate from its natural frequency (i.e., the preset frequency range), and simultaneously reduces the combustion amplitude. The reduction ratio of the combustion load Q1 is based on the heating capacity of the electric auxiliary heating load Q2. For example, if the user's required load is Q and the water flow rate is q, then Q = Q1 + Q2 = Q1 + c * ρ * q * ΔT.

[0094] Given the user's demand load Q, c (specific heat of water), ρ (water density), q, and ΔT (user-set outlet water temperature - inlet water temperature), we can obtain the maximum electric heating capacity and thus the minimum combustion load value Q1a. Q1 ≥ {Q1a, Q0}max, where Q0 is the minimum combustion load of the water heater.

[0095] When adjusting the ratio of combustion load Q1 and electric auxiliary heating load Q2, gradually reduce the combustion load Q1 and check whether the resonance risk has decreased to a low level. If the risk decreases to a low level before Q1 ≥ {Q1a, Q0}max, continue operating until the task is completed, and notify the user for maintenance. If the risk does not decrease to a low level after Q1 ≥ {Q1a, Q0}max, it indicates that the combustion load is already very small, and it is impossible to further reduce the combustion load or further reduction cannot meet the user's load requirements. In this case, immediately shut down the unit and notify the user for maintenance.

[0096] This embodiment uses the gas temperature and combustion pressure signal of smart home appliances to determine the risk of combustion resonance, and controls the smart home appliances according to the combustion resonance risk results to optimize the combustion resonance of smart home appliances and avoid the situation where combustion resonance is likely to occur when the temperature of smart home appliances is too low.

[0097] Example 2

[0098] Corresponding to the aforementioned embodiment of a control method for smart home appliances, this disclosure also provides an embodiment of a control system for smart home appliances.

[0099] Figure 2 This is a schematic diagram of a control system for a smart home appliance provided in Embodiment 2 of this disclosure, as shown below. Figure 2 As shown, the control system includes:

[0100] The first acquisition module 21 is used to acquire the gas temperature of smart home appliances;

[0101] The second acquisition module 22 is used to acquire the combustion pressure signal of the smart home appliance based on the comparison result between the gas temperature and the preset temperature.

[0102] In this embodiment, the preset temperature is set according to the actual situation, and no specific limitation is made here.

[0103] The third acquisition module 23 is used to determine the combustion resonance risk of smart home appliances based on combustion pressure signals and obtain combustion resonance risk results.

[0104] Control module 24 is used to control smart home appliances based on combustion resonance risk results to optimize combustion resonance of smart home appliances.

[0105] In this embodiment, the user load demand Q is first input. Before the smart home appliance (e.g., gas water heater) is run, it will first determine the relationship between the gas temperature Ta and the preset temperature T1. The preset temperature T1 is a preset parameter of the gas temperature. When the gas temperature Ta is lower than the preset temperature T1, combustion resonance is likely to occur.

[0106] In an optional embodiment, the second acquisition module is used to control the gas preheating device to start in response to the gas temperature not being greater than a preset temperature, and to acquire the combustion pressure signal, combustion load and electric auxiliary heating load of the smart home appliance.

[0107] In this embodiment, if Ta ≤ T1, the gas preheating device is turned on. Specifically, the condition for turning on the gas preheating device is that Ta ≤ T1. During the operation of smart home appliances (e.g., gas water heaters), monitoring is also continuous. However, the gas temperature is low, and combustion resonance may not necessarily occur. Therefore, it is necessary to simultaneously monitor the combustion pressure signal. After processing the combustion pressure signal, if it is determined that resonance will not occur, the gas preheating device will also be turned off. (Resonance is influenced by many factors, such as changes in system resistance at low gas temperatures, long-term use leading to flue blockage, longer flue pipes resulting in greater resistance, and changes in the gas source).

[0108] It should be noted that the electric auxiliary heating load Q2 = Q - Q1, where Q represents the user's load demand and Q1 represents the combustion load.

[0109] In addition, during ignition and combustion, the combustion load Q1 gradually increases from a small value.

[0110] In an optional embodiment, the third acquisition module includes:

[0111] The analysis unit is used to perform spectral analysis on the combustion pressure signal to obtain a spectrum diagram.

[0112] The acquisition unit is used to acquire the frequency peaks in the spectrum and the preset frequency range of smart home appliances;

[0113] The first determining unit is used to determine the combustion resonance risk result as low-risk resonance in response to the frequency peak being within a preset frequency range and the amplitude corresponding to the frequency peak being less than or equal to the first preset amplitude, and to continuously monitor the gas temperature and combustion pressure signals of the smart home appliance.

[0114] In this embodiment, regardless of the relationship between the gas temperature Ta and the preset temperature T1, a sensor continuously acquires combustion pressure signals during combustion. These signals are transmitted to a processor for signal processing and spectral analysis. The processed pressure signal is then combined with the gas temperature for further analysis. If the gas temperature Ta ≤ T1, the gas preheating device is kept on for heating time t1. If vibration persists after t1, the air-fuel ratio is further adjusted. If the gas temperature Ta > T1, but vibration still exists, the air-fuel ratio is adjusted to correct the current combustion conditions. If adjusting the gas intake temperature and air-fuel ratio fails to alleviate the vibration, the combustion load and electric heating load are allocated, and the user is notified that the equipment requires maintenance.

[0115] In an optional embodiment, the third acquisition module further includes:

[0116] The second determining unit is used to determine the combustion resonance risk result as medium-risk resonance in response to the fact that the frequency peak is within a preset frequency range and the amplitude corresponding to the frequency peak is less than the first preset amplitude and less than the second preset amplitude, and to adjust the air-fuel ratio of the smart home appliance.

[0117] The first preset amplitude is smaller than the second preset amplitude.

[0118] In an optional embodiment, the third acquisition module further includes:

[0119] The third determining unit is used to determine the combustion resonance risk result as high-risk resonance in response to the frequency peak being within a preset frequency range and the amplitude corresponding to the frequency peak being greater than or equal to the second preset amplitude, and to reduce the combustion load of the smart home appliance and increase the electric auxiliary heating load.

[0120] In this embodiment, the first preset amplitude and the second preset amplitude are both set according to the actual situation, and no specific limitation is made here.

[0121] In an optional embodiment, the second acquisition module is configured to, in response to the gas temperature being greater than a preset temperature, disable the gas preheating device, allow the smart home appliance to ignite and burn normally, and acquire the combustion pressure signal of the smart home appliance.

[0122] In this embodiment, if Ta > T1 (i.e., the gas temperature is greater than the preset temperature), the smart home appliance will start combustion at a low load while the gas preheating device is working, and then gradually increase the load until the user's heat load requirement Q is met. This is because when the gas temperature is low, high-load combustion is more prone to resonance. As the load gradually increases and the temperature inside the appliance rises, the gas heating device will shut off (Ta > T1). If the user's actual load requirement is greater than the current combustion load, the electric auxiliary heating device will be activated to heat the water, ensuring that the user's heat load requirement is met even when the initial combustion load is low.

[0123] In practice, a high-frequency dynamic pressure sensor installed near the combustion chamber can be used to capture rapid pressure fluctuations during the combustion process.

[0124] The collected combustion pressure signal contains noise and needs to be preprocessed. Specifically, low-frequency flow fluctuation noise and high-frequency electronic noise are filtered out, and slowly changing DC components or linear trends in the signal are removed, so that the combustion pressure signal fluctuates around the zero line.

[0125] Fast Fourier Transform (FFT): The FFT algorithm can decompose a time-domain signal (the curve of pressure changing with time) into a series of sine waves with different frequencies, amplitudes and phases, i.e., a spectrum. The X-axis is the frequency (Hz) and the Y-axis is the amplitude. Each "peak" on the spectrum represents a periodic fluctuation of that frequency in the original signal.

[0126] Obtaining the natural frequency of a smart home appliance (e.g., a gas water heater): The combustion chamber and the connected flue form an acoustic cavity with its own acoustic modal frequencies. These natural frequencies can be measured in the laboratory. However, due to differences in flue installation methods and lengths, the resistance varies from home to home. Therefore, after the smart home appliance is installed, a wideband excitation can be applied using a vibrator when the appliance is cold (not ignited). The pressure response spectrum can then be measured, and the frequency peak corresponding to the natural frequency is the natural frequency. This step can be performed by the after-sales installation technician. Alternatively, a vibrator can be configured in the smart home appliance (e.g., a gas water heater) to automatically complete the process after the appliance is installed and powered on.

[0127] For example, suppose that the combustion system of this type of smart home appliance (e.g., a gas water heater) has two main inherent acoustic modes around 125Hz and 350Hz.

[0128] a. Determine if the frequency peak is near the inherent frequency (e.g., within a preset frequency range). (For example, if the frequency peak spikes within the range of 125Hz ± 5Hz, it indicates that it falls near the inherent frequency. ± 5Hz can also be set to other values. A larger value results in a larger risk identification range, requiring more data processing; a smaller value results in a smaller risk identification range, requiring less data processing but potentially missing some combustion frequencies. If no explicit number is needed, letters can be used, such as F for inherent frequency and F±x for range.)

[0129] b. Determine whether the peak value in the real-time spectrum falls within the preset frequency range. If so, obtain the amplitude A corresponding to that peak value (amplitude means energy level). If the amplitude A exceeds the second preset amplitude and continues to increase, it means that positive feedback has been formed, combustion oscillation is being amplified, and the risk of resonance is extremely high. (The second preset amplitude needs to be determined based on a large amount of experimental data, with a certain safety margin. If a range is to be specified, several parameters can be assumed. If the amplitude at the peak is set as A, then the safety threshold can be set as the first preset amplitude A1 and the second preset amplitude A2, where A1 < A2).

[0130] c. If a is true, and the amplitude of b is small (A < A1), then the risk of resonance is low, no action is needed, and monitoring continues.

[0131] If a is true, and the amplitude of b is large (A1≤A≤A2), then it is judged to be in resonance risk. Adjusting the air-fuel ratio changes the combustion frequency, causing it to deviate from the natural frequency. Small adjustments to the air-fuel ratio are made (the process involves adjusting the fan current of the smart appliance and the current of the gas proportional valve. The adjustment strategy can be to decrease the proportional valve current and fan current, or to increase the proportional valve current and fan current. Small simultaneous increases / decreases in both currents ensure that the user's load demand remains unchanged).

[0132] If a is true, the amplitude of b is very large (A>A2), then it is judged that the resonance risk is high. The combustion load Q1 is immediately reduced (i.e., the combustion load is reduced to make the resonance point separate quickly), and the electric auxiliary heating load Q2 is increased (to ensure that Q1+Q2 meets the user's load demand Q). However, because the judgment of high resonance risk means that there is a problem with the smart home appliance, although the user's demand can be met by electric auxiliary heating in a short period of time, a fault code still needs to be reported to remind the user that the smart home appliance needs to be repaired.

[0133] Furthermore, the process of improving combustion resonance by adjusting the ratio of combustion load Q1 and electric auxiliary heating load Q2 is as follows:

[0134] If a high risk of resonance is identified, the combustion load Q1 is reduced while the electric auxiliary heating load Q2 is increased. This reduces the combustion load, causing the combustion frequency to deviate from its natural frequency (i.e., the preset frequency range), and simultaneously reduces the combustion amplitude. The reduction ratio of the combustion load Q1 is based on the heating capacity of the electric auxiliary heating load Q2. For example, if the user's required load is Q and the water flow rate is q, then Q = Q1 + Q2 = Q1 + c * ρ * q * ΔT.

[0135] Given the user's demand load Q, c (specific heat of water), ρ (water density), q, and ΔT (user-set outlet water temperature - inlet water temperature), we can obtain the maximum electric heating capacity and thus the minimum combustion load value Q1a. Q1 ≥ {Q1a, Q0}max, where Q0 is the minimum combustion load of the water heater.

[0136] When adjusting the ratio of combustion load Q1 and electric auxiliary heating load Q2, gradually reduce the combustion load Q1 and check whether the resonance risk has decreased to a low level. If the risk decreases to a low level before Q1 ≥ {Q1a, Q0}max, continue operating until the task is completed, and notify the user for maintenance. If the risk does not decrease to a low level after Q1 ≥ {Q1a, Q0}max, it indicates that the combustion load is already very small, and it is impossible to further reduce the combustion load or further reduction cannot meet the user's load requirements. In this case, immediately shut down the unit and notify the user for maintenance.

[0137] This embodiment uses the gas temperature and combustion pressure signal of smart home appliances to determine the risk of combustion resonance, and controls the smart home appliances according to the combustion resonance risk results to optimize the combustion resonance of smart home appliances and avoid the situation where combustion resonance is likely to occur when the temperature of smart home appliances is too low.

[0138] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs.

[0139] Example 3

[0140] Figure 3 This is a schematic diagram of the structure of an electronic device shown in Embodiment 3 of this disclosure. The electronic device can be a smart kitchen appliance. The electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor. When the processor executes the computer program, it implements the control method of the smart home appliance described in any of the above embodiments. Figure 3 The electronic device 90 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0141] like Figure 3As shown, the electronic device 90 can be manifested as a general-purpose computing device, such as a server device. The components of the electronic device 90 may include, but are not limited to: at least one processor 91, at least one memory 92, and a bus 93 connecting different system components (including memory 92 and processor 91).

[0142] Bus 93 includes a data bus, an address bus, and a control bus.

[0143] The memory 92 may include volatile memory, such as random access memory (RAM) 921 and / or cache memory 922, and may further include read-only memory (ROM) 923.

[0144] The memory 92 may also include a program tool 925 (or utility) having a set (at least one) program module 924, such program module 924 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0145] The processor 91 executes various functional applications and data processing by running computer programs stored in the memory 92, such as the smart home appliance control method provided in any of the above embodiments.

[0146] Electronic device 90 can also communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 95. Furthermore, electronic device 90 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 96. Figure 3 As shown, network adapter 96 communicates with other modules of electronic device 90 via bus 93. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with electronic device 90, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0147] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0148] Example 4

[0149] Embodiment 4 of this disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the control method for smart home appliances provided in any of the above embodiments.

[0150] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0151] Example 5

[0152] Embodiment 5 of this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the control method for smart home appliances described in any of the above claims.

[0153] The program code for executing the computer program product of this disclosure can be written in any combination of one or more programming languages, and the program code can be executed entirely on a user device, partially on a user device, as a stand-alone software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0154] While specific embodiments of this disclosure have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of this disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of this disclosure, but all such changes and modifications fall within the scope of protection of this disclosure.

Claims

1. A control method for smart home appliances, characterized in that, The control method includes: Obtain the gas temperature of smart home appliances; Based on the comparison between the gas temperature and the preset temperature, the combustion pressure signal of the smart home appliance is obtained. The combustion resonance risk of smart home appliances is determined based on the combustion pressure signal, and the combustion resonance risk result is obtained. The smart home appliance is controlled based on the combustion resonance risk results to optimize the combustion resonance of the smart home appliance.

2. The control method for smart home appliances as described in claim 1, characterized in that, The step of obtaining the combustion pressure signal of the smart home appliance based on the comparison result of the gas temperature and the preset temperature includes: In response to the gas temperature not exceeding a preset temperature, the gas preheating device is controlled to start, and the combustion pressure signal, combustion load, and electric auxiliary heating load of the smart home appliance are acquired.

3. The control method for smart home appliances as described in claim 2, characterized in that, The step of determining the combustion resonance risk of smart home appliances based on the combustion pressure signal and obtaining the combustion resonance risk result includes: The combustion pressure signal was subjected to spectral analysis to obtain a spectrum diagram; Obtain the frequency peak values ​​in the spectrum and the preset frequency range of the smart home appliances; In response to the frequency peak being within the preset frequency range and the amplitude corresponding to the frequency peak being less than or equal to the first preset amplitude, the combustion resonance risk result is determined to be low-risk resonance, and the gas temperature and combustion pressure signals of the smart home appliance are continuously monitored.

4. The control method for smart home appliances as described in claim 3, characterized in that, The step of determining the combustion resonance risk of smart home appliances based on the combustion pressure signal and obtaining the combustion resonance risk result further includes: In response to the fact that the peak frequency is within the preset frequency range, and the amplitude corresponding to the peak frequency is less than the first preset amplitude and less than the second preset amplitude, the combustion resonance risk result is determined to be medium-risk resonance, and the air-fuel ratio of the smart home appliance is adjusted. Wherein, the first preset amplitude is smaller than the second preset amplitude.

5. The control method for smart home appliances as described in claim 3, characterized in that, The step of determining the combustion resonance risk of smart home appliances based on the combustion pressure signal and obtaining the combustion resonance risk result further includes: In response to the frequency peak being within the preset frequency range and the amplitude corresponding to the frequency peak being greater than or equal to the second preset amplitude, the combustion resonance risk result is determined to be a high-risk resonance, and the combustion load of the smart home appliance is reduced while the electric auxiliary heating load is increased.

6. The control method for smart home appliances as described in claim 1, characterized in that, The step of obtaining the combustion pressure signal of the smart home appliance based on the comparison result of the gas temperature and the preset temperature includes: In response to the gas temperature being greater than the preset temperature, the gas preheating device is not activated, the smart home appliance ignites and burns normally, and the combustion pressure signal of the smart home appliance is acquired.

7. A control system for a smart home appliance, characterized in that, The control system includes: The first acquisition module is used to acquire the gas temperature of smart home appliances; The second acquisition module is used to acquire the combustion pressure signal of the smart home appliance based on the comparison result between the gas temperature and the preset temperature. The third acquisition module is used to determine the combustion resonance risk of smart home appliances based on the combustion pressure signal and obtain the combustion resonance risk result. A control module is used to control the smart home appliance based on the combustion resonance risk results, so as to optimize the combustion resonance of the smart home appliance.

8. A smart home appliance, comprising a memory, a processor, and a computer program stored in the memory and for running on the processor, characterized in that, When the processor executes the computer program, it implements the control method for the smart home appliance as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the control method for the smart home appliance as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the control method for smart home appliances as described in any one of claims 1 to 6.

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