Instability determination method, control method, instability determination system, device and medium for active noise reduction system

Abstract

This disclosure provides an instability determination method, control method, instability determination system, device, and medium for an active noise cancellation system. The instability determination method for the active noise cancellation system includes: acquiring the output power and historical instability data of the active noise cancellation system; and determining that the active noise cancellation system has become unstable in response to the output power meeting a preset condition. The preset condition includes: the output power is greater than a power threshold, which is determined based on the probability density function of the output power obtained by fitting an exponential distribution. This disclosure calculates the power threshold based on the probability density function obtained by fitting an exponential distribution and uses the power threshold as the instability determination condition. The power threshold is not a fixed value, which can adapt to environmental changes and reduce or avoid misjudgments. Using exponential distribution fitting eliminates the need to statistically analyze the output power over a large time window, resulting in better real-time performance and higher accuracy in instability determination, thus helping to improve the noise reduction effect of the active noise cancellation system.

Description

Technical Field

[0001] This disclosure relates to the field of active noise cancellation technology, and in particular to an instability determination method, control method, instability determination system, device and medium for an active noise cancellation system. Background Technology

[0002] Active noise cancellation is a noise control technology that can effectively reduce noise. However, active noise cancellation systems may experience instability during operation, producing high-frequency noise or feedback, which can reduce the noise cancellation effect or even increase noise levels, potentially damaging the speakers and microphones in the active noise cancellation system.

[0003] To prevent instability in active noise cancellation (ADC) systems, current methods typically involve statistically analyzing the output power of the ADC system within different time windows. This statistical approach determines the proportion of time windows in which the output power exceeds a power threshold. However, considering the real-time nature of instability assessment, only a small number of time windows can be statistically analyzed, leading to potential misjudgments and reduced accuracy. Conversely, to ensure accuracy, a larger number of time windows are needed, but this compromises real-time performance. Statistical methods for instability assessment cannot balance real-time performance and accuracy. For example, requiring the output power to exceed a threshold in 30% of the time windows necessitates collecting output power data from numerous different time windows, resulting in significant latency and poor real-time performance. This hinders optimization of ADC and negatively impacts its noise reduction effect. Conversely, collecting output power from only one time window increases the risk of misjudgments and reduces accuracy. Furthermore, existing technologies use fixed power thresholds, which fail to adapt to environmental variations, further complicating instability assessment accuracy. Summary of the Invention

[0004] The technical problem to be solved by this disclosure is to overcome the defect of poor accuracy in instability determination of existing active noise cancellation systems, and to provide an instability determination method, control method, instability determination system, device and medium for active noise cancellation systems.

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

[0006] In a first aspect, a method for determining the instability of an active noise cancellation system is provided, characterized in that the method for determining the instability of the active noise cancellation system includes:

[0007] Obtain the output power of the active noise cancellation system;

[0008] In response to the output power meeting a preset condition, it is determined that the active noise cancellation system has become unstable;

[0009] The preset conditions include: the output power is greater than a power threshold, and the power threshold is determined based on the probability density function of the output power obtained by fitting an exponential distribution.

[0010] Optionally, the active noise reduction system is applied to a range hood, where different operating conditions of the range hood correspond to different probability density functions. The instability determination method of the active noise reduction system further includes:

[0011] Obtain the current operating parameters of the range hood;

[0012] Determine the probability density function that matches the current operating condition parameters, wherein the current operating condition parameters are negatively correlated with the distribution parameters of the probability density function, and the power threshold is negatively correlated with the distribution parameters.

[0013] Optionally, the formula for calculating the power threshold based on the probability density function is as follows:

[0014] f(x) = λe -λx =P

[0015] Where x is the power threshold, f(x) is the probability density function, λ is the distribution parameter of the probability density function, e is the base of the natural logarithm, and P is the cumulative probability value;

[0016] Calculate the power threshold corresponding to the maximum possible value when the cumulative probability value is close to 1.

[0017] Optionally, the preset conditions further include:

[0018] The output power of the active noise cancellation system continues to increase;

[0019] Alternatively, the output power within the first time window is less than the output power within the second time window.

[0020] Alternatively, the output power in the first time window is less than the output power in the second time window, and the output power in the second time window is less than the output power in the third time window;

[0021] The first time window, the second time window, and the third time window are consecutive time windows, with the first time window being the furthest from the current time and the third time window being the closest to the current time.

[0022] Optionally, the active noise reduction system is applied to a range hood, wherein the window sizes of the first time window, the second time window, and the third time window are all larger than the reverberation time inside the range hood;

[0023] And / or, the window sizes of the first time window, the second time window, and the third time window are all less than 40ms.

[0024] Secondly, a control method for an active noise cancellation system is provided, characterized in that the control method for the active noise cancellation system includes:

[0025] Obtain historical instability data of the active noise cancellation system and the determination result of the instability determination method of the active noise cancellation system described above;

[0026] In response to instability of the active noise cancellation system, an instability intervention decision is determined based on the historical instability data.

[0027] Optionally, the historical instability data includes: the number of times instability occurred after the active noise cancellation system was turned on; the step of determining the instability intervention decision corresponding to the historical instability data includes:

[0028] If the number of instability occurrences exceeds a first threshold, the active noise cancellation system is shut down.

[0029] If the number of instability occurrences does not exceed the first threshold, the active noise cancellation system is shut down, restarted after a preset time, and the number of instability occurrences is recounted.

[0030] And / or, after the step of obtaining the determination result of the instability determination method of the active noise cancellation system described in any of the above claims, the control method of the active noise cancellation system further includes:

[0031] In response to the fact that the active noise cancellation system has not become unstable and the number of abnormal events exceeds the second threshold, the active noise cancellation system is shut down and a maintenance prompt is issued;

[0032] The number of anomalies is the number of times the instability occurs that is greater than the first threshold.

[0033] Thirdly, an instability determination system for an active noise cancellation system is provided, the instability determination system for the active noise cancellation system comprising:

[0034] The first acquisition module is used to acquire the output power and historical instability data of the active noise cancellation system;

[0035] The determination module is used to determine that the active noise cancellation system has become unstable in response to the output power meeting the preset conditions;

[0036] The preset conditions include: the output power is greater than a power threshold, and the power threshold is determined based on the probability density function of the output power obtained by fitting an exponential distribution.

[0037] Thirdly, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and used to run on the processor, characterized in that, when the processor executes the computer program, it implements the instability determination method and the control method of the active noise cancellation system described in any of the above claims.

[0038] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, it implements the instability determination method and the control method of the active noise cancellation system described in any of the preceding claims.

[0039] Fifthly, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the instability determination method and the control method of the active noise cancellation system described in any of the above claims.

[0040] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this disclosure.

[0041] The positive and progressive effects of this disclosure are as follows: The instability determination condition of this disclosure is a power threshold. Based on the probability density function of the output power obtained by fitting an exponential distribution, the power threshold is calculated according to the probability density function. Using the power threshold as the instability determination condition, the exponential distribution fitting does not require statistical analysis of the output power over a large number of time windows. Compared with statistical methods for determining instability, it has better real-time performance and higher accuracy in instability determination. The power threshold is not a fixed value but is calculated, which can adapt to changes in the environment, reduce or avoid misjudgments, and help improve the noise reduction effect of the active noise cancellation system. Attached Figure Description

[0042] Figure 1 A flowchart illustrating an instability determination method for an active noise reduction system provided as an exemplary embodiment of this disclosure;

[0043] Figure 2(a) is a probability distribution diagram of the output energy of a loudspeaker in an active noise cancellation system provided by an exemplary embodiment of the present disclosure;

[0044] Figure 2(b) is another probability distribution diagram of the output energy of a loudspeaker in an active noise cancellation system provided by an exemplary embodiment of the present disclosure;

[0045] Figure 3 A flowchart illustrating a control method for an active noise reduction system provided as an exemplary embodiment of this disclosure;

[0046] Figure 4 A schematic diagram of a module for determining the instability of an active noise reduction system, provided as an exemplary embodiment of this disclosure;

[0047] Figure 5 This is a schematic diagram of the structure of an electronic device provided as an exemplary embodiment of the present disclosure. Detailed Implementation

[0048] 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.

[0049] 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.

[0050] Figure 1 A flowchart illustrating an exemplary embodiment of this disclosure provides a method for determining the instability of an active noise cancellation system. The method includes:

[0051] Step S101: Obtain the output power of the active noise cancellation system.

[0052] An active noise cancellation system typically includes a reference microphone, a speaker, and a controller. The reference microphone is used to collect noise signals, the controller receives the noise signals and sends control signals to drive the speaker to output control signals. These control signals cancel out the noise signals emitted by the noise source, thus achieving active noise cancellation. An active noise cancellation system may include multiple reference microphones and speakers. Noise sources include, but are not limited to, range hoods; the specific configuration can be determined based on actual conditions and is not specifically limited here.

[0053] The output power of an active noise cancellation system refers to the output power of the loudspeaker, calculated using control signals from the controller. Specifically, the output power is the ratio of output energy to the number of time points. Output energy is the output energy of the loudspeaker in the active noise cancellation system, specifically the sum of the squares of the amplitudes of the control signals at several time points over a period of time. After calculating the output energy, the output energy is divided by the number of time points to obtain the output power. The number of time points is selected based on the actual situation and is not specifically limited here.

[0054] Step S102: In response to the output power meeting the preset conditions, it is determined that the active noise cancellation system has become unstable.

[0055] The preset conditions include: the output power is greater than the power threshold, and the power threshold is determined based on the probability density function of the output power obtained by fitting an exponential distribution.

[0056] Specifically, the time-domain data of the output power of the active noise cancellation system under normal operation is discretized into multiple time intervals of length T0. The probability density of the output power in each time interval is calculated to obtain the frequency of the output power falling within different power ranges in each time interval. This can be achieved using the Matlab function fitting toolkit, and is not specifically limited here. The process of obtaining the probability density function based on the frequency of the output power falling within different power ranges can be found in the relevant technical descriptions, and will not be elaborated here.

[0057] The probability density function of the exponential distribution is expressed as:

[0058] f(x) = λe -λx

[0059] Where f(x) is the probability density function, x is the power threshold, λ is the distribution parameter of the probability density function, and e is the base of the natural logarithm.

[0060] Given the distribution parameters, the power threshold is determined based on the probability density function. If the output power meets the preset condition, i.e., the output power is greater than the power threshold, it indicates that the speaker is malfunctioning, and the active noise cancellation system is determined to be unstable.

[0061] In this embodiment, the instability determination condition is a power threshold. Based on the probability density function of the output power obtained by fitting an exponential distribution, the power threshold is calculated according to the probability density function. The power threshold is used as the instability determination condition. Using exponential distribution fitting does not require statistical analysis of the output power over a large number of time windows. Compared with statistical methods, it has better real-time performance and higher accuracy in instability determination. The power threshold is not a fixed value but is calculated, which can adapt to changes in the environment, reduce or avoid misjudgments, and help improve the noise reduction effect of the active noise cancellation system.

[0062] In one embodiment, the active noise cancellation system is applied to a range hood, and different operating conditions of the range hood correspond to different probability density functions. The instability determination method for the active noise cancellation system further includes: acquiring the current operating condition parameters of the range hood; and determining the probability density function that matches the current operating condition parameters.

[0063] The current operating condition parameters are negatively correlated with the distribution parameters of the probability density function, and the power threshold is also negatively correlated with the distribution parameters. The larger the current operating condition parameters, the smaller the distribution parameters, and the larger the power threshold.

[0064] Operating parameters include, but are not limited to, motor speed and current. These can be selected according to actual conditions and are not specifically limited here.

[0065] In this embodiment, the power threshold is calculated based on the current operating parameters. The power threshold can be adjusted according to the current operating parameters. When the current operating parameters are large, it indicates that the range hood will generate more noise, so the output power of the active noise cancellation system will also increase, and the power threshold will also increase accordingly. Similarly, when the current operating parameters are small, it indicates that the range hood will generate less noise, so the output power of the active noise cancellation system will decrease, and the power threshold will also decrease accordingly.

[0066] In one embodiment, after obtaining the current operating parameters of the range hood, a probability density function matching the operating conditions that are similar to the current operating parameters is obtained.

[0067] Based on the output power of the active noise cancellation system under multiple different reference operating conditions, the probability density functions corresponding to these reference operating conditions are pre-calculated, obtaining the correspondence between the distribution parameters of the probability density functions and the reference operating conditions. The correspondence between the distribution parameters of the probability density functions and the reference operating conditions is stored in a memory chip in the controller, or it can be stored on other devices, depending on the actual situation; no specific limitation is made here.

[0068] When in use, based on the operating parameters of the reference operating condition, a reference operating condition that is similar to the current operating condition is determined, and the probability density function that matches the reference operating condition is obtained for calculating the power threshold.

[0069] In this embodiment, the probability density functions corresponding to multiple reference operating conditions are pre-calculated and stored. When in use, the similar reference operating conditions can be determined directly based on the current operating condition parameters, and the probability density function matching the reference operating conditions can be obtained for calculating the power threshold. This is simple and easy to implement, effectively saving computational load and improving computational efficiency.

[0070] In one embodiment, after obtaining the current operating parameters of the range hood, an interpolation method is used to determine the probability density function that matches the current operating parameters.

[0071] The distribution parameters of the probability density function and their correspondence with multiple different working conditions are obtained in advance. Since there are deviations between the actual working conditions and the reference working conditions, interpolation methods can be used to calculate the distribution parameters corresponding to the actual working conditions based on the distribution parameters of the probability density function and the correspondence with the working conditions. Interpolation methods include, but are not limited to, linear interpolation, and can be set according to the actual situation without special limitations here.

[0072] In this embodiment, the distribution parameters of the probability density function calculated by interpolation are more accurate, which helps to determine a more accurate power threshold, reduce or avoid misjudgments, improve the accuracy of instability judgment, and thus improve the noise reduction effect of the active noise cancellation system.

[0073] In one embodiment, the formula for calculating the power threshold based on the probability density function is as follows:

[0074] f(x) = λe -λx =P

[0075] Where x is the power threshold, f(x) is the probability density function, λ is the distribution parameter of the probability density function, e is the base of the natural logarithm, and P is the cumulative probability value.

[0076] Calculate the power threshold corresponding to the maximum possible value when the cumulative probability value is close to 1.

[0077] The cumulative probability value can be 0.999, and the specific value can be chosen according to the actual situation; no particular limitation is made here. Based on the experimental data analysis, the output power follows an exponential distribution; the higher the output power, the lower the probability. The probability density function represents the integral of the probability; specifically, f(x) = λe^(-λ / 2). -λx This represents the probability that the output power is less than the power threshold x. The power threshold x is calculated when the cumulative probability value is 0.999. The probability that the output power is greater than the power threshold x in a single time window is one in a thousand. The larger the cumulative probability value, the more accurate the power threshold.

[0078] In this embodiment, the power threshold is determined according to the above calculation formula. The probability that the output power of a single time window is greater than the power threshold is one in a thousand, which effectively reduces or avoids misjudgment, improves the accuracy of instability judgment, and thus improves the noise reduction effect of the active noise cancellation system.

[0079] In one embodiment, output energy is used instead of output power for instability determination. If the output energy exceeds an output energy threshold, the active noise cancellation system is determined to be unstable.

[0080] Output power is the ratio of output energy to the number of time points. Output energy refers to the output energy of the speaker in the active noise cancellation system, specifically the sum of the squares of the amplitudes of the control signals at several time points over a period of time. The number of time points is selected based on the actual situation and is not specifically limited here.

[0081] Since output power is proportional to output energy, for the sake of algorithm simplicity, output energy can be used instead of output power for instability detection, eliminating the need to calculate output power through division and reducing computation time. Assuming a multiplication calculation requires one clock cycle, while a division calculation might require hundreds of clock cycles, using output energy instead of output power avoids the need for division.

[0082] Referring to Figures 2(a) and 2(b), the horizontal axis DA (Energy) represents the estimated output energy of the loudspeaker within a time window, and the vertical axis Probability represents the probability of the output energy occurring, i.e., the ratio of the number of samples with output energy within a certain interval to the total number of samples. It should be noted that since the exact value of the loudspeaker's output energy within a time window cannot be directly calculated, an estimated value is used instead. The estimated value is directly proportional to the exact value of the output energy.

[0083] The formula for calculating the energy threshold based on the probability density function is as follows:

[0084] f(x) = λe -λx′ =P

[0085] Where x′ is the energy threshold, f(x) is the probability density function, λ is the distribution parameter of the probability density function, e is the base of the natural logarithm, and P is the cumulative probability value;

[0086] Calculate the energy threshold corresponding to the maximum possible value when the cumulative probability value is close to 1.

[0087] The cumulative probability value can be 0.999, and the specific value can be chosen according to the actual situation; no particular limitation is made here. Based on experimental data analysis, the output energy follows an exponential distribution, with higher output energy resulting in lower probability. f(x)=λe -λx′ This represents the probability that the output energy is less than the energy threshold x′. The solution is to calculate the energy threshold x′ corresponding to a cumulative probability value of 0.999. A higher cumulative probability value results in a more accurate energy threshold.

[0088] In this embodiment, for the sake of algorithm simplicity, output energy is used instead of output power for instability determination, which avoids division calculations. If the output energy is greater than the energy threshold, the active noise cancellation system is determined to be unstable, eliminating the need to calculate the output power through division, thus reducing computation time and improving computational efficiency.

[0089] In one embodiment, the preset condition further includes: the output power of the active noise cancellation system continuously increases.

[0090] If the output power continues to increase, it indicates that the output energy of the speaker in the active noise cancellation system is continuously increasing, and the output power exceeds the power threshold, suggesting that the active noise cancellation system has become unstable. Experimental data shows that the probability of this phenomenon—a continuous increase in the speaker's output energy—is extremely low when the active noise cancellation system is operating normally; it is a precursor to instability. Instability includes, but is not limited to, feedback and the generation of high-frequency abnormal sounds.

[0091] In this embodiment, continuously increasing the output power is used as the condition for instability determination to timely determine whether the active noise cancellation system becomes unstable and avoid the reduction of the noise cancellation effect after instability.

[0092] In one embodiment, the preset condition further includes: the output power within the first time window is less than the output power within the second time window.

[0093] Among them, the first time window and the second time window are consecutive time windows, and the first time window is the farthest from the current moment. The window sizes of the first time window, the second time window, and the third time window can be 4 - 10 ms, which can be specifically selected according to the actual situation and are not particularly limited here.

[0094] Calculate the output power P1 within the first time window and the output power P2 within the second time window respectively. If the output power meets the preset condition, that is, P1 is greater than the power threshold and P1 < P2, it means that the closer to the current moment, the greater the output power. Since the output power gradually increases, it is determined that the active noise cancellation system becomes unstable.

[0095] Assume that the cumulative probability value is 0.999, then the probability that the output power of a single time window is greater than the power threshold is one in a thousand. If the active noise cancellation system works normally and the output powers within each time window are independent events, that is, the output power within one time window is not affected by the output powers within other time windows, then the misjudgment probability when taking the output powers of two time windows as the condition for instability determination is approximately 0.001 2 .

[0096] In this embodiment, instability determination is performed by comparing the output powers within two consecutive time windows. If the output power within the first time window is less than the output power within the second time window, it is determined that the active noise cancellation system becomes unstable. Only the output powers within two time windows are required for judgment, with a small amount of calculation and good real-time performance, which helps to improve the noise cancellation effect of the active noise cancellation system.

[0097] In one embodiment, the preset condition further includes: the output power within the first time window is less than the output power within the second time window, and the output power within the second time window is less than the output power within the third time window.

[0098] Among them, the first time window, the second time window, and the third time window are consecutive time windows, the first time window is the farthest from the current moment, and the third time window is the closest to the current moment.

[0099] Calculate the output power P1 within the first time window, the output power P2 within the second time window, and the output power P3 within the third time window respectively. If the output power meets the preset conditions, that is, P1 is greater than the power threshold and P1 < P2 < P3, it means that the closer to the current moment, the greater the output power. Since the output power gradually increases, it is determined that the active noise cancellation system has become unstable.

[0100] Assume that the cumulative probability value is taken as 0.999, then the probability that the output power of a single time window is greater than the power threshold is one in a thousand. If the active noise cancellation system works normally and the output powers within each time window are independent events, that is, the output power within one time window is not affected by the output powers within other time windows, then the misjudgment probability when taking the output powers of two time windows as the condition for instability determination is approximately 0.001. 3 .

[0101] Taking the window size of 4 ms as an example, the active noise cancellation system is applied to an oil fume extractor. If the working time after the oil fume extractor is turned on is 2 h, corresponding to 1.8E6 4 - ms time windows, the probability of a single misjudgment is 0.18%. It can be understood that 1.8E6 is the representation in scientific notation, where E represents exponent and 6 represents the power of 10.

[0102] It should be noted that the output powers within more time windows can be obtained and compared to achieve instability determination. The number of output powers within different time windows obtained can be selected according to the actual situation and is not particularly limited here. The fewer the number of output powers within different time windows obtained, the higher the misjudgment probability of instability will be. Similarly, the more the number of output powers within different time windows obtained, the longer the response time of instability determination will be and the worse the real - time performance will be.

[0103] In this embodiment, instability determination is performed by comparing the output powers within three consecutive time windows. By judging through the output powers within three time windows, the misjudgment probability is effectively reduced, which helps to improve the noise reduction effect of the active noise cancellation system.

[0104] In one embodiment, the active noise cancellation system is applied to an oil fume extractor, and the window sizes of the first time window, the second time window, and the third time window are all greater than the reverberation time inside the oil fume extractor.

[0105] The window size of the time window being greater than the reverberation time means that a single external disturbance will only affect one time window, which can avoid the influence of multiple time windows by external disturbances. The acquisition of the reverberation time can refer to the relevant technical descriptions and will not be elaborated here.

[0106] In this embodiment, the window sizes of the first, second, and third time windows are selected with consideration of the reverberation time inside the range hood. This can prevent multiple time windows from being affected by external disturbances, improve the accuracy of the obtained output power, and thus improve the accuracy of the instability determination of the active noise cancellation system.

[0107] In one embodiment, the window sizes of the first time window, the second time window, and the third time window are all less than 40ms.

[0108] Human tapping frequency is generally no more than 20Hz. To avoid the active noise cancellation system being misjudged as unstable due to high-speed manual tapping, the window size can be less than 40ms. The specific size can be set according to the actual situation. The only requirement is to avoid the active noise cancellation system being misjudged as unstable due to high-speed manual tapping. No special restrictions are imposed here.

[0109] Since Hz is a unit of frequency, representing the number of periodic events per second, if a periodic event occurs once every 40ms, then within 1 second, this periodic time will occur 1000ms divided by 40ms, which is 25 times. Therefore, the frequency of this periodic event is 25Hz. For a detailed explanation of the relationship between frequency and time, please refer to the relevant technical descriptions; it will not be elaborated upon here.

[0110] 40ms corresponds to 25Hz. 25Hz is larger than 20Hz, so humans cannot interfere with multiple consecutive time windows, thus avoiding misjudgment.

[0111] In this embodiment, the window sizes of the first, second, and third time windows are selected with consideration of the human tapping frequency, which can avoid misjudgments caused by manual tapping, improve the accuracy of the obtained output power, and thus improve the accuracy of the instability judgment of the active noise cancellation system.

[0112] Figure 3 A flowchart illustrating a control method for an active noise cancellation system provided as an exemplary embodiment of this disclosure. The control method for the active noise cancellation system includes:

[0113] Step S201: Obtain historical instability data of the active noise cancellation system and the determination results of the instability determination method of the active noise cancellation system.

[0114] The controller of the active noise cancellation system includes an instability counting module, which specifically comprises an instability counter and an anomaly counter, both initially set to 0. Upon determining that instability has occurred in the active noise cancellation system, the controller sends an instability signal. When the determination result confirms instability, the value of the instability counter is incremented by 1. Since reducing output power can cause random fluctuations, to minimize the impact of these random fluctuations on the control process of the active noise cancellation system, if no instability signal is detected after time T1 and the value of the instability counter is greater than 0, the value of the instability counter is decremented by 1.

[0115] T1 is the time threshold, which can be set according to the reliability requirements of the ANC system in the application scenario. It is usually set to 2 hours, but the specific value can be selected based on the actual situation, without any particular limitation here. A larger T1 will not be able to reduce the impact of random fluctuations in output power on the control process of the active noise cancellation system, resulting in incorrect instability intervention decisions when the active noise cancellation system has not yet become unstable. Conversely, a smaller T1 will not be able to accurately determine instability, resulting in the inability to execute instability intervention decisions in a timely manner when instability occurs in the active noise cancellation system.

[0116] Step S202: In response to the instability of the active noise cancellation system, determine the instability intervention decision corresponding to the historical instability data.

[0117] The controller of the active noise cancellation system also includes a decision intervention module, which is used to determine the instability intervention decision corresponding to the historical instability data.

[0118] In this embodiment, based on the historical instability data of the active noise cancellation system and the determination results of the instability determination method of the active noise cancellation system, an instability intervention decision corresponding to the historical instability data is determined, which reduces or avoids the negative impact of the instability of the active noise cancellation system on the noise reduction effect and helps to improve the noise reduction effect of the active noise cancellation system.

[0119] In one embodiment, historical instability data includes the number of times instability occurred after the active noise cancellation system was turned on. Step S202 includes: turning off the active noise cancellation system in response to the number of instability occurrences exceeding a first threshold; turning off the active noise cancellation system in response to the number of instability occurrences not exceeding the first threshold, restarting the active noise cancellation system after a preset time, and recounting the number of instability occurrences.

[0120] Specifically, if the number of instability occurrences after the active noise cancellation system is turned on exceeds the first threshold, meaning the instability counter value is greater than the first threshold, it indicates that the active noise cancellation system has experienced multiple instabilities since power-on. In this case, the active noise cancellation system is turned off, and the anomaly counter value is incremented by 1. It should be noted that the instability counter value is reset to zero after a restart, while the anomaly counter value is saved to non-volatile storage media and will remain after a restart.

[0121] Non-volatile storage media include, but are not limited to, disks. The specific choice can be made according to the actual situation, and no particular limitation is made here.

[0122] If the number of instability occurrences is not greater than the first threshold, that is, if the value of the instability counter is not greater than the first threshold, it means that the active noise cancellation system has experienced fewer instability occurrences since power-on this time. In this case, the active noise cancellation system is turned off, restarted after a preset time, and the number of instability occurrences is recounted, that is, the value of the instability counter is reset to zero.

[0123] The first threshold is usually greater than 3, and the preset time is usually greater than 50ms. The specific time can be set according to the actual situation, and no special limit is made here.

[0124] When the obtained judgment result is instability, the value of the instability counter is incremented by 1. Since the instability intervention decision includes shutting down the active noise cancellation system, the instability counter can also be regarded as a record of the number of times the active noise cancellation system has been shut down after this round of power-on.

[0125] When the number of instability occurrences after the active noise cancellation system is turned on exceeds the first threshold, that is, when the value of the instability counter exceeds the first threshold, the value of the anomaly counter is incremented by 1. Since the value of the instability counter exceeding the first threshold may cause the active noise cancellation system to lock up, the anomaly counter can also be seen as a way to record the number of times the active noise cancellation system has been locked up due to excessive restarts.

[0126] In this embodiment, the instability intervention decision is determined based on the number of times instability occurs after activation, which effectively reduces or avoids the negative impact of instability of the active noise cancellation system on the noise reduction effect, and helps to improve the noise reduction effect of the active noise cancellation system.

[0127] In one embodiment, after step S201, the control method for the active noise cancellation system further includes: in response to the active noise cancellation system not becoming unstable and the number of abnormalities exceeding a second threshold, shutting down the active noise cancellation system and issuing a maintenance prompt.

[0128] The anomaly count is the number of times instability occurs that exceeds the first threshold, and it is the value of the anomaly counter. When the active noise cancellation system is turned on, if the number of instability occurrences exceeds the first threshold, that is, when the value of the instability counter exceeds the first threshold, the value of the anomaly counter is incremented by 1.

[0129] Specifically, when the active noise cancellation system does not become unstable, the anomaly counter is checked first. If the value of the anomaly counter is greater than the second threshold, that is, the number of anomalies is greater than the second threshold, the active noise cancellation system is shut down and a maintenance prompt is issued. After the engineer performs maintenance, the value of the anomaly counter is reset to avoid the active noise cancellation system being locked.

[0130] The second threshold is usually greater than 5, and can be set according to the actual situation. No special limit is made here.

[0131] In this embodiment, if the active noise cancellation system does not become unstable and the number of abnormal events exceeds the second threshold, the active noise cancellation system is shut down and a maintenance prompt is issued, which effectively reduces or avoids the negative impact of active noise cancellation system instability on the noise reduction effect and prevents the active noise cancellation system from being locked.

[0132] Corresponding to the aforementioned embodiments of the instability determination method for active noise cancellation systems, this disclosure also provides embodiments of an instability determination system for active noise cancellation systems.

[0133] Figure 4 A schematic diagram of a module for an instability determination system of an active noise cancellation system is provided as an exemplary embodiment of this disclosure. The instability determination system of the active noise cancellation system includes:

[0134] The first acquisition module is used to acquire the output power and historical instability data of the active noise cancellation system.

[0135] The determination module is used to determine that the active noise cancellation system has become unstable in response to the output power meeting the preset conditions.

[0136] The preset conditions include: the output power is greater than the power threshold, and the power threshold is determined based on the probability density function of the output power obtained by fitting an exponential distribution.

[0137] The output power of an active noise cancellation system refers to the output power of the loudspeaker, calculated using control signals from the controller. Specifically, the output power is the ratio of output energy to the number of time points. Output energy is the output energy of the loudspeaker in the active noise cancellation system, specifically the sum of the squares of the amplitudes of the control signals at several time points over a period of time. After calculating the output energy, the output energy is divided by the number of time points to obtain the output power. The number of time points is selected based on the actual situation and is not specifically limited here.

[0138] In this embodiment, the instability determination condition is a power threshold. Based on the probability density function of the output power obtained by fitting an exponential distribution, the power threshold is calculated according to the probability density function. The power threshold is used as the instability determination condition. Using exponential distribution fitting does not require statistical analysis of the output power over a large number of time windows. Compared with statistical methods, it has better real-time performance and higher accuracy in instability determination. The power threshold is not a fixed value but is calculated, which can adapt to changes in the environment, reduce or avoid misjudgments, and help improve the noise reduction effect of the active noise cancellation system.

[0139] In one embodiment, the active noise reduction system is applied to a range hood, where different operating conditions of the range hood correspond to different probability density functions. The instability determination system of the active noise reduction system further includes:

[0140] The second acquisition module acquires the current operating parameters of the range hood.

[0141] The determination module is used to determine the probability density function that matches the current operating condition parameters.

[0142] The current operating parameters are negatively correlated with the distribution parameters of the probability density function, and the power threshold is negatively correlated with the distribution parameters.

[0143] Operating parameters include, but are not limited to, motor speed and current. These can be selected according to actual conditions and are not specifically limited here.

[0144] In this embodiment, the power threshold is calculated based on the current operating parameters. The power threshold can be adjusted according to the current operating parameters. When the current operating parameters are large, it indicates that the range hood will generate more noise, so the output power of the active noise cancellation system will also increase, and the power threshold will also increase accordingly. Similarly, when the current operating parameters are small, it indicates that the range hood will generate less noise, so the output power of the active noise cancellation system will decrease, and the power threshold will also decrease accordingly.

[0145] 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.

[0146] Figure 5 This is a schematic diagram of the structure of an electronic device according to an example embodiment of the present disclosure. 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 instability determination method and the control method of the active noise cancellation system described in any of the above embodiments. Figure 5 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.

[0147] like Figure 5 As 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).

[0148] Bus 93 includes a data bus, an address bus, and a control bus.

[0149] 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.

[0150] The memory 92 may also include a program tool 925 (or utility) having a set (at least one) 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.

[0151] The processor 91 executes various functional applications and data processing by running computer programs stored in the memory 92, such as the instability determination method and control method of the active noise cancellation system provided in any of the above embodiments.

[0152] Electronic device 90 can also communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). This communication can be performed through 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 network, such as the Internet) via network adapter 96. 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 can 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.

[0153] 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.

[0154] This disclosure also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the instability determination method and the control method of the active noise cancellation system provided in any of the above embodiments.

[0155] 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.

[0156] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the instability determination method and the control method of the active noise cancellation system described in any of the above embodiments.

[0157] 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.

[0158] 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 method for determining the instability of an active noise reduction system, characterized in that, The instability determination method of the active noise cancellation system includes: Obtain the output power of the active noise cancellation system; In response to the output power meeting a preset condition, it is determined that the active noise cancellation system has become unstable; The preset conditions include: the output power is greater than a power threshold, and the power threshold is determined based on the probability density function of the output power obtained by fitting an exponential distribution; The active noise reduction system is applied to a range hood, and different operating conditions of the range hood correspond to different probability density functions. The instability determination method of the active noise reduction system further includes: Obtain the current operating parameters of the range hood; Determine the probability density function that matches the current operating condition parameters, wherein the current operating condition parameters are negatively correlated with the distribution parameters of the probability density function, and the power threshold is negatively correlated with the distribution parameters; The formula for calculating the power threshold based on the probability density function is as follows: Where x is the power threshold. Let be the probability density function. The distribution parameters of the probability density function are... is the base of the natural logarithm. This is the cumulative probability value; Calculate the power threshold corresponding to the maximum possible value when the cumulative probability value is close to 1.

2. The instability determination method for an active noise cancellation system as described in claim 1, characterized in that, The preset conditions also include: The output power of the active noise cancellation system continues to increase; Alternatively, the output power within the first time window is less than the output power within the second time window. Alternatively, the output power in the first time window is less than the output power in the second time window, and the output power in the second time window is less than the output power in the third time window; The first time window, the second time window, and the third time window are consecutive time windows, with the first time window being the furthest from the current time and the third time window being the closest to the current time.

3. The instability determination method for an active noise cancellation system as described in claim 2, characterized in that, The active noise reduction system is applied to a range hood, and the window sizes of the first time window, the second time window, and the third time window are all larger than the reverberation time inside the range hood. And / or, the window sizes of the first time window, the second time window, and the third time window are all less than 40ms.

4. A control method for an active noise reduction system, characterized in that, The control method of the active noise cancellation system includes: Obtain historical instability data of the active noise cancellation system and the determination result of the instability determination method of the active noise cancellation system according to any one of claims 1-3; In response to instability of the active noise reduction system, an instability intervention decision is determined based on historical instability data.

5. The control method for the active noise cancellation system as described in claim 4, characterized in that, The historical instability data includes the number of times instability occurred after the active noise cancellation system was activated; the step of determining the instability intervention decision corresponding to the historical instability data includes: If the number of instability occurrences exceeds a first threshold, the active noise cancellation system is shut down. If the number of instability occurrences does not exceed the first threshold, the active noise cancellation system is shut down, restarted after a preset time, and the number of instability occurrences is recounted. And / or, after the step of obtaining the determination result of the instability determination method of the active noise cancellation system according to any one of claims 1-3, the control method of the active noise cancellation system further includes: In response to the fact that the active noise cancellation system has not become unstable and the number of abnormal events exceeds the second threshold, the active noise cancellation system is shut down and a maintenance prompt is issued; The number of anomalies is the number of times the instability occurs that is greater than the first threshold.

6. An instability determination system for an active noise reduction system, characterized in that, The instability determination system of the active noise cancellation system includes: The first acquisition module is used to acquire the output power and historical instability data of the active noise cancellation system; The determination module is used to determine that the active noise cancellation system has become unstable in response to the output power meeting the preset conditions; The preset conditions include: the output power is greater than a power threshold, and the power threshold is determined based on the probability density function of the output power obtained by fitting an exponential distribution; The active noise reduction system is applied to a range hood, and different operating conditions of the range hood correspond to different probability density functions. The instability determination system of the active noise reduction system also includes: The second acquisition module acquires the current operating parameters of the range hood; A determination module is used to determine a probability density function that matches the current operating condition parameters; the current operating condition parameters are negatively correlated with the distribution parameters of the probability density function, and the power threshold is negatively correlated with the distribution parameters; wherein, the calculation formula for determining the power threshold based on the probability density function is: Where x is the power threshold. Let be the probability density function. The distribution parameters of the probability density function are... is the base of the natural logarithm. This is the cumulative probability value; Calculate the power threshold corresponding to the maximum possible value when the cumulative probability value is close to 1.

7. An electronic device 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 instability determination method of the active noise cancellation system according to any one of claims 1 to 3 and the control method of the active noise cancellation system according to claim 4 or 5.

8. 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 instability determination method of the active noise cancellation system according to any one of claims 1 to 3 and the control method of the active noise cancellation system according to claim 4 or 5.

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