A multi-sensor-based active noise reduction control method for a range hood, a range hood, and a storage medium

Abstract

This invention provides a multi-sensor-based active noise reduction control method for range hoods, a range hood itself, and a storage medium, belonging to the field of range hood technology. The method includes acquiring environmental data collected by each sensor; analyzing the environmental data collected by each sensor to determine if any abnormal operating conditions exist; if an abnormal operating condition is determined to exist, recording the duration of the abnormal operating condition; determining whether the duration exceeds a preset threshold, and if so, reducing the noise reduction intensity of the active noise reduction module by a preset amount. This invention solves the safety hazard of alarm sounds being masked when an ANC (Active Noise Cancellation) is installed on a range hood.

Description

Technical Field

[0001] This invention relates to the field of range hood technology, and in particular to an active noise reduction control method for range hoods based on multiple sensors, a range hood, and a storage medium. Background Technology

[0002] As people's living standards continue to improve, noise issues in the kitchen environment are receiving increasing attention. As a core piece of equipment in modern kitchens, range hoods generate fan rotation noise, airflow turbulence noise, and mechanical vibration noise during operation, which has long troubled cooks. This not only affects communication and the auditory experience during cooking but may also potentially harm the user's physical and mental health. To improve this situation, active noise cancellation technology has gradually been introduced into the range hood industry. This technology uses speakers to emit sound waves with the opposite phase to the noise, effectively canceling out the noise and improving the user's cooking experience.

[0003] However, while active noise cancellation technology improves auditory comfort, it also introduces new safety hazards. Various unexpected situations can occur during cooking, such as boiling liquid overflowing and extinguishing the gas flame, cookware drying out and causing a rapid temperature increase, cooking oil overheating and posing a fire risk, or food burning and producing harmful fumes. Timely detection and handling of these abnormal situations are crucial for ensuring the safety of users and their property. When abnormal situations occur, they are often accompanied by audible warning signals, such as the alarm sound of the gas stove's flameout protection device, the buzzer of the smoke detector, or the abnormal noise from the cookware due to dry burning. These warning signals alert users to take timely action. However, when active noise cancellation is activated, these alarm sounds and warnings are significantly weakened or even completely masked along with the noise from the range hood, preventing users from perceiving the danger in time and thus missing the best opportunity to take action, leading to a safety accident. Summary of the Invention

[0004] In view of this, the present invention provides a multi-sensor-based active noise reduction control method for range hoods, a range hood, and a storage medium, aiming to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a multi-sensor-based active noise reduction control method for range hoods. The range hood includes multiple sensors and an active noise reduction module. The method includes the following steps: S1. Acquire environmental data collected by each of the sensors; S2. Analyze the environmental data collected by each sensor to determine whether there are any abnormal operating conditions; S3. If it is determined that the abnormal working condition exists, the duration of the abnormal working condition is recorded. S4. Determine whether the duration exceeds a preset threshold. If so, reduce the noise reduction intensity of the active noise reduction module by a preset amount.

[0006] Optionally, the plurality of said sensors include at least two of the following: temperature sensor, humidity sensor, vibration sensor, sound sensor, pressure sensor, human body sensor, gas sensor, and smoke sensor.

[0007] Optionally, the step of analyzing the environmental data collected by each of the sensors to determine whether there are abnormal operating conditions includes the following steps: Determine whether the environmental data collected by each sensor exceeds a preset value. If the environmental data collected by at least one sensor exceeds the preset value, analyze the environmental data collected by the other sensors to determine whether the abnormal operating condition exists.

[0008] Optionally, the step of determining whether the environmental data collected by each of the sensors exceeds a preset value, and if the environmental data collected by at least one of the sensors exceeds the preset value, then analyzing the environmental data collected by the other sensors to determine whether the abnormal operating condition exists includes the following steps: Determine whether the environmental data collected by each sensor exceeds a preset value. If the environmental data collected by at least one sensor exceeds the preset value, then mark the corresponding sensor as an abnormal sensor. The environmental data collected by the abnormal sensor and associated sensors are analyzed to determine whether the abnormal operating condition exists.

[0009] Optionally, the step of recording the duration of the abnormal operating condition if it is determined that such an abnormal operating condition exists further includes: Analyze the environmental data collected by each sensor to determine the type of abnormal operating condition; In step S4, the preset amount is determined according to the type of abnormal operating condition.

[0010] Optionally, the step of determining whether the duration exceeds a preset threshold, and if so, reducing the noise reduction intensity of the active noise reduction module by a preset amount, specifically includes the following steps: If the duration exceeds a preset threshold, then every time the duration exceeds the preset threshold, the noise reduction intensity of the active noise reduction module is reduced by a preset amount until the active noise reduction module is turned off.

[0011] Optionally, after determining whether the duration exceeds a preset threshold, and if so, reducing the noise reduction intensity of the active noise cancellation module by a preset amount every time the duration exceeds the preset threshold until the active noise cancellation module is turned off, the following steps are further included: An alarm is triggered when the active noise cancellation module is turned off.

[0012] Optionally, after determining whether the duration exceeds a preset threshold, and if so, reducing the noise reduction intensity of the active noise reduction module by a preset amount, the method further includes the following steps: Determine whether the abnormal operating condition has been eliminated; if so, restore the noise reduction intensity of the active noise reduction.

[0013] In addition, to achieve the above objectives, the present invention also provides a range hood, which includes multiple sensors and an active noise reduction module. The active noise reduction module includes a speaker, which is used to emit noise reduction waves to cancel out noise. The range hood applies the active noise reduction control method for range hoods based on multiple sensors described in any of the preceding claims.

[0014] In addition, to achieve the above objectives, the present invention also provides a storage medium containing computer-executable instructions, characterized in that the computer-executable instructions, when executed by a computer processor, are used to execute any of the aforementioned multi-sensor-based active noise reduction control methods for range hoods.

[0015] The beneficial effects of the present invention include at least the following: (1) Eliminate the safety hazard of ANC causing alarm sounds to be masked.

[0016] This embodiment uses multiple sensors to collect multi-dimensional environmental data in real time in step S1, and multi-sensor collaborative analysis in step S2 to effectively distinguish between normal disturbances and real abnormal operating conditions. Step S3 records the duration of the abnormality, and step S4 reduces the intensity of active noise cancellation when the duration exceeds a preset threshold. This solves the technical problem in the prior art where alarm sounds are masked by ANC and cannot be perceived by users, achieving an effective balance between auditory comfort and cooking safety.

[0017] (2) Avoid frequent false triggers caused by instantaneous disturbances.

[0018] This embodiment introduces a duration accumulation judgment mechanism through steps S3 and S4. That is, after the sensor data exceeds the threshold, the noise reduction intensity is not immediately adjusted. Instead, the abnormal duration is recorded first, and the adjustment operation is only performed when the duration exceeds the preset threshold. This effectively filters the instantaneous data disturbance caused by cooking operations, greatly improves the anti-interference capability and judgment accuracy of the system, and avoids the ANC being frequently and unexpectedly adjusted due to instantaneous disturbances, which would affect the user experience. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] To gain a more complete understanding of this application and its beneficial effects, the following description will be provided in conjunction with the accompanying drawings. In the following description, the same reference numerals denote the same parts.

[0021] Figure 1 This is a schematic flowchart illustrating an embodiment of the active noise reduction control method for a range hood based on multiple sensors described in this application. Figure 2 This is a schematic flowchart illustrating an embodiment of the active noise reduction control method for a range hood based on multiple sensors described in this application. Figure 3 This is a schematic flowchart illustrating an embodiment of the active noise reduction control method for a range hood based on multiple sensors described in this application. Figure 4 This is a schematic flowchart illustrating an embodiment of the active noise reduction control method for a range hood based on multiple sensors described in this application. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0023] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0024] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0025] Example 1: refer to Figure 1This invention discloses a multi-sensor-based active noise reduction control method for range hoods. This method acquires environmental data collected by multiple sensors in real time, identifies abnormal conditions during the cooking process, and reduces the intensity of active noise reduction after the abnormal conditions persist for a preset time threshold. This forms a closed-loop control system from environmental perception to abnormal identification, then to dynamic adjustment, and finally to safety alerts. This ensures auditory comfort during active noise reduction while eliminating the safety hazard of alarm sounds being masked by active noise reduction.

[0026] The following is a detailed description of an active noise reduction control method for range hoods based on multiple sensors disclosed in this embodiment. For example... Figure 1 As shown, Figure 1 This is a flowchart illustrating a multi-sensor-based active noise reduction control method for a range hood provided in this embodiment. The range hood hardware system adapted in this embodiment includes at least multiple sensors and an active noise control (ANC) module. The multi-sensor-based active noise reduction control method for a range hood in this embodiment includes the following steps: S1. Acquire environmental data collected by each of the sensors.

[0027] In this embodiment, environmental data during the cooking process is collected in real time using multiple sensors. These sensors are of different types, preferably including, but not limited to, at least two of the following: temperature sensors, humidity sensors, vibration sensors, sound sensors, pressure sensors, human body sensors, gas sensors, and smoke sensors. These sensors can be installed on the range hood itself, such as on the side wall of the smoke collection chamber, or at preset locations within the cooking environment, such as on the cooktop panel or kitchen wall. It is important to note that the installation locations of each sensor in this embodiment should be able to effectively sense the environmental parameters of the cooking area to collect environmental data that accurately reflects the cooking status.

[0028] In this embodiment, the environmental data refers to the data information that each sensor senses and outputs in real time during the cooking process, characterizing the current state of the cooking environment. Specifically, it can be as follows: The environmental data collected by the temperature sensor includes, but is not limited to, at least one of the following: cookware surface temperature, stove flame temperature, and ambient air temperature. Specifically, the cookware surface temperature can be used to determine if the cookware is dry-burning or if the oil temperature is too high; the stove flame temperature can be used to determine if the stove is functioning correctly; and the ambient air temperature can be used to help assess the overall environmental conditions of the cooking area. The rate and magnitude of temperature change can be used to distinguish between normal cooking and abnormal conditions.

[0029] The humidity sensor collects environmental data, including but not limited to the humidity level in the cooking area. A sharp increase in humidity usually corresponds to the generation of a large amount of water vapor, which can be used to help determine whether a boil-over situation has occurred.

[0030] The environmental data collected by the vibration sensor includes, but is not limited to, at least one of the vibration amplitude, vibration frequency, and vibration acceleration values ​​of the range hood body. Abnormal patterns in the vibration data can be used to determine whether the fan is operating abnormally.

[0031] The environmental data collected by the sound sensor includes, but is not limited to, at least one of the following: ambient sound pressure level of the cooking area, sound pressure level of a specific frequency sound wave, and acoustic characteristic values. Changes in the sound data can be used to help determine changes in the cooking process.

[0032] The environmental data collected by the pressure sensor includes, but is not limited to, at least one of the wind pressure and air pressure values ​​within the range hood duct. Changes in wind pressure data can be used to determine whether the fan's operating status is abnormal.

[0033] The environmental data collected by the human body sensor includes, but is not limited to, at least one of the following: human presence status values ​​and human distance values ​​within the cooking area. Human presence status data can be used to determine whether a user is located within the cooking area.

[0034] The environmental data collected by the gas sensors includes, but is not limited to, at least one of the following: methane concentration, carbon monoxide concentration, and volatile organic compound concentration. Abnormal increases in these gas concentrations can be used to identify abnormal operating conditions such as gas leaks and food burning.

[0035] The environmental data collected by the smoke sensor includes, but is not limited to, at least one of the following: the concentration of cooking fumes and the concentration of particulate matter in the cooking area. An abnormal increase in smoke concentration can be used to determine whether abnormal conditions such as burnt food or excessive fumes have occurred.

[0036] In this embodiment, the range hood acquires environmental data collected by each sensor. In a preferred embodiment, after acquiring the environmental data, a preprocessing step is included. This preprocessing may include data validity verification, data filtering, and data format conversion. Data validity verification refers to determining whether the environmental data is within the reasonable measurement range of the corresponding sensor; if it exceeds the reasonable range, it is marked as invalid data and discarded. Data filtering refers to using filtering algorithms to suppress random noise in the environmental data to improve data stability and reliability. Data format conversion refers to unifying the different formats of data output from different sensors into a preset data format, such as unifying data units, data precision, and data encoding methods, to facilitate subsequent data storage, transmission, and processing. The preprocessed environmental data is stored in the range hood and can be classified and arranged according to timestamps to establish a time-indexed environmental data sequence. Each data frame preferably includes a sensor identifier, a collection timestamp, and the corresponding environmental data value, enabling accurate association and correspondence of environmental data collected by different sensors at the same time.

[0037] Understandably, this step involves continuously collecting environmental data of the cooking area in real time using multiple sensors installed on the range hood, providing a raw data source for subsequent identification of abnormal operating conditions.

[0038] S2. Analyze the environmental data collected by each sensor to determine whether there are any abnormal operating conditions.

[0039] In this step, a data analysis algorithm is used to analyze the environmental data of each sensor obtained in step S1 in order to determine whether there are any abnormal working conditions in the cooking environment where the range hood is located.

[0040] Specifically, the abnormal operating conditions described here refer to events or situations that deviate from the normal state during cooking. These abnormal operating conditions preferably include, but are not limited to, overflowing, dry burning, excessively high oil temperature, gas leakage, excessive smoke concentration, and unattended operation. Overflowing refers to the overflow of liquid from the pot during cooking, which may extinguish the gas flame and lead to a gas leak. Dry burning refers to the continuous heating of a pot without water or food, causing a rapid temperature increase that may ignite a fire. Excessively high oil temperature refers to the cooking oil exceeding its smoke point or ignition point, which may spontaneously combust and cause a fire. Gas leakage refers to a leak in the gas pipeline or stove, causing combustible gas to accumulate in the room to a dangerous concentration. Excessively high smoke concentration refers to food burning and producing a large amount of harmful smoke, endangering the user's respiratory health. Unattended operation refers to the stove being unattended for an extended period; if these abnormal operating conditions are not detected and dealt with in a timely manner, they may threaten the user's personal safety or property.

[0041] In one optional implementation, the specific method for analyzing the environmental data collected by each sensor is as follows: the control unit of the range hood compares the environmental data of each sensor obtained in step S1 with their respective preset normal ranges to determine whether each environmental data exceeds its corresponding preset normal range. If it exceeds the aforementioned preset normal range, it is determined that there is an abnormal operating condition.

[0042] Specifically, the preset normal range here is a set of data pre-stored in the control unit's memory, representing the numerical range that each sensor should be in under normal cooking conditions. Each sensor corresponds to an independent and unique preset normal range, which varies depending on the sensor type and the environmental parameters it senses. This can be manifested as different numerical ranges and upper and lower thresholds. For example, the preset normal range for a temperature sensor is a temperature range, such as 20℃-260℃. When the temperature data is below the lower limit or above the upper limit, it is determined to be outside the preset normal range. Similarly, the preset normal range for a gas sensor is set according to the safe concentration threshold of the target gas. For example, the upper limit of the preset normal range for methane can be set to 15% LEL (Lower Explosion Limit).

[0043] The preset normal range values ​​here can be preset and stored by the range hood manufacturer based on existing technology.

[0044] In practical applications, the cooking environment is a complex, dynamic environment influenced by various factors. A single sensor's environmental data exceeding the preset normal range can be caused by a variety of reasons, such as instantaneous fluctuations due to cooking operations, sensor noise, or environmental interference. If an abnormal condition is immediately identified when a single sensor's data exceeds the preset normal range, it may lead to frequent false triggers of the range hood system during normal cooking operations. This causes subsequent ANC (Automatic Control) adjustments to be made repeatedly and unexpectedly, impacting the user experience and system reliability.

[0045] Regarding the aforementioned issues, refer to Figure 2 In a preferred embodiment, the step of analyzing the environmental data collected by each sensor to determine whether there are abnormal operating conditions includes the following steps: Determine whether the environmental data collected by each sensor exceeds a preset value. If the environmental data collected by at least one sensor exceeds the preset value, analyze the environmental data collected by the other sensors to determine whether the abnormal operating condition exists.

[0046] The preferred method is to use a multi-sensor integrated judgment mechanism to determine whether the environmental data collected by each sensor exceeds a preset value. When the environmental data collected by a sensor exceeds its preset normal range, it is not immediately judged as an abnormal working condition, but is comprehensively verified by combining the environmental data collected by other sensors.

[0047] Specifically, firstly, the control unit of the range hood compares the environmental data collected by each sensor with its corresponding preset normal range to determine whether each environmental data exceeds its preset normal range. When it detects that the environmental data collected by at least one sensor exceeds its corresponding preset normal range, the control unit does not immediately confirm an abnormal condition, but rather identifies it as a suspected abnormal condition and initiates a multi-sensor comprehensive analysis program. That is, the control unit comprehensively judges whether the environmental data from each sensor also shows a trend that matches the suspected abnormal condition to determine whether an abnormal condition exists. For example, when the temperature sensor detects that the pot temperature exceeds the preset normal range, the temperature increase alone is not enough to determine whether an abnormal condition exists, because normal cooking methods such as stir-frying will also cause the pot temperature to rise rapidly. At this time, the control unit further acquires the smoke concentration data from the smoke sensor and the gas concentration data from the gas sensor. If both the smoke concentration and the volatile organic compound concentration rise abnormally at the same time, it indicates that the food in the pot may have been burnt or the oil temperature has reached the level of producing a large amount of oil smoke. Combined with the abnormal temperature, it can confirm that there is an abnormal condition of dry burning or excessively high oil temperature; if neither the smoke concentration nor the gas concentration is abnormal, it is determined to be normal stir-frying cooking and is not considered an abnormal condition. For example, in determining if a pot is overflowing, when the humidity sensor detects a sharp increase in humidity data exceeding the preset normal range, the control unit further acquires temperature data from the temperature sensor and sound data from the sound sensor. If the temperature sensor shows no abnormal drop in the pot temperature (when overflowing, a large amount of liquid evaporates, taking away heat, and the pot temperature usually drops), and the sound sensor does not detect characteristic acoustic signals of liquid dripping or boiling overflow, then it is determined to be normal steam generation and is not considered an overflow condition. Conversely, if the humidity data rises abnormally while the temperature data fluctuates and decreases, and the sound data contains acoustic characteristics consistent with liquid dripping or boiling overflow, then an overflow condition is confirmed.

[0048] It is understandable that, through the above-mentioned multi-sensor comprehensive analysis, this implementation method can effectively distinguish between normal operational disturbances and real abnormal working conditions during the cooking process, and significantly reduce the false trigger rate.

[0049] However, the above analysis in conjunction with other sensors still has room for further optimization in practical implementation. When there are many sensors, if all the data from other sensors need to be analyzed every time a suspected abnormal condition occurs, it will increase unnecessary computational overhead. Moreover, some sensors have no physical connection with the current suspected abnormal type, and introducing their data may introduce noise interference.

[0050] Regarding the aforementioned issues, refer to Figure 3 As another preferred embodiment, based on the aforementioned embodiment, the step of determining whether the environmental data collected by each of the sensors exceeds a preset value, and if the environmental data collected by at least one of the sensors exceeds the preset value, then analyzing the environmental data collected by the other sensors to determine whether the abnormal operating condition exists includes the following steps: Determine whether the environmental data collected by each sensor exceeds a preset value. If the environmental data collected by at least one sensor exceeds the preset value, then mark the corresponding sensor as an abnormal sensor. The environmental data collected by the abnormal sensor and associated sensors are analyzed to determine whether the abnormal operating condition exists.

[0051] This preferred method, based on preset sensor association rules, marks sensors corresponding to abnormal environmental data as abnormal sensors, and analyzes the environmental data collected by the abnormal sensors and their associated sensors to determine whether there is an abnormal operating condition, rather than analyzing all environmental data from other sensors.

[0052] Specifically, the control unit determines whether the environmental data collected by each sensor exceeds a preset normal range. When at least one sensor's environmental data exceeds its preset normal range, the control unit first marks that sensor as an abnormal sensor. Then, the control unit determines the associated sensors that are related to the abnormal sensor according to a preset sensor association table. This sensor association table is pre-stored in the control unit's memory and records the logical relationships between the sensors and their corresponding abnormal operating conditions. For example, in identifying dry burning or excessively high oil temperature conditions, the temperature sensor is associated with the smoke sensor and the gas sensor; in identifying overflow conditions, the humidity sensor is associated with the temperature sensor and the sound sensor, and so on. Since not all sensor data changes when different types of abnormal operating conditions occur, but rather a specific group of sensors synchronously exhibits data change characteristics corresponding to the abnormal type, when a sensor's data is abnormal, it is only necessary to query its associated sensors to complete the abnormality confirmation, without needing to comprehensively analyze the data of all sensors. This reduces computational overhead and avoids interference that might be caused by introducing sensor data unrelated to the type of abnormal operating condition into the judgment.

[0053] After identifying the associated sensors, the control unit acquires environmental data from the abnormal sensor and the associated sensor at the current time and within a preset time window, and analyzes this data to determine whether there are any abnormal operating conditions, without needing to analyze environmental data collected by other sensors.

[0054] In one embodiment, the control unit matches the environmental data from the abnormal sensor and the associated sensor with a preset abnormal operating condition determination rule. The abnormal operating condition determination rule preferably includes at least one of the following: the direction of data change, the magnitude of change, and the temporal relationship that each associated sensor should exhibit under the corresponding abnormal operating condition. When the environmental data from the abnormal sensor and the associated sensor meet the abnormal operating condition determination rule, the control unit confirms the existence of a corresponding abnormal operating condition. When only the data from the abnormal sensor is abnormal while the data from all associated sensors are normal, the control unit marks the event as a transient disturbance, does not determine it as an abnormal operating condition, and continues to maintain the monitoring state. Alternatively, when the environmental data collected by the abnormal sensor is abnormal and the environmental data collected by at least one associated sensor also shows an abnormal change matching the same abnormal operating condition, the control unit confirms the existence of the abnormal operating condition.

[0055] For example, in identifying overflow conditions, when the humidity sensor is marked as an abnormal sensor, the control unit queries the sensor association table to find the associated sensors as the temperature sensor and the sound sensor. The control unit acquires environmental data from the temperature and sound sensors. If the temperature data shows a fluctuating downward trend (due to heat absorption from liquid evaporation leading to cooling), and the sound data contains acoustic characteristics such as dripping or boiling overflow sounds, while the humidity data remains consistently high, the data from all three sensors together meet the overflow condition judgment rules, thus confirming the existence of an abnormal overflow condition. If only the humidity data is abnormal, while the temperature and sound data are normal, the overflow condition judgment rules are not met, and it is not considered an abnormal condition.

[0056] For example, in identifying dry-burning conditions, when the temperature sensor is marked as abnormal, the control unit uses a sensor association table to find the associated sensors as the smoke sensor and the gas sensor. The control unit obtains smoke concentration data from the smoke sensor and volatile organic compound (VOC) concentration data from the gas sensor. If the temperature data continuously rises beyond the preset normal range, and the smoke concentration and VOC concentration data also rise abnormally simultaneously, the data from all three sensors meet the dry-burning condition determination rules, then dry-burning is confirmed. Conversely, if only the temperature data is abnormal while the smoke and gas data are normal, it is determined to be a normal cooking operation such as stir-frying, and is not considered an abnormal condition.

[0057] It should be noted that the relationships in the sensor association table can be configured and updated according to the actual application scenario. They can be pre-set by the manufacturer using existing technology and stored in the control unit before the range hood leaves the factory, or they can be dynamically adjusted during use based on user habits and environmental characteristics using machine learning algorithms. After confirming the abnormal operating condition, the control unit proceeds to step S3.

[0058] Through the above-mentioned preferred method, the control unit only needs to analyze the data of the abnormal sensor and its associated sensors, rather than combining the data of all sensors, thus avoiding the interference of irrelevant data on the judgment result and reducing the consumption of computing resources.

[0059] Understandably, by using multi-sensor collaborative analysis in step S2 to determine whether there are abnormal operating conditions, an accurate and reliable triggering premise is provided for duration recording and noise reduction intensity adjustment in subsequent steps, avoiding ineffective adjustments caused by false triggering and ensuring that noise reduction exit is only for real abnormal operating conditions.

[0060] S3. If it is determined that the abnormal working condition exists, the duration of the abnormal working condition is recorded.

[0061] In this step, after step S2 confirms the existence of an abnormal operating condition, the duration of the abnormal operating condition is recorded. Specifically, in an optional embodiment, when the abnormal operating condition is confirmed in step S2, the control unit obtains the current system time as the starting timestamp and stores the starting timestamp in the control unit's memory. The initial value of the timer is set to 0, and the timer count is incremented in a preset time unit (preferably 1 second) starting from the time corresponding to the starting timestamp. During the timing process, the control unit continuously receives environmental data from each sensor and continuously analyzes the environmental data according to the method in step S2 to determine whether the abnormal operating condition still exists. If the control unit determines that the abnormal operating condition has been eliminated based on the latest environmental data analysis results, the aforementioned determination that the abnormal operating condition has been eliminated can be that the environmental data of all sensors has recovered to the corresponding preset normal range and has continued to reach the preset elimination confirmation time, then the control unit stops timing, calculates the difference between the current system time and the starting timestamp, and this difference is the current duration of the abnormal operating condition. At this time, the timer is reset to zero, and the starting timestamp is cleared. The elimination confirmation time is preferably 3 to 10 seconds. Setting an elimination confirmation time can prevent the timer from being frequently reset and restarted when the abnormal condition disappears briefly and then quickly reappears, thereby ensuring the stability and continuity of the duration recording. If the control unit determines that the abnormal condition still exists during the timing process, the timer continues to accumulate, and the duration is continuously extended.

[0062] Understandably, this step, by recording the duration of abnormal operating conditions, provides an accurate time basis for determining when noise reduction adjustment should be triggered in subsequent steps.

[0063] S4. Determine whether the duration exceeds a preset threshold. If so, reduce the noise reduction intensity of the active noise reduction module by a preset amount.

[0064] In this step, the duration of the abnormal working condition recorded in step S3 is obtained in real time, and the duration is compared with a preset threshold. Based on the comparison result, it is determined whether to perform noise reduction intensity adjustment operation.

[0065] In an optional embodiment, the preset threshold refers to a time length value pre-stored in the range hood, which serves as the time boundary condition for triggering the active noise cancellation intensity adjustment operation. ANC refers to the functional unit in the range hood used to generate and output a reverse sound wave with the opposite phase to the noise signal to cancel the original noise. Noise cancellation intensity refers to the degree to which the reverse sound output by the ANC cancels the original noise, specifically reflected in the amplitude or power of the reverse sound signal output by the ANC. Higher noise cancellation intensity results in a larger amplitude of the reverse sound signal, a more significant cancellation effect on the original noise, and a lower perceived ambient noise level; conversely, lower noise cancellation intensity results in a smaller amplitude of the reverse sound signal, a weaker cancellation effect on the original noise, and a higher perceived ambient noise level. Lowering the noise cancellation intensity of the active noise cancellation module means sending a control command to the ANC, instructing the ANC to reduce the amplitude or power of its output reverse sound signal, thereby weakening the cancellation effect of the reverse sound on the original noise. The preset amount refers to the adjustment step value pre-stored in the range hood corresponding to a single noise cancellation intensity adjustment.

[0066] Specifically, the control unit compares the duration of the abnormal condition recorded in step S3 with a preset threshold. The preset threshold is preferably between 5 and 30 seconds, and more preferably 10 seconds. If the threshold is too short (e.g., less than 5 seconds), the system may intervene prematurely before the user is aware of the abnormality, reducing noise reduction intensity and interfering with normal cooking operations and auditory experience. If the threshold is too long (e.g., greater than 30 seconds), the abnormal condition may have already progressed to a dangerous stage, and the user may not have received any warning signal, delaying the optimal time for intervention. Therefore, the preset threshold value strikes a balance between giving the user time to handle the situation independently and ensuring timely warnings.

[0067] If the duration is less than or equal to a preset threshold, no noise reduction adjustment is performed. If the duration is greater than the preset threshold, it indicates that the duration of the abnormal condition has exceeded the system's allowable buffer range, and noise reduction intervention measures are initiated. The control unit sends a noise reduction intensity adjustment command to the active noise reduction module to reduce the noise reduction intensity of the active noise reduction module by a preset amount. The preset amount is preferably 10% to 25% of the maximum noise reduction intensity of the active noise reduction module, and more preferably 15%. For example, if the amplitude of the reverse sound signal currently output by the active noise reduction module is 100% of the maximum amplitude, and the preset amount is 15%, the control unit instructs the active noise reduction module to adjust the amplitude of the reverse sound signal to 85% of the maximum amplitude.

[0068] refer to Figure 4In a preferred embodiment, if the abnormal operating condition persists after the noise reduction intensity is lowered, the control unit repeats the above adjustment operation once every preset threshold period (i.e., the time length corresponding to each preset threshold after the abnormal operating condition is confirmed), gradually reducing the noise reduction intensity by the same preset amount until the ANC is completely turned off.

[0069] For example, in a specific application scenario, the preset threshold is set to 10 seconds, and the preset amount is set to 15% of the maximum noise reduction intensity. When an abnormal condition is confirmed, the control unit starts timing. If the abnormal condition is not eliminated after 10 seconds, the control unit reduces the noise reduction intensity by 15%, for example, from 100% to 85%. If the abnormal condition is still not eliminated after 20 seconds, the control unit further reduces the noise reduction intensity from 85% to 70%. If the abnormal condition persists after 30 seconds, it continues to be reduced to 55%, and so on, until the noise reduction intensity drops to 0%, and ANC is completely turned off.

[0070] This step-by-step adjustment mechanism, compared to a complete shutdown, avoids the abrupt auditory impact caused by sudden changes in noise reduction intensity. The human auditory system is sensitive to sudden changes in ambient sound. If the noise reduction intensity drops from 100% to 0% instantaneously, the user will experience a strong auditory contrast from extreme quiet to sudden noise, causing discomfort. Step-by-step adjustment allows the ambient noise to recover slowly in a gradual manner, enabling the user's auditory system to smoothly adapt to this change. At the same time, step-by-step noise reduction itself is a gradual warning. As the noise reduction intensity decreases step by step, the user can perceive the ambient noise gradually increasing. This gradual change in ambient sound can attract the user's attention and alertness, prompting the user to actively check the cooking status. In addition, it provides the user with a time window for self-handling. At each stage of the gradual decrease in noise reduction intensity, the user has the opportunity to independently discover and handle abnormalities before the noise reduction is completely shut off, achieving a positive interaction between system intervention and user autonomy. More importantly, the longer the abnormal operating condition lasts, the more likely the user is not to notice the abnormality. The noise reduction intensity decreases by one level after each cycle, and the audibility of the alarm sound caused by the abnormal operating condition increases step by step. Finally, the maximum audibility is reached when the active noise reduction module is completely turned off, ensuring that the user can detect and deal with the danger in time.

[0071] In this embodiment, when the ANC is turned off, the control unit preferably also triggers an alarm. "Triggering an alarm" means that the control unit controls the alarm device of the range hood to issue a prompt signal. The alarm device includes at least one of a buzzer, a speaker, a display panel, and an indicator light. The prompt signal includes at least one of sound, light, and text signals. In this embodiment, when the ANC is adjusted to be completely turned off due to a persistent abnormal condition, the control unit immediately triggers an alarm by emitting a continuous or intermittent buzzing sound through the buzzer, and / or displaying text prompts such as "Abnormal condition detected, please handle promptly" through the display panel, and / or emitting light signals by flashing the indicator light at a preset frequency. It is understood that after the ANC is completely turned off, the alarm sounds and warning sounds in the cooking environment can be clearly heard by the user. However, to prevent the user from leaving the cooking area for a short time and not hearing the alarm, the control unit simultaneously triggers an active alarm at this time, using a combination of buzzing sounds, display information, and flashing lights to remind the user.

[0072] In this embodiment, while triggering the alarm, the control unit also preferably performs at least one of the following operations: controls the range hood to automatically reduce the fan speed or turn off the fan to reduce the noise source intensity and further assist the user in hearing the external alarm sound; sends an alarm notification to the mobile terminal (such as a mobile phone, smartwatch, etc.) bound to the range hood through the wireless communication module, including but not limited to the type of abnormal working condition, the time of occurrence, and suggested handling measures, so that the user can be informed of the abnormal information in a timely manner even if he / she is not in the cooking area.

[0073] In this embodiment, a feedback verification sub-step is also preferably included: after each adjustment of the noise reduction intensity, the control unit continues to acquire environmental data from each sensor to determine whether the abnormal operating condition has been alleviated or eliminated. If the abnormal operating condition persists after the noise reduction intensity is reduced, the noise reduction intensity is further reduced in the aforementioned stepwise manner; if the abnormal operating condition is eliminated after the noise reduction intensity is reduced (for example, the user actively turns off the engine or handles the abnormality after hearing increased ambient noise), the control unit stops further noise reduction adjustment, and preferably gradually restores the ANC noise reduction intensity to a normal level after the abnormal operating condition is eliminated and remains stable for a preset time. By gradually restoring the noise reduction intensity, a smooth transition of the auditory environment after the abnormality is eliminated is achieved, avoiding auditory discomfort caused by sudden restoration of noise reduction and improving the continuity of the user experience.

[0074] The beneficial effects of this embodiment include at least the following aspects: (1) Eliminate the safety hazard of ANC causing alarm sounds to be masked.

[0075] Existing active noise cancellation (ANC) technologies, while reducing range hood noise, simultaneously weaken ambient alarm and warning sounds, preventing users from promptly perceiving potential hazards. This embodiment addresses this by using multiple sensors to collect multi-dimensional environmental data in real-time during step S1, followed by collaborative analysis in step S2 to effectively distinguish between normal disturbances and actual abnormal conditions, recording the duration of the abnormality in step S3, and reducing the ANC intensity in step S4 when the duration exceeds a preset threshold. This solves the technical problem in existing technologies where alarm sounds are masked by ANC and thus undetectable to users, achieving an effective balance between auditory comfort and cooking safety.

[0076] (2) Avoid frequent false triggers caused by instantaneous disturbances.

[0077] Normal operations during cooking, such as tossing the pan and opening the lid, can cause momentary fluctuations in sensor data. Immediately responding to any momentary event exceeding the threshold would lead to frequent false triggers. This embodiment introduces a duration accumulation judgment mechanism in steps S3 and S4. Instead of immediately adjusting the noise reduction intensity after the sensor data exceeds the threshold, it first records the abnormal duration. The adjustment is only performed when the duration exceeds a preset threshold. This effectively filters out momentary data disturbances caused by cooking operations, significantly improving the system's anti-interference capability and judgment accuracy, and preventing the ANC from being frequently and unpredictably adjusted due to momentary disturbances, thus affecting the user experience.

[0078] Example 2: This embodiment is a further description of the aforementioned Embodiment 1.

[0079] The active noise reduction control method for range hoods based on multiple sensors disclosed in this embodiment further includes the step of recording the duration of the abnormal operating condition if it is determined that the abnormal operating condition exists: Analyze the environmental data collected by each sensor to determine the type of abnormal operating condition; In step S4, the preset amount is determined according to the type of abnormal operating condition.

[0080] The difference between this embodiment and Embodiment 1 is that: after determining the existence of abnormal working conditions in step S3, the type of abnormal working conditions is also identified, and in step S4, the preset amount of noise reduction intensity adjustment is dynamically determined according to the type of abnormal working conditions, thereby realizing a differentiated noise reduction exit strategy.

[0081] Specifically, in step S3, after step S2 confirms the existence of an abnormal operating condition, while recording the duration of the abnormal operating condition, the environmental data collected by each sensor is also analyzed to determine the specific type of the current abnormal operating condition. As mentioned above, abnormal operating conditions include various types such as overflow, dry burning, excessively high oil temperature, gas leakage, excessive smoke concentration, and unattended operation.

[0082] In one specific embodiment, the abnormal operating condition type is determined as follows: the control unit of the range hood matches the environmental data characteristics detected by the abnormal sensors in step S2 with the data feature templates corresponding to each type of abnormal operating condition in a preset abnormal operating condition judgment rule library. The judgment rule library stores sensor data feature patterns corresponding to different types of abnormal operating conditions. For example, the data characteristics corresponding to the dry burning condition are that the temperature sensor data continuously rises abnormally and exceeds a first preset temperature threshold, and the smoke concentration data and volatile organic compound concentration data rise abnormally simultaneously; the data characteristics corresponding to the overflow condition are that the humidity sensor data rises sharply and the temperature sensor data shows a fluctuating decrease, and the sound sensor detects acoustic features of liquid dripping or boiling overflow, etc. The control unit compares the current environmental data characteristics of each sensor with the data feature templates of each type of abnormal operating condition mentioned above. When the matching degree exceeds a preset matching threshold (preferably 80%), the type of the current abnormal operating condition is determined.

[0083] In other embodiments, a machine learning classification model can be used to identify abnormal operating condition types. This involves pre-collecting multi-sensor data under various abnormal operating conditions as training samples, and then training the model using classification algorithms such as random forests or neural networks to obtain an abnormal operating condition type classification model. During actual operation, the control unit inputs environmental data from each sensor into the classification model, and the model outputs the type of the current abnormal operating condition.

[0084] After determining the type of abnormal operating condition, in one specific embodiment, the control unit queries a preset value from a pre-defined operating condition type-adjustment parameter mapping table based on the identified abnormal operating condition type. The operating condition type-adjustment parameter mapping table is pre-stored in the control unit's memory, and records the mapping relationship between different types of abnormal operating conditions and their corresponding preset noise reduction intensity adjustment values.

[0085] The table below illustrates an example of a working condition type-adjustment parameter mapping relationship: Understandably, different types of abnormal operating conditions pose varying degrees of threat to user safety, and therefore, the urgency at which the alarm sound can be heard also differs. For example, gas leaks are extremely dangerous, requiring the noise reduction intensity to be lowered as quickly as possible to ensure the alarm is delivered immediately; while excessive smoke concentration also requires attention, its urgency is relatively lower, allowing for a slower noise reduction exit speed, giving users more time to handle the situation independently.

[0086] It is understood that, based on Embodiment 1, this embodiment further identifies the specific types of abnormal working conditions and dynamically determines the preset amount of noise reduction intensity adjustment according to the abnormal type, thereby achieving differentiated responses to abnormal working conditions of different risk levels. High-risk working conditions correspond to large step size for rapid exit of noise reduction, while medium- and low-risk working conditions correspond to small step size for gradual exit. This maximizes the noise reduction experience in normal cooking scenarios while ensuring safety.

[0087] Example 3 The present invention also provides a range hood, which includes multiple sensors and an active noise reduction module. The active noise reduction module includes a speaker for emitting noise-reducing waves to cancel out noise. The range hood applies the multi-sensor-based active noise reduction control method for range hoods described in any of the foregoing embodiments. The beneficial effects of the multi-sensor-based active noise reduction control method for range hoods described in any of the foregoing embodiments are not elaborated further here.

[0088] Example 4: According to embodiments of the present invention, a computer-readable storage medium is provided, comprising various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. The computer-readable storage medium stores computer-executable instructions. When these computer-executable instructions are executed by a computer processor, the processor executes the multi-sensor-based active noise reduction control method for range hoods described in any of the foregoing embodiments.

[0089] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general-purpose hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to enable a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; under the concept of the present invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the present invention as described above, which are not provided in detail for the sake of brevity; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A multi-sensor-based active noise reduction control method for range hoods, characterized in that, The range hood includes multiple sensors and an active noise reduction module, and the method includes the following steps: S1. Acquire environmental data collected by each of the sensors; S2. Analyze the environmental data collected by each sensor to determine whether there are any abnormal operating conditions; S3. If it is determined that the abnormal working condition exists, the duration of the abnormal working condition is recorded. S4. Determine whether the duration exceeds a preset threshold. If so, reduce the noise reduction intensity of the active noise reduction module by a preset amount.

2. The active noise reduction control method for range hoods based on multiple sensors as described in claim 1, characterized in that, The plurality of said sensors include at least two of the following: temperature sensor, humidity sensor, vibration sensor, sound sensor, pressure sensor, human body sensor, gas sensor, and smoke sensor.

3. The active noise reduction control method for range hoods based on multiple sensors as described in claim 1, characterized in that, The step of analyzing the environmental data collected by each sensor to determine whether there are abnormal operating conditions specifically includes the following steps: Determine whether the environmental data collected by each sensor exceeds a preset value. If the environmental data collected by at least one sensor exceeds the preset value, analyze the environmental data collected by the other sensors to determine whether the abnormal operating condition exists.

4. The active noise reduction control method for range hoods based on multiple sensors as described in claim 3, characterized in that, The step of determining whether the environmental data collected by each of the sensors exceeds a preset value, and if the environmental data collected by at least one of the sensors exceeds the preset value, then analyzing the environmental data collected by the other sensors to determine whether the abnormal operating condition exists includes the following steps: Determine whether the environmental data collected by each sensor exceeds a preset value. If the environmental data collected by at least one sensor exceeds the preset value, then mark the corresponding sensor as an abnormal sensor. The environmental data collected by the abnormal sensor and associated sensors are analyzed to determine whether the abnormal operating condition exists.

5. The active noise reduction control method for range hoods based on multiple sensors as described in claim 1, characterized in that, The step of recording the duration of the abnormal operating condition if it is determined that the abnormal operating condition exists further includes: analyzing the environmental data collected by each sensor to determine the type of the abnormal operating condition; In step S4, the preset amount is determined according to the type of abnormal operating condition.

6. The active noise reduction control method for range hoods based on multiple sensors as described in claim 1, characterized in that, The step of determining whether the duration exceeds a preset threshold, and if so, reducing the noise reduction intensity of the active noise reduction module by a preset amount, specifically includes the following steps: If the duration exceeds a preset threshold, then every time the duration exceeds the preset threshold, the noise reduction intensity of the active noise reduction module is reduced by a preset amount until the active noise reduction module is turned off.

7. The active noise reduction control method for range hoods based on multiple sensors as described in claim 6, characterized in that, The step of determining whether the duration exceeds a preset threshold, and if so, reducing the noise reduction intensity of the active noise cancellation module by a preset amount every time the duration exceeds the preset threshold, until the active noise cancellation module is turned off, further includes the following steps: An alarm is triggered when the active noise cancellation module is turned off.

8. The active noise reduction control method for range hoods based on multiple sensors as described in claim 1, characterized in that, The step of determining whether the duration exceeds a preset threshold, and if so, reducing the noise reduction intensity of the active noise reduction module by a preset amount, further includes the following steps: Determine whether the abnormal operating condition has been eliminated; if so, restore the noise reduction intensity of the active noise reduction.

9. A range hood, characterized in that, The range hood includes multiple sensors and an active noise reduction module. The active noise reduction module includes a speaker, which is used to emit noise reduction waves to cancel out noise. The range hood applies the active noise reduction control method for range hoods based on multiple sensors as described in any one of claims 1-8.

10. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the active noise reduction control method for a range hood based on multiple sensors as described in any one of claims 1-8.

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