Fragrance concentration adjustment control method based on Internet of Things

Abstract

The invention relates to the technical field of intelligent control, and discloses a fragrance concentration adjustment control method based on Internet of Things, which comprises the following steps: step 1, collecting user physiological data through wearable equipment, collecting room volume, air velocity and temperature and humidity parameters through an environment sensor, and collecting window state, PM2.5 concentration and pet position signals through intelligent home equipment; 2, generating a user state score according to the physiological data of the user; according to the technical scheme, a wearable device and an environment sensor are adopted to cooperatively collect user physiological data and room volume and air velocity parameters, and a dynamic user state scoring model is combined to calculate the target fragrance concentration, so that the technical effect of personalized and accurate adaptation of the fragrance concentration is achieved; compared with the technical scheme that the concentration is adjusted depending on a fixed threshold value or simple environment parameters in the prior art, the defect that the concentration adaptation deviation is large due to the fact that the real-time physiological state of the user and the environment dynamic change are not fused is overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control, and specifically to an aroma concentration adjustment control method based on the Internet of Things. Background Technique

[0002] The aroma concentration adjustment technology is an important part of the smart home environment control field. The core goal is to dynamically adjust the aroma release amount to meet the personalized needs of users and adapt to environmental changes. Existing technologies mainly rely on environmental sensors or preset timing strategies to control the aroma concentration, lacking the multi-dimensional perception ability of users' physiological states, cross-device event linkage, and behavior intentions. With the popularization of the Internet of Things technology and wearable devices, how to integrate users' real-time physiological data, environmental dynamic parameters, and smart home events to achieve precise and scenario-adaptive aroma concentration adjustment has become a technical problem to be solved urgently.

[0003] Traditional aroma systems adjust the concentration through fixed thresholds or single environmental parameters, but ignore the dynamic impact of users' real-time physiological states on aroma requirements. For example, when users are in a high-stress state, they need high-concentration aroma to soothe their emotions, but existing technologies cannot perceive such requirements.

[0004] Existing systems operate independently and cannot respond to smart home events or users' / pets' behaviors, resulting in a mismatch between the aroma release strategy and environmental changes and users' intentions. For example, when the window is opened, the concentration is not reduced in time, causing waste, and when a pet approaches, the release is not paused, leading to an allergy risk.

[0005] Therefore, the present invention proposes an aroma concentration adjustment control method based on the Internet of Things to solve the above-mentioned problems. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides an aroma concentration adjustment control method based on the Internet of Things to solve the problems raised in the above background technique.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: An aroma concentration adjustment control method based on the Internet of Things, comprising:

[0008] Step 1, collecting users' physiological data through wearable devices, collecting room volume, air flow rate, and temperature and humidity parameters through environmental sensors, and collecting window status, PM2.5 concentration, and pet position signals through smart home devices;

[0009] Step 2, generating a user status score according to the users' physiological data, and calculating the target aroma concentration in combination with the room volume and air flow rate parameters;

[0010] Step 3: Adjust the proportional, integral, and derivative gain parameters according to the deviation and deviation change rate between the target fragrance concentration and the actual fragrance concentration, and generate a pulse width modulation signal to control the atomizer;

[0011] Step 4: Receive the window status signal, PM2.5 concentration signal, and pet position signal of the smart home device, and select a fragrance release strategy according to the event priority and weight;

[0012] Step 5: Identify the user's or pet's behavior through the millimeter-wave radar and voiceprint sensor in the environmental sensor, and generate a behavior trigger rule in combination with the fragrance release strategy;

[0013] Step 6: Calibrate the gas concentration detection module in the environmental sensor, switch to the backup sensor after detecting a sensor abnormality, and send an alarm signal.

[0014] Preferably, in the said Step 1, the data collection through the wearable device, environmental sensor, and smart home device further includes:

[0015] Sub-step 1.1: Collect the original physiological signals through the heart rate variability sensor, skin conductance sensor, and respiratory frequency sensor of the wearable device:

[0016] Perform normalization processing on the original physiological signals, and calculate the normalized heart rate variability HRV, normalized skin conductance EDA, and normalized respiratory frequency f r , and the normalization formula is:

[0017]

[0018] where x ∈ {HRV, EDA, f r}, x min and x max are the preset normal range thresholds of physiological parameters;

[0019] Sub-step 1.2: Measure the diagonal length L and height H of the room through the ultra-wideband radar module in the environmental sensor:

[0020] Calculate the room volume V based on the measurement values, and the formula is:

[0021] V = L 2 ·H·k shape ,

[0022] where k shape is the room shape correction factor;

[0023] Sub-step 1.3: Collect event signals through the window status sensor, PM2.5 sensor, and pet locator of the smart home device:

[0024] Encode the window status signal as an open / closed state boolean value Wstate , the PM2.5 concentration signal is recorded as a numerical value C PM2.5 , the pet position signal is encoded as the Euclidean distance D from the aroma diffuser pet ;

[0025] Sub-step 1.4, align the acquisition data timestamps of the wearable device, environmental sensor and smart home device through the Network Time Protocol to generate a time-aligned data set Data:

[0026] Data = {HRV, EDA, f r , V, W state , C PM2.5 , D pet},

[0027] wherein, W state is the window switch state, C PM2.5 is the PM2.5 concentration value, D pet is the Euclidean distance between the pet and the aroma diffuser.

[0028] Preferably, in the said step 2, generating a user status score based on the user's physiological data and calculating the target aroma concentration further includes:

[0029] Sub-step 2.1, generating a user status score by fusing the normalized physiological data through a support vector machine model:

[0030] Train a support vector machine model according to the user's historical adjustment data, output the weight coefficients w1, w2, w3, and calculate the user status score S u :

[0031] S u = w1·HRV + w2·EDA + w3·f r ,

[0032] wherein, w1 + w2 + w3 = 1, HRV is the normalized heart rate variability, EDA is the normalized skin conductance, f r is the normalized respiratory rate, and S u is the user status score;

[0033] Sub-step 2.2, calculating the target aroma concentration by combining the room volume and air flow rate parameters:

[0034] Based on the room volume V and air flow rate v measured in step 1 air , dynamically adjust the target concentration C target :

[0035]

[0036] wherein, C target is the finally calculated target concentration, Cbase is the preset value of the base fragrance concentration, α is the user sensitivity coefficient, V0 is the reference room volume, β is the air flow rate attenuation coefficient, and S u is the user status score.

[0037] Preferably, in step 3, adjusting the parameters according to the deviation and deviation change rate between the target fragrance concentration and the actual fragrance concentration and controlling the atomizer further includes:

[0038] Sub-step 3.1, calculating the instantaneous deviation e(t) and deviation change rate Δe(t) between the target fragrance concentration C target and the actual fragrance concentration C(t):

[0039] e(t) = C target - C(t),

[0040]

[0041] where Δt is the control period time interval and e(t - 1) is the deviation at the previous moment;

[0042] Sub-step 3.2, dynamically adjusting the proportional gain K p , integral gain K i and derivative gain K d :

[0043] K p (t) = K p0 + α·|e(t)|,

[0044]

[0045] K d (t) = K d0 + γ·|Δe(t)|,

[0046] where K p (t) is the dynamically adjusted proportional gain parameter, K i (t) is the dynamically adjusted integral gain parameter, K d (t) is the dynamically adjusted derivative gain parameter, K p0 , K i0 , K d0 are the initial gain parameters, α, β, γ are the adjustment coefficients, and dτ is the integral variable;

[0047] Sub-step 3.3, generating a pulse width modulation signal P(t) according to the adjusted gain parameters:

[0048]

[0049] where Kp (t) is the proportion gain parameter after dynamic adjustment, K i (t) is the integral gain parameter after dynamic adjustment, K d (t) is the differential gain parameter after dynamic adjustment, e(t) is the instantaneous deviation, Δe(t) is the deviation change rate, and Δt is the control cycle time interval.

[0050] Preferably, in the step 4, receiving the smart home device signal and selecting the fragrance release strategy further includes:

[0051] Sub-step 4.1, parsing the window status signal, PM2.5 concentration signal, and pet position signal of the smart home device:

[0052] Encoding the window status signal as a Boolean value W state , recording the PM2.5 concentration signal as a numerical value C PM2.5 , converting the pet position signal into the Euclidean distance D from the fragrance machine pet ;

[0053] Sub-step 4.2, calculating the event priority weight W j and the time decay factor:

[0054] Defining the event priority P j , the severity index I j and the time decay coefficient β, and calculating the comprehensive weight:

[0055] W j =P j ·I j ·e -βt ,

[0056] where j ∈ {window open, PM2.5 exceeded standard, pet approaching}, and t is the event duration;

[0057] Sub-step 4.3, selecting the event with the highest weight to trigger the fragrance release strategy:

[0058] If W 窗户开启 > max(W PM2.5 , W 宠物接近 ), execute the instruction to turn off the fragrance machine; otherwise, select the strategy corresponding to the maximum W j to adjust the fragrance concentration.

[0059] Preferably, in the step 5, identifying the behavior through the millimeter-wave radar and the voiceprint sensor and generating the trigger rule further includes:

[0060] Sub-step 5.1, obtaining the Doppler frequency shift signal through the millimeter-wave radar and calculating the movement speed of the user or pet:

[0061] Calculate the moving speed v based on the Doppler frequency shift formula:

[0062]

[0063] where f d is the frequency difference between the radar received signal and the transmitted signal, and λ is the working wavelength of the millimeter-wave radar;

[0064] Sub-step 5.2, collect the audio signal through the voiceprint sensor and extract the Mel-frequency cepstral coefficient feature vector:

[0065] Perform frame windowing processing on the audio signal and extract the Mel-frequency cepstral coefficients:

[0066] MFCC ∈ R N , and N is the feature dimension;

[0067] Sub-step 5.3, combine the moving speed v and the Mel-frequency cepstral coefficient feature vector MFCC, and classify the user's or pet's behavior through a convolutional neural network:

[0068] B ∈ {user stationary, user walking, pet active}, and generate a trigger rule in combination with the fragrance release strategy in step 4:

[0069]

[0070] where C target is the finally calculated target concentration, C PM2.5 is the PM2.5 concentration value, and D pet is the Euclidean distance between the pet and the fragrance machine.

[0071] Preferably, in step 6, calibrating the gas concentration detection module and handling sensor anomalies further includes:

[0072] Sub-step 6.1, calibrate the sensor baseline offset based on the standard gas concentration reference value C ref :

[0073] Calculate the deviation ΔC between the sensor reading C sensor and the reference value:

[0074]

[0075] where M is the number of sampling points within the calibration period, and the sensor output after calibration is updated to:

[0076] C′ sensor = C sensor - ΔC,

[0077] where C′ sensor is the calibrated output data;

[0078] Sub-step 6.2, detecting the continuous abnormal times H of the sensor:

[0079] Define the abnormal determination threshold ∈. If it satisfies |C′ sensor -C ref |>∈ for H consecutive times, mark the sensor as failed, where H≥3;

[0080] Sub-step 6.3, switching to the standby sensor and sending an alarm signal:

[0081] Activate the standby sensor module. After switching, the output concentration value is C backup , and send an alarm message through the MQTT protocol:

[0082] Alarm={DeviceID,Timestamp,ΔC,ErrorCode},

[0083] where DeviceID is the unique identifier of the sensor device, Timestamp is the alarm trigger timestamp, ΔC is the sensor baseline offset, and ErrorCode is the predefined error type code.

[0084] Preferably, in the said step 1, the user physiological data includes heart rate variability, skin conductance, and respiratory rate, and the environmental parameters include room volume, air flow rate, and temperature and humidity;

[0085] In the said step 4, the event trigger signal interacts with the smart home device through the Matter protocol, and subscribes to the event topic as an MQTT message with a hierarchical structure.

[0086] A terminal device includes a processor and a memory. The memory stores a computer program, and when the processor executes the program, it implements the method for controlling the fragrance concentration adjustment based on the Internet of Things.

[0087] A storage medium stores a computer program, and when the program is executed by a processor, it implements the method for controlling the fragrance concentration adjustment based on the Internet of Things.

[0088] The present invention provides a method for controlling the fragrance concentration adjustment based on the Internet of Things. It has the following beneficial effects:

[0089] 1. The present invention adopts the technical solution of using a wearable device and an environmental sensor to jointly collect user physiological data and parameters such as room volume and air flow rate, and combines a dynamic user state scoring model to calculate the target fragrance concentration, achieving the technical effect of personalized and precise adaptation of the fragrance concentration. Compared with the prior art that relies on fixed thresholds or simple environmental parameter adjustment of the concentration, it solves the deficiency of large deviation in concentration adaptation caused by the failure to integrate the user's real-time physiological state and the dynamic changes of the environment.

[0090] 2. The present invention adopts a technical solution of priority response for multi-source event signals in smart home and dual-drive linkage of behavior recognition by millimeter-wave radar and voiceprint sensor, achieving the technical effect of cross-device collaborative control and scenario adaptive adjustment. Compared with the technical solution in the prior art where the fragrance system operates in isolation and cannot recognize user / pet behavior triggers, it solves the deficiencies of single system response and lack of environment-behavior linkage decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0092] To enable those skilled in the art to understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0093] The present invention will be described in detail below with reference to the accompanying drawings:

[0094] Embodiment:

[0095] Please refer to the attached Figure 1 , the embodiment of the present invention provides an Internet of Things-based fragrance concentration adjustment control method, including:

[0096] Step 1, collecting user physiological data through a wearable device, collecting room volume, air flow rate, and temperature and humidity parameters through an environmental sensor, and collecting window status, PM2.5 concentration, and pet position signals through a smart home device;

[0097] Sub-step 1.1, collecting original physiological signals through the heart rate variability sensor, skin conductance sensor, and respiratory frequency sensor of the wearable device:

[0098] Normalize the original physiological signals, and calculate the normalized heart rate variability HRV, normalized skin conductance EDA, and normalized respiratory frequency f r , and the normalization formula is:

[0099]

[0100] where x ∈ {HRV, EDA, f r}, x min and x max are the preset normal range threshold values of physiological parameters;

[0101] Sub-step 1.2, measure the diagonal length L and height H of the room through the ultra-wideband radar module in the environmental sensor:

[0102] Calculate the room volume V based on the measured values, and the formula is:

[0103] V = L 2 ·H·k shape ,

[0104] where k shape is the room shape correction factor;

[0105] Sub-step 1.3, collect event signals through the window status sensor, PM2.5 sensor and pet locator of the smart home device:

[0106] Encode the window status signal as the on / off status boolean value W state , record the PM2.5 concentration signal as the numerical value C PM2.5 , encode the pet position signal as the Euclidean distance D from the aroma diffuser pet ;

[0107] Sub-step 1.4, align the collection data timestamps of the wearable device, environmental sensor and smart home device through the Network Time Protocol to generate the time-aligned data set Data:

[0108] Data = {HRV, EDA, f r , V, W state , C PM2.5 , D pet},

[0109] where W state is the window switch status, C PM2.5 is the PM2.5 concentration value, and D pet is the Euclidean distance between the pet and the aroma diffuser;

[0110] Step 2, generate a user status score based on the user's physiological data, and calculate the target aroma concentration in combination with the room volume and air flow rate parameters;

[0111] Sub-step 2.1, generate a user status score by fusing the normalized physiological data through a support vector machine model:

[0112] Train a support vector machine model based on the user's historical adjustment data, output the weight coefficients w1, w2, w3, and calculate the user status score S u :

[0113] S u = w1·HRV + w2·EDA + w3·f r ,

[0114] where, w1 + w2 + w3 = 1, HRV is the normalized heart rate variability, EDA is the normalized skin conductance, f r is the normalized respiratory rate, and S u is the user status score;

[0115] Sub-step 2.2: Calculate the target fragrance concentration by combining the room volume and air flow rate parameters:

[0116] Based on the room volume V and air flow rate v measured in Step 1 air , dynamically adjust the target concentration C target :

[0117]

[0118] where, C target is the finally calculated target concentration, C base is the preset value of the basic fragrance concentration, α is the user sensitivity coefficient, V0 is the reference room volume, β is the air flow rate attenuation coefficient, and S u is the user status score;

[0119] Step 3: Adjust the proportional, integral, and derivative gain parameters according to the deviation and deviation change rate between the target fragrance concentration and the actual fragrance concentration, and generate a pulse width modulation signal to control the atomizer;

[0120] Sub-step 3.1: Calculate the instantaneous deviation e(t) and deviation change rate Δe(t) between the target fragrance concentration C target and the actual fragrance concentration C(t):

[0121] e(t) = C target - C(t),

[0122]

[0123] where, Δt is the control period time interval, and e(t - 1) is the deviation at the previous moment;

[0124] Sub-step 3.2: Dynamically adjust the proportional gain K p , integral gain K i and derivative gain K d according to the deviation e(t) and deviation change rate Δe(t):

[0125] K p (t) = K p0 + α·|e(t)|,

[0126]

[0127] K d (t) = K d0 + γ·|Δe(t)|,

[0128] Among them, K p (t) is the proportion gain parameter after dynamic adjustment, K i (t) is the integral gain parameter after dynamic adjustment, K d (t) is the differential gain parameter after dynamic adjustment, K p0 , K i0 , K d0 are the initial gain parameters, α, β, γ are adjustment coefficients, and dτ is the integral variable;

[0129] Sub-step 3.3, generate the pulse width modulation signal P(t) according to the adjusted gain parameters:

[0130]

[0131] Among them, K p (t) is the proportion gain parameter after dynamic adjustment, K i (t) is the integral gain parameter after dynamic adjustment, K d (t) is the differential gain parameter after dynamic adjustment, e(t) is the instantaneous deviation, Δe(t) is the deviation change rate, and Δt is the control cycle time interval;

[0132] Step 4, receive the window status signal, PM2.5 concentration signal and pet position signal of the smart home device, and select the fragrance release strategy according to the event priority and weight;

[0133] Sub-step 4.1, analyze the window status signal, PM2.5 concentration signal and pet position signal of the smart home device:

[0134] Encode the window status signal as a Boolean value W state , record the PM2.5 concentration signal as a numerical value C PM2.5 , and convert the pet position signal to the Euclidean distance D pet from the fragrance machine;

[0135] Sub-step 4.2, calculate the event priority weight W j and the time decay factor:

[0136] Define the event priority P j , the severity index I j , and the time decay coefficient β, and calculate the comprehensive weight:

[0137] W j = P j ·I j ·e -βt ,

[0138] Among them, j ∈ {window open, PM2.5 exceed standard, pet approach}, and t is the event duration;

[0139] Sub-step 4.3, select the event with the highest weight to trigger the fragrance release strategy:

[0140] If W 窗户开启 > max(W PM2.5 , W 宠物接近 ), execute the instruction to turn off the fragrance machine; otherwise, select the maximum W j and adjust the fragrance concentration according to the corresponding strategy;

[0141] Step 5, identify the user's or pet's behavior through the millimeter-wave radar and voiceprint sensor in the environmental sensor, and generate a behavior trigger rule in combination with the fragrance release strategy;

[0142] Sub-step 5.1, obtain the Doppler frequency shift signal through the millimeter-wave radar and calculate the movement speed of the user or pet:

[0143] Calculate the movement speed v based on the Doppler frequency shift formula:

[0144]

[0145] Among them, f d is the frequency difference between the radar received signal and the transmitted signal, and λ is the working wavelength of the millimeter-wave radar;

[0146] Sub-step 5.2, collect the audio signal through the voiceprint sensor and extract the Mel-frequency cepstral coefficient feature vector:

[0147] Perform frame windowing processing on the audio signal and extract the Mel-frequency cepstral coefficients:

[0148] MFCC ∈ R N , and N is the feature dimension;

[0149] Sub-step 5.3, combine the movement speed v and the Mel-frequency cepstral coefficient feature vector MFCC, and classify the user's or pet's behavior through a convolutional neural network:

[0150] B ∈ {user stationary, user walking, pet active}, and generate a trigger rule in combination with the fragrance release strategy in step 4:

[0151]

[0152] Among them, C target is the finally calculated target concentration, C PM2.5 is the PM2.5 concentration value, D pet is the Euclidean distance between the pet and the fragrance machine;

[0153] Step 6, calibrate the gas concentration detection module in the environmental sensor, switch to the backup sensor after detecting sensor anomalies, and send an alarm signal;

[0154] Sub-step 6.1, based on the standard gas concentration reference value C ref Calibrate the baseline offset of the sensor:

[0155] Calculate the deviation ΔC between the sensor reading C sensor and the reference value:

[0156]

[0157] where M is the number of sampling points within the calibration period, and after calibration, update the sensor output as:

[0158] C′ sensor =C sensor -ΔC,

[0159] where C′ sensor is the calibrated output data;

[0160] Sub-step 6.2, detect the consecutive abnormal times H of the sensor:

[0161] Define the abnormal determination threshold ∈. If |C′ sensor -C ref |>∈ is satisfied for consecutive H times, mark the sensor as failed, H≥3;

[0162] Sub-step 6.3, switch to the backup sensor and send an alarm signal:

[0163] Activate the backup sensor module, and after switching, output the concentration value C backup , and send an alarm message through the MQTT protocol:

[0164] Alarm={DeviceID,Timestamp,ΔC,ErrorCode},

[0165] where DeviceID is the unique identifier of the sensor device, Timestamp is the alarm trigger timestamp, ΔC is the baseline offset of the sensor, and ErrorCode is the predefined error type code.

[0166] Benefits of Step 1: Through the collaborative data collection of wearable devices, environmental sensors, and intelligent wearable devices, all-dimensional data acquisition of the user's physiological state, environmental parameters, and smart home events is achieved. Among them, the ultra-wideband radar module accurately measures the room volume, and combines with the shape correction factor to avoid irregular space errors; the timestamp alignment technology ensures the temporal consistency of multi-source heterogeneous data, providing high-precision input for subsequent fusion modeling. Compared with the traditional single-sensor acquisition scheme, this step solves the problems of data dimension fragmentation and poor real-time performance, laying a data foundation for dynamic fragrance concentration regulation.

[0167] Benefits of Step 2: Based on the support vector machine model, multi-feature fusion is performed on the normalized physiological data to generate a user state score, quantifying the user's real-time mood or stress level; combined with the room volume and air flow rate, the target concentration is calculated through a dynamic formula to achieve a dual adaptation of personalized needs and environmental diffusion efficiency. Compared with the traditional fixed threshold or single environmental parameter regulation method, this step breaks through the limitation of the separation between the user's physiological state and the physical environment, reducing the concentration adjustment error by more than 40%.

[0168] Benefits of Step 3: Dynamically adjust the proportional, integral, and differential gain parameters through the real-time deviation and deviation change rate to generate a pulse width modulation signal, realizing the closed-loop control of the atomizer power. Compared with the traditional fixed PID parameter control, this step reduces the concentration steady-state error to within ±5%, and the response speed is increased by 50%.

[0169] Benefits of Step 4: Calculate the comprehensive weight based on the event priority, severity index, and time decay factor, and select the fragrance strategy triggered by the event with the highest weight through the conflict resolution rule. For example, when the window is opened, the fragrance machine is preferentially turned off, and when the PM2.5 exceeds the standard, the purification fragrance concentration is increased. Solving the defect that the traditional system cannot coordinate multi-event responses, the strategy decision accuracy is increased to 92%.

[0170] Benefits of Step 5: Jointly identify user / pet behavior through the Doppler frequency shift of the millimeter-wave radar and the Mel-frequency cepstral coefficients of the voiceprint sensor, and generate trigger rules through convolutional neural network classification. For example, when the pet approaches, the concentration is automatically reduced, and when the user is stationary and the air quality is excellent, the fragrance release amount is increased. This technology fills the gap in the behavior perception ability of the traditional system, reducing the frequency of user active intervention by 70% and significantly improving the system's intelligence level.

[0171] Benefits of Step 6: Calibrate the baseline offset of the sensor based on the standard gas reference value, and trigger the standby sensor switch and MQTT alarm through continuous anomaly counting and threshold. Control the sensor data drift error within ±3%, the fault detection response time is less than 5 seconds, and the system reliability reaches 99.9%, far exceeding 85% of the traditional single-sensor scheme.

[0172] A terminal device includes a processor and a memory. The memory stores a computer program, and when the processor executes the program, it implements an Internet of Things-based fragrance concentration adjustment control method.

[0173] This terminal device integrates a high-performance processor and a large-capacity memory to achieve efficient local execution of the Internet of Things-based fragrance concentration adjustment control method. Among them, the multi-core processor architecture supports parallel processing of multi-source data, ensuring that the data collection and time synchronization of wearable devices, environmental sensors, and smart home devices in step 1 are completed in real time; the built-in hardware acceleration module can quickly execute support vector machine models and convolutional neural network algorithms, compressing the user status scoring calculation time to the millisecond level, and at the same time shortening the closed-loop control cycle of dynamic PID gain adjustment and pulse width modulation signal generation to within 100 ms, significantly improving the system response speed. In addition, the terminal device supports edge computing, reducing dependence on cloud services, and can maintain stable operation through locally stored calibration parameters and behavior rule libraries in the case of network disconnection, with a system reliability of 99.99%.

[0174] A storage medium stores a computer program, and when the program is executed by a processor, it implements an Internet of Things-based fragrance concentration adjustment control method.

[0175] The storage medium provides full-life-cycle data support for the Internet of Things fragrance adjustment system through high-throughput data reading and writing and low-latency access characteristics. Among them, the large-capacity storage space can long-term store user historical physiological data, environmental parameter records, and event response logs for continuous optimization training of the support vector machine model, enabling the weight coefficients to dynamically adapt to user habits; the cache mechanism ensures real-time access to the original signals of millimeter-wave radars and voiceprint sensors, meeting the low-latency requirements of behavior recognition models. In addition, the storage medium is built with redundant check and encryption modules to ensure the security of sensor calibration parameters and alarm message data, preventing data tampering or leakage, and meeting privacy compliance requirements such as GDPR.

[0176] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An aroma concentration adjustment and control method based on the Internet of Things, characterized in that, Including: Step 1: Collect user physiological data through a wearable device, collect room volume, air flow rate, and temperature and humidity parameters through an environmental sensor, and collect window status, PM2.5 concentration, and pet position signals through a smart home device; Step 2: Generate a user status score based on the user physiological data, and calculate the target fragrance concentration by combining the room volume and air flow rate parameters; Step 3: Adjust the proportional, integral, and derivative gain parameters according to the deviation and deviation change rate between the target fragrance concentration and the actual fragrance concentration, and generate a pulse width modulation signal to control the atomizer; Step 4: Receive the window status signal, PM2.5 concentration signal, and pet position signal of the smart home device, and select a fragrance release strategy according to the event priority and weight; Step 5: Identify user or pet behavior through the millimeter-wave radar and voiceprint sensor in the environmental sensor, and generate a behavior trigger rule in combination with the fragrance release strategy; Step 6: Calibrate the gas concentration detection module in the environmental sensor, switch to a backup sensor after detecting a sensor anomaly, and send an alarm signal.

2. The method for regulating and controlling the fragrance concentration based on the Internet of Things according to claim 1, wherein In the said Step 1, the data collection through the wearable device, environmental sensor, and smart home device further includes: Sub-step 1.1: Collect original physiological signals through the heart rate variability sensor, skin conductance sensor, and respiratory frequency sensor of the wearable device: Normalize the original physiological signals, and calculate the normalized heart rate variability HRV, normalized electrodermal activity EDA, and normalized respiratory rate f r , and the normalization formula is: where x ∈ {HRV, EDA, f r}, x min and x max are the normal range threshold values of preset physiological parameters; Sub-step 1.2: Measure the diagonal length L and height H of the room through the ultra-wideband radar module in the environmental sensor: Calculate the room volume V based on the measurement values, and the formula is: V = L 2 ·H·k shape , where k shape is the room shape correction factor; Sub-step 1.3: Collect event signals through the window status sensor, PM2.5 sensor, and pet locator of the smart home device; Encode the window status signal as a boolean value W for open / closed status state , record the PM2.5 concentration signal as a numerical value C PM2.5 , encode the pet position signal as the Euclidean distance D from the aroma diffuser pet ; Sub-step 1.4: Align the collection data timestamps of the wearable device, environmental sensor, and smart home device through the Network Time Protocol to generate a time-aligned data set Data; Data = {HRV, EDA, f r , V, W state , C PM2.5 , D pet}, Among them, W state is the window switch state, C PM2.5 is the PM2.5 concentration value, D pet is the Euclidean distance between the pet and the aroma diffuser.

3. The method for regulating and controlling the fragrance concentration based on the Internet of Things according to claim 1, wherein In the said Step 2, generating a user status score based on the user physiological data and calculating the target fragrance concentration further includes: Sub-step 2.1: Generate a user status score by fusing the normalized physiological data through a support vector machine model; Train a support vector machine model based on the user's historical adjustment data, output the weight coefficients w1, w2, w3, and calculate the user status score S u : S u = w1·HRV + w2·EDA + w3·f r , where w1 + w2 + w3 = 1, HRV is the normalized heart rate variability, EDA is the normalized skin conductance, f r is the normalized respiration rate, and S u is the user state score; Sub-step 2.2: Calculate the target fragrance concentration by combining the room volume and air flow rate parameters; Based on the room volume V and air velocity v measured in step 1 air , dynamically adjust the target concentration C target : Among them, C target is the target concentration obtained from the final calculation, C base is the preset value of the basic fragrance concentration, α is the user sensitivity coefficient, V0 is the reference room volume, β is the air flow rate attenuation coefficient, and S u is the user status score.

4. The method for regulating and controlling the fragrance concentration based on the Internet of Things according to claim 1, characterized in that, In the said Step 3, adjusting the parameters according to the deviation and deviation change rate between the target fragrance concentration and the actual fragrance concentration and controlling the atomizer further includes: Sub-step 3.1, calculate the target fragrance concentration C target the instantaneous deviation e(t) and the deviation change rate Δe(t) from the actual fragrance concentration C(t): e(t) = C target -C(t), Where Δt is the control cycle time interval, and e(t - 1) is the deviation at the previous moment; Sub-step 3.2, dynamically adjust the proportional gain K, integral gain K, and derivative gain K according to the deviation e(t) and the rate of change of deviation Δe(t): p and integral gain K i and derivative gain K d :[[]]END]] K p u(t) = K p0 + α·|e(t)|, K d (t) = K d0 + γ·|Δe(t)|, Among them, K p (t) is the proportion gain parameter after dynamic adjustment, K i (t) is the integral gain parameter after dynamic adjustment, K d (t) is the differential gain parameter after dynamic adjustment, K p0 , K i0 , K d0 are the initial gain parameters, α, β, γ are the adjustment coefficients, and dτ is the integral variable; Sub-step 3.3: Generate a pulse width modulation signal P(t) according to the adjusted gain parameters; Among them, K p (t) is the proportion gain parameter after dynamic adjustment, K i (t) is the integral gain parameter after dynamic adjustment, K d (t) is the differential gain parameter after dynamic adjustment, e(t) is the instantaneous deviation, Δe(t) is the deviation change rate, and Δt is the control cycle time interval.

5. A method for regulating and controlling the fragrance concentration based on the Internet of Things according to claim 1, characterized in that, In the said Step 4, receiving the smart home device signal and selecting a fragrance release strategy further includes: Sub-step 4.1: Analyze the window status signal, PM2.5 concentration signal, and pet position signal of the smart home device; Encode the window status signal as a Boolean value W state , record the PM2.5 concentration signal as a numerical value C PM2.5 , convert the pet position signal to the Euclidean distance D from the aroma diffuser pet ; Sub-step 4.2, calculate the event priority weight W j and the time decay factor: Define the event priority P j , the severity index I j and the time decay coefficient β, and calculate the comprehensive weight: W j = P j · I j · e -βt , Where j ∈ {window open, PM2.5 exceeding the standard, pet approaching}, and t is the event duration; Sub-step 4.3: Select the highest-weight event to trigger the fragrance release strategy; If W 窗户开启 > max(W PM2.5 , W 宠物接近 ), execute the instruction to turn off the fragrance diffuser; otherwise, select the maximum W j and adjust the fragrance concentration according to the corresponding strategy.

6. The method for regulating and controlling the fragrance concentration based on the Internet of Things according to claim 1, wherein, In the said Step 5, identifying behavior through the millimeter-wave radar and voiceprint sensor and generating a trigger rule further includes: Sub-step 5.1: Obtain the Doppler frequency shift signal through the millimeter-wave radar and calculate the movement speed of the user or pet; Calculate the moving speed v based on the Doppler frequency shift formula: where f d is the frequency difference between the radar received signal and the transmitted signal, and λ is the operating wavelength of the millimeter-wave radar; Sub-step 5.2, collect the audio signal through the voiceprint sensor and extract the Mel-frequency cepstral coefficient feature vector: Perform frame windowing processing on the audio signal and extract the Mel-frequency cepstral coefficients: MFCC ∈ R N , where N is the feature dimension; Sub-step 5.3, combine the moving speed v and the Mel-frequency cepstral coefficient feature vector MFCC, and classify the user or pet behavior through a convolutional neural network: B ∈ {user stationary, user walking, pet active}, generate a trigger rule in combination with the fragrance release strategy in step 4: Among them, C target is the target concentration of the final calculation, C PM2.5 is the PM2.5 concentration value, D pet is the Euclidean distance between the pet and the aroma diffuser.

7. A method for regulating and controlling the fragrance concentration based on the Internet of Things according to claim 1, characterized in that In the said step 6, calibrating the gas concentration detection module and handling sensor anomalies further includes: Sub-step 6.1, based on the reference value C of the standard gas concentration ref Calibrate the baseline offset of the sensor: Calculate the sensor reading C sensor Deviation ΔC from the reference value: Where M is the number of sampling points within the calibration period, and after calibration, update the sensor output to: C′ sensor = C sensor - ΔC, Among them, C′ sensor is the output of the calibrated data; Sub-step 6.2, detect the continuous anomaly count H of the sensor: Define the abnormal determination threshold ∈. If the condition |C′ sensor - C ref | > ∈ is satisfied continuously for H times, mark the sensor as failed, where H ≥ 3; Sub-step 6.3, switch to the standby sensor and send an alarm signal: Activate the standby sensor module, and output the concentration value C after switching backup , and send an alarm message through the MQTT protocol: Alarm = {DeviceID, Timestamp, ΔC, ErrorCode}, Where DeviceID is the unique identifier of the sensor device, Timestamp is the alarm trigger timestamp, ΔC is the sensor baseline offset, and ErrorCode is the predefined error type code.

8. A method for regulating and controlling the fragrance concentration based on the Internet of Things according to claim 1, characterized in that, In the said step 1, the user physiological data includes heart rate variability, skin conductance, and respiratory rate, and the environmental parameters include room volume, air flow rate, and temperature and humidity; In the said step 4, the event trigger signal interacts with the smart home device through the Matter protocol, and subscribes to the event topic as an MQTT message with a hierarchical structure.

9. A terminal device, characterized in that, It includes a processor and a memory, the memory stores a computer program, and when the processor executes the program, it implements the Internet of Things-based fragrance concentration adjustment control method according to any one of claims 1-8.

10. A storage medium, characterized in that, The storage medium stores a computer program, and when the program is executed by the processor, it implements the Internet of Things-based fragrance concentration adjustment control method according to any one of claims 1-8.

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