Bladeless ventilation device control system and method based on multi-sensor data

Abstract

The invention relates to a bladeless ventilation device control system and method based on multi-sensor data. The system comprises a bladeless fan body, an air quality monitoring module, an infrared pyroelectric sensor, an infrared distance sensor, an air purification module and a controller. User presence state detection, infrared thermal signal and distance data processing, user position distribution modeling based on Gaussian kernel density estimation and a Markov chain user behavior prediction method are fused to perceive user presence state and spatial position change in real time. And the controller adaptively adjusts the air speed mode and the air purification intensity according to the environment comfort index model and the user position prediction result, and dynamic directional air supply and precise purification control with the user activity area as the core are achieved. The indoor air treatment efficiency and the user experience are improved, comfort, energy conservation and health are considered, and the method is suitable for various application scenes such as families and offices.

Description

Technical Field

[0001] The present invention belongs to the technical field of ventilation devices, and in particular relates to a control system and method for a bladeless ventilation device based on multi-sensor data. Background Art

[0002] As people's demand for indoor air quality and personalized comfort continues to rise, traditional ventilation equipment such as fans and air conditioners can no longer meet the requirements of refined control and health-oriented use cases. Especially in relatively confined spaces such as homes and offices, users not only want to obtain appropriate wind speed adjustment, but also want to achieve effective improvement in air quality at the same time.

[0003] While some existing bladeless ventilation devices achieve safe, low-noise air delivery, they often suffer from single control methods, unintelligent responses, and limited purification effectiveness. For example, some devices only offer simple wind speed adjustment based on a single sensor (such as a temperature sensor), failing to comprehensively consider user location, air quality fluctuations, and environmental comfort. This results in a disconnect between wind speed control and purification mode, impacting both user experience and energy efficiency.

[0004] Furthermore, existing air purification systems often rely on fixed filtration modes and lack the ability to intelligently identify and adjust responses to varying pollution levels, leading to wasteful filter material resources or insufficient purification capacity. Furthermore, the lack of predictive mechanisms limits the system's ability to proactively adapt to changes in user behavior and environmental conditions.

[0005] In the prior art, Chinese patent CN110030703A discloses an indoor air quality control system and method based on personnel positioning detection, which includes a data acquisition system, a computer system and an indoor environment control system; the data acquisition system includes a personnel counter, an infrared array sensor and an air quality sensor module; the indoor environment control system includes an indoor overall main ventilation and purification system and a ventilation and purification system for a local area space; the indoor overall main ventilation and purification system performs overall indoor ventilation and purification based on the total number of people in the room transmitted to the computer system; the ventilation and purification system for the local area space determines whether to start the ventilation and purification system for the local area space based on the data transmitted to the computer system by the infrared array sensor and the air quality sensor module.

[0006] However, this solution still has the following shortcomings: First, its personnel detection method is mainly based on total headcount and whether there are people in the area. It lacks the refined identification of individual user locations, relative activity status, and spatial behavior trends. This results in a coarse granularity of system control and makes it difficult to achieve personalized air supply or purification responses. Second, the control method is a passive response based on the current detection results. It does not introduce an intelligent prediction model and cannot make forward-looking adjustments based on user behavior patterns or air pollution trends, which affects comfort and system efficiency. In addition, this solution is mainly used in large indoor ventilation systems. It has a complex structure and low integration, making it difficult to adapt to small ventilation equipment such as bladeless fans.

[0007] Therefore, there is an urgent need to provide a new ventilation device that combines multi-source perception, intelligent prediction and adaptive control, which can achieve deep integration of wind speed adjustment and purification functions according to the user's actual presence status, air quality level and behavioral trends, thereby improving air purification efficiency, energy saving and user experience. Summary of the Invention

[0008] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a control system and method for a bladeless ventilation device based on multi-sensor data.

[0009] The purpose of the present invention can be achieved by the following technical solutions:

[0010] In one aspect, the present invention provides a bladeless ventilation device control system based on multi-sensor data, comprising:

[0011] The main control module, based on a high-performance microcontroller MCU, is used to receive various sensor data, perform data preprocessing, fusion analysis and control strategy calculations, and output ventilation control signals;

[0012] The sensor module includes: a human body sensing submodule for detecting the presence and relative position of a user, the human body sensing submodule includes a pyroelectric infrared sensor and an infrared distance sensor; an environmental sensing submodule for collecting real-time ambient temperature, humidity, and air duct pressure information, the environmental sensing submodule includes a temperature and humidity sensor and a pressure sensor; an air quality monitoring submodule for monitoring the concentration of particulate matter and volatile organic gases in the air, the air quality monitoring submodule includes a PM2.5 sensor and a VOC sensor;

[0013] The control execution module is used to control the bladeless ventilation device according to the ventilation control signal output by the main control module;

[0014] The air purification module includes a coarse-efficiency filter layer, a medium-efficiency filter layer and a high-efficiency filter layer, and is used to automatically switch the purification mode according to the air quality level under the control of the control execution module.

[0015] Furthermore, the control execution module includes: a PWM drive submodule, which is used to adjust the speed of the brushless motor of the bladeless ventilation device according to the wind speed control signal output by the main control module to achieve stepless adjustment of the wind speed; a purification control submodule, which is used to control the working state and filtration level of the air purification module; and a communication submodule, which is used to upload the system status and receive remote control instructions via Bluetooth or WiFi.

[0016] Furthermore, the main control module is configured with a multi-sensor data fusion algorithm, which constructs a user presence status model, an environmental comfort index model and an air pollution level model based on the user status, environmental parameters and air quality information collected by the sensor module. Based on the fuzzy logic controller and predictive adjustment algorithm, it outputs control instructions such as wind speed and purification intensity to achieve intelligent adjustment and energy consumption optimization of the ventilation device.

[0017] Another aspect of the present invention provides a bladeless ventilation device control method of the bladeless ventilation device control system based on multi-sensor data as described above, comprising the following steps:

[0018] The sensor modules collect human body sensing data, environmental parameter data and air quality data respectively;

[0019] The main control module pre-processes the data collected by the sensor module;

[0020] The main control module performs fusion operations on the pre-processed data to construct a user presence status model, an environmental comfort index model, and an air pollution level model;

[0021] The main control module, based on the constructed user presence model, environmental comfort index model and air pollution level model, combines the fuzzy logic controller to perform decision operations on the wind speed and purification intensity control variables and generate control instructions;

[0022] The control execution module drives the PWM drive module to adjust the speed of the brushless motor according to the control instructions output by the main control module, and links the air purification module to perform the switching operation of the corresponding filtration level;

[0023] The communication submodule uploads environmental status parameters and equipment operating status to the mobile terminal, and receives user remote control instructions to realize system status monitoring and intervention operations.

[0024] Furthermore, the human body sensing data includes infrared heat signal data obtained by the pyroelectric infrared sensor and distance data between the user and the ventilation device obtained by the infrared distance sensor;

[0025] The environmental parameter data includes ambient temperature data and ambient humidity data obtained by the temperature and humidity sensor, and air duct pressure data obtained by the pressure sensor;

[0026] The air quality data includes the concentration data of inhalable particulate matter in the air obtained by the PM2.5 sensor and the concentration data of volatile organic gas in the air obtained by the VOC sensor.

[0027] Furthermore, the main control module pre-processes the data collected by the sensor module, specifically including:

[0028] De-noising is performed on various raw sensor data, using the Kalman filter algorithm or sliding average algorithm to eliminate short-term interference signals and abnormal fluctuations;

[0029] Add timestamp information to the denoised data and write it to the sensor fusion buffer according to data category for subsequent fusion operation calls.

[0030] Furthermore, the user presence status model building process is as follows:

[0031] At multiple sampling moments, the infrared heat signal intensity data P of the area around the user is obtained through the pyroelectric infrared sensor. i , and obtain the distance data D between the user and the device through the infrared distance sensor i , where i is the sampling number;

[0032] For infrared heat signal intensity data series {P i Perform sliding window average filtering to eliminate short-term jitter interference and obtain a smooth thermal signal sequence The formula is:

[0033]

[0034] Among them, w is the sliding window width, P j represents the infrared heat signal intensity at the jth moment, is the infrared heat signal intensity at the i-th moment after smoothing;

[0035] The processed smoothed thermal signal sequence With the threshold value P th Compare, when satisfied , it is determined that the user is within the detection range of the device at the current sampling moment;

[0036] Sequence of moments based on user presence Combined with its corresponding distance data D tk , use the Gaussian kernel density estimation method to fit the user appearance probability distribution f(D), as the user relative position probability model, and estimate the user's main activity range in space [D min , D max ],in, represents the time sequence of the user’s existence, Dtk represents the distance between the user and the ventilation device at time tk, f(D) is the user position probability distribution function fitted by the Gaussian kernel density estimation method, D min , D max is the minimum and maximum distance of the user activity interval determined by the probability distribution f(D);

[0037] Under the premise of the user's continuous existence, a first-order Markov chain is used to analyze the user's position change sequence {D tk}Establish a transition probability matrix:

[0038] P ij =Pr(D t+1 =d j ∣D t =d i )

[0039] Among them, P ij Indicates that the user distance at time t is d i When the user arrives at time t+1, the distance is d j The transition probability, D t 、D t+1 denotes the distance between the user and the ventilation device at time t and time t+1, respectively, and Pr(·) denotes the probability operator;

[0040] Based on the transition probability matrix P of the Markov chain, the possible location state of the user at the next moment is predicted.

[0041] Furthermore, the environmental comfort index model is:

[0042]

[0043] C env (t) = w1·f temp (T env (t))+w2·f lumid (H env (t))+w3·f pressure (P env (t))

[0044] Among them, I comfort (t) is the environmental comfort index at time t, C env (t) is the environmental comfort factor at time t, C envmin 、C envmax are the minimum and maximum values ​​of the preset environmental comfort factor; w1, w2, and w3 are weight coefficients, which are adaptively adjusted based on user behavior data and environmental changes; T env (t), H env (t), P env(t) are the ambient temperature, ambient humidity and air duct pressure at time t; f temp (T env (t))、f lumid (H env (t))、f pressure (P env (t)) are the influence functions of ambient temperature, ambient humidity and duct pressure on the comfort factor, respectively, and the formula is:

[0045]

[0046] Among them, k temp is the sensitivity constant to temperature change, α temp is the adjustment factor for the effect of temperature change on comfort, T opt The most comfortable temperature for the human body, ΔT env (t) = T env (t)-T env (t-1) is the instantaneous rate of change of temperature; β humid is the sensitivity of humidity to comfort factor, γ humid is the influence factor of humidity accumulation effect, H min is the minimum comfortable threshold of humidity, is the time cumulative effect of humidity, that is, the humidity integral from time 0 to time t, reflecting the long-term humidity impact; P opt is the pressure that the human body is most adapted to, δ pressure , γ are regulatory factors.

[0047] Furthermore, the air pollution level model is:

[0048] A pollution (t) = w4·f PM2.5 (C PM2.5 (t))+w5·f VOC (C VOC (t))

[0049]

[0050] Among them, A pollution (t) is the air pollution level index at time t, C PM2.5 (t), C VOC (t) is the concentration of inhalable particulate matter and volatile organic gas at time t, w4 and w5 are weight coefficients, and f PM2.5 (C PM2.5 (t))、f VOC (C VOC (t)) are the influence functions of the concentration of inhalable particulate matter and the concentration of volatile organic gases on the air pollution level index, k PM2.5is the adjustment parameter for the sensitivity of PM2.5 concentration changes to the pollution index, C PM2.5,opt is the optimal reference concentration of PM2.5, β VOC is the scaling factor of the impact of VOC concentration on the pollution index, C VOC,min It is the minimum safety threshold of VOC.

[0051] Furthermore, the main control module performs decision operations on the wind speed and purification intensity control variables based on the constructed user presence status model, environmental comfort index model and air pollution level model, combined with the fuzzy logic controller, and generates control instructions, specifically including:

[0052] Obtain the sensor data at the current moment, and calculate the current environmental comfort index I based on the sensor data at the current moment and the constructed user presence state model, environmental comfort index model and air pollution level model comfort (t) and air pollution index A Pollution (t), and based on the Markov chain transfer matrix P constructed in the user presence state model, predict the user's location state S at the next moment t+1 ;

[0053] The environmental comfort index I comfort (t), Air pollution level index A pollution (t) and the predicted user's location status S at the next moment t+1 As input variables, they are input to the fuzzy logic controller for fuzzy reasoning. The fuzzy rules include:

[0054]

[0055] Wherein, Filter Mode(t) is the purification mode of the air purification module at time t, High-efficiency indicates that the air purification module uses a high-efficiency filter layer, Medium-efficiency indicates that the air purification module uses a medium-efficiency filter layer, and Low-efficiency indicates that the air purification module uses a coarse-efficiency filter layer. A1 and A2 are the first and second preset values, respectively, and A1>A2;

[0056]

[0057] Where WindSpeed(t) is the speed mode of the brushless motor of the bladeless ventilation device at time t, High, Medium, Low, and Close represent high speed, medium speed, low speed, and off, respectively, I1 is the preset environmental comfort value, and D is the preset distance threshold.

[0058] Compared with the prior art, the present invention has the following advantages:

[0059] (1) The present invention provides an air quality monitoring module and a human body sensing module connected to the fan body. The controller adjusts the wind speed according to the human body position and air quality parameters. This design eliminates the need for the fan to operate in a fixed manner, but instead provides an intelligent control closed loop of "sense-judgment-execution". This achieves dynamic optimization of wind speed and purification intensity to align with the human breathing zone and activity zone, which not only enhances user comfort, but also improves purification efficiency and avoids resource waste.

[0060] (2) The present invention integrates an air quality monitoring module and a purification module to automatically adjust the purification intensity based on air quality data. The controller determines the activation and intensity adjustment of the purification mode based on real-time air quality monitoring data, thereby ensuring that the air purification effect meets the current environmental requirements, avoiding the waste of resources in a fixed purification mode, and improving the intelligence and efficiency of air quality control.

[0061] (3) The present invention uses a human body sensing module to accurately detect the user's presence and location, and dynamically adjusts the wind speed based on environmental parameters. Wind speed adjustment is based not only on environmental data such as temperature and humidity, but also on the user's real-time activity status and location. This allows the device to more personalized adapt to user needs, providing a more comfortable indoor air environment and avoiding the inadaptability of traditional fans in fixed wind speed mode.

[0062] (4) Through a multi-sensor data fusion algorithm, the present invention can simultaneously collect and analyze information such as user distance, ambient temperature and humidity, and air quality, and comprehensively determine environmental comfort and air pollution levels based on this information. Based on a fuzzy logic controller and a predictive adjustment algorithm, the control system can intelligently adjust wind speed and purification intensity to ensure that the device always maintains the optimal working state, improving the device's intelligence level and automatic adaptability, and enhancing the user experience.

[0063] (5) The present invention eliminates short-term jitter interference by performing sliding window averaging filtering on the infrared heat signal intensity data sequence, thereby improving the stability and accuracy of the infrared heat signal. This processing technology effectively improves the reliability of the signal and avoids misjudgments caused by environmental changes or signal fluctuations, thereby enhancing the device's recognition accuracy of the user's presence status and avoiding both misjudgments and missed detections.

[0064] (6) The present invention adopts the Gaussian kernel density estimation method, combined with the user's infrared heat signal data and distance data, to fit the user's relative position probability model. This technical means can estimate the user's main activity range in space, thereby helping the device predict the user's activity range and behavior pattern. In this way, the device can not only identify the user's location, but also understand its possible activity area, providing more accurate information for subsequent adjustment and control. A first-order Markov chain is used to establish a transition probability matrix for the user's position change sequence, thereby predicting the user's possible location at the next moment. This Markov chain-based prediction mechanism enables the device to understand the user's dynamic position changes in real time, and make response adjustments in advance based on the prediction results, further improving the intelligence and adaptability of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a diagram of the overall system architecture of the present invention;

[0066] Figure 2 Schematic diagram of the bladeless ventilation device of the present invention;

[0067] Figure 3 This is a flow chart of the control method of the present invention. DETAILED DESCRIPTION

[0068] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0069] Example 1:

[0070] This embodiment provides a bladeless ventilation device control system based on multi-sensor data, such as Figure 1 As shown, including:

[0071] The main control module, based on a high-performance microcontroller MCU, is used to receive various sensor data, perform data preprocessing, fusion analysis and control strategy calculations, and output ventilation control signals;

[0072] The sensor module includes: a human body sensing submodule for detecting the presence and relative position of the user, which includes a pyroelectric infrared sensor and an infrared distance sensor; an environmental sensing submodule for real-time acquisition of ambient temperature, humidity, and air duct pressure information, which includes a temperature and humidity sensor and a pressure sensor; an air quality monitoring submodule for monitoring the concentration of particulate matter and volatile organic gases in the air, which includes a PM2.5 sensor and a VOC sensor;

[0073] The control execution module is used to control the bladeless ventilation device according to the ventilation control signal output by the main control module; wherein the bladeless ventilation device is as follows Figure 2 shown.

[0074] The air purification module includes a coarse-efficiency filter layer, a medium-efficiency filter layer and a high-efficiency filter layer, and is used to automatically switch the purification mode according to the air quality level under the control of the control execution module.

[0075] The control execution module includes: a PWM drive submodule, which is used to adjust the speed of the brushless motor of the bladeless ventilation device according to the wind speed control signal output by the main control module to achieve stepless adjustment of the wind speed; a purification control submodule, which is used to control the working status and filtration level of the air purification module; and a communication submodule, which is used to upload the system status and receive remote control instructions via Bluetooth or WiFi.

[0076] The main control module is equipped with a multi-sensor data fusion algorithm. Based on the user status, environmental parameters and air quality information collected by the sensor module, it constructs a user presence status model, an environmental comfort index model and an air pollution level model. Based on the fuzzy logic controller and predictive adjustment algorithm, it outputs control instructions such as wind speed and purification intensity to achieve intelligent adjustment of the ventilation device and energy consumption optimization.

[0077] Example 2:

[0078] This embodiment provides a bladeless ventilation device control method based on the multi-sensor data bladeless ventilation device control system as described above, such as Figure 3 As shown, the following steps are included:

[0079] Step S1: The sensor module collects human body sensing data, environmental parameter data and air quality data respectively;

[0080] Step S2: The main control module pre-processes the data collected by the sensor module;

[0081] Step S3: The main control module performs fusion calculations on the pre-processed data to construct a user presence status model, an environmental comfort index model, and an air pollution level model;

[0082] Step S4: The main control module performs decision operations on the wind speed and purification intensity control variables based on the constructed user presence status model, environmental comfort index model, and air pollution level model, combined with the fuzzy logic controller, and generates control instructions;

[0083] Step S5: The control execution module drives the PWM drive module to adjust the speed of the brushless motor according to the control instruction output by the main control module, and links the air purification module to perform the switching operation of the corresponding filtration level;

[0084] Step S6: The communication submodule uploads the environmental status parameters and the equipment operation status to the mobile terminal, and receives the user's remote control instructions to realize the monitoring and intervention operations of the system status.

[0085] The human body sensing data includes infrared heat signal data obtained by the pyroelectric infrared sensor and the distance data between the user and the ventilation device obtained by the infrared distance sensor;

[0086] Environmental parameter data includes ambient temperature data and ambient humidity data obtained by the temperature and humidity sensor, and air duct pressure data obtained by the pressure sensor;

[0087] Air quality data includes the concentration data of inhalable particulate matter in the air obtained by the PM2.5 sensor and the concentration data of volatile organic gas in the air obtained by the VOC sensor.

[0088] The main control module pre-processes the data collected by the sensor module, including:

[0089] De-noising is performed on various raw sensor data, using the Kalman filter algorithm or sliding average algorithm to eliminate short-term interference signals and abnormal fluctuations;

[0090] Add timestamp information to the denoised data and write it to the sensor fusion buffer according to data category for subsequent fusion operation calls.

[0091] The process of building the user presence model is as follows:

[0092] At multiple sampling moments, the infrared heat signal intensity data P of the area around the user is obtained through the pyroelectric infrared sensor. i , and obtain the distance data D between the user and the device through the infrared distance sensor i , where i is the sampling number;

[0093] For infrared heat signal intensity data series {P i Perform sliding window average filtering to eliminate short-term jitter interference and obtain a smooth thermal signal sequence The formula is:

[0094]

[0095] Among them, w is the sliding window width, P j represents the infrared heat signal intensity at the jth moment, is the infrared heat signal intensity at the i-th moment after smoothing;

[0096] The processed smoothed thermal signal sequence With the threshold value P th Compare, when satisfied , it is determined that the user is within the detection range of the device at the current sampling moment;

[0097] Sequence of moments based on user presence Combined with its corresponding distance data D tk , use the Gaussian kernel density estimation method to fit the user appearance probability distribution f(D), as the user relative position probability model, and estimate the user's main activity range in space [D min , D max ],in, represents the time sequence of the user’s existence, D tk represents the distance between the user and the ventilation device at time tk, f(D) is the user position probability distribution function fitted by the Gaussian kernel density estimation method, D min , D max is the minimum and maximum distance of the user activity interval determined by the probability distribution f(D);

[0098] Under the premise of the user's continuous existence, a first-order Markov chain is used to analyze the user's position change sequence {D tk}Establish a transition probability matrix:

[0099] P ij =Pr(D t+1 =d j ∣D t =d i )

[0100] Among them, P ij Indicates that the user distance at time t is d i When the user arrives at time t+1, the distance is d j The transition probability, D t 、D t+1 denotes the distance between the user and the ventilation device at time t and time t+1, respectively, and Pr(·) denotes the probability operator;

[0101] Based on the transition probability matrix P of the Markov chain, the possible location state of the user at the next moment is predicted.

[0102] The environmental comfort index model is:

[0103]

[0104] C env (t) = w1·f temp (T env (t))+w2·f lumid (H env (t))+w3·f pressure (P env (t))

[0105] Among them, I comfort (t) is the environmental comfort index at time t, C env (t) is the environmental comfort factor at time t, C envmin 、C envmax are the minimum and maximum values ​​of the preset environmental comfort factor; w1, w2, and w3 are weight coefficients, which are adaptively adjusted based on user behavior data and environmental changes; T env (t), H env (t), P env (t) are the ambient temperature, ambient humidity and air duct pressure at time t; f temp (T env (t))、f lumid (H env (t))、f pressure (P env (t)) are the influence functions of ambient temperature, ambient humidity and duct pressure on the comfort factor, respectively, and the formula is:

[0106]

[0107] Among them, k temp is the sensitivity constant to temperature change, α temp is the adjustment factor for the effect of temperature change on comfort, T opt The most comfortable temperature for the human body, ΔT env (t) = T env (t)-T env (t-1) is the instantaneous rate of change of temperature; β humid is the sensitivity of humidity to comfort factor, γ humid is the influence factor of humidity accumulation effect, H min is the minimum comfortable threshold of humidity, is the time cumulative effect of humidity, that is, the humidity integral from time 0 to time t, reflecting the long-term humidity impact; P opt is the pressure that the human body is most adapted to, δ pressure , γ are regulatory factors.

[0108] Among them, w1, w2, and w3 are weight coefficients, which are adaptively adjusted based on user behavior data and environmental changes. Specifically, they include:

[0109] Through the human body sensing module and the user operation recording module, the user's interactive behaviors such as turning on and off equipment, adjusting wind speed, and moving positions are collected in real time to form a behavior label data set; after each user operation or automatic adjustment by the system, the environmental conditions such as temperature, humidity, and pollution index at that time are recorded; based on the time series correlation between user behavior and environmental conditions, the deviation loss function between the predicted environmental comfort value and the user's actual operation preference is defined, and using optimization algorithms such as stochastic gradient descent (SGD), the weights w1, w2, and w3 are iteratively updated to make the comfort value output by the model as consistent as possible with the user's actual behavior preferences. Whenever the system accumulates a certain number of user behavior samples or the environment changes significantly, the weight retraining mechanism is triggered to further improve the prediction accuracy.

[0110] The air pollution level model is:

[0111] A pollution (t) = w4·f PM2.5 (C PM2.5 (t))+w5·f VOC (C VOC (t))

[0112]

[0113] Among them, A pollution (t) is the air pollution level index at time t, C PM2.5 (t), C VOC (t) is the concentration of inhalable particulate matter and volatile organic gas at time t, w4 and w5 are weight coefficients, and f PM2.5 (C PM2.5 (t))、f VOC (C VOC (t)) are the influence functions of the concentration of inhalable particulate matter and the concentration of volatile organic gases on the air pollution level index, k PM2.5 is the adjustment parameter for the sensitivity of PM2.5 concentration changes to the pollution index, C PM2.5,opt is the optimal reference concentration of PM2.5, β VOC is the scaling factor of the impact of VOC concentration on the pollution index, C VOC,mim It is the minimum safety threshold of VOC.

[0114] The main control module, based on the constructed user presence model, environmental comfort index model, and air pollution level model, combines with the fuzzy logic controller to perform decision operations on the wind speed and purification intensity control variables and generate control instructions, including:

[0115] Obtain the sensor data at the current moment, and calculate the current environmental comfort index I based on the sensor data at the current moment and the constructed user presence state model, environmental comfort index model and air pollution level modelcomfort (t) and air pollution index A pollution (t), and based on the Markov chain transfer matrix P constructed in the user presence state model, predict the user's location state S at the next moment t+1 ;

[0116] The environmental comfort index I comfort (t), Air pollution level index A pollution (t) and the predicted user's location status S at the next moment t+1 As input variables, they are input to the fuzzy logic controller for fuzzy reasoning. The fuzzy rules include:

[0117]

[0118] Wherein, Filter Mode(t) is the purification mode of the air purification module at time t, High-efficiency indicates that the air purification module uses a high-efficiency filter layer, Medium-efficiency indicates that the air purification module uses a medium-efficiency filter layer, and Low-efficiency indicates that the air purification module uses a coarse-efficiency filter layer. A1 and A2 are the first and second preset values, respectively, and A1>A2;

[0119] That is, if the air pollution index A pollution (t) is greater than the first preset value, the air purification module is controlled to use a high-efficiency filter layer; if the air pollution level index A pollution (t) is less than or equal to the first preset value and greater than the second preset value, the air purification module is controlled to use the medium efficiency filter layer. If the air pollution level index A pollution (t) is less than or equal to a second preset value, the air purification module is controlled to adopt a coarse filter layer;

[0120]

[0121] Where WindSpeed(t) is the speed mode of the brushless motor of the bladeless ventilation device at time t, High, Medium, Low, and Close represent high speed, medium speed, low speed, and off, respectively, I1 is the preset environmental comfort value, and D is the preset distance threshold.

[0122] That is, if the environmental comfort index is less than or equal to the first preset value and the predicted user position is less than the preset distance threshold, the bladeless ventilation device is controlled to operate in high-speed mode to provide a stronger wind speed to meet the heat dissipation needs of the user when using it at close range; if the environmental comfort index is less than or equal to the first preset value and the predicted user position is greater than or equal to the preset distance threshold, the control device operates in medium-speed mode (Medium), maintaining a moderate wind speed to balance comfort and energy consumption; if the environmental comfort index is greater than the first preset value and the predicted user position is less than the preset distance threshold, the control device operates in low-speed mode (Low) to avoid discomfort to close users at a higher comfort level; if the environmental comfort index is greater than the first preset value and the predicted user position is greater than or equal to the preset distance threshold, the ventilation device is turned off (Close) to save energy consumption and prevent excessive interference to the user.

[0123] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0124] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A bladeless ventilation device control system based on multi-sensor data, characterized in that: include: The main control module, based on a high-performance microcontroller MCU, is used to receive various sensor data, perform data preprocessing, fusion analysis and control strategy calculations, and output ventilation control signals; The sensor module includes: a human body sensing submodule for detecting the presence and relative position of a user, the human body sensing submodule includes a pyroelectric infrared sensor and an infrared distance sensor; an environmental sensing submodule for collecting real-time ambient temperature, humidity, and air duct pressure information, the environmental sensing submodule includes a temperature and humidity sensor and a pressure sensor; an air quality monitoring submodule for monitoring the concentration of particulate matter and volatile organic gases in the air, the air quality monitoring submodule includes a PM2.5 sensor and a VOC sensor; The control execution module is used to control the bladeless ventilation device according to the ventilation control signal output by the main control module; The air purification module includes a coarse-efficiency filter layer, a medium-efficiency filter layer and a high-efficiency filter layer, and is used to automatically switch the purification mode according to the air quality level under the control of the control execution module.

2. A bladeless ventilation device control system based on multi-sensor data according to claim 1, characterized in that: The control execution module includes: a PWM drive submodule, which is used to adjust the speed of the brushless motor of the bladeless ventilation device according to the wind speed control signal output by the main control module to achieve stepless adjustment of the wind speed; a purification control submodule, which is used to control the working state and filtration level of the air purification module; and a communication submodule, which is used to upload the system status and receive remote control instructions via Bluetooth or WiFi.

3. The bladeless ventilation device control system based on multi-sensor data according to claim 1, characterized in that: The main control module is equipped with a multi-sensor data fusion algorithm. Based on the user status, environmental parameters and air quality information collected by the sensor module, it constructs a user presence status model, an environmental comfort index model and an air pollution level model. Based on the fuzzy logic controller and the predictive adjustment algorithm, it outputs control instructions such as wind speed and purification intensity to achieve intelligent adjustment and energy consumption optimization of the ventilation device.

4. A bladeless ventilation device control method of a bladeless ventilation device control system based on multi-sensor data according to any one of claims 1 to 3, characterized in that: The following steps are involved: The sensor modules collect human body sensing data, environmental parameter data and air quality data respectively; The main control module pre-processes the data collected by the sensor module; The main control module performs fusion operations on the pre-processed data to construct a user presence status model, an environmental comfort index model, and an air pollution level model; The main control module, based on the constructed user presence model, environmental comfort index model and air pollution level model, combines the fuzzy logic controller to perform decision operations on the wind speed and purification intensity control variables and generate control instructions; The control execution module drives the PWM drive module to adjust the speed of the brushless motor according to the control instructions output by the main control module, and links the air purification module to perform the switching operation of the corresponding filtration level; The communication submodule uploads environmental status parameters and equipment operating status to the mobile terminal, and receives user remote control instructions to realize system status monitoring and intervention operations.

5. A bladeless ventilation device control method according to claim 4, characterized in that: The human body sensing data includes infrared heat signal data obtained by the pyroelectric infrared sensor and distance data between the user and the ventilation device obtained by the infrared distance sensor; The environmental parameter data includes ambient temperature data and ambient humidity data obtained by the temperature and humidity sensor, and air duct pressure data obtained by the pressure sensor; The air quality data includes the concentration data of inhalable particulate matter in the air obtained by the PM2.5 sensor and the concentration data of volatile organic gas in the air obtained by the VOC sensor.

6. A bladeless ventilation device control method according to claim 4, characterized in that: The main control module pre-processes the data collected by the sensor module, specifically including: De-noising is performed on various raw sensor data, using the Kalman filter algorithm or sliding average algorithm to eliminate short-term interference signals and abnormal fluctuations; Add timestamp information to the denoised data and write it to the sensor fusion buffer according to data category for subsequent fusion operation calls.

7. A bladeless ventilation device control method according to claim 4, characterized in that: The user presence status model construction process is as follows: At multiple sampling moments, the infrared heat signal intensity data P of the area around the user is obtained through the pyroelectric infrared sensor. i , and obtain the distance data D between the user and the device through the infrared distance sensor i , where i is the sampling number; For infrared heat signal intensity data series {P i Perform sliding window average filtering to eliminate short-term jitter interference and obtain a smooth thermal signal sequence The formula is: Among them, w is the sliding window width, p j represents the infrared heat signal intensity at the jth moment, is the infrared heat signal intensity at the i-th moment after smoothing; The processed smoothed thermal signal sequence With the threshold value P th Compare, when satisfied , it is determined that the user is within the detection range of the device at the current sampling moment; Sequence of moments based on user presence Combined with its corresponding distance data D tk , use the Gaussian kernel density estimation method to fit the user appearance probability distribution f(D), as the user relative position probability model, and estimate the user's main activity range in space [D min , D max ],in, represents the time sequence of the user’s existence, D tk represents the distance between the user and the ventilation device at time tk, f(D) is the user position probability distribution function fitted by the Gaussian kernel density estimation method, D min , D max is the minimum and maximum distance of the user activity interval determined by the probability distribution f(D); Under the premise of the user's continuous existence, a first-order Markov chain is used to analyze the user's position change sequence {D tk }Establish a transition probability matrix: P ij =Pr(D t+1 =d j |D t =d i , Among them, P ij Indicates that the user distance at time t is d i When the user arrives at time t+1, the distance is d j The transition probability, D t 、D t+1 denotes the distance between the user and the ventilation device at time t and time t+1, respectively, and Pr(·) denotes the probability operator; Based on the transition probability matrix P of the Markov chain, the possible location state of the user at the next moment is predicted.

8. A bladeless ventilation device control method according to claim 4, characterized in that: The environmental comfort index model is: C env (t)=w1·f temp (T env (t))+w2·f lumid (H env (t))+w3·f pressure (P env (t)) Among them, I comfort (t) is the environmental comfort index at time t, C env (t) is the environmental comfort factor at time t, C envmin 、C envmax are the minimum and maximum values ​​of the preset environmental comfort factor; w1, w2, and w3 are weight coefficients, which are adaptively adjusted based on user behavior data and environmental changes; T env (t), H env (t), P env (t) are the ambient temperature, ambient humidity and air duct pressure at time t; f temp (T env (t))、f lumid (H env (t))、f pressure (P env (t)) are the influence functions of ambient temperature, ambient humidity and duct pressure on the comfort factor, respectively, and the formula is: Among them, k temp is the sensitivity constant to temperature change, α temp is the adjustment factor for the effect of temperature change on comfort, T opt The most comfortable temperature for the human body, ΔT env (t) = T env (t)-T env (t-1) is the instantaneous rate of change of temperature; β humid is the sensitivity of humidity to comfort factor, γ humid is the influence factor of humidity accumulation effect, H min is the minimum comfortable threshold of humidity, is the time cumulative effect of humidity, that is, the humidity integral from time 0 to time t, reflecting the long-term humidity impact; P opt is the pressure that the human body is most adapted to, δ pressure , γ are regulatory factors.

9. A bladeless ventilation device control method according to claim 4, characterized in that: The air pollution level model is: A pollution (t)=w4·f PM2.5 (C PM2.5 (t))+w5·f VOC (C VOC (t)) Among them, A pollution (t) is the air pollution level index at time t, C PM2.5 (t), C VOC (t) is the concentration of inhalable particulate matter and volatile organic gas at time t, w4 and w5 are weight coefficients, and f PM2.5 (C PM2.5 (t))、f VOC (C VOC (t)) are the influence functions of the concentration of inhalable particulate matter and the concentration of volatile organic gases on the air pollution level index, k PM2.5 is the adjustment parameter for the sensitivity of PM2.5 concentration changes to the pollution index, C PM2.5,opt is the optimal reference concentration of PM2.5, β VOC is the scaling factor of the impact of VOC concentration on the pollution index, C VIC,min It is the minimum safety threshold of VOC.

10. A bladeless ventilation device control method according to claim 4, characterized in that: The main control module, based on the constructed user presence model, environmental comfort index model and air pollution level model, combines with the fuzzy logic controller to perform decision operations on the wind speed and purification intensity control variables and generate control instructions, specifically including: Obtain the sensor data at the current moment, and calculate the current environmental comfort index I based on the sensor data at the current moment and the constructed user presence state model, environmental comfort index model and air pollution level model comfort (t) and air pollution index A pollution (t), and based on the Markov chain transfer matrix P constructed in the user presence state model, predict the user's location state S at the next moment t+1 ; The environmental comfort index I comfort (t), Air pollution level index A pollutiin (t) and the predicted user's location status S at the next moment t+1 As input variables, they are input to the fuzzy logic controller for fuzzy reasoning. The fuzzy rules include: Wherein, Filter Mode(t) is the purification mode of the air purification module at time t, High-efficiency indicates that the air purification module uses a high-efficiency filter layer, Medium-efficiency indicates that the air purification module uses a medium-efficiency filter layer, and Low-efficiency indicates that the air purification module uses a coarse-efficiency filter layer. A1 and A2 are the first and second preset values, respectively, and A1>A2; Where WindSpeed(t) is the speed mode of the brushless motor of the bladeless ventilation device at time t, High, Medium, Low, and Close represent high speed, medium speed, low speed, and off, respectively, I1 is the preset environmental comfort value, and D is the preset distance threshold.

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