Intelligent light control system and method based on WiFi wireless sensing

Abstract

The invention relates to the technical field of control methods, and provides a light intelligent control system and method based on WiFi wireless sensing, and the system comprises a data collection module which is used for receiving and collecting CSI data in an environment in real time; the data processing module is used for pre-processing the received CSI data to obtain pre-processed data, and analyzing the pre-processed data according to a preset or self-adaptive threshold to obtain a personnel activity state; and the light control module is used for controlling on, off or brightness adjustment of light equipment according to the activity state of the personnel. By using existing network facilities, extra hardware cost is reduced, flexible deployment can be achieved, different environment requirements can be met, and the problems that in practical application, an infrared sensor is prone to being affected by temperature changes or object shielding, the risk of privacy disclosure exists in a camera, the technical cost of radar and ultrasonic waves is high, and the cost is low are solved. And wide application of the system in home and office environments is limited.

Description

Technical Field

[0001] This application relates to the technical field of control methods, and in particular to a smart lighting control system and method based on WiFi wireless sensing. Background Art

[0002] With the rapid development of smart home technology, automated control systems have gradually entered daily life. As an important part of smart homes, lighting control is widely used in homes, offices, and public places, etc. Smart lighting systems not only improve user convenience but also play an important role in energy conservation and environmental protection. Traditional lighting control methods usually rely on timers, manual switches, or sensor-based automatic switches, but these methods often have limitations and cannot fully meet the requirements of modern smart homes for efficient and precise control.

[0003] Existing smart lighting control systems mostly use technologies such as infrared sensors, cameras, and lidar for personnel activity monitoring. For example, infrared sensors determine the presence of personnel by detecting the thermal radiation of the human body. Although it is simple to implement, it is easily affected by environmental factors (such as temperature changes, object occlusion, etc.). Camera monitoring can provide more abundant data, but it involves privacy issues and is costly. Technologies such as lidar and ultrasonic sensors can sense activities more precisely, but they are costly, take up a large amount of space, and are highly dependent on the environment. Therefore, there are generally problems such as complex deployment, high cost, and privacy issues in the prior art.

[0004] For the above technical solutions, although through existing technical means, smart lighting systems can achieve basic automatic control and energy-saving functions, in actual applications, infrared sensors are easily affected by temperature changes or object occlusion, cameras have the risk of privacy leakage, while the technologies of radar and ultrasonic are costly and have relatively strict environmental requirements, which limit their wide application in home and office environments. Summary of the Invention

[0005] In order to improve the problems that in actual applications, infrared sensors are easily affected by temperature changes or object occlusion, cameras have the risk of privacy leakage, while the technologies of radar and ultrasonic are costly and have relatively strict environmental requirements, which limit their wide application in home and office environments, this application provides a smart lighting control system and method based on WiFi wireless sensing.

[0006] The present invention provides an intelligent lighting control system based on WiFi wireless sensing, which is applied to lighting devices. The control system includes: a data acquisition module for receiving and acquiring CSI data in the environment in real time; a data processing module for preprocessing the received CSI data to obtain preprocessed data, and analyzing the preprocessed data according to a preset or adaptive threshold to obtain the personnel activity state in the environment; a lighting control module for controlling the turning on, turning off or brightness adjustment of the lighting devices according to the personnel activity state.

[0007] As a preferred solution, after receiving the CSI data, the data processing module performs fast Fourier transform on the CSI data to obtain low-frequency energy and high-frequency energy; compares the low-frequency energy and the high-frequency energy with a preset threshold, and analyzes the personnel activity state in the environment based on the comparison result.

[0008] As a preferred solution, the control system further includes a threshold adaptive calibration module, which is used to collect background noise CSI data when there is no one in the environment and update the threshold based on the background noise CSI data.

[0009] As a preferred solution, after receiving the personnel activity state, the lighting control module classifies and identifies the personnel activity state to obtain a detailed level of the activity state, generates a corresponding control signal based on the detailed level, and sends the control signal to the lighting device.

[0010] As a preferred solution, after receiving the control signal, the lighting device decodes and analyzes the control signal to obtain a control instruction, and controls the turning on, turning off or brightness adjustment of the light based on the control instruction.

[0011] As a preferred solution, the lighting device is any one of the following: a dimmable LED bulb, a programmable RGB LED strip.

[0012] As a preferred solution, several Wi-Fi CSI-supported nodes are deployed in the control system, and the activity detection results of all nodes are integrated through a local area network or a cloud platform.

[0013] The present application also provides a method for intelligent control of lights based on Wi-Fi wireless sensing, which is applied to the above-mentioned intelligent control system of lights based on Wi-Fi wireless sensing and lighting devices. The control method includes: receiving and collecting Wi-Fi signals in the environment in real time to obtain CSI data; preprocessing the CSI data to obtain preprocessed data; analyzing the preprocessed data, calculating the low-frequency energy and high-frequency energy respectively by using the fast Fourier transform method, and comparing them with preset or adaptive thresholds, and analyzing the human activity state in the environment based on the comparison results; generating a control signal according to the human activity state, and controlling the lighting device according to the control signal to adjust the turning on, turning off or brightness adjustment of the lights.

[0014] Compared with the prior art, the present application has the following beneficial effects: strong flexibility and high accuracy. Through the existing Wi-Fi network infrastructure, it is possible to sense and analyze the human activities in the environment without additional deployment of dedicated sensors. The system realizes efficient lighting management through real-time collection of CSI data, precise signal analysis and intelligent lighting control; the data collection module can capture subtle signal changes, the data processing module performs denoising, filtering and frequency-domain analysis on the data to ensure the accuracy of activity state recognition, and the lighting control module can automatically adjust the switch and brightness of the lights according to the human activities, avoiding manual intervention or fixed-time setting, improving the user's comfort and energy-saving effect, and achieving efficient automatic control on the premise of ensuring low cost, making full use of the existing Wi-Fi network facilities, reducing the additional hardware cost, and being able to be flexibly deployed to adapt to different environmental requirements, improving the problems that in practical applications, infrared sensors are easily affected by temperature changes or object occlusion, cameras have the risk of privacy leakage, and the technical costs of radar and ultrasonic are relatively high and the environmental requirements are relatively strict, restricting their wide application in home and office environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] The structures, proportions, sizes, etc. shown in the accompanying drawings of this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limiting conditions under which the present invention can be implemented. Therefore, they do not have substantial technical significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0017] Figure 1 is a schematic block diagram of the structure of the intelligent lighting control system based on WiFi wireless sensing provided by an embodiment of the present invention; Figure 2 is a schematic block diagram of the structure of the intelligent lighting control system based on WiFi wireless sensing provided by an embodiment of the present invention, including a threshold adaptive calibration module; Figure 3 is a schematic flowchart of the intelligent lighting control method based on WiFi wireless sensing provided by an embodiment of the present invention.

[0018] Explanation of reference numerals: 10. Intelligent lighting control system based on WiFi wireless sensing; 11. Data acquisition module; 12. Data processing module; 13. Lighting control module; 14. Threshold adaptive calibration module. Detailed implementation manners

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] The flowcharts shown in the accompanying drawings are only illustrative examples, and do not necessarily include all the contents and operations / steps, nor do they necessarily need to be carried out in the described order. For example, some operations / steps can also be decomposed, combined, or partially merged. Therefore, the actual order of execution may be changed according to the actual situation.

[0021] It should also be understood that the terms used in this specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0022] It should also be further understood that the term "and / or" used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0023] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings and through specific embodiments.

[0024] Embodiment 1: As Figure 1 shown, a lighting intelligent control system 10 based on Wi-Fi wireless sensing of the present application is applied to lighting devices. The control system includes a data acquisition module 11, a data processing module 12, and a lighting control module 13.

[0025] The data acquisition module 11 is used to receive and collect CSI data in the environment in real time.

[0026] In this module, the data acquisition module 11 receives Wi-Fi signals in the environment in real time through at least one microcontroller supporting Wi-Fi Channel State Information (CSI) function and extracts CSI data. Specifically, the microcontroller can communicate with one or more Wi-Fi access points to obtain CSI data of each access point in the environment, including information such as the amplitude, phase, and frequency characteristics of the signal. These CSI data reflect the interaction characteristics between Wi-Fi signals and objects (such as human bodies) in the environment, so they can be used to infer the activity status and location information of human bodies.

[0027] For example, when a Wi-Fi signal passes through a human body, certain attenuation and phase changes will occur, and the microcontroller can capture these changes and generate corresponding CSI data. These CSI data provide a way of activity detection without additional sensors.

[0028] The data processing module 12 is used to preprocess the received CSI data to obtain preprocessed data, and analyze the preprocessed data according to a preset or adaptive threshold to obtain the activity status of personnel in the environment.

[0029] In this module, the data processing module 12 first performs preprocessing operations such as denoising and filtering on the received CSI data to remove environmental noise and interference signals. Specifically, the data processing module 12 can remove high-frequency noise signals through a low-pass filter, an average filtering algorithm, or median filtering, etc., so as to improve the accuracy of the data. Then, the data processing module 12 analyzes the CSI data through fast Fourier transform (FFT), extracts the energy information of low frequency and high frequency, and compares it with a preset threshold to determine whether there is personnel activity.

[0030] For example, when a significant change in low-frequency energy is detected, the data processing module 12 will consider that there is a large amount of human activity in the environment, such as walking or approaching; while when there is a change in high-frequency energy, it indicates minor activities, such as hand movements or slight vibrations.

[0031] The lighting control module 13 is used to control the turning on, turning off or brightness adjustment of lighting devices according to the personnel activity status.

[0032] In this module, the lighting control module 13 determines whether to turn on or off the lights or adjust the brightness of the lights according to preset rules by receiving the activity status information from the data processing module 12. Specifically, if the data processing module 12 detects that there are people in the environment, the lighting control module 13 will automatically adjust the status of the lighting device. If people leave, the lights will be automatically turned off. If people are present, the lights will remain on, and the brightness of the lights will be adjusted according to the distance or activity intensity of the people.

[0033] For example, when the system detects that a person stays in a certain area for a long time, the lighting control module 13 will gradually increase the brightness of the lights according to the brightness adjustment rules; while when the person leaves, the system will gradually decrease the brightness or turn off the lights.

[0034] In this embodiment, the data acquisition module 11 receives Wi-Fi signals in the environment in real time and obtains Channel State Information (CSI) data. The received CSI data undergoes preprocessing by the data processing module 12, including operations such as denoising and filtering, and then feature extraction is performed, such as variance and frequency-domain energy analysis. By comparing with preset or adaptive thresholds, the personnel activity status in the environment is analyzed. If personnel activity is detected, the lighting control module 13 will generate a control signal to adjust the turning on, turning off or brightness of the lighting device to achieve intelligent lighting control. Compared with traditional sensor technologies, it has the advantages of low cost, high deployment flexibility and privacy friendliness. By using the existing Wi-Fi network infrastructure, efficient personnel activity perception can be achieved without additional hardware sensors, and the lights can be automatically adjusted according to the real-time status to achieve energy-saving effects, which not only reduces the hardware cost, but also improves the intelligence and accuracy of control, and is widely applicable to environments such as homes, offices and public places. It improves the problems that in practical applications, infrared sensors are easily affected by temperature changes or object occlusion, cameras have the risk of privacy leakage, and the technical costs of radar and ultrasonic are relatively high and the environmental requirements are relatively strict, which limit their wide application in home and office environments.

[0035] Embodiment 2: Among them, when the data processing module 12 receives the CSI data, the CSI data is utilized by fast Fourier transform to obtain low-frequency energy and high-frequency energy.

[0036] By performing a Fast Fourier Transform (FFT) on the received CSI data, the data processing module 12 converts the time-domain data into frequency-domain data. Specifically, the FFT can convert the amplitude and phase information in the CSI data into frequency components, thereby obtaining the energy distribution of the signal in different frequency bands. By analyzing the low-frequency energy and high-frequency energy, the data processing module 12 can distinguish different types of human activities, such as micro-movements, breathing, or violent walking. Changes in low-frequency energy are usually related to small movements, such as human breathing or slight movements, while high-frequency energy is related to more violent activities such as walking, jumping, etc.

[0037] For example, when someone makes a slight movement or waves their hand, the FFT result shows an increase in low-frequency energy, while when this person walks or runs quickly, the high-frequency energy in the frequency domain increases significantly.

[0038] Compare the low-frequency energy and high-frequency energy with preset thresholds, and analyze the human activity state in the environment based on the comparison results.

[0039] By comparing the calculated low-frequency energy and high-frequency energy with preset thresholds, the data processing module 12 can determine whether there is an activity occurring in the current environment. Specifically, the module sets two thresholds: a low-frequency energy threshold and a high-frequency energy threshold. When the low-frequency energy exceeds the preset threshold, the system determines that there is a micro-movement or breathing activity; when the high-frequency energy exceeds the preset threshold, the system determines that there is a violent activity, such as walking or running.

[0040] For example, when the low-frequency energy reaches or exceeds the preset threshold, it indicates that someone is present and there is a slight activity. The system can adjust the brightness of the light based on this information; when the high-frequency energy exceeds the threshold, it indicates a violent activity, and the system can immediately turn on the light and increase the brightness to adapt to environmental changes.

[0041] As Figure 2 shown, the control system further includes a threshold adaptive calibration module 14. The threshold adaptive calibration module 14 is used to collect background noise CSI data when there is no one in the environment, and update the thresholds based on the background noise CSI data.

[0042] Through the threshold adaptive calibration module 14, the system can collect CSI data of background noise when the environment is empty. These data reflect the normal noise level in the environment when there is no one. Specifically, the module starts when there is no one in the environment, collects CSI data for a period of time, and calculates the reference value of the background noise, such as the average amplitude, variance, etc. Then, based on these background data, the system will dynamically adjust the thresholds of low-frequency and high-frequency energy to adapt to environmental changes.

[0043] For example, when the system is first deployed in a new environment or when the environment changes (such as adding large furniture or changing the layout), the threshold adaptive calibration module 14 will automatically adjust the sensitivity of activity detection to ensure accurate perception of human activities under the new environmental conditions.

[0044] After receiving the human activity status, the lighting control module 13 classifies and identifies the human activity status, obtains the detailed level of the activity status, generates a corresponding control signal based on the detailed level, and sends the control signal to the lighting device.

[0045] In the lighting control module 13, the human activity status is first classified into different activity levels, such as "micro motion", "light activity", and "vigorous activity". Specifically, based on the low-frequency and high-frequency energy information analyzed by the data processing module 12, the system can subdivide the activity status into several types: micro motion such as breathing, light activity such as stationary walking, and vigorous activity such as running. Then, the system generates corresponding control signals according to these activity statuses to adjust the brightness or color of the lighting device. For example, the system can adjust the lighting brightness according to light activity to provide soft lighting, while for vigorous activity, the lighting brightness can be increased.

[0046] For example, when the system detects micro motion activities in the environment, the lighting control module 13 will keep the lighting brightness at a low level, while when fast walking is detected, the lighting will automatically brighten to ensure sufficient lighting.

[0047] After receiving the control signal, the lighting device decodes and analyzes the control signal to obtain the control instruction, and controls the turning on, turning off, or brightness adjustment of the lighting based on the control instruction.

[0048] After the lighting device receives the control signal, the decoding module of the device decodes and analyzes the control signal. Specifically, the decoding module determines the operation type of the lighting according to the instruction in the control signal, such as turning on, turning off, or adjusting the brightness. The lighting control instruction is then transmitted to the corresponding drive circuit to perform the actual control of the lighting. For example, when the control signal indicates turning on the lighting, the drive circuit transmits current to the lighting device to make it light up; when indicating turning off, the current is cut off and the lighting goes out; when indicating adjusting the brightness, the current or pulse width modulation (PWM) signal of the lighting is adjusted to change the brightness.

[0049] For example, when the system detects no human activity for a long time, the control signal will indicate dimming or turning off the lighting to save energy. While when human activity is detected, the control signal will make the lighting turn on and adjust the brightness according to the intensity of the activity.

[0050] The lighting device is any one of the following: dimmable LED bulbs, programmable RGB LED light strips.

[0051] This system supports multiple types of lighting devices, including dimmable LED bulbs and programmable RGB LED strip lights. Specifically, the LED bulbs can control the brightness by adjusting the current through PWM, while the RGB LED strip lights can control the color and effect of the light by changing the brightness of the three primary colors (red, green, and blue) of each LED.

[0052] For example, if the system detects human activity and the activity intensity is high, the control system can adjust the light color through the RGB LED strip lights to make it brighter white light or make the strip lights display different colors to match the atmosphere of the activity.

[0053] The control system deploys several Wi-Fi CSI-supported nodes and integrates the activity detection results of all nodes through a local area network or a cloud platform.

[0054] This system supports the deployment of multiple Wi-Fi CSI-supported nodes in multiple areas. For example, in a large room or a multi-room environment, each room can be configured with a Wi-Fi node to sense the activities within that area. All nodes share data through a local area network or a cloud platform, aggregating the activity detection results of each node to achieve lighting linkage across the entire house.

[0055] For example, in an office, multiple Wi-Fi CSI-supported nodes can monitor the activities in different areas in real time. When any one node detects an activity, other nodes will synchronously adjust the light status to ensure that the lighting needs of the entire area are met.

[0056] In this embodiment, through the real-time analysis of Wi-Fi CSI data and the cooperation of multiple nodes, intelligent lighting control based on human activities is achieved. Through the coordination of the data acquisition module 11 and the data processing module 12, the system can accurately sense the micro and intense activities in the environment and automatically adjust the brightness and switch status of the lighting devices, thereby achieving the purpose of energy conservation and enhancing the user experience. The introduction of the threshold adaptive calibration module 14 further improves the adaptability of the system in different environments, ensuring accurate judgment of human activity status in various complex environments. The entire system, through flexible deployment and low-cost hardware configuration, not only provides an efficient lighting control solution but also can be compatible with different types of lighting devices and application scenarios.

[0057] Embodiment 3: As Figure 3 shown, this application also provides a method for intelligent lighting control based on WiFi wireless sensing, which is applied to the WiFi wireless sensing lighting intelligent control system and lighting devices in Embodiment 1. The control method includes steps S100 to S400.

[0058] Step S100: Receive and collect Wi-Fi signals in the environment in real time to obtain CSI data.

[0059] In this step, the data acquisition module receives Wi-Fi signals from the environment in real time through Wi-Fi access points or other network devices, and extracts Channel State Information (CSI) data. Specifically, the module uses a Wi-Fi communication module (such as ESP32, etc.) to enable the CSI function, and transmits the amplitude, phase and other information of the Wi-Fi signal to the processing unit for storage and processing in a callback manner. This process does not require additional sensors and only relies on the existing Wi-Fi infrastructure to complete data collection.

[0060] For example, the system collects CSI data within each time window by periodically calling the Wi-Fi interface and stores the data in a buffer for subsequent analysis.

[0061] Step S200: pre-process the CSI data to obtain pre-processed data.

[0062] In this step, the data processing module removes noise, filters and processes the received raw CSI data to obtain clean and stable pre-processed data. Specifically, a low-pass filter is used to remove noise signals to ensure that valid signals within the frequency band can be clearly extracted. The CSI data is smoothed by a sliding window algorithm to eliminate errors caused by instantaneous changes.

[0063] For example, the system processes the collected CSI data through low-pass filtering and median filtering to ensure that environmental noise and sudden changes are effectively isolated and the validity and accuracy of the data are maintained.

[0064] Step S300: Analyze the preprocessed data, use the fast Fourier transform method to calculate the low-frequency energy and high-frequency energy respectively, and compare them with the preset or adaptive thresholds, and analyze the activity status of people in the environment based on the comparison results.

[0065] In this step, the data processing module uses fast Fourier transform (FFT) to perform frequency domain conversion on the pre-processed CSI data and calculate the low-frequency energy and high-frequency energy. Specifically, FFT converts the time domain signal into the frequency domain signal, and the system identifies different types of activities by analyzing the energy changes of low and high frequencies in the frequency domain. Low-frequency energy changes are usually associated with micro-movements or subtle activities, while high-frequency energy changes are associated with more intense activities such as walking.

[0066] For example, when the system detects a significant change in low-frequency energy, it indicates that there is human micro-movement or breathing; while a significant increase in high-frequency energy indicates the presence of larger activities such as fast walking or running, and the system can adjust the lighting state accordingly.

[0067] Step S400: Generate a control signal according to the human activity state, and control the lighting device according to the control signal to adjust the turning on, turning off or brightness adjustment of the light.

[0068] In this step, the lighting control module generates a corresponding control signal according to the human activity state to control the turning on, turning off or brightness adjustment of the lighting device. Specifically, if activity is detected in the environment, the lighting control module will send a signal to turn on or increase the lighting brightness; if there is no activity in the environment for a long time, the system will generate a control signal to turn off or dim the light.

[0069] For example, when it is detected that someone is moving in the room, the lighting control module sends a signal to automatically increase the lighting brightness; when it is detected that the person has left, the system will automatically turn off the light or dim it to the lowest brightness after a delay.

[0070] In this embodiment, by collecting and analyzing the CSI data of Wi-Fi signals in real time, accurate perception of human activities in the environment can be achieved without additional hardware sensors. The system first obtains the CSI data through the Wi-Fi module and performs denoising and preprocessing on it to improve the accuracy of the data. Then, the fast Fourier transform (FFT) method is used to analyze the frequency-domain characteristics in the CSI data to accurately detect the energy changes of micro-movement and intense activities. According to the detection results, the system automatically controls the turning on, turning off or brightness adjustment of the lighting device, thereby achieving the effects of energy saving and improved comfort. The introduction of the threshold adaptive calibration module further enhances the adaptability of the system in different environments, ensuring accurate perception of human activities under changing environmental conditions. Through flexible deployment and a low-cost solution, the system can be widely applied to multiple scenarios such as homes and offices, providing an efficient, intelligent and environmentally friendly lighting control solution.

[0071] It should be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described method can refer to the corresponding process in the foregoing Embodiment 1 and will not be elaborated here.

[0072] The structures, proportions, sizes, etc. shown in the accompanying drawings of this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented. Therefore, they do not have any substantial technical significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope that can be covered by the technical content disclosed in the present invention.

[0073] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A lighting intelligent control system based on WiFi wireless perception, applied to lighting equipment, characterized in that: The control system comprises: Data acquisition module, used to receive and collect CSI data in the environment in real time; A data processing module, configured to pre-process the received CSI data to obtain pre-processed data, and analyze the pre-processed data according to a preset or adaptive threshold to obtain the activity status of personnel in the environment; The lighting control module is used to control the lighting equipment to be turned on, off or adjust the brightness according to the activity status of the personnel.

2. The lighting intelligent control system based on WiFi wireless perception according to claim 1 is characterized in that: After receiving the CSI data, the data processing module performs fast Fourier transform on the CSI data to obtain low-frequency energy and high-frequency energy; The low-frequency energy and the high-frequency energy are compared with a preset threshold, and the activity status of people in the environment is analyzed based on the comparison result.

3. The lighting intelligent control system based on WiFi wireless perception according to claim 1 is characterized in that: The control system further includes a threshold adaptive calibration module, which is used to collect background noise CSI data when no one is in the environment, and update the threshold based on the background noise CSI data.

4. The lighting intelligent control system based on WiFi wireless perception according to claim 1 is characterized in that: After receiving the activity status of the personnel, the lighting control module classifies and identifies the activity status of the personnel, obtains the subdivision level of the activity status, generates a corresponding control signal based on the subdivision level, and sends the control signal to the lighting device.

5. The lighting intelligent control system based on WiFi wireless perception according to claim 4 is characterized in that: After receiving the control signal, the lighting device decodes and analyzes the control signal to obtain a control instruction, and controls the turning on, turning off or adjusting the brightness of the light based on the control instruction.

6. The lighting intelligent control system based on WiFi wireless perception according to claim 1 is characterized in that: The lighting device is any one of the following: a dimmable LED bulb, a programmable RGB LED light strip.

7. The lighting intelligent control system based on WiFi wireless perception according to claim 1 is characterized in that: The control system is deployed with several Wi-Fi CSI-supported nodes, and the activity detection results of all nodes are integrated through a local area network or a cloud platform.

8. A lighting intelligent control method based on WiFi wireless perception, applied to the lighting intelligent control system and lighting equipment based on WiFi wireless perception as described in claims 1 to 7, characterized in that: The control method comprises: Receive and collect Wi-Fi signals in the environment in real time to obtain CSI data; Preprocessing the CSI data to obtain preprocessed data; Analyze the preprocessed data, calculate low-frequency energy and high-frequency energy respectively by using a fast Fourier transform method, and compare them with preset or adaptive thresholds, and analyze the activity status of people in the environment based on the comparison results; A control signal is generated according to the activity status of the personnel, and the lighting equipment is controlled according to the control signal to adjust the turning on, off or brightness of the light.

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