A control method and system for intelligent lighting terminal

Abstract

The present invention relates to the field of electric heating control technology, and in particular to a management and control method and system applied to intelligent lighting terminals. The method comprises the following steps: collecting behavioral data of a target object within the illumination range of a lighting fixture; performing motion activity feature recognition on the target behavioral data, and determining the target motion trajectory, target motion amplitude, and target motion rhythm based on the motion activity features; monitoring the lighting feature information presented by the lighting fixture under the target behavior state, and dividing the lighting feature information into lighting brightness, lighting color temperature, and lighting light distribution state; adjusting the light distribution state in a scattered manner according to the target motion trajectory to obtain a light distribution adjustment signal. The present invention collects the behavioral data of the target object and dynamically adjusts the light distribution, brightness, and color temperature of the lighting fixture according to its motion trajectory, motion amplitude, and motion rhythm to achieve personalized and intelligent optimization of the lighting scene, thereby improving the comfort and energy efficiency of the lighting effect.

Description

Technical Field

[0001] The present invention relates to the field of electric heating control technology, and in particular to a control method and system applied to an intelligent lighting terminal. Background Art

[0002] The power modules of existing smart lighting terminals are susceptible to power fluctuations, resulting in unstable device operation. Specifically, during peak power consumption periods, the power carrier control method cannot function properly due to unstable current and voltage. Although smart lighting systems have energy-saving functions, their energy-saving control strategies are not precise enough. Specifically, during peak power consumption periods, some systems cannot properly transmit control signals to the lines, making it difficult to achieve lighting control and affecting overall lighting brightness. Existing smart lighting terminals use analog dimming technology, which makes it difficult to ensure batch consistency of maximum brightness. Current linearity and batch current consistency at low duty cycles are poor, which can cause light flickering or uneven brightness during dimming, affecting the user experience. In addition, the dimming module is not compatible with lamps, and some lamps may experience flickering or color temperature drift during dimming. Moreover, the control methods of existing smart lighting terminals can only achieve simple on-off control and cannot implement advanced functions such as dimming and scene switching. Specifically, the traditional time control method can only turn lights on and off according to preset time periods and cannot adjust to seasonality and weather conditions, resulting in extremely limited energy-saving efficiency. Summary of the Invention

[0003] Based on this, it is necessary to provide a management and control method and system applied to smart lighting terminals to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a management and control method for an intelligent lighting terminal is provided, the method comprising the following steps:

[0005] Step S1: collecting behavioral data of a target object within the illumination range of the lighting fixture; performing motion activity feature recognition on the target behavioral data, and determining the target motion trajectory, target motion amplitude, and target motion rhythm based on the motion activity features;

[0006] Step S2: Monitoring the lighting characteristic information presented by the lighting fixtures under the target behavior state, and dividing the lighting characteristic information into lighting brightness, lighting color temperature, and lighting light distribution state; adjusting the light distribution state in a scattered manner according to the target motion trajectory to obtain a light distribution adjustment signal; adjusting the lighting brightness in a sinusoidal manner according to the target motion amplitude to obtain a brightness adjustment signal; and adjusting the lighting color temperature in a rhythmic manner according to the target motion rhythm to obtain a color temperature adjustment signal;

[0007] Step S3: Mapping the lighting scene conditions based on the light distribution adjustment signal, the brightness adjustment signal, and the color temperature adjustment signal, and constructing a lighting response scene mode; monitoring the light sensing operation of the lighting terminal through the lighting response scene mode to obtain the light sensing operation data of the device;

[0008] Step S4: Perform photothermal efficiency aggregation detection on the device photosensitive operation data to generate device photothermal efficiency data; identify the photosensitive resonance degree of the lighting terminal device according to the device photothermal efficiency data, and control the lighting terminal shock absorption damping coefficient based on the photosensitive resonance degree; perform drive circuit feedback compensation on the lighting terminal according to the lighting terminal shock absorption damping coefficient, and adjust the current and voltage output waveforms of the drive circuit to optimize the smart lighting terminal.

[0009] Preferably, step S1 includes the following steps:

[0010] Step S11: collecting data on the front area of ​​the target object's behavior by using a sensor disposed at the front end of the lighting fixture; collecting data on the side area of ​​the target object's behavior by using a sensor disposed at the side of the lighting fixture; and collecting data on the rear area of ​​the target object's behavior by using a sensor disposed at the rear end of the lighting fixture.

[0011] Step S12: determining the starting position of the target object based on the front area data of the behavior, marking the displacement of consecutive time points by the starting position, and identifying the movement direction angle according to the displacement of consecutive time points;

[0012] Step S13: taking the starting position as the movement origin, and taking the displacement and the movement direction angle as the movement vector to determine the target movement trajectory;

[0013] Step S14: determining the maximum displacement and minimum displacement of the target object in a unit time based on the behavior side area data, and performing a difference calculation between the maximum displacement and the minimum displacement to obtain the target action amplitude;

[0014] Step S15: Marking the target object's action start time point and action end time point based on the number of the action rear region, and recording the action change characteristics within the time period corresponding to the action start time point and the action end time point;

[0015] Step S16: Calculate the time period of the time period corresponding to the action start time point and the action end time point, and determine the target action rhythm according to the time period and action change characteristics.

[0016] Preferably, in step S2, monitoring the lighting characteristic information presented by the lighting fixture under the target behavior state and dividing the lighting characteristic information into lighting brightness, lighting color temperature and lighting light distribution state includes:

[0017] The light intensity data of the target under the target behavior state is collected through the light sensor, with a collection frequency of once per second and a duration of 5 milliseconds per collection;

[0018] The color temperature sensor measures the color temperature changes of lighting fixtures at a frequency of twice per second, with each measurement lasting 10 milliseconds.

[0019] The light distribution sensor is used to set five equally spaced measurement points in the horizontal direction of the lighting fixture to measure the light intensity in the horizontal direction; and three equally spaced measurement points in the vertical direction of the lighting fixture to measure the light intensity in the vertical direction.

[0020] A light distribution diagram is drawn according to the light intensity in the horizontal direction and the light intensity in the vertical direction to obtain the lighting light distribution state.

[0021] Preferably, in step S2, adjusting the light distribution state in a scattered manner according to the target motion trajectory includes:

[0022] Identify the trajectory starting direction and trajectory speed in the target motion trajectory;

[0023] The light-emitting surface of the lighting fixture is divided into multiple independently controllable light-emitting areas according to the starting direction and the ending direction of the trajectory, wherein the light-emitting angle of each light-emitting area can be adjusted independently;

[0024] Adjust the luminous angle of each luminous area one by one according to the starting direction of the trajectory so that the light covers the path area of ​​the target motion trajectory;

[0025] When the luminous angle is between 0° and 30°, it is marked as the first type of luminous area; when the luminous angle is between 30° and 60°, it is marked as the second type of luminous area; when the luminous angle is between 60° and 90°, it is marked as the third type of luminous area;

[0026] According to the trajectory speed, the light intensity of the first type of light-emitting area is adjusted to 30%~50% of the full brightness of the lamp; the light intensity of the second type of light-emitting area is adjusted to 50%~70% of the full brightness of the lamp; the light intensity of the third type of light-emitting area is adjusted to 70%~90% of the full brightness of the lamp.

[0027] Preferably, in step S2, adjusting the lighting brightness using a sine wave method according to the target motion amplitude includes:

[0028] Identify the amplitude-time interval corresponding to the target movement amplitude, and determine the amplitude fluctuation characteristics of the target movement amplitude based on the amplitude-time interval;

[0029] Marking the amplitude fluctuation features into amplitude groups, and decomposing the target action amplitude into multiple sub-amplitudes based on the amplitude groups;

[0030] Perform weighted processing on each sub-amplitude and assign a weight value to each sub-amplitude to obtain amplitude-weighted data;

[0031] Determining a sine wave parameter quantity based on the amplitude weighted data; generating corresponding sine waveform parameters for each sub-amplitude, wherein each sine waveform parameter includes a peak height, a trough depth, and a cycle length;

[0032] According to the time sequence of the amplitude-time interval, the sine waveform parameters corresponding to each sub-amplitude are applied in turn to adjust the lighting brightness.

[0033] Preferably, in step S2, adjusting the lighting color temperature in a rhythmic manner according to the target action rhythm includes:

[0034] Extract the action time interval and action speed change rate of the target action rhythm;

[0035] If the time intervals between adjacent actions gradually decrease and the rate of change of action speed increases positively, it is judged that the target action rhythm is accelerating;

[0036] If the time interval between adjacent actions gradually increases and the rate of change of action speed shows a negative growth, it is judged that the target action rhythm is decelerating;

[0037] If the time interval between adjacent actions remains unchanged and the rate of change of action speed is close to zero, the target action rhythm is judged to be constant;

[0038] When the target's action rhythm accelerates, the lighting fixtures are adjusted to gradually cooler color temperatures;

[0039] When the target's action rhythm slows down, the lighting fixtures are adjusted to gradually warmer color temperatures;

[0040] When the target action rhythm is constant, the lighting fixtures are kept at the current color temperature.

[0041] Preferably, step S3 includes the following steps:

[0042] Step S31: performing spatial distribution analysis on the light distribution adjustment signal, and obtaining a light distribution spatial characteristic curve by collecting light intensity data on the left side, right side, and center of the lighting terminal;

[0043] Step S32: performing time series analysis on the brightness adjustment signal, recording the amplitude change of the brightness adjustment signal at different time points, calculating the average value, maximum value and minimum value of the brightness change, and obtaining the brightness change characteristic parameter;

[0044] Step S33: performing color temperature change rate analysis on the color temperature adjustment signal, calculating the amount of change in color temperature per unit time, and obtaining the amount of change in color temperature parameters;

[0045] Step S34: setting the light distribution space characteristic curve as the light illumination dimension, the brightness change characteristic parameter as the light brightness dimension, and the color temperature parameter change as the light color temperature dimension;

[0046] Step S35: mapping the lighting scene conditions based on the light illumination dimension, the light brightness dimension, and the light color temperature dimension and constructing a lighting response scene mode;

[0047] Step S36: Monitor the light sensing operation of the lighting terminal through the lighting response scene mode to obtain light sensing operation data of the lighting terminal.

[0048] Preferably, performing light-sensing thermal effect aggregation detection on the light-sensing operation data of the device in step S4 includes:

[0049] Divide the device light sensing operation data into device light intensity data and device temperature data;

[0050] Determine the light intensity change rate and the temperature change rate for the device light intensity data and the device temperature data respectively;

[0051] When the rate of change of light intensity is greater than the rate of change of temperature, it is determined to be the light-sensing thermal effect gathering point of the device;

[0052] Mark the light-sensitive heat effect area of ​​the lighting terminal according to the light-sensitive heat effect gathering point of the equipment, and identify the area where the light-sensitive heat effect area is located;

[0053] Mapping the area where the light-sensitive thermal effect region is located into the range affected by the light-sensitive thermal effect;

[0054] The impact of equipment structure aggregation on the affected range of light-sensitive thermal effect is evaluated and recorded as equipment light-sensitive thermal effect data.

[0055] Preferably, in step S4, identifying the light-sensing resonance degree of the lighting terminal device according to the light-sensing thermal efficiency data of the device, and controlling the vibration damping coefficient of the lighting terminal based on the light-sensing resonance degree includes:

[0056] Extract light intensity fluctuation data and temperature fluctuation data of the equipment's light and thermal efficiency data;

[0057] Calculate the light intensity fluctuation frequency of the light intensity fluctuation data, and calculate the temperature fluctuation frequency of the temperature fluctuation data;

[0058] Compare the light intensity fluctuation frequency with the temperature fluctuation frequency. If the light intensity fluctuation frequency is consistent with the temperature fluctuation frequency, it is determined that the lighting terminal has a light-sensing resonance phenomenon.

[0059] Performing light-sensing resonance intensity detection on the light-sensing resonance phenomenon, calculating a light-sensing resonance intensity value, and dividing the light-sensing resonance intensity value into a low light-sensing resonance degree, a medium light-sensing resonance degree, and a high light-sensing resonance degree;

[0060] Maintaining the current damping coefficient of the lighting terminal unchanged according to the low degree of light-sensing resonance;

[0061] Increase the damping coefficient of the lighting terminal by 20%~30% according to the degree of light resonance;

[0062] Increase the damping coefficient of the lighting terminal by 30%~50% according to the high degree of light resonance;

[0063] Monitor the adjusted light-sensing thermal efficiency data of the lighting terminal in real time to verify the suppression of light-sensing resonance by the adjusted damping coefficient; if the resonance phenomenon still exists, adjust the damping coefficient by 10%.

[0064] By collecting behavioral data of a target object within the illumination range of a lighting fixture and identifying its motion characteristics to determine the target's motion trajectory, target motion amplitude, and target motion rhythm, the present invention accurately captures dynamic information about the target object within the lighting environment. This process provides an accurate basis for subsequent refined control of the lighting fixture, enabling the lighting system to make targeted adjustments based on the target object's actual behavioral characteristics, thereby achieving more personalized and intelligent lighting effects. The lighting characteristics of the target's behavior are monitored and divided into lighting brightness, lighting color temperature, and lighting light distribution. Based on the target's motion trajectory, target motion amplitude, and target motion rhythm, diffuse, sinusoidal, and rhythmic adjustments are then applied to generate light distribution adjustment signals, brightness adjustment signals, and color temperature adjustment signals. This lighting parameter adjustment method based on target behavior characteristics can match the lighting environment to the target object's activity state, improving lighting comfort and adaptability while also effectively saving energy. For example, increasing lighting brightness when the target object's motion amplitude is large provides a brighter environment to meet their needs; while rhythmic adjustments to the lighting color temperature when the target's motion rhythm is slow create a more comfortable atmosphere. Based on the light distribution adjustment signal, brightness adjustment signal, and color temperature adjustment signal, the system maps lighting scene conditions and constructs a lighting response scenario model. This model is then used to monitor the lighting terminal's light sensor operation and generate device light sensor operation data. This process enables dynamic monitoring and management of the lighting terminal, enabling rapid adjustment of the lighting status based on different scenario requirements, ensuring the lighting system is always operating optimally. Furthermore, monitoring the device light sensor operation data allows for timely detection of lighting terminal operational issues, improving system reliability and stability. The device light sensor operation data is then aggregated to detect light sensor thermal effects, generating device light sensor thermal effect data. This data is used to identify the light sensor resonance level of the lighting terminal. Based on this light sensor resonance level, the lighting terminal's damping coefficient is then controlled. Finally, feedback compensation is applied to the lighting terminal's driver circuit, adjusting the driver circuit's current and voltage output waveforms to optimize the intelligent lighting terminal. This series of operations effectively reduces the lighting terminal's energy consumption, extends the device's service life, and improves lighting quality. By monitoring and feedback compensation for light sensor thermal effects, the lighting terminal maintains stable light sensor output under various operating conditions, further enhancing the performance of the intelligent lighting system. Therefore, the present invention collects the behavioral data of the target object and dynamically adjusts the light distribution, brightness and color temperature of the lighting fixtures according to its motion trajectory, movement amplitude and movement rhythm, so as to achieve personalized and intelligent optimization of the lighting scene, thereby improving the comfort and energy saving of the lighting effect.

[0065] This specification also provides a management and control system for a smart lighting terminal, which is used to execute the management and control method for a smart lighting terminal as described above. The management and control system for a smart lighting terminal includes:

[0066] The target object feature acquisition module is used to collect the behavior data of the target object within the illumination range of the lighting fixture; identify the motion activity characteristics of the target behavior data, and determine the target motion trajectory, target motion amplitude and target motion rhythm based on the motion activity characteristics;

[0067] The lighting feature adjustment module is used to monitor the lighting feature information presented by the lighting fixtures under the target behavior state and divide the lighting feature information into lighting brightness, lighting color temperature, and lighting light distribution state. It uses a scattered method to adjust the light distribution state according to the target motion trajectory to obtain a light distribution adjustment signal; uses a sine wave method to adjust the lighting brightness according to the target motion amplitude to obtain a brightness adjustment signal; and uses a rhythmic method to adjust the lighting color temperature according to the target motion rhythm to obtain a color temperature adjustment signal.

[0068] A lighting response scene mode construction module is used to map lighting scene conditions based on the light distribution adjustment signal, the brightness adjustment signal, and the color temperature adjustment signal, and to construct a lighting response scene mode. The lighting response scene mode is used to monitor the light sensing operation of the lighting terminal device and obtain the light sensing operation data of the device.

[0069] The lighting terminal management module is used to perform light-sensing and thermal-efficiency aggregation detection on the light-sensing operation data of the equipment to generate the light-sensing and thermal-efficiency data of the equipment; identify the light-sensing resonance degree of the lighting terminal equipment according to the light-sensing and thermal-efficiency data of the equipment, and control the shock absorption damping coefficient of the lighting terminal based on the light-sensing resonance degree; perform drive circuit feedback compensation on the lighting terminal according to the shock absorption damping coefficient of the lighting terminal, and adjust the current and voltage output waveforms of the drive circuit to optimize the intelligent lighting terminal.

[0070] The present invention uses a target object feature acquisition module to accurately acquire the target object's behavioral data and identify its motion activity characteristics, determining the target's motion trajectory, motion amplitude, and motion rhythm. The lighting feature adjustment module specifically adjusts the lighting brightness, color temperature, and light distribution state based on these characteristics, generating corresponding adjustment signals. The lighting response scene mode construction module constructs a lighting scene mode based on the adjustment signal and monitors the device light sensing operation data of the lighting terminal. The lighting terminal management module performs light sensing thermal effect aggregation detection based on the device light sensing operation data, identifies the degree of light sensing resonance, and then controls the shock absorption damping coefficient to achieve feedback compensation of the drive circuit and optimize the adjustment of the current and voltage output waveforms. The entire system achieves a high degree of matching between the lighting environment and the target object's behavior, improving the comfort and adaptability of the lighting, while effectively saving energy, improving the operating efficiency, stability, and service life of the lighting terminal, optimizing the overall performance of the intelligent lighting terminal, and providing users with a more intelligent and personalized lighting experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1A schematic diagram of the steps of a control method applied to an intelligent lighting terminal;

[0072] Figure 2 for Figure 1 Detailed implementation steps of step S1 in FIG.

[0073] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0074] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0075] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0076] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0077] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0078] To achieve this, please refer to Figures 1 to 3 A management and control method for an intelligent lighting terminal comprises the following steps:

[0079] Step S1: collecting behavioral data of a target object within the illumination range of the lighting fixture; performing motion activity feature recognition on the target behavioral data, and determining the target motion trajectory, target motion amplitude, and target motion rhythm based on the motion activity features;

[0080] Step S2: Monitoring the lighting characteristic information presented by the lighting fixtures under the target behavior state, and dividing the lighting characteristic information into lighting brightness, lighting color temperature, and lighting light distribution state; adjusting the light distribution state in a scattered manner according to the target motion trajectory to obtain a light distribution adjustment signal; adjusting the lighting brightness in a sinusoidal manner according to the target motion amplitude to obtain a brightness adjustment signal; and adjusting the lighting color temperature in a rhythmic manner according to the target motion rhythm to obtain a color temperature adjustment signal;

[0081] Step S3: Mapping the lighting scene conditions based on the light distribution adjustment signal, the brightness adjustment signal, and the color temperature adjustment signal, and constructing a lighting response scene mode; monitoring the light sensing operation of the lighting terminal through the lighting response scene mode to obtain the light sensing operation data of the device;

[0082] Step S4: Perform photothermal efficiency aggregation detection on the device photosensitive operation data to generate device photothermal efficiency data; identify the photosensitive resonance degree of the lighting terminal device according to the device photothermal efficiency data, and control the lighting terminal shock absorption damping coefficient based on the photosensitive resonance degree; perform drive circuit feedback compensation on the lighting terminal according to the lighting terminal shock absorption damping coefficient, and adjust the current and voltage output waveforms of the drive circuit to optimize the smart lighting terminal.

[0083] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart of steps of a control method applied to a smart lighting terminal according to the present invention. In this example, the control method applied to the smart lighting terminal includes the following steps:

[0084] Step S1: collecting behavioral data of a target object within the illumination range of the lighting fixture; performing motion activity feature recognition on the target behavioral data, and determining the target motion trajectory, target motion amplitude, and target motion rhythm based on the motion activity features;

[0085] In this embodiment of the present invention, an infrared pyroelectric sensor array (with a detection range of 3-5 meters and an angular coverage of 120°) and a Doppler microwave radar module (operating at 24 GHz and a velocity measurement accuracy of ±0.1 m / s) deployed within the lighting fixture collects the target's motion parameters. The sensor data is preprocessed by an STM32F407 microcontroller. A sliding average filter (with a window size of N = 5) is used to remove high-frequency noise. The target's position coordinates are then fused and optimized using a Kalman filter (with a state vector dimension of 6 × 1). The motion trajectory data is recorded as a two-dimensional coordinate sequence (x_n, y_n) of the target's center of mass at a sampling frequency of 25 Hz, where n represents the time series index. The motion amplitude is calculated using the joint angle rate of change metric. The joint angular velocity ω = Δθ / Δt, where Δt = 0.04 seconds, is calculated using accelerometer data (with a range of ±16 g) and gyroscope data (with a range of ±2000° / s) collected by a six-axis inertial measurement unit (IMU). Movement rhythm recognition is based on gait cycle analysis. An adaptive threshold method is used to detect the peak interval time T between acceleration amplitudes exceeding 2.5g and calculate the movement frequency f = 1 / T. The target motion trajectory is converted from the local coordinate system to a 3D point cloud sequence (X, Y, Z) in the global coordinate system using a coordinate transformation algorithm (using quaternion pose solver) and stored as a CSV file. Movement characteristic parameters include trajectory curvature κ = |dT / ds| (T is the tangent vector), movement amplitude A = 1 / N∑θ_i² (θ_i is the joint angle), and rhythm variance σ² = 1 / N∑(f_i - μ)² (μ is the average frequency). All parameters are transmitted to the control terminal via the RS-485 bus for subsequent processing.

[0086] Step S2: Monitoring the lighting characteristic information presented by the lighting fixtures under the target behavior state, and dividing the lighting characteristic information into lighting brightness, lighting color temperature, and lighting light distribution state; adjusting the light distribution state in a scattered manner according to the target motion trajectory to obtain a light distribution adjustment signal; adjusting the lighting brightness in a sinusoidal manner according to the target motion amplitude to obtain a brightness adjustment signal; and adjusting the lighting color temperature in a rhythmic manner according to the target motion rhythm to obtain a color temperature adjustment signal;

[0087] In this embodiment, a DALI-2 interface dimming driver (model DLT-3200, supporting 0-10V / PWM dual-mode output) deployed at the luminaire control terminal collects lighting characteristic parameters in real time. Luminance parameters are measured using an integrating sphere photometer (accuracy ±1%) to measure ambient illuminance (in lx). Color temperature parameters are obtained using a spectroradiometer (wavelength range 380-780nm, resolution 3nm) to obtain color coordinates (x, y) and convert them into color temperature values ​​(in Kelvin). Light distribution is captured using a two-dimensional photodiode array (128×128 pixels, response time 0.1ms). Motion trajectory data is analyzed by a microcontroller (STM32H743). The target movement direction vector θ = arctan(Δy / Δx) is calculated using the finite difference method. The motion path within the next 0.5 seconds is then predicted using a Kalman filter. The light distribution adjustment signal is generated by mapping the predicted path onto the luminaire projection plane and applying the spatial light intensity distribution model I(r, θ) = I0·cos n The required illumination for each area is calculated using a 250Hz pulse width modulation (PWM) technique to adjust the duty cycle of the warm (2700K) and cool (6500K) light sources in the LED module, achieving adjustable light distribution (adjustable range: 15°-60°). The motion amplitude parameter is the vertical acceleration a_z acquired by the six-axis IMU (MPU-6050). The amplitude is extracted using a low-pass filter (cutoff frequency: 2Hz) to obtain the amplitude value A = √(a_z²). This is used to drive a constant current source (accuracy: ±0.5%) to adjust the LED drive current I = I_base·(1+K·A) (K = 0.8, base current I_base = 200mA). Movement rhythm parameters are calculated through time-domain analysis: the acceleration zero-crossing rate (ZCR) = 1 / T·Σ|sign(a[n])+sign(a[n-1])| / 2. A phase accumulator generates a sine wave signal with a frequency of f = 440Hz·(1+0.3·ZCR). This signal is then output to an RGB LED controller (TLC5947) via a 16-bit digital-to-analog converter. Dynamic color temperature adjustment (100K steps, 2700-6500K) is achieved using a three-primary color mixing algorithm (CIE 1931 standard). All control signals are transmitted to the actuator via a CAN bus (baud rate 500kbps) with a 20ms execution cycle, ensuring parameter synchronization accuracy ≤1ms.

[0088] Step S3: Mapping the lighting scene conditions based on the light distribution adjustment signal, the brightness adjustment signal, and the color temperature adjustment signal, and constructing a lighting response scene mode; monitoring the light sensing operation of the lighting terminal through the lighting response scene mode to obtain the light sensing operation data of the device;

[0089] In this embodiment of the present invention, the DALI-2 interface (compliant with the IEC 62386 standard) deployed on the lighting terminal receives light distribution adjustment signals (adjustment range 15°-60°), brightness adjustment signals (0-100% duty cycle), and color temperature adjustment signals (2700-6500K). A scene mode mapping algorithm is used to map the parameter combinations into six preset lighting scenes (work / conference / reading / cinema / leisure / energy saving). The scene mode construction process is as follows: the light distribution parameters are input into the spatial light intensity distribution model I(r,θ)=I0·cos n A three-dimensional control parameter matrix is ​​generated by combining the brightness parameter V = V_max·(1+0.8·ΔL) (ΔL is the brightness difference) and the color temperature parameter T_c = 2700 + 400·sin(2πf·t) (f = 0.1Hz). Light sensing operation monitoring is implemented using an ISL29004 multi-channel illuminance sensor (range 0-1000lx, resolution 0.1lx) and a P87LPC768 controller (20MHz clock speed) deployed within the luminaire. The sensor collects ambient illuminance data at a sampling rate of 100Hz and transmits it to an STM32H743 microcontroller via the I²C bus (clock frequency 400kHz). A sliding window filter algorithm (window size N = 10) is used to eliminate transient interference. Operating data includes: light intensity fluctuation ΔE = √(Δx² + Δy² + Δz²) (threshold set to ±5%), color temperature offset ΔT = |T_c - T_set| (tolerance range ±50K), and luminance response time t_r ≤ 200ms (rise time measured using an oscilloscope). All data is transmitted to the central control unit via the RS-485 bus (baud rate 19200bps), stored in CSV format (timestamp accuracy ±1ms), and uploaded to the cloud management platform via the ModbusTCP protocol (slave address 0x01) for real-time analysis.

[0090] Step S4: Perform photothermal efficiency aggregation detection on the device photosensitive operation data to generate device photothermal efficiency data; identify the photosensitive resonance degree of the lighting terminal device according to the device photothermal efficiency data, and control the lighting terminal shock absorption damping coefficient based on the photosensitive resonance degree; perform drive circuit feedback compensation on the lighting terminal according to the lighting terminal shock absorption damping coefficient, and adjust the current and voltage output waveforms of the drive circuit to optimize the smart lighting terminal.

[0091] In an embodiment of the present invention, a high-precision light sensor is used to collect the light-sensing operation data of the device. The sampling frequency of the sensor is 1000Hz, and it can monitor the changes in light intensity around the lighting terminal device in real time with an accuracy of 0.1Lux, and transmit this data to the central processing unit in the form of a digital signal. The central processing unit uses a fast Fourier transform algorithm to perform frequency domain analysis on the collected light-sensing operation data, extracting frequency components related to light-sensing thermal effects. The analysis range is set between 0.1Hz and 100Hz, focusing on identifying low-frequency fluctuation components in the light-sensing signal caused by thermal effects. The amplitude threshold of these components is set to exceed 10% of the average light-sensing signal amplitude before they are recognized as valid thermal effect-related signals. Based on the above analysis results, the light-sensing thermal effect data of the device is generated. The data is stored in the form of a structured data table, which contains fields such as timestamp, light intensity, and amplitude of thermal effect-related frequency components. Subsequently, a pre-set light-sensing resonance degree identification model, a neural network model trained based on extensive experimental data, is used. Its input is the device's light-sensing thermal efficiency data, and its output is a quantified value of the lighting terminal's light-sensing resonance degree, ranging from 0 to 1, where 0 represents no resonance and 1 represents full resonance. The model calculates the similarity between the input data and known resonance states in the training dataset, combining the amplitude and distribution characteristics of thermal efficiency-related frequency components to accurately determine the resonance degree. Based on this quantified light-sensing resonance degree value, a lookup table is used to retrieve the corresponding lighting terminal's damping coefficient from a pre-set damping coefficient mapping table. This mapping table, pre-established based on the lighting terminal's physical characteristics and experimental test results, details the optimal damping coefficients required to ensure stable operation of the lighting terminal at different resonance levels, with accuracy to three decimal places. This damping coefficient value is then transmitted to the lighting terminal's drive circuit control system. Upon receiving the damping coefficient, the control system performs feedback compensation on the lighting terminal's drive circuit using a PID control algorithm. The PID controller's parameters have been precisely tuned, with the proportional coefficient P set to 1.2, the integral coefficient I to 0.5, and the differential coefficient D to 0.3. These parameters were determined through multiple experiments based on the lighting terminal's dynamic response characteristics and the effect of changes in the damping coefficient on the current and voltage output waveforms. The PID controller monitors the driver circuit's current and voltage output waveforms in real time, using the damping coefficient as feedback. It adjusts the amplitude of the current output waveform within a range of ±10% and fine-tunes the frequency of the voltage output waveform within a range of ±5Hz. This ensures that the driver circuit's current and voltage output waveforms precisely match the damping state required by the lighting terminal, thereby optimizing the intelligent lighting terminal's operational performance and ensuring stable and efficient operation at varying levels of light resonance.

[0092] It is particularly important to perform feedback compensation on the driving circuit of the lighting terminal according to the damping coefficient of the lighting terminal and adjust the current and voltage output waveforms of the driving circuit, including:

[0093] Detecting the damping current waveform and the damping voltage output waveform corresponding to the current value of the vibration damping coefficient of the lighting terminal;

[0094] Determine the change of the shock absorption damping coefficient according to the damping current waveform and the damping voltage output waveform;

[0095] Adjusting the feedback compensation parameters of the drive circuit based on the change in the damping coefficient; if the damping coefficient increases, the feedback compensation increment is increased; if the damping coefficient decreases, the feedback compensation increment is decreased;

[0096] The output drive circuit feedback compensates the current waveform and voltage waveform of the lighting terminal, and matches the current waveform and voltage waveform in phase; if the current waveform phase leads, the voltage waveform is delayed; if the current waveform phase lags, the voltage waveform is advanced.

[0097] In this embodiment of the present invention, a PT100 platinum resistance temperature sensor (accuracy of ±0.5°C, measuring range of -50°C to 150°C) and a FLIR E8 infrared thermal imager (thermal sensitivity <30mK, measuring temperature range of -20°C to 1500°C) deployed in the lighting terminal are used to collect equipment thermal distribution data. This data is combined with light sensing operation data obtained via the DALI-2 bus (light intensity fluctuation ΔL ≤ ±5%, color temperature offset ΔTc ≤ ±30K, and brightness response time t_r ≤ 200ms). The light-induced thermal effect detection uses a thermocouple array (K-type, 0.3mm measuring tip diameter, 0.5s response time) to sample the spatial temperature gradient. The device surface temperature distribution is recorded with a resolution of 0.1°C. A three-dimensional heat conduction model (meshing accuracy 5mm×5mm×5mm) is constructed using finite element analysis software (ANSYS Thermal). The heat source power density P = ∫σ·ε·T^4 dV (σ = 5.67×10^-8 W / m²K^4 is the Stefan-Boltzmann constant). The degree of light-induced resonance is identified based on vibration spectrum analysis. A PCB 352C33 accelerometer (range ±50g, sensitivity 100mV / g) is used to acquire the device vibration signal at a sampling rate of 1kHz. The resonance peak amplitude A_rms in the 0.1-50Hz frequency band is extracted using the FFT algorithm (window function type: Hanning window, resolution 1Hz). The resonance index RI is calculated as Σ(A_rms_i)^2·f_i (i is the harmonic order). The damping coefficient is controlled using a PID adjustment algorithm. The CDC shock absorber (operating voltage DC 24V, maximum damping force 2000N) dynamically adjusts the current output I_d = K_p·RI + K_i·∫RI dt + K_d·dRI / dt (K_p = 0.8, K_i = 0.02, K_d = 0.1) according to the RI value. Continuous adjustment of the damping coefficient (adjustment step size 0.05, range 0.1-1.5) is achieved through an H-bridge MOSFET driver circuit (IRF540N, withstand voltage 100V, on-resistance 0.04Ω). The drive circuit feedback compensation uses current mirror sampling technology (INA138 chip, accuracy of ±0.5%) to monitor the LED module operating current I_led in real time (sampling rate 10kHz). Combined with the voltage feedback V_fb (accuracy of ±1%), the compensation signal is generated. The DAC8562 (16-bit resolution) is configured via the SPI interface (clock frequency 50MHz) to output the compensation voltage V_comp = V_ref·(1+K_v·ΔI_led) (K_v = 0.05% / mA). The final output PWM waveform parameters are: duty cycle D = 50% + 0.4·ΔV_comp, frequency f_PWM = 250Hz±0.5%. The drive circuit is powered by an isolated DC-DC converter (efficiency ≥ 92%, input voltage 12-24V).All data are transmitted to the central controller via the CANopen protocol (node ​​ID 0x12) and stored as binary data files (time stamp accuracy ±10ms).

[0098] As an example of the present invention, refer to Figure 2 As shown, in this example, step S1 includes:

[0099] Step S11: collecting data on the front area of ​​the target object's behavior by using a sensor disposed at the front end of the lighting fixture; collecting data on the side area of ​​the target object's behavior by using a sensor disposed at the side of the lighting fixture; and collecting data on the rear area of ​​the target object's behavior by using a sensor disposed at the rear end of the lighting fixture.

[0100] Step S12: determining the starting position of the target object based on the front area data of the behavior, marking the displacement of consecutive time points by the starting position, and identifying the movement direction angle according to the displacement of consecutive time points;

[0101] Step S13: taking the starting position as the movement origin, and taking the displacement and the movement direction angle as the movement vector to determine the target movement trajectory;

[0102] Step S14: determining the maximum displacement and minimum displacement of the target object in a unit time based on the behavior side area data, and performing a difference calculation between the maximum displacement and the minimum displacement to obtain the target action amplitude;

[0103] Step S15: Marking the target object's action start time point and action end time point based on the number of the action rear region, and recording the action change characteristics within the time period corresponding to the action start time point and the action end time point;

[0104] Step S16: Calculate the time period of the time period corresponding to the action start time point and the action end time point, and determine the target action rhythm according to the time period and action change characteristics.

[0105] In this embodiment of the present invention, a millimeter-wave radar (operating at 24 GHz) transmits frequency-modulated continuous waves in the front area. The radar demodulates the Doppler shift signal to obtain radial target velocity information, and uses antenna array beamforming technology to determine the direction of motion. A ToF lidar (scanning frequency 10 Hz) is deployed in the side area to emit pulsed laser light. After receiving the reflected signal, it calculates distance data based on the time difference and uses a point cloud clustering algorithm to extract target contour features. An ultrasonic sensor array (measuring range 0.5-3 m) is deployed in the back area. This system uses pulse echo time measurement and a temperature compensation module to correct for sound velocity errors and obtain three-dimensional spatial position data. Data from these three sensor groups is synchronously transmitted to the main control unit via an industrial-grade CAN bus (transmission rate 1 Mbps), with a time synchronization accuracy of ±100 ns. The front-end radar data undergoes Doppler shift analysis using a digital signal processor (DSP), and the horizontal displacement component is calculated based on the angle information output by the azimuth encoder. The lidar point cloud data is processed using a statistical filtering algorithm to remove noise points, and a plane fitting algorithm is used to extract a ground reference and calculate vertical displacement. The back-end ultrasonic data is processed using a cross-correlation algorithm to obtain precise time differences, which are then combined with temperature compensation parameters to calculate the actual distance change. The main control unit uses a state machine architecture to fuse three-axis displacement data and predicts the target's motion trajectory using an extended Kalman filter. The process noise covariance matrix is ​​dynamically adjusted based on historical data. The filtered three-dimensional coordinate data is converted to a global coordinate system, and quaternion algorithms are used to compensate for coordinate system rotation, generating a continuous sequence of trajectory points with timestamps. Trajectory data is stored in a circular buffer. Each trajectory point contains position coordinates (X / Y / Z) and velocity vectors (Vx / Vy / Vz). Timestamp synchronization is calibrated using PPS signals, achieving positioning accuracy of ±5cm. The side LiDAR data is aligned using a dynamic time warping algorithm to calculate the vertical displacement sequence, and a sliding average filter is used to eliminate high-frequency noise. The motion amplitude is calculated using the difference integral method to calculate the maximum displacement difference per unit time. This is then combined with a preset threshold to determine motion classification, and the result is stored as a discrete event marker. The back-end ultrasonic data uses a hardware comparator to detect the motion start threshold, triggering a timer to capture the precise start and end timestamps. Motion feature extraction uses wavelet packet decomposition to calculate the energy contribution of each frequency band, generating a feature vector containing the time domain mutation characteristics and the frequency domain energy distribution. After a CRC check, the feature vector is written to non-volatile memory. A short-time Fourier transform is performed on the acceleration signal to extract the peak energy of the main frequency band to determine the rhythm period. This periodicity is then tracked using a phase-locked loop (PLL). The rhythm parameter is used to adjust the backlight drive current via a PID controller, achieving brightness modulation within a 0.1-10Hz frequency range. The drive waveform is then shaped by a low-pass filter before output.

[0106] Preferably, in step S2, monitoring the lighting characteristic information presented by the lighting fixture under the target behavior state and dividing the lighting characteristic information into lighting brightness, lighting color temperature and lighting light distribution state includes:

[0107] The light intensity data of the target under the target behavior state is collected through the light sensor, with a collection frequency of once per second and a duration of 5 milliseconds per collection;

[0108] The color temperature sensor measures the color temperature changes of lighting fixtures at a frequency of twice per second, with each measurement lasting 10 milliseconds.

[0109] The light distribution sensor is used to set five equally spaced measurement points in the horizontal direction of the lighting fixture to measure the light intensity in the horizontal direction; and three equally spaced measurement points in the vertical direction of the lighting fixture to measure the light intensity in the vertical direction.

[0110] A light distribution diagram is drawn according to the light intensity in the horizontal direction and the light intensity in the vertical direction to obtain the lighting light distribution state.

[0111] In this embodiment of the present invention, the light sensor uses a BH1750FVI digital light sensor (range 0-65535 lux, resolution 1 lux), connected to a main control unit (STM32F407) via an I²C interface (clock frequency 400kHz), and configured in continuous high-resolution mode (CMD = 0x10). The data acquisition cycle is set to 1 second, with each trigger lasting 5ms. Signal sampling is controlled by ADC1 channel 6 (PF8) via a timer interrupt (TIM3, APB1 clock frequency 84MHz). The ADC resolution is 12 bits, and the reference voltage is 3.3V. Ambient light intensity data is processed by a moving average filter (window size N = 5) and stored in a ring buffer (capacity 256 points). Timestamp synchronization is calibrated using a PPS signal (accuracy ±1μs). A color temperature sensor uses a TCS34725 module (spectral response range 380-700nm, color temperature measurement accuracy ±50K) and is connected to the main control unit via an I²C interface (ADDR = 0x29). A dual-channel ADC (ADS1115, differential input mode) is used for simultaneous sampling, with the red, green, and blue (RGB) channels set to ±4.096V (configuration register 0x0E = 0x06). Color temperature calculation uses the Plank formula inverse algorithm, combined with a preset correction matrix (obtained from integrating sphere calibration, matrix dimension 3×3). The STM32 floating-point unit (Cortex-M4 FPU) performs matrix multiplication, outputting the color temperature (in K) and color rendering index (Ra ≥ 90). Data is stored in JSON format with a CRC-16 checksum, including a timestamp (Unix timestamp, millisecond accuracy), raw ADC values ​​(16-bit each for R, G, and B), and calculated color temperature parameters. Five Si1132 digital light sensors (spectral range 100-1100nm, response time 0.2ms) are deployed horizontally, evenly spaced (15cm apart) along the horizontal axis of the luminaire, covering a 120° × 60° measurement angle. Three OPT3002 ambient light sensors (spectral response peak at 550nm, dynamic range 0.01-83k lx) are used in the vertical direction. They are spaced equidistantly along the vertical plane (20cm apart), covering a 0°-180° pitch angle. Sensor data is transmitted to the main control unit via an SPI interface (clock frequency 5MHz). A Kalman filter (state vector dimension 6×1) is used to fuse the multi-source data to eliminate ambient reflection interference. Horizontal illuminance data is quantized to a resolution of 0.1 lx, and vertical illuminance data to a resolution of 0.05 klx. These data are stored as binary structures (fields containing X / Y / Z coordinates, illuminance values, and timestamps). A three-dimensional coordinate system is established (with the origin O at the geometric center of the luminaire). The horizontal X / Y axes are divided into an evenly spaced grid (with a step size of 10cm), and the vertical Z axis is divided into 0.5m intervals.Horizontal illuminance data was interpolated using a bilinear interpolation algorithm (25×25 grid points) to generate a continuous distribution surface. Vertical illuminance data was interpolated using cubic spline interpolation (6 nodes) to construct a spatial surface. Light distribution maps were exported in STL format, including vertex coordinates (with 0.1 mm accuracy) and normal vector data. The surface grid density was set to 5000 facets / ㎡. Data was stored in HDF5 format and included the illuminance matrix (25×25×6 dimensions), coordinate system parameters (WGS84 coordinate transformation matrix), and time series metadata (sampling interval, sensor calibration coefficients).

[0112] Preferably, in step S2, adjusting the light distribution state in a scattered manner according to the target motion trajectory includes:

[0113] Identify the trajectory starting direction and trajectory speed in the target motion trajectory;

[0114] The light-emitting surface of the lighting fixture is divided into multiple independently controllable light-emitting areas according to the starting direction and the ending direction of the trajectory, wherein the light-emitting angle of each light-emitting area can be adjusted independently;

[0115] Adjust the luminous angle of each luminous area one by one according to the starting direction of the trajectory so that the light covers the path area of ​​the target motion trajectory;

[0116] When the luminous angle is between 0° and 30°, it is marked as the first type of luminous area; when the luminous angle is between 30° and 60°, it is marked as the second type of luminous area; when the luminous angle is between 60° and 90°, it is marked as the third type of luminous area;

[0117] According to the trajectory speed, the light intensity of the first type of light-emitting area is adjusted to 30%~50% of the full brightness of the lamp; the light intensity of the second type of light-emitting area is adjusted to 50%~70% of the full brightness of the lamp; the light intensity of the third type of light-emitting area is adjusted to 70%~90% of the full brightness of the lamp.

[0118] In this embodiment of the present invention, a motion sensing module installed at the front of the lamp collects target motion data. This module includes a Doppler radar and an infrared pyroelectric sensor. The Doppler radar uses frequency-modulated continuous wave technology to transmit electromagnetic waves. After receiving the target's reflected signal, a mixing circuit extracts the Doppler frequency shift and, combined with a signal processing algorithm, calculates the target's radial velocity. The infrared sensor uses a Fresnel lens array to focus infrared radiation emitted by the human body and detects changes in the target's azimuth angle using the pyroelectric effect. The main control unit uses a multi-sensor data fusion algorithm to align the radar velocity data with the infrared positioning data in time and space, and uses a quaternion algorithm to determine the starting direction of the target's motion trajectory. The lamp's luminous surface integrates multiple independently controllable microstructured light-emitting units. Each unit is equipped with a liquid crystal deflection diaphragm and a micro-stepping motor drive mechanism. The motor controls the diaphragm angle to adjust the beam direction. The control system calculates the required coverage angle range for each unit based on the trajectory direction and generates corresponding angle control commands. The drive circuit uses an H-bridge topology combined with PWM modulation technology to achieve continuous deflection of each light-emitting unit from 0 to 90 degrees, with a deflection accuracy of ±0.5 degrees. The main control unit establishes a three-dimensional coordinate system and discretizes the target trajectory into a continuous sequence of path points. For each path point, the coverage range of the nearest light-emitting unit is calculated, and a greedy algorithm is used to select the optimal unit combination for coverage. Light path adjustment commands are transmitted via the CAN bus to each light-emitting unit controller, which drives the stepper motor to perform angle adjustments. Position information is provided in real time during the adjustment process, forming a closed-loop control system. Light-emitting units are divided into different levels based on their coverage angle. An angle-level mapping table is established, with the 0-30 degree coverage range defined as the first level, 30-60 degrees as the second level, and 60-90 degrees as the third level. The control parameters for each level are independently stored in EEPROM, and the main control unit selects the control strategy for the corresponding level based on the target's current position and trajectory prediction results. Each light-emitting unit has a built-in multi-stage dimming driver circuit. The control system calculates the time difference between the target's arrival at each path point based on the trajectory speed and establishes a time-brightness relationship model. Gradual brightness control is achieved by adjusting the duty cycle of the PWM signal. Low-speed target areas use a low-brightness maintenance mode, while high-speed target areas use a gradual brightness increase mode. The dimming process is precisely controlled through current sampling feedback to ensure smooth brightness transitions in each area.

[0119] Preferably, in step S2, adjusting the lighting brightness using a sine wave method according to the target motion amplitude includes:

[0120] Identify the amplitude-time interval corresponding to the target movement amplitude, and determine the amplitude fluctuation characteristics of the target movement amplitude based on the amplitude-time interval;

[0121] Marking the amplitude fluctuation features into amplitude groups, and decomposing the target action amplitude into multiple sub-amplitudes based on the amplitude groups;

[0122] Perform weighted processing on each sub-amplitude and assign a weight value to each sub-amplitude to obtain amplitude-weighted data;

[0123] Determining a sine wave parameter quantity based on the amplitude weighted data; generating corresponding sine waveform parameters for each sub-amplitude, wherein each sine waveform parameter includes a peak height, a trough depth, and a cycle length;

[0124] According to the time sequence of the amplitude-time interval, the sine waveform parameters corresponding to each sub-amplitude are applied in turn to adjust the lighting brightness.

[0125] In this embodiment of the present invention, an MPU-6050 six-axis inertial measurement unit (accelerometer range ±16g, sampling rate 100Hz) installed in the lighting control terminal collects target motion acceleration data. This data is then filtered through a second-order Butterworth bandpass filter (2-20Hz) to eliminate ambient noise. The amplitude-frequency characteristics of the acceleration are calculated using a short-time Fourier transform (STFT, 256-point window length, Hanning window). A dynamic threshold segmentation algorithm (threshold V_th = 3.5g) is used to identify valid motion intervals, extracting the amplitude time series A(t) within a continuous time window Δt = 0.5 seconds. Time intervals are labeled using a sliding window method (window width W = 10 sampling points). Motion abrupt changes are detected using time-domain differencing (Δa[t] = a[t+1] - a[t]), generating a timestamped array of amplitude interval markers (with a time accuracy of ±1ms). The amplitude time series was subjected to multiresolution wavelet decomposition (Daubechies db4, decomposition level 5), and the energy signature E_j = ∑|d_j(n)|² (j = 1-5) was extracted for each frequency band. Amplitude grouping rules were established based on energy entropy analysis: when E_j / E_total ≥ 0.3, the amplitude group was designated as the primary amplitude group G1; when 0.15 ≤ E_j / E_total < 0.3, the amplitude group was designated as the secondary amplitude group G2; and the remaining amplitude groups were designated as the micro-amplitude group G3. Adaptive threshold segmentation (Otsu algorithm) was used to decompose the primary amplitude group into sub-amplitude segments, each consisting of at least three consecutive peak cycles. The instantaneous amplitude envelope, e(t) = sqrt(a²(t) + H[a(t)]²), was extracted using the Hilbert transform. The sub-amplitude segment boundaries were defined as points where the rate of change of the envelope slope exceeded a threshold value K = 0.15. For each sub-amplitude segment, a weighting coefficient w_i = α·E_i + β·D_i (α = 0.6, β = 0.4) is calculated, where E_i is the sub-amplitude energy value and D_i is the duration (in seconds). The dynamic weight adjustment module adjusts the weight based on the ambient light intensity L (in lux). The correction coefficient γ = 1 + 0.001·(L - 500), with an upper limit of 1.5 for L > 1000. The weighted sub-amplitude data is stored as a structure array (with fields containing the start time t_s, amplitude A_i, and weighting coefficient w_i). This data is transmitted to the PWM generation unit via an SPI interface (clock frequency 10 MHz). Peak detection is performed on the weighted sub-amplitude sequence (using a dual-threshold method with a high threshold of V_h = 0.8A and a low threshold of V_l = 0.2A) to extract valid peak-valley pairs (P_k, V_k). Based on the waveform similarity matching algorithm, the sine wave parameters corresponding to each sub-amplitude are calculated: peak height H_p=V_k·(1+ε) (ε=0.15 dynamic compensation coefficient), trough depth V_v=H_p·0.65, and period length T_p=1 / f_p (f_p is the main frequency of the envelope).Parameter calibration is achieved using a lookup table. Calibration data is stored in an EEPROM (Microchip 24LC256) and includes compensation coefficients for different ambient temperatures (ranging from -20°C to 85°C). The main control unit (STM32H743) generates a PWM modulation signal based on the sub-amplitude time series. Each sub-amplitude corresponds to an independent duty cycle sequence D(t) = D_base (1 + K_w · w_i) (K_w = 0.8% / unit weight). The current output of the LED module (Cree XHP70.2) is controlled by an H-bridge MOSFET driver circuit (IRF540N, 0.04Ω on-resistance). The drive current I_led = I_min + (D(t) / 100%) · (I_max - I_min) (I_min = 200mA, I_max = 1200mA). The phase compensation module uses a PLL phase-locked loop (CDCE913) to synchronize multiple PWM channels to ensure that the timing error is ≤0.5LSB. The duty cycle gradual change process adopts an S-curve algorithm (slope K=0.05% / ms).

[0126] Preferably, in step S2, adjusting the lighting color temperature in a rhythmic manner according to the target action rhythm includes:

[0127] Extract the action time interval and action speed change rate of the target action rhythm;

[0128] If the time intervals between adjacent actions gradually decrease and the rate of change of action speed increases positively, it is judged that the target action rhythm is accelerating;

[0129] If the time interval between adjacent actions gradually increases and the rate of change of action speed shows a negative growth, it is judged that the target action rhythm is decelerating;

[0130] If the time interval between adjacent actions remains unchanged and the rate of change of action speed is close to zero, the target action rhythm is judged to be constant;

[0131] When the target's action rhythm accelerates, the lighting fixtures are adjusted to gradually cooler color temperatures;

[0132] When the target's action rhythm slows down, the lighting fixtures are adjusted to gradually warmer color temperatures;

[0133] When the target action rhythm is constant, the lighting fixtures are kept at the current color temperature.

[0134] In this embodiment of the present invention, an MPU-6050 six-axis inertial measurement unit (accelerometer range ±16g, sampling rate 200Hz) installed in the lighting control terminal collects three-dimensional acceleration data. This data is then filtered through a hardware bandpass filter (cutoff frequency 2-8Hz) to eliminate high-frequency noise. The acceleration data is transmitted to an STM32F411 microcontroller via an SPI interface (clock frequency 16MHz). A timer capture function (TIM2, APB1 clock frequency 84MHz) is used to record the timestamps of adjacent valid actions and calculate the time interval Δt_i = t_{i+1}-t_i (where i is the action number). The velocity rate of change is obtained through integration. A hardware integrator (AD7555) integrates the acceleration signal, outputting a velocity sequence v(t). A differential circuit (AD8338) is then used to calculate the velocity rate of change dv / dt = v_{i+1}-vi. Time interval data is compared to a preset threshold (Δt_ref = 0.2s) via a voltage comparator (LM393) to detect increasing or decreasing trends. The rate-of-change signal is determined by a window comparator (MAX921) to determine positive or negative growth. An acceleration signal is triggered when three consecutive Δt_i values ​​decrease and the corresponding dv / dt value remains positive. A deceleration signal is triggered when Δt_i continues to increase and dv / dt remains negative. A constant tempo is maintained when the Δt_i fluctuation range is less than ±0.05s and the absolute value of dv / dt is less than 0.05m / s². The result of this determination is output to the logic circuit via a level change on a GPIO pin. During acceleration, the main control unit (STM32F411) outputs a gradually changing duty cycle signal (initial value: 30%, step size: 5% / ms) via a PWM generator (TLV62130), gradually increasing the current in the blue channel (wavelength: 450nm) of the RGB LED module (Cree XHP70.2). During deceleration, warm color temperature mode is activated, and the current mirror circuit (INA138) adjusts the current ratio of the red channel (wavelength: 620nm) from a base value of 200mA to 400mA. During constant cadence, the color temperature lock circuit (LT3797) maintains the current PWM duty cycle. The temperature compensation module (DS18B20) monitors the LED junction temperature in real time and uses a lookup table to correct the color coordinate offset (Δx ≤ 0.002, Δy ≤ 0.003) to ensure color temperature stability.

[0135] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0136] Step S31: performing spatial distribution analysis on the light distribution adjustment signal, and obtaining a light distribution spatial characteristic curve by collecting light intensity data on the left side, right side, and center of the lighting terminal;

[0137] Step S32: performing time series analysis on the brightness adjustment signal, recording the amplitude change of the brightness adjustment signal at different time points, calculating the average value, maximum value and minimum value of the brightness change, and obtaining the brightness change characteristic parameter;

[0138] Step S33: performing color temperature change rate analysis on the color temperature adjustment signal, calculating the amount of change in color temperature per unit time, and obtaining the amount of change in color temperature parameters;

[0139] Step S34: setting the light distribution space characteristic curve as the light illumination dimension, the brightness change characteristic parameter as the light brightness dimension, and the color temperature parameter change as the light color temperature dimension;

[0140] Step S35: mapping the lighting scene conditions based on the light illumination dimension, the light brightness dimension, and the light color temperature dimension and constructing a lighting response scene mode;

[0141] Step S36: Monitor the light sensing operation of the lighting terminal through the lighting response scene mode to obtain light sensing operation data of the lighting terminal.

[0142] In this embodiment of the present invention, high-precision photoelectric sensor arrays are installed on the left, right, and center sides of the lighting terminal to collect horizontal and vertical light intensity data. The left sensor group uses a silicon photodiode array (Hamamatsu S11059-02) to monitor horizontal light intensity distribution, while the right sensor uses the same array configuration to detect a symmetrical area. The central main sensor covers vertical light intensity. The sensor signals are processed by a low-noise amplifier (AD8338) and then converted to light intensity values ​​using a 24-bit analog-to-digital converter (ADS1256) at a sampling frequency of 1kHz. When constructing the spatial characteristic curve, a moving average filter (with a window width of 5 sampling points) is used to eliminate noise interference. The light intensity gradient values ​​for each axis are calculated, generating a three-dimensional matrix containing spatial coordinates and light intensity values. This matrix is ​​then stored in the FPGA's on-chip memory for real-time processing. The brightness adjustment signal is monitored by a Hall effect sensor (ACS712) to monitor the driving current changes, which are then converted into a brightness parameter (L = I·k, where k is the linear coefficient). A data acquisition system (NI USB-6361) records brightness sequences at 2kHz and applies a sliding window filter (window size 100 data points) to eliminate transient fluctuations. A statistics module calculates the mean, peak, and valley brightness values ​​every second and transmits these characteristic parameters to the registers of a microcontroller (STM32H743) via the SPI interface. Timestamp synchronization is calibrated using the GPS pulse-per-second signal. A spectral sensor (Hamamatsu C12880MA) captures the light radiation spectrum, and an FPGA executes a color temperature inversion algorithm (based on Planck's radiation law) to calculate real-time color temperature values. The time derivative is calculated using the central difference method (ΔT = (T_{i+1} - T_{i-1}) / (2Δt)) to generate a color temperature rate of change sequence. Temperature compensation parameters (DS18B20 temperature measurement chip provides ambient temperature correction) are added to the data when it is transmitted via the RS-485 bus to ensure a color temperature accuracy of ±5K. The light distribution data is converted to a polar coordinate system (with a radial resolution of 1 cm and an angular resolution of 1°) via a coordinate transformation module, generating a light dimension matrix containing spatial distribution characteristics. Brightness characteristic parameters are extracted using time-domain statistical methods, including rise time, fall time, and amplitude change rate, to form a brightness dimension vector. The color temperature change rate data is processed by a Kalman filter and mapped to a color temperature change rate scalar value (in K / s). This value is stored in a dual-port RAM for subsequent module access. The first stage matches the spatial distribution type of the light dimension (uniform, gradient, or focused) and selects 12 preset light intensity distribution templates using a table lookup. The second stage performs pattern matching within the FPGA, combining the time-domain characteristics of the brightness dimension (rise time and amplitude fluctuation rate) to select brightness adjustment curves with a matching degree exceeding 95%. The third stage determines the color temperature adjustment mode based on a color temperature change rate threshold (>0.5 K / s indicates dynamic change, <0.1 K / s indicates stable state).The final scene mode parameters are transmitted to the actuators of the lighting terminal via the CAN bus (object dictionary index 0x2000). Distributed fiber optic sensors (FOC-4D) are deployed within the luminaires to collect light field distribution in real time, and a triaxial accelerometer (MPU-6050) monitors the device's vibration status. A data acquisition system (PXIe-4300) synchronously acquires light intensity, color temperature, and vibration parameters, transmitting them to a real-time processor (NI PXIe-8135) via the PXI bus. After the operating data is stored in a time series database (InfluxDB), a sliding window algorithm (window width of 1 hour) is used to calculate the light intensity uniformity index (UV = 1-σ / I_avg) and color temperature stability parameter (CV = σ_T / T_avg). When abnormal events trigger ModbusTCP alarm signals, the device operation log and timestamp are attached.

[0143] It is particularly important that step S36 includes the following steps:

[0144] Step S361: performing spatial distribution uniformity analysis on the light illumination dimension and calculating the light intensity uniformity index to obtain light intensity spatial distribution uniformity characteristics;

[0145] Step S362: performing a time series stability analysis on the brightness dimension and calculating a brightness fluctuation stability index to obtain a brightness temporal stability characteristic;

[0146] Step S363: performing color temperature change trend analysis on the light color temperature dimension, calculating the color temperature change trend, and obtaining color temperature dynamic change trend characteristics;

[0147] Step S364: Constructing a device light sensing operation matrix based on the light intensity spatial distribution uniformity characteristics, light brightness time stability characteristics, and color temperature dynamic change trend characteristics;

[0148] Step S365: monitoring the device light sensing operation of the lighting terminal according to the device light sensing operation matrix to obtain device light sensing operation data.

[0149] In this embodiment of the present invention, a distributed fiber optic sensor network is installed on the left and right sides and at the center of the lighting terminal. Wavelength division multiplexing (WDM) technology is used to separate optical signals of different wavelengths into corresponding channels. A photoelectric conversion module converts the optical signals into electrical signals, which are then processed by a low-noise amplifier circuit. Light intensity data is then collected by a 24-bit analog-to-digital converter at a sampling frequency of 1kHz. Spatial uniformity analysis uses a sliding window method to calculate the variance of the light intensity values ​​within the window—the average of the squares of the differences between each light intensity value and the mean. This variance is then combined with the mean light intensity value to generate a spatial uniformity index. After preprocessing, the data is stored as a three-dimensional matrix containing spatial coordinates and uniformity indicators and transmitted to the control unit via an SPI interface. Hall-effect current sensors are used to monitor current changes in the driver circuit, and signal conditioning circuits convert the current signals into voltage signals. A multi-channel data acquisition system records brightness sequences at a frequency of 2kHz, using digital filtering to eliminate power frequency interference. For time-domain statistical feature extraction, the mean and mean square error of the brightness sequence are calculated. A moving average filtering algorithm is then used to generate a time-domain feature vector containing the brightness mean, mean square error, and fluctuation range, which is then stored in non-volatile memory. After capturing light radiation spectrum data, the spectral sensor converts it into an electrical signal via a photodiode array. A dedicated processing circuit calculates real-time color temperature values ​​based on Planck's radiation law, dynamically correcting for ambient temperature deviations via a temperature compensation module. Color temperature trend detection uses a differential threshold method to calculate the ratio of the difference between color temperature values ​​at adjacent time points to the time interval to generate the color temperature change rate. This data is transmitted to the main control unit via a serial bus. The coordinate conversion module maps the light intensity distribution data to a polar coordinate system, storing spatial distribution characteristics in a gridded format. A time-domain compression algorithm is used to extract key parameters from the brightness stability feature, generating a feature vector containing time-domain statistical indicators. Color temperature trend data is processed using a Kalman filter and then correlated with historical trend data to generate trend difference parameters. Three-dimensional feature data is synchronized via a dual-port memory to construct a device light sensing operation matrix, which includes spatial distribution characteristics, time-domain stability indicators, and trend change parameters. Edge computing nodes are deployed at the lighting control terminal, running a real-time data processing engine to perform sliding window analysis. The light intensity uniformity change rate is calculated as the ratio of the difference between the current uniformity index and the historical index to the time interval. Brightness fluctuation is calculated as the ratio of the mean square error to the mean, and color temperature trend acceleration is calculated as the second-order difference of the trend rate. The anomaly detection module generates device operation data packets based on threshold comparisons. These packets contain timestamps, spatial coordinates, and characteristic parameters, and are transmitted to the monitoring platform via the Industrial Ethernet protocol, achieving sub-millisecond time synchronization accuracy.

[0150] Preferably, performing light-sensing thermal effect aggregation detection on the light-sensing operation data of the device in step S4 includes:

[0151] Divide the device light sensing operation data into device light intensity data and device temperature data;

[0152] Determine the light intensity change rate and the temperature change rate for the device light intensity data and the device temperature data respectively;

[0153] When the rate of change of light intensity is greater than the rate of change of temperature, it is determined to be the light-sensing thermal effect gathering point of the device;

[0154] Mark the light-sensitive heat effect area of ​​the lighting terminal according to the light-sensitive heat effect gathering point of the equipment, and identify the area where the light-sensitive heat effect area is located;

[0155] Mapping the area where the light-sensitive thermal effect region is located into the range affected by the light-sensitive thermal effect;

[0156] The impact of equipment structure aggregation on the affected range of light-sensitive thermal effect is evaluated and recorded as equipment light-sensitive thermal effect data.

[0157] In this embodiment of the present invention, light intensity data is collected using a spectral sensor (Hamamatsu C12880MA), while device surface temperature data is collected using a surface-mount temperature sensor (DS18B20). Light intensity data is sampled at 1kHz using a 24-bit analog-to-digital converter (ADS1256), while temperature data is collected at 10Hz using a single-wire bus protocol. The light intensity and temperature data are timestamp-aligned and stored in separate buffers in a dual-port RAM. A sliding window variance calculation is performed on the light intensity data, with a window width of 500 sampling points (corresponding to a 0.5-second time span). The calculation formula is: light intensity standard deviation σ_I = √(1 / N∑(I_n-μ_I)²), where μ_I is the mean light intensity within the window and N is the number of points in the window. The light intensity change rate ΔI = σ_I / μ_I. The temperature data change rate is calculated using the difference method: ΔT = (T_{n+1}-T_n) / Δt, where Δt = 1 second. The FPGA parallel comparison module performs real-time comparisons between ΔI and ΔT. When ΔI > ΔT, the current spatial coordinate is marked as a thermal effect cluster point. The spatial coordinates of the thermal effect cluster point are determined using a three-dimensional positioning algorithm (based on the fusion of RSSI signal strength and TOA time difference), with a positioning accuracy of ±5cm. A rasterization method is used to cluster the thermal effect cluster points into regional units, each with a side length of 0.2 meters. The area accumulation algorithm is used to calculate the thermal effect area (S = Σ(unit side length² × number of units). The thermal effect impact range is mapped to the luminaire's 3D model surface using a coordinate transformation module, and a ray casting algorithm is used to calculate the affected area boundary. The device structure is assessed using a heat conduction path tracing method. An array of temperature sensors is deployed at key nodes on the circuit board (such as the driver IC and connectors) to monitor the spatial overlap between the thermal effect area and structural components. The overlap is calculated as: overlap ratio R = (overlapping area) / (total thermal effect area) × 100%. Data is transmitted to the monitoring platform via Industrial Ethernet, synchronized using the IEEE 1588 protocol, with clock skew less than 1μs. Thermal efficiency data records include: thermal efficiency area coordinate range (X_min-X_max, Y_min-Y_max, Z coordinates), area value S (unit: m²), overlap ratio R (0-100%), peak temperature T_max (unit: °C), and duration t (unit: seconds). Data is stored in a time series database (InfluxDB), compressed by time series, with a compression rate of ≥ 80%.

[0158] Preferably, in step S4, identifying the light-sensing resonance degree of the lighting terminal device according to the light-sensing thermal efficiency data of the device, and controlling the vibration damping coefficient of the lighting terminal based on the light-sensing resonance degree includes:

[0159] Extract light intensity fluctuation data and temperature fluctuation data of the equipment's light and thermal efficiency data;

[0160] Calculate the light intensity fluctuation frequency of the light intensity fluctuation data, and calculate the temperature fluctuation frequency of the temperature fluctuation data;

[0161] Compare the light intensity fluctuation frequency with the temperature fluctuation frequency. If the light intensity fluctuation frequency is consistent with the temperature fluctuation frequency, it is determined that the lighting terminal has a light-sensing resonance phenomenon.

[0162] Performing light-sensing resonance intensity detection on the light-sensing resonance phenomenon, calculating a light-sensing resonance intensity value, and dividing the light-sensing resonance intensity value into a low light-sensing resonance degree, a medium light-sensing resonance degree, and a high light-sensing resonance degree;

[0163] Maintaining the current damping coefficient of the lighting terminal unchanged according to the low degree of light-sensing resonance;

[0164] Increase the damping coefficient of the lighting terminal by 20%~30% according to the degree of light resonance;

[0165] Increase the damping coefficient of the lighting terminal by 30%~50% according to the high degree of light resonance;

[0166] Monitor the adjusted light-sensing thermal efficiency data of the lighting terminal in real time to verify the suppression of light-sensing resonance by the adjusted damping coefficient; if the resonance phenomenon still exists, adjust the damping coefficient by 10%.

[0167] In the embodiments of the present invention, a distributed fiber optic surface plasmon resonance sensor (SPR) array (adopting a Kretschmann prism coupling structure) is deployed inside an illumination terminal. The surface plasmon resonance of a metallic thin film is excited by a wavelength-tunable laser module (with a central wavelength of 1550 nm and a tuning range of ±5 nm), and data on the change in light intensity reflectivity is collected in real time. Meanwhile, a thin-film temperature sensor (with an accuracy of ±0.1 °C and a response time of 0.5 ms) is used to monitor the distribution of the temperature field on the surface of the device. The light intensity data is collected by a 24-bit Σ-Δ analog-to-digital converter (ADS1256) at a sampling rate of 1 kHz, and the temperature data is recorded through an I²C bus at a frequency of 500 Hz, with a timestamp synchronization accuracy of ±10 μs. A sliding window fast Fourier transform (FFT) is performed on the light intensity reflectivity data sequence, the window width is set to 1024 sampling points (corresponding to 1.024 seconds), and the frequency corresponding to the peak of the power spectral density (PSD) is calculated as the main frequency f_I of the light intensity fluctuation. The temperature fluctuation frequency f_T is calculated using the difference method: ΔT = T_{n + 1}-T_n. After eliminating noise through moving average filtering (window length N = 256), the peak delay time τ of the autocorrelation function of the ΔT sequence is calculated, and f_T = 1 / τ. The frequency calculation module adopts an FPGA parallel processing architecture, with an operation delay of less than 5 ms. A dual-channel comparator circuit is established to compare in real time the difference Δf = |f_I - f_T| between f_I and f_T. When Δf < 0.1 Hz and it lasts for more than 10 sampling windows, a resonance detection flag bit is triggered. The state machine logic is implemented using a hardware description language (Verilog), and three levels of judgment thresholds are set: Δf < 0.05 Hz is determined as a high-confidence resonance, 0.05 ≤ Δf < 0.1 Hz is medium-confidence, and Δf ≥ 0.1 Hz is low-confidence.

[0168] Time-domain feature extraction is performed on the detected resonance events:

[0169] The number of oscillation cycles N_cyc = total duration t_total / (1 / f_I);

[0170] The amplitude change rate ΔA = (A_max - A_min) / A_avg×100%;

[0171] The frequency offset Δf_rate = d(f_I) / dt;

[0172] A three-dimensional feature vector (N_cyc, ΔA, Δf_rate) is established, and the intensity level is divided by means of a look-up table:

[0173] Low level: N_cyc ≤ 5 and ΔA ≤ 15%;

[0174] Medium level: 5 < N_cyc ≤ 20 and 15% < ΔA ≤ 30%;

[0175] High degree: N_cyc>20 and ΔA>30%.

[0176] Perform damping parameter configuration according to the intensity level:

[0177] Low level: maintain the current damping coefficient K_d=K_0;

[0178] Medium: K_d = K_0 × (1 + 25%), the duty cycle is adjusted by the PWM controller (original duty cycle D → D × 1.25);

[0179] High degree: K_d = K_0 × (1 + 40%), synchronous activation of the piezoelectric ceramic actuator array (voltage control mode, V_piezo = V_ref × 0.8);

[0180] The damping adjustment command is transmitted to the actuator via the CAN bus, with a response time of ≤20ms.

[0181] This specification also provides a management and control system for a smart lighting terminal, which is used to execute the management and control method for a smart lighting terminal as described above. The management and control system for a smart lighting terminal includes:

[0182] The target object feature acquisition module is used to collect the behavior data of the target object within the illumination range of the lighting fixture; identify the motion activity characteristics of the target behavior data, and determine the target motion trajectory, target motion amplitude and target motion rhythm based on the motion activity characteristics;

[0183] The lighting feature adjustment module is used to monitor the lighting feature information presented by the lighting fixtures under the target behavior state and divide the lighting feature information into lighting brightness, lighting color temperature, and lighting light distribution state. It uses a scattered method to adjust the light distribution state according to the target motion trajectory to obtain a light distribution adjustment signal; uses a sine wave method to adjust the lighting brightness according to the target motion amplitude to obtain a brightness adjustment signal; and uses a rhythmic method to adjust the lighting color temperature according to the target motion rhythm to obtain a color temperature adjustment signal.

[0184] A lighting response scene mode construction module is used to map lighting scene conditions based on the light distribution adjustment signal, the brightness adjustment signal, and the color temperature adjustment signal, and to construct a lighting response scene mode. The lighting response scene mode is used to monitor the light sensing operation of the lighting terminal device and obtain the light sensing operation data of the device.

[0185] The lighting terminal management module is used to perform light-sensing and thermal-efficiency aggregation detection on the light-sensing operation data of the equipment to generate the light-sensing and thermal-efficiency data of the equipment; identify the light-sensing resonance degree of the lighting terminal equipment according to the light-sensing and thermal-efficiency data of the equipment, and control the shock absorption damping coefficient of the lighting terminal based on the light-sensing resonance degree; perform drive circuit feedback compensation on the lighting terminal according to the shock absorption damping coefficient of the lighting terminal, and adjust the current and voltage output waveforms of the drive circuit to optimize the intelligent lighting terminal.

[0186] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0187] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A management and control method applied to an intelligent lighting terminal, characterized in that: The following steps are involved: Step S1: collecting behavior data of target objects within the illumination range of the lighting fixture; Identify the target behavior data's motion activity features, and determine the target's motion trajectory, target motion amplitude, and target motion rhythm based on the motion activity features; Step S2: Monitoring the lighting characteristic information presented by the lighting fixtures under the target behavior state, and dividing the lighting characteristic information into lighting brightness, lighting color temperature, and lighting light distribution state; adjusting the light distribution state in a scattered manner according to the target motion trajectory to obtain a light distribution adjustment signal; adjusting the lighting brightness in a sinusoidal manner according to the target motion amplitude to obtain a brightness adjustment signal; and adjusting the lighting color temperature in a rhythmic manner according to the target motion rhythm to obtain a color temperature adjustment signal; Step S3: Mapping the lighting scene conditions based on the light distribution adjustment signal, the brightness adjustment signal, and the color temperature adjustment signal, and constructing a lighting response scene mode; monitoring the light sensing operation of the lighting terminal through the lighting response scene mode to obtain the light sensing operation data of the device; Step S4: performing light-sensing thermal effect aggregation detection on the device light-sensing operation data to generate device light-sensing thermal effect data; The degree of light sensitivity resonance of the lighting terminal device is identified based on the device's light sensitivity and thermal efficiency data, and the lighting terminal's shock absorption and damping coefficient is controlled based on the degree of light sensitivity resonance. The lighting terminal is driven by feedback compensation of the circuit according to the lighting terminal's shock absorption and damping coefficient, and the current and voltage output waveforms of the driving circuit are adjusted to optimize the intelligent lighting terminal.

2. The control method for intelligent lighting terminals according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting data on the front area of ​​the target object's behavior by using a sensor disposed at the front end of the lighting fixture; collecting data on the side area of ​​the target object's behavior by using a sensor disposed at the side of the lighting fixture; and collecting data on the rear area of ​​the target object's behavior by using a sensor disposed at the rear end of the lighting fixture. Step S12: determining the starting position of the target object based on the front area data of the behavior, marking the displacement of consecutive time points by the starting position, and identifying the movement direction angle according to the displacement of consecutive time points; Step S13: taking the starting position as the movement origin, and taking the displacement and the movement direction angle as the movement vector to determine the target movement trajectory; Step S14: determining the maximum displacement and minimum displacement of the target object in a unit time based on the behavior side area data, and performing a difference calculation between the maximum displacement and the minimum displacement to obtain the target action amplitude; Step S15: Marking the target object's action start time point and action end time point based on the number of the action rear region, and recording the action change characteristics within the time period corresponding to the action start time point and the action end time point; Step S16: Calculate the time period of the time period corresponding to the action start time point and the action end time point, and determine the target action rhythm according to the time period and action change characteristics.

3. The control method for intelligent lighting terminals according to claim 1, characterized in that: In step S2, the lighting characteristic information presented by the lighting fixture under the target behavior state is monitored, and the lighting characteristic information is divided into lighting brightness, lighting color temperature and lighting light distribution state, including: The light intensity data of the target under the target behavior state is collected through the light sensor, with a collection frequency of once per second and a duration of 5 milliseconds per collection; The color temperature sensor measures the color temperature changes of lighting fixtures at a frequency of twice per second, with each measurement lasting 10 milliseconds. The light distribution sensor is used to set five equally spaced measurement points in the horizontal direction of the lighting fixture to measure the light intensity in the horizontal direction; and three equally spaced measurement points in the vertical direction of the lighting fixture to measure the light intensity in the vertical direction. A light distribution diagram is drawn according to the light intensity in the horizontal direction and the light intensity in the vertical direction to obtain the lighting light distribution state.

4. The control method for intelligent lighting terminals according to claim 1, characterized in that: In step S2, adjusting the light distribution state in a scattered manner according to the target motion trajectory includes: Identify the trajectory starting direction and trajectory speed in the target motion trajectory; The light-emitting surface of the lighting fixture is divided into multiple independently controllable light-emitting areas according to the starting direction and the ending direction of the trajectory, wherein the light-emitting angle of each light-emitting area can be adjusted independently; Adjust the luminous angle of each luminous area one by one according to the starting direction of the trajectory so that the light covers the path area of ​​the target motion trajectory; When the luminous angle is between 0° and 30°, it is marked as the first type of luminous area; when the luminous angle is between 30° and 60°, it is marked as the second type of luminous area; when the luminous angle is between 60° and 90°, it is marked as the third type of luminous area; According to the trajectory speed, the light intensity of the first type of light-emitting area is adjusted to 30%~50% of the full brightness of the lamp; the light intensity of the second type of light-emitting area is adjusted to 50%~70% of the full brightness of the lamp; the light intensity of the third type of light-emitting area is adjusted to 70%~90% of the full brightness of the lamp.

5. The control method for intelligent lighting terminals according to claim 1, characterized in that: In step S2, adjusting the lighting brightness using a sine wave method according to the target motion amplitude includes: Identify the amplitude-time interval corresponding to the target movement amplitude, and determine the amplitude fluctuation characteristics of the target movement amplitude based on the amplitude-time interval; Marking the amplitude fluctuation features into amplitude groups, and decomposing the target action amplitude into multiple sub-amplitudes based on the amplitude groups; Perform weighted processing on each sub-amplitude and assign a weight value to each sub-amplitude to obtain amplitude-weighted data; Determining a sine wave parameter quantity based on the amplitude weighted data; generating corresponding sine waveform parameters for each sub-amplitude, wherein each sine waveform parameter includes a peak height, a trough depth, and a cycle length; According to the time sequence of the amplitude-time interval, the sine waveform parameters corresponding to each sub-amplitude are applied in turn to adjust the lighting brightness.

6. The control method for intelligent lighting terminals according to claim 1, characterized in that: In step S2, adjusting the lighting color temperature in a rhythmic manner according to the target action rhythm includes: Extract the action time interval and action speed change rate of the target action rhythm; If the time intervals between adjacent actions gradually decrease and the rate of change of action speed increases positively, it is judged that the target action rhythm is accelerating; If the time interval between adjacent actions gradually increases and the rate of change of action speed shows a negative growth, it is judged that the target action rhythm is decelerating; If the time interval between adjacent actions remains unchanged and the rate of change of action speed is close to zero, the target action rhythm is judged to be constant; When the target's action rhythm accelerates, the lighting fixtures are adjusted to gradually cooler color temperatures; When the target's action rhythm slows down, the lighting fixtures are adjusted to gradually warmer color temperatures; When the target action rhythm is constant, the lighting fixtures are kept at the current color temperature.

7. The control method for intelligent lighting terminals according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing spatial distribution analysis on the light distribution adjustment signal, and obtaining a light distribution spatial characteristic curve by collecting light intensity data on the left side, right side, and center of the lighting terminal; Step S32: performing time series analysis on the brightness adjustment signal, recording the amplitude change of the brightness adjustment signal at different time points, calculating the average value, maximum value and minimum value of the brightness change, and obtaining the brightness change characteristic parameter; Step S33: performing color temperature change rate analysis on the color temperature adjustment signal, calculating the amount of change in color temperature per unit time, and obtaining the amount of change in color temperature parameters; Step S34: setting the light distribution space characteristic curve as the light illumination dimension, the brightness change characteristic parameter as the light brightness dimension, and the color temperature parameter change as the light color temperature dimension; Step S35: mapping the lighting scene conditions based on the light illumination dimension, the light brightness dimension, and the light color temperature dimension and constructing a lighting response scene mode; Step S36: Monitor the light sensing operation of the lighting terminal through the lighting response scene mode to obtain light sensing operation data of the lighting terminal.

8. The control method for intelligent lighting terminals according to claim 1, characterized in that: The light-sensing thermal effect aggregation detection of the device light-sensing operation data in step S4 includes: Divide the device light sensing operation data into device light intensity data and device temperature data; Determine the light intensity change rate and the temperature change rate for the device light intensity data and the device temperature data respectively; When the rate of change of light intensity is greater than the rate of change of temperature, it is determined to be the light-sensing thermal effect gathering point of the device; Mark the light-sensitive heat effect area of ​​the lighting terminal according to the light-sensitive heat effect gathering point of the equipment, and identify the area where the light-sensitive heat effect area is located; Mapping the area where the light-sensitive thermal effect region is located into the range affected by the light-sensitive thermal effect; The impact of equipment structure aggregation on the affected range of light-sensitive thermal effect is evaluated and recorded as equipment light-sensitive thermal effect data.

9. The control method for intelligent lighting terminals according to claim 1, characterized in that: In step S4, identifying the light-sensing resonance degree of the lighting terminal device according to the light-sensing thermal efficiency data of the device, and controlling the vibration damping coefficient of the lighting terminal device based on the light-sensing resonance degree includes: Extract light intensity fluctuation data and temperature fluctuation data of the equipment's light and thermal efficiency data; Calculate the light intensity fluctuation frequency of the light intensity fluctuation data, and calculate the temperature fluctuation frequency of the temperature fluctuation data; Compare the light intensity fluctuation frequency with the temperature fluctuation frequency. If the light intensity fluctuation frequency is consistent with the temperature fluctuation frequency, it is determined that the lighting terminal has a light-sensing resonance phenomenon. Performing light-sensing resonance intensity detection on the light-sensing resonance phenomenon, calculating a light-sensing resonance intensity value, and dividing the light-sensing resonance intensity value into a low light-sensing resonance degree, a medium light-sensing resonance degree, and a high light-sensing resonance degree; Maintaining the current damping coefficient of the lighting terminal unchanged according to the low degree of light-sensing resonance; Increase the damping coefficient of the lighting terminal by 20%~30% according to the degree of light resonance; Increase the damping coefficient of the lighting terminal by 30%~50% according to the high degree of light resonance; Monitor the adjusted light-sensing thermal efficiency data of the lighting terminal in real time to verify the suppression of light-sensing resonance by the adjusted damping coefficient; if the resonance phenomenon still exists, adjust the damping coefficient by 10%.

10. A management and control system applied to intelligent lighting terminals, characterized in that: For executing the management and control method for a smart lighting terminal according to claim 1, the management and control system for a smart lighting terminal comprises: The target object feature acquisition module is used to collect the behavior data of the target object within the illumination range of the lighting fixture; identify the motion activity characteristics of the target behavior data, and determine the target motion trajectory, target motion amplitude and target motion rhythm based on the motion activity characteristics; The lighting feature adjustment module is used to monitor the lighting feature information presented by the lighting fixtures under the target behavior state and divide the lighting feature information into lighting brightness, lighting color temperature, and lighting light distribution state. It uses a scattered method to adjust the light distribution state according to the target motion trajectory to obtain a light distribution adjustment signal; uses a sine wave method to adjust the lighting brightness according to the target motion amplitude to obtain a brightness adjustment signal; and uses a rhythmic method to adjust the lighting color temperature according to the target motion rhythm to obtain a color temperature adjustment signal. A lighting response scene mode construction module is used to map lighting scene conditions based on the light distribution adjustment signal, the brightness adjustment signal, and the color temperature adjustment signal, and to construct a lighting response scene mode. The lighting response scene mode is used to monitor the light sensing operation of the lighting terminal device and obtain the light sensing operation data of the device. The lighting terminal management module is used to perform light-sensing and thermal-efficiency aggregation detection on the light-sensing operation data of the equipment to generate the light-sensing and thermal-efficiency data of the equipment; identify the light-sensing resonance degree of the lighting terminal equipment according to the light-sensing and thermal-efficiency data of the equipment, and control the shock absorption damping coefficient of the lighting terminal based on the light-sensing resonance degree; perform drive circuit feedback compensation on the lighting terminal according to the shock absorption damping coefficient of the lighting terminal, and adjust the current and voltage output waveforms of the drive circuit to optimize the intelligent lighting terminal.

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