Illumination control method and system based on differential threshold and trajectory tracking

Abstract

The invention belongs to the technical field of illumination control, and particularly discloses an illumination control method and system based on differential threshold values and trajectory tracking, and the method comprises the steps: collecting historical illumination adjustment data in a preset illumination region, carrying out the classification analysis and calculation of the collected data, generating the optimal illumination threshold values corresponding to various scenes, and obtaining the optimal illumination threshold values; a plurality of sensors are arranged in the illumination area to form a latticed detection area, and the current position and the moving track of a person are calculated in real time; dynamically generating a brightness distribution field according with a Gaussian distribution rule according to the position of the person, and controlling the brightness output of each unit in real time; and secondary compensation brightness adjustment of the illumination area is realized based on the personnel speed and the moving direction. According to the technical scheme, regional differentiation self-adaptive adjustment of the illumination threshold value and dynamic tracking control of the personnel trajectory are achieved, the intelligent level and the energy-saving effect of the illumination system are effectively improved, the illumination experience of a user is improved, and the method is suitable for various complex illumination environments.

Description

Technical Field

[0001] The present invention belongs to the technical field of lighting control, and in particular relates to a lighting control method and system based on differentiated thresholds and trajectory tracking. Background Art

[0002] The lighting system is an indispensable and important component. Traditional lighting systems usually use fixed lighting brightness settings and cannot be intelligently adjusted according to the actual ambient light intensity and human activities. For example, when there is sufficient natural light during the day, indoor lamps still maintain a high brightness, resulting in unnecessary energy consumption. In unmanned areas, lamps are also continuously turned on, wasting a lot of electricity, increasing operating costs, and not in line with the development concept of energy conservation and environmental protection.

[0003] Existing lighting control systems often use fixed thresholds or single logic for regulation, often relying on manual switches, timed controls, or single sensor triggers, which present significant limitations. First, traditional "one-size-fits-all" control strategies fail to meet the lighting needs of different areas, resulting in insufficient brightness in high-importance areas and wasted light energy in less important areas. Second, existing control logic lacks flexibility, making it difficult to adapt to dynamic scenarios like conference rooms, and lacks intelligent response mechanisms for the real-time movement of people. Furthermore, traditional solutions often struggle to strike a balance between energy conservation and user experience. For example, while overall dimming can save energy, it can also reduce the lighting experience in key areas, impacting user satisfaction.

[0004] Patent CN202411979967.9 discloses an IoT-based intelligent lighting control system that combines light sensors, human body sensors, and meteorological linkage to achieve lighting control based on fixed thresholds and compensation coefficients. Although this method has a certain degree of intelligence, the threshold setting is relatively rigid and lacks the ability to dynamically adjust the thresholds for different regions, seasons, and weather conditions, making it difficult to meet changing lighting needs. In addition, its control rules based on human body sensors are relatively simple. When people move within the area, the current lighting cannot adapt to the lighting needs of the new location in a timely manner, resulting in a response lag, which reduces the level of intelligence and requires users to make manual adjustments. Summary of the Invention

[0005] In response to the problems in the prior art, the present invention provides a lighting control method and system based on differentiated thresholds and trajectory tracking to solve the problems in the above-mentioned background technology where the threshold setting is relatively rigid and lacks the ability to dynamically adjust the threshold under different regions, seasons and weather conditions. At the same time, it solves the problem that when people move within the area, the current lighting cannot adapt to the lighting requirements of the new location in a timely manner, there is a response lag, and the user needs to make manual adjustments.

[0006] The technical solution adopted in the present invention is as follows: In a first aspect, the present application provides a lighting control method based on differentiated thresholds and trajectory tracking, the method comprising the following steps: Step S1: collecting historical lighting adjustment data in multiple preset lighting areas; Step S2: classify the historical lighting adjustment data, analyze each category of data based on a clustering algorithm, and calculate and generate the corresponding optimal lighting threshold; Step S3: Deploy sensors in the lighting area to form a grid-like detection area, fuse trigger information from multiple sensors, and calculate the current position and real-time movement trajectory of the person in the lighting area; Step S4: Divide the lighting area into multiple independent lighting areas, generate a brightness distribution field centered on the person's current position within the lighting area, and update it in real time within a set time interval; Step S5: controlling the brightness output of the corresponding lighting area according to the optimal lighting threshold and the brightness distribution field; Step S6: Based on the moving speed and direction of the person, perform secondary compensation adjustment on the brightness of the corresponding lighting area.

[0007] Furthermore, in step S1, the historical lighting adjustment data includes the lighting intensity of the lighting area, the user's expected brightness corresponding to the user's manual brightness adjustment, the current time, season, weather type, natural light intensity and the lighting intensity of the adjacent area, and the data is associated with the corresponding lighting area identification and stored.

[0008] Furthermore, in step S2, newly collected data is periodically introduced to retrain the clustering algorithm, the newly collected data is merged with the existing historical light adjustment data, the optimal lighting threshold of each area is updated, and the original cluster center parameters are replaced.

[0009] Furthermore, in step S3, the sensor is an infrared array sensor or a millimeter wave radar, the detection grids of the sensor are numbered, the time series of the personnel triggering each detection grid is recorded, and the current position coordinates and real-time movement trajectory vector of the personnel in the lighting area are calculated based on the time series.

[0010] Furthermore, calculating the current position coordinates and real-time movement trajectory vector of the person includes: Use sensors to trigger the spatial coordinates of the grid and trigger time , construct the weighted centroid formula:

[0011] Among them, the weight , is the time attenuation coefficient; The personnel positions at consecutive moments and Perform the difference to obtain the personnel movement speed vector:

[0012] The modulus of the velocity vector is , the direction angle is:

[0013] And the multi-time velocity vectors are fitted into a smooth trajectory curve.

[0014] Furthermore, in step S4, the brightness distribution field conforms to the Gaussian function distribution law, and the brightness distribution field formula is:

[0015] in, is the current position coordinate, is the center brightness, is the standard deviation of brightness attenuation.

[0016] Furthermore, in step S6, when predicting the next position of the person, according to the velocity vector With direction vector Compensation is performed, and the brightness distribution field formula after compensation is:

[0017] in:

[0018] From the current location Point to coordinate point The direction angle, is the speed compensation coefficient;

[0019] is the amplification factor of speed on brightness diffusion, when The larger the angle, The smaller it is, the greater the compensation value.

[0020] In a second aspect, the present application provides a lighting control system based on differentiated thresholds and trajectory tracking, which is used to implement the lighting control method based on differentiated thresholds and trajectory tracking as described in the first aspect. The system includes: A data acquisition module is used to obtain historical lighting adjustment data of a preset lighting area; The threshold generation module is used to classify the historical light adjustment data, analyze each type of data based on the clustering algorithm, and calculate and generate the corresponding optimal lighting threshold; A sensor deployment module is used to deploy sensors in the lighting area to form a grid-like detection area and collect sensor trigger information; Position trajectory calculation module, used to integrate multiple sensor trigger information to calculate the current position and real-time movement trajectory of personnel in the lighting area; The lighting area control module is used to generate a brightness distribution field based on the current position of the personnel and control the brightness output of the corresponding lighting area according to the optimal lighting threshold; The trajectory prediction module is used to perform secondary compensation and adjust the brightness of the corresponding lighting area based on the real-time movement speed and direction of the personnel.

[0021] Furthermore, the sensor includes an infrared array sensor or a millimeter wave radar, and the position trajectory calculation module specifically includes a weighted centroid calculation unit based on the sensor trigger grid number and a trajectory smoothing processing unit.

[0022] Furthermore, the brightness distribution field conforms to the Gaussian function distribution law, and the trajectory prediction module performs brightness distribution compensation adjustment according to the velocity vector and direction angle.

[0023] It can be seen from the above technical solutions that the advantages of the present invention are: By introducing a regional differentiated threshold learning mechanism and a dynamic dimming method based on personnel trajectories, the intelligence level and energy-saving effect of lighting control are significantly improved.

[0024] An independent threshold library is established for different lighting areas. Combining historical light adjustment data, time, season, weather and other multi-dimensional information, the optimal threshold is dynamically generated through a clustering algorithm to achieve adaptive adjustment of the threshold, avoiding the rigidity of the traditional unified threshold, effectively matching the lighting needs of different areas and environments, and reducing light energy waste.

[0025] By deploying infrared array sensors or millimeter-wave radar within the lighting area to form a grid-like detection zone, accurate tracking of a person's location and real-time movement trajectory is achieved. Using an algorithm based on weighted centroid and trajectory smoothing, a Gaussian-distributed brightness field centered on the person's current location is dynamically generated, enabling local dimming. This system adapts to the person's movement in real time, enhancing the user experience.

[0026] This invention predicts the position of people and pre-adjusts brightness based on their speed and direction, further optimizing the response and balancing energy conservation with lighting comfort. Experimental results show that compared to traditional threshold algorithms, this invention significantly reduces energy consumption while maintaining a consistent lighting experience, achieving the dual goals of energy conservation and intelligent lighting. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for the description. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 2 is a step diagram of a lighting control method based on differentiated thresholds and trajectory tracking in an embodiment. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] See also Figure 1 As shown, the present invention provides a lighting control method based on differentiated thresholds and trajectory tracking, comprising the following steps: Step S1: Collect historical lighting adjustment data in multiple preset lighting areas. The collected data includes the regional light intensity detected by the light sensor, the brightness value manually adjusted by the user (the user's expected brightness), the current time, season, weather type, natural light intensity, and the light intensity of adjacent areas. The above data are associated with the corresponding lighting area identification and stored. The sampling period is a preset fixed time interval.

[0031] Step S2: Classify the historical light adjustment data collected in step S1, and group and analyze the data according to region type, weather type, and time period based on a clustering algorithm (such as the K-means algorithm). For each type of data, calculate and generate the corresponding optimal lighting threshold to guide subsequent automatic dimming control.

[0032] In this embodiment, newly collected data is periodically introduced to retrain the clustering algorithm, the newly collected data is merged with the existing historical light adjustment data, the optimal lighting threshold of each area is updated, and the original cluster center parameters are replaced.

[0033] During the initial system operation, light sensors and necessary environmental data collection devices (such as temperature and humidity sensors and weather interface modules) are deployed in each pre-set lighting area to continuously record ambient light intensity, user-adjusted brightness (i.e., user-desired brightness), time information (including hour, day of the week, and season), weather type, and the area type corresponding to the data. At the same time, the area number is used to map the data to the physical space, forming a data set containing the following fields: Data={AreaType,WeatherType,TimeSlot,AmbientLux,UserLux} Among them, TimeSlot is the time period, which can be divided into discrete segments (for example, 08:00–10:00 is the morning period); WeatherType is the weather, which can be derived from a real-time meteorological API or inferred by local sensors (sunny, cloudy, rainy, etc.); AreaType is the area type, defined on-site (such as conference room, corridor, office area); AmbientLux is the ambient light intensity, reflecting the objective value of the current natural light or mixed light; UserLux is the user's expected brightness, which is used as supervision data in subsequent threshold calculations to reflect the user's preference for the brightness of the scene.

[0034] In the data preprocessing stage, missing data are interpolated by mean or neighboring values, outliers (such as sudden changes in light intensity but no weather changes) are removed, and discrete features such as AreaType, WeatherType, and TimeSlot are encoded as numerical features (such as one-hot encoding or integer encoding). The resulting feature vector is in the form of: x=[AreaCode,WeatherCode,TimeCode,AmbientLux] AreaCode is the area code, WeatherCode is the weather code, and TimeCode is the time period code.

[0035] All feature vectors are input into the K-means clustering algorithm. The choice of K can be determined using the Elbow Method, which calculates the total intra-class squared error (SSE) for different K values ​​and selects the K value at the inflection point as the optimal number of clusters. The iterative process of K-means clustering is as follows: Step 1: Randomly select K initial cluster centers.

[0036] Step 2: Assign each piece of data to the nearest cluster center (using the Euclidean distance metric).

[0037] Step 3: Calculate the new center of each cluster (take the mean of each feature).

[0038] Step 4: Repeat steps 2 and 3 until the cluster centers converge or the number of iterations reaches the upper limit.

[0039] After clustering is complete, each cluster corresponds to a composite scene data of "region type + weather type + time period". For each cluster's data, the optimal balance point between UserLux and AmbientLux within the cluster is calculated as the optimal lighting threshold for the cluster. In this embodiment, the following formula is used for calculation:

[0040] in, 、 are the weights of ambient light and user expected brightness, which can be adjusted according to energy saving priority or experience priority strategy (such as =0.4, =0.6).

[0041] The optimal lighting thresholds of all clusters are stored in the threshold library in the form of key-value pairs, where the key is (AreaType, WeatherType, TimeSlot) and the value is the corresponding T_opt.

[0042] When the system is running in real time, the currently detected area type, weather type and time period information will be encoded as a feature vector, matched to the corresponding cluster category in the threshold library, and the pre-calculated T_opt will be called as the lighting threshold of the current scene to achieve differentiated and adaptive lighting control.

[0043] When the system collects new historical data, it can re-execute the above clustering and threshold generation process regularly (such as daily or weekly) to achieve dynamic updating and self-optimization of the threshold.

[0044] Step S3: Infrared array sensors or millimeter-wave radars are deployed within the illuminated area to form a grid-like detection area. The trigger information from multiple sensors is integrated. The person's current location coordinates are obtained in real time by calculating the weighted centroid of the spatial coordinates and trigger times of the sensor trigger grid. The person's location is then differentiated at consecutive moments to obtain the person's velocity vector and azimuth. The velocity vectors at multiple moments are then fitted to form a smooth trajectory.

[0045] Calculating the personnel's current position coordinates and real-time movement trajectory vector includes: Use sensors to trigger the spatial coordinates of the grid and trigger time , construct the weighted centroid formula:

[0046] Among them, the weight , is the time attenuation coefficient; The personnel positions at consecutive moments and Perform the difference to obtain the personnel movement speed vector:

[0047] The modulus of the velocity vector is , the direction angle is:

[0048] And the multi-time velocity vectors are fitted into a smooth trajectory curve.

[0049] Step S4: Divide the lighting area into multiple independent lighting zones, typically 60 cm x 60 cm in size. Based on the person's current location, a brightness distribution field conforming to a Gaussian function is generated within the corresponding lighting zone, centered at the person's location. The brightness distribution field includes the central brightness and the standard deviation of the brightness attenuation. The system updates the brightness distribution field in real time at a set interval to achieve dynamic response.

[0050] The brightness distribution field conforms to the Gaussian function distribution law, and the brightness distribution field formula is:

[0051] in, is the current position coordinate, is the center brightness, is the standard deviation of brightness attenuation.

[0052] Step S5: Based on the optimal lighting threshold generated in step S2 and the brightness distribution field generated in step S4, the brightness output of the corresponding lighting area is controlled. Through local dimming control, the light intensity that meets the threshold condition is achieved while ensuring spatial gradient of lighting, achieving a balance between energy saving and user experience.

[0053] Step S6: Based on the moving speed and direction of the person, perform secondary compensation adjustment on the brightness of the corresponding lighting area.

[0054] The brightness distribution field is compensated and adjusted according to the velocity vector and direction angle to achieve secondary compensation adjustment of the brightness of the corresponding lighting area, ensuring the forward-looking and continuity of the lighting response.

[0055] When predicting the next position of a person, according to the velocity vector With direction vector Compensation is performed, and the brightness distribution field formula after compensation is:

[0056] in:

[0057] From the current location Point to coordinate point The direction angle, is the speed compensation coefficient;

[0058] is the amplification factor of speed on brightness diffusion, when The larger the angle, The smaller it is, the greater the compensation value.

[0059] In some embodiments, the present application provides a lighting control system based on differentiated thresholds and trajectory tracking, the system comprising: The data acquisition module is used to acquire historical lighting adjustment data within a pre-defined lighting area. This data includes ambient light intensity, user-adjusted brightness, time of day, season, weather type, natural light intensity, and adjacent area light intensity. The data is then associated with the corresponding area identifier and stored.

[0060] The threshold generation module is responsible for classifying and processing the collected historical light adjustment data. Based on a clustering algorithm (such as the K-means algorithm), it divides and analyzes the data according to regional characteristics, weather type, and time period, and generates the optimal lighting threshold corresponding to each category for subsequent automatic dimming control.

[0061] The sensor deployment module arranges infrared array sensors or millimeter-wave radars in the lighting area to form a grid-shaped detection area, collecting trigger information from multiple sensors in real time to ensure comprehensive coverage of human activities.

[0062] The position trajectory calculation module integrates the trigger data of the above multiple sensors and determines the precise position of people and their movement trajectory in the lighting area in real time through weighted centroid calculation and timing analysis, providing an accurate spatial reference for dynamic dimming.

[0063] The lighting area control module generates a brightness distribution field that conforms to the Gaussian distribution law based on the current position of the personnel. At the same time, combined with the optimal lighting threshold output by the threshold generation module, it dynamically controls the brightness output of each lighting area to achieve refined and energy-saving lighting adjustment within the area.

[0064] The trajectory prediction module is used to perform secondary compensation and adjust the brightness of the corresponding lighting area based on the real-time movement speed and direction of the personnel, improving the response speed and adaptability of the lighting system and ensuring the continuity and comfort of the user experience.

[0065] In some embodiments, the present application provides a terminal, including: A memory for storing a lighting control program based on differentiated thresholds and trajectory tracking; A processor is configured to implement the steps of the lighting control method based on differentiated thresholds and trajectory tracking when executing the lighting control system based on differentiated thresholds and trajectory tracking.

[0066] In some embodiments, the present application provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the lighting control method based on differentiated thresholds and trajectory tracking.

[0067] It is understood that the systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or physical devices, or by products having certain functions. A typical implementation device is a computer, which may be a personal computer, a laptop computer, a personal digital assistant, a tablet computer, a wearable device, or a combination of any of these devices.

[0068] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0069] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0070] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be used to store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0071] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0072] It should be understood that although the terms first, second, third, etc. may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when..." or "when..." or "in response to determining."

[0073] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this specification shall be included in the scope of protection of one or more embodiments of this specification.

Claims

1. A lighting control method based on differentiated threshold and trajectory tracking, characterized in that: The following steps are involved: Step S1: collecting historical lighting adjustment data in multiple preset lighting areas; Step S2: classify the historical lighting adjustment data, analyze each category of data based on a clustering algorithm, and calculate and generate the corresponding optimal lighting threshold; Step S3: Deploy sensors in the lighting area to form a grid-like detection area, fuse trigger information from multiple sensors, and calculate the current position and real-time movement trajectory of the person in the lighting area; Step S4: Divide the lighting area into multiple independent lighting areas, generate a brightness distribution field centered on the personnel position within the lighting area, and update it in real time within a set time interval; Step S5: controlling the brightness output of the corresponding lighting area according to the optimal lighting threshold and the brightness distribution field; Step S6: Based on the moving speed and direction of the person, perform secondary compensation adjustment on the brightness of the corresponding lighting area.

2. The lighting control method based on differentiated threshold and trajectory tracking according to claim 1, characterized in that: In step S1, the historical lighting adjustment data includes the lighting intensity of the lighting area, the user's expected brightness corresponding to the user's manual brightness adjustment, the current time, season, weather type, natural light intensity and the lighting intensity of the adjacent area, and the data is associated with the corresponding lighting area identification and stored.

3. The lighting control method based on differentiated threshold and trajectory tracking according to claim 2, characterized in that: In step S2, newly collected data is periodically introduced to retrain the clustering algorithm, the newly collected data is merged with the existing historical light adjustment data, the optimal lighting threshold of each area is updated, and the original cluster center parameters are replaced.

4. The lighting control method based on differentiated threshold and trajectory tracking according to claim 1, characterized in that: In step S3, the sensor is an infrared array sensor or a millimeter wave radar, the detection grids of the sensor are numbered, the time series of the personnel triggering each detection grid is recorded, and the current position coordinates and real-time movement trajectory vector of the personnel in the lighting area are calculated based on the time series.

5. The lighting control method based on differentiated threshold and trajectory tracking according to claim 4, characterized in that: Calculating the personnel's current position coordinates and real-time movement trajectory vector includes: Use sensors to trigger the spatial coordinates of the grid and trigger time , construct the weighted centroid formula: Among them, the weight , is the time attenuation coefficient; The personnel positions at consecutive moments and Perform the difference to obtain the personnel movement speed vector: The modulus of the velocity vector is , the direction angle is: And the multi-time velocity vectors are fitted into a smooth trajectory curve.

6. The lighting control method based on differentiated threshold and trajectory tracking according to claim 5, characterized in that: In step S4, the brightness distribution field conforms to the Gaussian function distribution law, and the brightness distribution field formula is: in, is the current position coordinate, is the center brightness, is the standard deviation of brightness attenuation.

7. The lighting control method based on differentiated threshold and trajectory tracking according to claim 6, characterized in that: In step S6, when predicting the next position of the person, according to the velocity vector With direction vector Compensation is performed, and the brightness distribution field formula after compensation is: in: From the current location Point to coordinate point The direction angle, is the speed compensation coefficient; is the amplification factor of speed on brightness diffusion, when The larger the angle, The smaller it is, the greater the compensation value.

8. A lighting control system based on differentiated thresholds and trajectory tracking, used to implement the lighting control method based on differentiated thresholds and trajectory tracking as claimed in claim 1, characterized in that: The system includes: A data acquisition module is used to obtain historical lighting adjustment data of a preset lighting area; The threshold generation module is used to classify the historical light adjustment data, analyze each type of data based on the clustering algorithm, and calculate and generate the corresponding optimal lighting threshold; A sensor deployment module is used to deploy sensors in the lighting area to form a grid-like detection area and collect sensor trigger information; Position trajectory calculation module, used to integrate multiple sensor trigger information to calculate the current position and real-time movement trajectory of personnel in the lighting area; The lighting area control module is used to generate a brightness distribution field based on the current position of the personnel and control the brightness output of the corresponding lighting area according to the optimal lighting threshold; The trajectory prediction module is used to perform secondary compensation and adjust the brightness of the corresponding lighting area based on the real-time movement speed and direction of the personnel.

9. The lighting control system based on differentiated thresholds and trajectory tracking according to claim 8, characterized in that: The sensor includes an infrared array sensor or a millimeter wave radar, and the position trajectory calculation module specifically includes a weighted centroid calculation unit based on the sensor trigger grid number and a trajectory smoothing processing unit.

10. The lighting control system based on differentiated thresholds and trajectory tracking according to claim 8 or 9, characterized in that: The brightness distribution field conforms to the Gaussian function distribution law, and the trajectory prediction module performs brightness distribution compensation adjustment according to the velocity vector and direction angle.

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