Dynamic lighting control method and apparatus, electronic device, and storage medium

Abstract

The application discloses a dynamic lighting control method and device, electronic equipment and storage medium, and is used for solving the technical problems of single perception dimension, poor decision real-time and lack of dynamic optimization in current lighting control related technologies. When a new object appears in a monitoring area, the current ambient light intensity of the monitoring area, the motion speed information of the new object, radar point cloud data and infrared thermal imaging data are acquired; temperature calculation based on space-time registration is performed based on the radar point cloud data and the infrared thermal imaging data, and temperature information is acquired; the motion state of the new object is judged through double verification in combination with the temperature information and the motion speed information; adaptive dimming of the environment is performed based on the motion state and the ambient light intensity, illumination brightness is acquired, and the output brightness of the lighting equipment in the monitoring area is converted into the illumination brightness.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to a dynamic lighting control method, device, electronic device, and storage medium. Background Technology

[0002] Lighting control technology is a crucial component of modern architecture and smart parks. Through intelligent management and adjustment, lighting control optimizes the energy efficiency of lighting systems, enhances user experience, and ensures safety. Traditional lighting control relies primarily on manual switches or simple timers, resulting in high energy consumption, poor flexibility, and inconvenient management. With technological advancements, intelligent lighting systems are becoming increasingly widespread. These systems not only significantly reduce energy consumption but also provide personalized lighting experiences, improving indoor comfort. Currently, intelligent lighting is widely used in homes, offices, and public spaces.

[0003] Current lighting control is mainly achieved through the following methods: firstly, timed control based on a preset schedule for switching lighting equipment on and off; secondly, single-sensor control using infrared or sound-activated sensors to trigger lighting switches; thirdly, centralized cloud control by uploading all sensor data to the cloud and generating control strategies from a central server; fourthly, simple light-sensing control that adjusts brightness based on ambient light intensity; and finally, a group control strategy that manages lighting fixtures in groups and controls them uniformly by area.

[0004] The above-mentioned methods have the following problems: First, a single sensor is greatly affected by the environment, leading to a high false trigger rate, frequent lighting in unoccupied areas, and accidental operation triggered by animals / equipment. Second, fixed threshold responses cannot adapt to dynamic environments, resulting in excessive brightness during the day and insufficient darkness at night, failing to utilize natural light. Third, cloud-based decision-making has high latency, with response times typically exceeding 500ms, requiring personnel to wait for lighting to activate, resulting in a poor user experience and wasted energy. Furthermore, the current passive response mode lacks predictive capabilities, causing lighting to lag behind personnel movement, posing safety hazards. Finally, the inability to optimize manually set parameters leads to policy rigidity, making lighting control unable to adapt to seasonal / human traffic changes, resulting in a long-term decline in energy-saving effects. Therefore, current mainstream solutions suffer from three major limitations: a single sensing dimension, poor real-time decision-making, and a lack of dynamic optimization. Summary of the Invention

[0005] This invention provides a dynamic lighting control method, device, electronic device, and storage medium to solve or partially solve the technical problems of single perception dimension, poor real-time decision-making, and lack of dynamic optimization in current lighting control technologies.

[0006] This invention provides a dynamic lighting control method, the method comprising:

[0007] When a new object is detected in the monitoring area, the current ambient light intensity of the monitoring area, the motion speed information of the new object, radar point cloud data and infrared thermal imaging data are obtained.

[0008] Temperature information is obtained by performing temperature calculations based on spatiotemporal registration using the radar point cloud data and the infrared thermal imaging data.

[0009] By combining the temperature information and the speed information, the motion state of the new object is determined through dual verification.

[0010] Based on the motion state and the ambient light intensity, adaptive dimming is performed to obtain the illumination brightness, and the output brightness of the lighting equipment in the monitoring area is controlled to be converted into the illumination brightness.

[0011] Optionally, the infrared thermal imaging data includes thermal imaging temperature matrix information; the step of performing temperature calculation based on spatiotemporal registration using the radar point cloud data and the infrared thermal imaging data to obtain temperature information includes:

[0012] Based on the radar point cloud data, coordinate transformation is performed to obtain thermal imaging plane coordinate information;

[0013] ICP registration is performed based on the thermal imaging temperature matrix information and the thermal imaging plane coordinate information to obtain coordinate registration information;

[0014] Based on the coordinate registration information, the temperature information of the new object is obtained through bilinear interpolation.

[0015] Optionally, the step of combining the temperature information and the motion speed information to determine the motion state of the new object through dual verification includes:

[0016] Temperature zone verification is performed based on the temperature information;

[0017] Determine the speed range based on the aforementioned motion speed information;

[0018] Based on the combined results of temperature range verification and velocity range judgment, the motion state of the new object is determined.

[0019] Optionally, the determination of the motion state of the new object based on the combined temperature range verification results and velocity range judgment results includes:

[0020] When the temperature zone verification result indicates that the temperature of the new object is within the preset temperature range, and the speed zone judgment result indicates that the current speed of the new object is zero, the new object is determined to be a person entering the monitoring area, and the person's movement state is a stationary state.

[0021] When the temperature zone verification result indicates that the temperature of the new object is within the preset temperature range, and the speed zone judgment result indicates that the current movement speed of the new object is within the preset speed range, the new object is determined to be a person entering the monitoring area, and the movement state of the person is an active state.

[0022] When the temperature zone verification result indicates that the temperature of the new object is outside the preset temperature zone, or when the speed zone judgment result indicates that the current speed of the new object is neither zero nor within the preset speed zone, the motion state of the new object is determined to be in a failure state, and the failure state indicates that there is currently no one in the monitoring area.

[0023] Optionally, the motion state of the new object is a stationary state, or an active state, or a disabled state; the step of adaptive dimming based on the motion state and the ambient light intensity to obtain illumination brightness includes:

[0024] When the new object is in an active state, the compensation brightness is calculated based on the ambient light intensity, and the compensation brightness is used as the illumination brightness.

[0025] When the motion state of the new object is stationary, the preset base brightness is used as the illumination brightness;

[0026] When the motion state of the new object is in a failed state, the value of the lighting brightness is set to zero to control the lighting to be turned off.

[0027] Optionally, the motion state of the new object is a stationary state, or an active state, or a disabled state; the conversion of the output brightness of the lighting device in the monitoring area into the lighting brightness includes:

[0028] When the motion state of the new object is stationary or in a failed state, the output brightness of the lighting equipment in the monitoring area is directly controlled to be converted into the lighting brightness.

[0029] When the new object is in an active state, the new object is a person entering the monitoring area. At this time, the location information of the target lighting device closest to the person in the monitoring area is obtained.

[0030] Based on the radar point cloud data, the personnel coordinate information is determined, and path planning is performed based on the personnel coordinate information to obtain the predicted path of the personnel.

[0031] Based on the location information and the predicted path of the personnel, the shortest straight-line distance between the personnel and the target lighting equipment is calculated in real time.

[0032] Taking into account response delay, the pre-start time of the target lighting device is calculated based on the shortest straight distance and the movement speed information;

[0033] When the pre-start time exceeds the preset pre-start threshold, the system enters a monitoring standby state.

[0034] When the pre-start time is less than or equal to the preset pre-start threshold, the control will convert the output brightness of the target lighting device into the lighting brightness in advance.

[0035] Optionally, the method further includes:

[0036] Obtain the lighting control dataset of the monitoring area within a preset historical time period. The lighting control dataset includes pedestrian traffic data and lighting control data for different time periods throughout the day in the monitoring area.

[0037] The lighting control dataset is input into a pre-trained LSTM prediction model to predict lighting demand and obtain the probability distribution of lighting demand in the monitored area over a future period.

[0038] The present invention also provides a dynamic lighting control device, comprising:

[0039] The data acquisition unit is used to acquire the current ambient light intensity of the monitoring area, the motion speed information of the new object, radar point cloud data and infrared thermal imaging data when a new object is detected in the monitoring area.

[0040] The temperature information calculation unit is used to perform temperature calculation based on the radar point cloud data and the infrared thermal imaging data to obtain temperature information;

[0041] A motion state determination unit is used to determine the motion state of the new object by combining the temperature information and the motion speed information through dual verification.

[0042] The lighting control unit is used to perform environmental adaptive dimming based on the motion state and the ambient light intensity to obtain the lighting brightness, and to control the output brightness of the lighting equipment in the monitoring area to be converted into the lighting brightness.

[0043] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0044] The memory is used to store program code and transmit the program code to the processor;

[0045] The processor is configured to execute the dynamic lighting control method as described above, according to the instructions in the program code.

[0046] The present invention also provides a computer-readable storage medium for storing program code for performing the dynamic lighting control method as described in any of the preceding claims.

[0047] As can be seen from the above technical solutions, the present invention has the following advantages:

[0048] A dynamic lighting control method is provided. When a new object is detected in the monitoring area, the current ambient light intensity, the object's motion speed, radar point cloud data, and infrared thermal imaging data are acquired. Based on multi-source data fusion, subsequent calculations are performed by integrating various monitoring data within the current area, reducing the false trigger rate. Temperature information is obtained through spatiotemporal registration-based temperature calculation using radar point cloud data and infrared thermal imaging data. A dual-mode verification combining millimeter-wave radar and thermal imaging is then implemented to better filter biometric features, enabling the identification of individuals and their specific states within the detected area, providing a decision-making basis for subsequent dynamic lighting control. Combining temperature and motion speed information, the motion state of the new object is determined through dual verification; environmental adaptive dimming is performed based on the motion state and ambient light intensity to obtain the lighting brightness, and the output brightness of lighting equipment in the monitoring area is controlled to convert it into the desired lighting brightness. Based on the state judgment results and ambient light intensity, and combined with dynamic light compensation for adaptive dimming, real-time edge decision-making can not only obtain a more environmentally adaptable light intensity, improve the overall power saving rate, reduce energy consumption, and balance the fluctuation of the annual power saving rate, but also shorten the control response time and improve the user experience when performing lighting control in the future. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of the implementation architecture of a dynamic lighting control system;

[0051] Figure 2 This is a flowchart illustrating the steps of a dynamic lighting control method.

[0052] Figure 3 This is a schematic diagram of the processing flow of the sensing layer in a dynamic lighting control system.

[0053] Figure 4 This is a schematic diagram of the execution process for light intensity control under environmental adaptive dimming.

[0054] Figure 5 This is a schematic diagram of the target brightness control execution process under environmental adaptive dimming.

[0055] Figure 6 This is a schematic diagram of a pre-startup execution process based on trajectory prediction;

[0056] Figure 7 This is a schematic diagram of the execution process for lighting demand forecasting.

[0057] Figure 8 A schematic diagram of the overall process of a dynamic lighting control method;

[0058] Figure 9 This is a structural block diagram of a dynamic lighting control device. Detailed Implementation

[0059] This invention provides a dynamic lighting control method, device, electronic device, and storage medium to solve or partially solve the technical problems of single perception dimension, poor real-time decision-making, and lack of dynamic optimization in current lighting control technologies.

[0060] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0061] As an example, current lighting control for modern homes or smart parks is mainly achieved through the following methods: firstly, timed control based on preset schedules for switching lighting equipment on and off; secondly, single-sensor control using infrared or sound-activated sensors to trigger lighting switches; thirdly, centralized cloud control by uploading all sensor data to the cloud and generating control strategies from a central server; fourthly, simple light-sensing control that adjusts brightness based on ambient light intensity; and finally, a group control strategy that manages lighting fixtures in groups and controls them uniformly by area.

[0062] The above-mentioned methods have the following problems: First, a single sensor is greatly affected by the environment, leading to a high false trigger rate, frequent lighting in unoccupied areas, and accidental operation triggered by animals / equipment. Second, fixed threshold responses cannot adapt to dynamic environments, resulting in excessive brightness during the day and insufficient darkness at night, failing to utilize natural light. Third, cloud-based decision-making has high latency, with response times typically exceeding 500ms, requiring personnel to wait for lighting to activate, resulting in a poor user experience and wasted energy. Furthermore, the current passive response mode lacks predictive capabilities, causing lighting to lag behind personnel movement, posing safety hazards. Finally, the inability to optimize manually set parameters leads to policy rigidity, making lighting control unable to adapt to seasonal / human traffic changes, resulting in a long-term decline in energy-saving effects. Therefore, current mainstream solutions suffer from three major limitations: a single sensing dimension, poor real-time decision-making, and a lack of dynamic optimization.

[0063] Therefore, one of the core inventive points of this invention is: addressing the shortcomings of current technologies, to achieve real-time dynamic lighting control based on multi-source sensing, and significantly reduce ineffective lighting energy consumption while ensuring comfort, a more intelligent and flexible dynamic lighting control method is provided. First, based on multi-source data fusion, subsequent calculations are performed by integrating various monitoring data within the current area, which can reduce the false trigger rate. Setting up dual-mode verification combining millimeter-wave radar and thermal imaging can better filter biometric features, enabling the determination of personnel and specific states of objects detected within the current area, providing a decision-making basis for subsequent dynamic lighting control. Second, based on the state judgment results and ambient light intensity, and combined with dynamic light compensation and pre-start mechanisms, real-time edge decision-making not only achieves better environmental adaptability light intensity, improving overall energy saving rate, reducing energy consumption, and balancing annual energy saving rate fluctuations, but also shortens control response time during subsequent lighting control, improving user experience. Simultaneously, hourly automatic optimization based on an LSTM prediction model can reduce operation and maintenance costs.

[0064] Reference Figure 1 The diagram illustrates a schematic representation of the implementation architecture of a dynamic lighting control system provided in an embodiment of the present invention.

[0065] Combination Figure 1 The dynamic lighting control system provided in this embodiment of the invention is actually a cloud-edge collaborative optimization system. Specifically, this system is a layered system that includes an edge decision gateway and a cloud optimization platform. It mainly includes a perception layer 101, an edge computing layer 102, and a cloud platform layer 103.

[0066] The perception layer 101 can be further divided into: a millimeter-wave radar for acquiring radar point cloud data; an infrared thermal imaging module for acquiring infrared thermal imaging data; a light sensor for acquiring ambient light intensity; a spatiotemporal registration module for ICP registration based on radar point cloud data and infrared thermal imaging data (thermal imaging temperature matrix); and a dynamic strategy engine for adaptive ambient lighting based on ambient light intensity (lux value) and the registered coordinate registration information.

[0067] The edge computing layer 102 (edge ​​gateway) performs edge computing in conjunction with the perception layer 101, and performs real-time control (response <100ms) based on the lighting brightness data transmitted by the perception layer 101. Specifically, based on the lighting brightness, it generates a PWM (Pulse Width Modulation) control signal through instruction generation, and sends it to the lighting device that needs to perform lighting intensity conversion, so that the lighting device can perform lighting intensity adjustment based on the PWM control signal.

[0068] Cloud platform layer 103 uses historical lighting control data (such as pedestrian traffic data from the past three days, and lighting control information for different time periods, including normal and peak hours each day) to predict lighting demand for a future period (such as the next hour) using an LSTM (Long Short-Term Memory) model. Based on model optimization, optimized parameters are distributed to edge nodes daily, forming a real-time, dynamic, cyclical optimization process. Simultaneously, cloud platform layer 103 also includes digital twin functionality, which can be mapped to the operations and maintenance end through 3D mapping for better system operation and maintenance management.

[0069] Reference Figure 2 The diagram illustrates a flowchart of a dynamic lighting control method provided by an embodiment of the present invention, which may specifically include the following steps:

[0070] Step 201: When a new object is detected in the monitoring area, the current ambient light intensity of the monitoring area, the motion speed information of the new object, radar point cloud data and infrared thermal imaging data are obtained.

[0071] In practical applications, ambient light can be sensed in real time using a light sensor. Specifically, the ambient light intensity is obtained by collecting the visible spectral intensity of the monitored area using a photodiode and a filter (380-780nm). (Unit: lux).

[0072] Electromagnetic wave reflection signals are detected using millimeter-wave radar. Based on FMCW (Frequency Modulated Continuous Wave) scanning (77 GHz), the polar coordinate data of newly appearing objects within the monitoring area are obtained, including distance. Azimuth and speed .

[0073] Infrared radiation energy is acquired by an infrared thermal imaging module, and the thermal imaging temperature matrix of the new object is obtained based on an uncooled microbolo array (160×120 pixels). (Accuracy ±0.5℃).

[0074] By using multiple devices to collect data from different data sources as basic data, subsequent multi-source data fusion is performed, and lighting control is dynamically executed based on the current actual situation.

[0075] Step 202: Perform temperature calculation based on the radar point cloud data and the infrared thermal imaging data to obtain temperature information;

[0076] This step mainly involves performing temperature calculations based on spatiotemporal registration using radar point cloud data and infrared thermal imaging data to obtain temperature information. The point cloud coordinates are then registered with the thermal image using the ICP (Iterative Closest Point) algorithm.

[0077] Based on the preceding information, infrared thermal imaging data includes thermal imaging temperature matrix information. Therefore, in the specific implementation, temperature calculation based on spatiotemporal registration using radar point cloud data and infrared thermal imaging data to obtain temperature information can include: first, performing coordinate transformation based on the radar point cloud data to obtain thermal imaging plane coordinate information; then, performing ICP registration based on the thermal imaging temperature matrix information and the thermal imaging plane coordinate information to obtain coordinate registration information; finally, using the coordinate registration information, obtaining the temperature information of the new object through bilinear interpolation.

[0078] The above process is mainly implemented based on the spatiotemporal registration module of the perception layer. For example, some key code implementing this step is shown below:

[0079] Sensor data acquisition (50ms cycle)

[0080] / / Pseudocode: Millimeter-wave radar data acquisition

[0081] struct RadarPoint {

[0082] float range; / / Distance (m)

[0083] float azimuth; / / Azimuth angle (rad)

[0084] float velocity; / / Velocity (m / s)

[0085] };

[0086] void read_sensors() {

[0087] RadarPoint points

[128] ;

[0088] thermal_matrix_t thermal = read_thermal(); / / Get the 160x120 thermal matrix

[0089] float lux = read_light_sensor(); / / Light intensity

[0090] / / Send using MQTT-SN protocol (payload < 128 bytes)

[0091] mqttsn_publish("sensor / data",&points, sizeof(points));

[0092] }

[0093] The key implementation code for coordinate transformation and ICP registration is as follows:

[0094] def spatio_temporal_fusion(radar_pts, thermal_img):

[0095] # Coordinate system transformation (radar polar coordinates → thermal imaging plane coordinates)

[0096] cartesian_pts = []

[0097] for pt in radar_pts:

[0098] x = pt.range * cos(pt.azimuth) # Azimuth to Cartesian X

[0099] y = pt.range * sin(pt.azimuth) # Convert azimuth to Cartesian y

[0100] cartesian_pts.append([x, y])

[0101] # Iterative Nearest Point Matching (ICP Algorithm)

[0102] transformation = ICP(

[0103] source=cartesian_pts,

[0104] target=thermal_img.get_keypoints(), # Thermal imaging feature points

[0105] max_iterations=100,

[0106] tolerance=1e-5 )

[0108] Edge data processing (response time <100ms)

[0109] # Core code for ICP point cloud registration

[0110] def icp_registration(source, target):

[0111] prev_error = float('inf')

[0112] T = identity_matrix()

[0113] for _ in range(100): # Maximum number of iterations: 100

[0114] # Find the nearest point

[0115] correspondences = find_nearest(source, target)

[0116] # Calculate the optimal transformation (SVD decomposition)

[0117] R, t = svd_transform(source, correspondences)

[0118] # Update transformation matrix

[0119] T = combine_transform(T, R, t)

[0120] # Check convergence

[0121] error = calculate_error(source, target, T) if abs(prev_error - error)< 1e-5:

[0122] break

[0123] prev_error = error

[0124] return T

[0125] Step 203: Combining the temperature information and the motion speed information, determine the motion state of the new object through dual verification;

[0126] This step primarily combines temperature and speed information to determine the motion state of the new object through dual verification. Specifically, temperature zone verification is performed based on temperature information to verify whether the new object is within a preset temperature range (e.g., setting a human body temperature range of 36.0-37.5℃ to initially determine if it is a person); speed zone judgment is performed based on speed information to determine whether the new object is in a normal stationary or moving state (e.g., a speed of 0 indicates a stationary state; a speed of 0.5~2m / s indicates a normal moving state; a speed greater than 2m / s indicates an abnormal speed state, in which case the judgment is invalid); combining the temperature zone verification results and the speed zone judgment results, the motion state of the new object is determined.

[0127] Furthermore, by combining the temperature range verification results and the velocity range judgment results, the motion state of the new object can be determined, which may include the following three scenarios:

[0128] In the first scenario, when the temperature zone verification result indicates that the temperature of the new object is within the preset temperature range, and the speed zone judgment result indicates that the current speed of the new object is zero, the new object is determined to be a person entering the monitoring area, and the person's movement state is stationary.

[0129] In the second scenario, when the temperature zone verification result indicates that the temperature of the new object is within the preset temperature range, and the speed zone judgment result indicates that the current speed of the new object is within the preset speed range, the new object is determined to be a person entering the monitoring area, and the person's movement state is an active state.

[0130] In the third scenario, if the temperature zone verification result indicates that the temperature of the new object is outside the preset temperature zone (indicating that the detected object is not a person entering the monitoring area, but may be a mobile robot or other object that has mistakenly entered; if the monitoring area is outdoors, it may be a small animal with a high or low body temperature, etc.), or if the speed zone judgment result indicates that the current movement speed of the new object is neither zero nor within the preset speed zone (indicating that the current movement speed of the object is abnormal, such as a small animal darting by or a robot moving at a high speed, etc.), then the movement state of the new object is determined to be in a failed state. In this failed state, it means that the monitoring area is currently unoccupied, requiring no lighting or adaptive dimming.

[0131] For example, the system first verifies whether the target temperature of a newly entered object within the monitoring area is within the human body temperature range of 36.0-37.5℃. If so, it can be preliminarily identified as a person. Then, the speed is used to further verify whether the person is currently stationary or active. Some key code implementing this process is shown below:

[0132] # Data Fusion (with Additional Temperature Attribute)

[0133] fused_data = []

[0134] for i, pt in enumerate(cartesian_pts):

[0135] world_pt = transformation.apply(pt) # Coordinate transformation

[0136] temp = thermal_img.get_temp(world_pt[0], world_pt[1])

[0137] if 36.0 <= temp <= 37.5: # Human body temperature verification

[0138] fused_data.append({

[0139] "x": pt[0], "y": pt[1],

[0140] "v": radar_pts[i].velocity,

[0141] "status": "ACTIVE" if abs(radar_pts[i].velocity) > 0.5 else "IDLE"})

[0142] return fused_data

[0143] Combining steps 202 to 203, refer to Figure 3 This diagram illustrates the processing flow of the sensing layer in a dynamic lighting control system proposed in this invention. On one hand, a mapping relationship is established through ICP (Integrated Circuit Processing), aligning the coordinate-transformed radar point cloud with the thermal imaging feature points in space. The registered coordinate points can then be used to obtain accurate temperature values ​​via bilinear interpolation for subsequent temperature verification. Through temperature-velocity dual-condition verification, the monitored target is more accurately identified as a human body, and further, the current state of the human body—whether stationary or active—is determined. Specifically, if the temperature range is acceptable and the speed is within a reasonable walking range (0-2 m / s), it can be identified as a human body. Further, based on the speed, it can be determined whether the target is in an IDLE (stationary) state or an ACTIVE (active) state. In other cases, such as abnormal temperature range or abnormal speed, the target is determined to be in an INVALID (inactive) state.

[0144] Step 204: Based on the motion state and the ambient light intensity, perform environmental adaptive dimming to obtain the illumination brightness, and control the output brightness of the lighting equipment in the monitoring area to convert it into the illumination brightness.

[0145] This step performs adaptive dimming based on motion status and ambient light intensity to obtain illumination brightness, and controls the output brightness of lighting equipment in the monitoring area to convert it into illumination brightness.

[0146] Based on the preceding discussion, the motion state of a new object within the monitoring area can be either stationary (IDLE), active (ACTIVE), or inactive (INVALID). In practical implementation, adaptive dimming based on the motion state and ambient light intensity to obtain illumination brightness can include the following three scenarios:

[0147] In the first scenario, when the new object is in an active state, the compensation brightness is calculated based on the ambient light intensity and used as the illumination brightness.

[0148] In the second scenario, when the new object is in a stationary state, the preset base brightness is used as the illumination brightness.

[0149] In the third scenario, when the motion state of the new object is in a failed state, the illumination brightness value is set to zero to control the lighting to be turned off.

[0150] The key code for implementing ambient adaptive dimming is as follows:

[0151] L_comp = max(300 - L_amb, 100) if active else 150 if idle else 0

[0152] A schematic diagram of the light intensity control execution process under environmental adaptive dimming is shown below. Figure 4 As shown.

[0153] Among them, for the calculation of compensated brightness, when At that time, forced To ensure basic safety lighting, PWM smooth transition is implemented during lighting control to avoid eye discomfort. Specifically, the rate of brightness change is limited to ±100 lux / s.

[0154] The calculation formula for meeting dynamic brightness requirements through ambient adaptive dimming is shown below:

[0155]

[0156] in, Indicates the brightness that needs to be compensated; The values ​​represent ambient light intensity; 300 represents the standard illuminance (international lighting standard) when people are active; 100 represents the minimum compensation illuminance to ensure basic lighting is provided even when ambient light is strong; 150 represents the maintenance illuminance when people are stationary; 0 represents the lighting being off in a failure state (no one is present); all illuminance parameters are in lux.

[0157] For example, assuming the ambient light intensity is 200 lux, in the ACTIVE state, Assuming the ambient light intensity is 400 lux, under the ACTIVE state, Assuming the state is IDLE, Assuming the state is INVALID, .

[0158] The formula for calculating PWM dimming output is as follows:

[0159]

[0160] in, This indicates the PWM duty cycle, ranging from 0% to 100%. This indicates the maximum brightness of the lighting equipment, which is set to 500 lux here.

[0161] For example, when When it is 100 lux, ;when At 150 lux, .

[0162] Referring to the previous steps, Figure 5 A schematic diagram of the target brightness control execution process under environmental adaptive dimming is shown.

[0163] Figure 5The process steps shown are largely similar to the previous steps, with the difference being that... This indicates the target brightness, which is the brightness that the working plane needs to achieve. To compensate for brightness, that is, the additional light intensity that the lighting equipment needs to emit.

[0164] Furthermore, the implementation process for converting the output brightness of lighting equipment in the control monitoring area into illumination brightness can include the following two scenarios:

[0165] The first method is clear: when the motion state of a new object is stationary or in a failed state, the output brightness of the lighting equipment in the monitoring area is directly controlled to be converted into illumination brightness.

[0166] In the second scenario, when the new object is in an active state, it indicates that the new object is a person entering the monitoring area, and the pre-start mechanism can be executed at this time.

[0167] A schematic diagram of the pre-startup execution process based on trajectory prediction is shown below. Figure 6 As shown, the specific implementation process is as follows: First, obtain the location information of the target lighting device closest to the person in the monitoring area; second, determine the person's coordinates based on radar point cloud data, and perform path planning based on the coordinates (combining the person's velocity vector to calculate the movement trajectory) to obtain the predicted path; third, calculate the shortest straight-line distance between the person and the target lighting device in real time based on the location information and the predicted path; fourth, considering response delay, calculate the pre-start time of the target lighting device based on the shortest straight-line distance and movement speed information to determine the start-up timing; fifth, when the pre-start time is greater than a preset pre-start threshold, enter a monitoring standby state; sixth, when the pre-start time is less than or equal to the preset pre-start threshold, control the output brightness of the target lighting device to be converted to illumination brightness in advance. Alternatively, control the output brightness of the target lighting device to be converted to a certain percentage of illumination brightness in advance. For example, trigger the activation of 30% base brightness 2 seconds before the person arrives at the target lighting device, and then control the conversion of brightness to illumination brightness when the person is about to arrive.

[0168] The formula for calculating the pre-start time in the pre-start mechanism is as follows:

[0169]

[0170] In the formula, Indicates the pre-start time (in seconds); This represents the perpendicular distance between the target lighting equipment and the predicted path of the personnel, i.e., the shortest straight-line distance (unit: meters). This indicates the distance (in meters) between the person's current location and the starting point, where the starting point is the location where the light fixture first sensed the person. Indicates the speed of movement of personnel (unit: meters per second); This represents a constant for hardware response latency, set to 0.3 seconds.

[0171] For example, assuming a person is 3 meters away from the target lighting device and moves at a speed of 1 meter per second, the pre-start time is 3.3 seconds, that is, the target lighting device is turned on 3.3 seconds in advance.

[0172] It should be noted that in actual dynamic lighting control applications, the current monitoring area may contain more than one person, and may also contain more than one lighting device. For ease of explanation, this embodiment of the invention assumes that only one person has entered the monitoring area, and the lighting device closest to the person is identified as the target lighting device, and the above process is executed. When the monitoring area is large and there are multiple people, the dynamic lighting control process for other people can be executed according to the process provided in the embodiment. When the monitoring area is small but contains multiple people, or when the monitoring area is large but more than one person who is close to the person has entered a certain sub-area, these people can be treated as a whole, and the relevant process can be executed according to the aforementioned embodiment. It is understood that this invention does not impose any limitations on this.

[0173] In summary, when the monitored area is unoccupied (in a failed state), the lighting is turned off. When a monitoring person is in the IDLE state, the pre-start logic is skipped, and the system directly enters the basic lighting mode. When a monitoring person is in the ACTIVE state, the pre-start mechanism is used to compensate for brightness changes to control the lighting brightness. It can be seen that the pre-start depends on the ACTIVE state output from the previous steps and the person's movement speed v. Therefore, for newly detected objects within the monitored area, it determines whether they are human, and then, based on the state under different circumstances, executes the corresponding lighting control mechanism, improving the flexibility and applicability of dynamic lighting control within the area.

[0174] In some embodiments, lighting demand in the future can be predicted based on historical lighting control data using machine learning (an LSTM prediction model built on a cloud platform layer) to obtain the corresponding probability distribution. This allows for adaptive optimization of the area's lighting demand in the future, taking into account different time periods, pedestrian traffic, and lighting control conditions, thus providing better long-term optimization capabilities. Specifically, a lighting control dataset for the monitored area within a preset historical time period (e.g., the past three days) is obtained. This dataset includes pedestrian traffic data and lighting control data for different time periods throughout the day (using hours, 5 minutes, or other suitable time points as statistical time granularities). The lighting control dataset is then input into a pre-trained LSTM prediction model to predict lighting demand and obtain the probability distribution of lighting demand in the monitored area in the future.

[0175] For example, Figure 7 A schematic diagram of the execution flow for lighting demand forecasting is shown.

[0176] Table 1: Network Architecture and Training-Related Parameters of the LSTM Prediction Model

[0177]

[0178] The model training mainly involves the following key parameters:

[0179] Input features: [pedestrian traffic, ambient light, day of the week factor, holiday markers]

[0180] Tag: [Actual Lighting Energy Consumption]

[0181] Loss function: MAPE (Mean Absolute Percentage Error)

[0182] The LSTM prediction model obtained through training can be used to predict lighting demand over a future period of time.

[0183] Combining the LSTM prediction described above, in another embodiment, the following equation gives the mathematical model of the pre-startup algorithm containing peak period coefficients:

[0184]

[0185] in, express ; This represents the time period coefficient, with 1.2 for peak hours and 1.0 for regular hours.

[0186] For example, suppose it is currently peak time, the ambient light intensity is 200 lux, and the distance is... The distance is 4 meters, and the personnel movement speed is 1 meter / second. Therefore, the PWM value can be calculated to be 24%, and the pre-start time is... It takes 4.3 seconds.

[0187] Based on the content described in the preceding embodiments, refer to Figure 8 This diagram illustrates the overall flow of a dynamic lighting control method according to an embodiment of the present invention. The specific implementation process of each step can be understood by referring to the relevant content in the foregoing embodiments; it will not be elaborated upon here. It is understood that the present invention does not impose any limitations on this.

[0188] This invention provides a real-time dynamic lighting control method based on multi-source sensing. First, by fusing multi-source data and integrating various monitoring data within the current area for subsequent calculations, the false trigger rate can be reduced. A dual-mode verification system combining millimeter-wave radar and thermal imaging can better filter biometric features, enabling the determination of personnel and specific states of objects detected within the current area, providing a decision-making basis for subsequent dynamic lighting control. Second, based on the state judgment results and ambient light intensity, and combined with dynamic light compensation and pre-start mechanisms, real-time edge decision-making not only achieves better environmental adaptability to light intensity, improving overall energy saving rate, reducing energy consumption, and balancing annual energy saving rate fluctuations, but also shortens control response time during subsequent lighting control, improving user experience. Simultaneously, hourly automatic optimization based on an LSTM prediction model can reduce operation and maintenance costs.

[0189] Compared with current technologies, the technical solution provided by this invention has the following advantages:

[0190] (1) Sensing accuracy: The current technology uses a single sensor for data acquisition, and its accuracy is less than 85%. However, the present invention can achieve an accuracy of 98.2% and reduce the false trigger rate by 82% through multi-source fusion.

[0191] (2) Response speed: Current technology relies on cloud-based decision-making. Its response speed requires 300~500ms. However, the technical solution of this invention relies on real-time edge decision-making, and its response speed can be reduced to 80~150ms, which can realize dynamic lighting control without waiting and improve user experience.

[0192] (3) Energy saving effect: The current technology has a power saving rate of 15-30%. However, by implementing the technical solution provided by this invention, the overall power saving rate can be controlled at around 53.2%, and energy consumption can be reduced by more than 40%.

[0193] (4) Environmental adaptability: Current technologies mainly adopt a fixed threshold control strategy. However, the technical solution of this invention uses dynamic light compensation and a pre-start mechanism to control the annual power saving rate fluctuation within ±5%.

[0194] (5) Fault tolerance: In current technology, a single point of failure will lead to system paralysis. However, the technical solution of this invention can achieve three-level fault-tolerant control (72 hours of autonomous operation), and the availability is improved to 99.98%.

[0195] (6) Long-term optimization capability: Current technology requires manual quarterly adjustments, resulting in high operation and maintenance costs. However, the technical solution of this invention can achieve hourly automatic optimization by using an LSTM model, reducing operation and maintenance costs by 90%.

[0196] (7) Scalability: Current technology can only achieve lighting control. However, the technical solution provided by this invention can be connected to the light-temperature-carbon synergistic optimization system through the Virtual Power Plant (VPP) interface, supporting the virtual power plant to participate in grid peak shaving.

[0197] Reference Figure 9 The diagram illustrates a structural block diagram of a dynamic lighting control device provided in an embodiment of the present invention, which may specifically include:

[0198] The data acquisition unit 901 is used to acquire the current ambient light intensity of the monitoring area, the motion speed information of the new object, radar point cloud data and infrared thermal imaging data when a new object is detected in the monitoring area.

[0199] Temperature information calculation unit 902 is used to perform temperature calculation based on spatiotemporal registration based on the radar point cloud data and the infrared thermal imaging data to obtain temperature information.

[0200] The motion state determination unit 903 is used to combine the temperature information and the motion speed information to determine the motion state of the new object through dual verification.

[0201] The lighting control unit 904 is used to perform environmental adaptive dimming based on the motion state and the ambient light intensity to obtain the lighting brightness, and to control the output brightness of the lighting equipment in the monitoring area to be converted into the lighting brightness.

[0202] In one optional embodiment, the infrared thermal imaging data includes thermal imaging temperature matrix information; the temperature information calculation unit 902 includes:

[0203] The coordinate transformation unit is used to perform coordinate transformation based on the radar point cloud data to obtain thermal imaging plane coordinate information;

[0204] The ICP registration unit is used to perform ICP registration based on the thermal imaging temperature matrix information and the thermal imaging plane coordinate information to obtain coordinate registration information.

[0205] A bilinear interpolation unit is used to obtain the temperature information of the new object through bilinear interpolation based on the coordinate registration information.

[0206] In one optional embodiment, the motion state determination unit 903 includes:

[0207] Temperature zone verification unit, used to perform temperature zone verification based on the temperature information;

[0208] A speed range determination unit is used to determine the speed range based on the motion speed information.

[0209] The comprehensive judgment unit is used to combine the temperature range verification results and the speed range judgment results to determine the motion state of the new object.

[0210] In one optional embodiment, the comprehensive judgment unit includes:

[0211] The stationary state determination unit is used to determine that the new object is a person entering the monitoring area and the movement state of the person is stationary when the temperature zone verification result indicates that the temperature of the new object is within the preset temperature range and the speed zone determination result indicates that the current movement speed of the new object is zero.

[0212] The activity status determination unit is used to determine that the new object is a person entering the monitoring area and the movement status of the person is an active state when the temperature zone verification result indicates that the temperature of the new object is within a preset temperature range and the speed zone determination result indicates that the current movement speed of the new object is within a preset speed range.

[0213] The failure state determination unit is used to determine the motion state of the new object as a failure state when the temperature zone verification result indicates that the temperature of the new object is outside the preset temperature zone, or the speed zone determination result indicates that the current motion speed of the new object is neither zero nor within the preset speed zone. The failure state indicates that there is currently no one in the monitoring area.

[0214] In one optional embodiment, the motion state of the new object is a stationary state, or an active state, or a disabled state; the lighting control unit 904 includes:

[0215] An active state lighting brightness calculation unit is used to calculate a compensation brightness based on the ambient light intensity when the new object is in an active state, and to use the compensation brightness as the lighting brightness.

[0216] A stationary illumination brightness calculation unit is used to use a preset base brightness as the illumination brightness when the motion state of the new object is stationary.

[0217] The failure state lighting brightness calculation unit is used to set the lighting brightness value to zero when the motion state of the new object is in a failure state, so as to control the lighting to be turned off.

[0218] In one optional embodiment, the motion state of the new object is a stationary state, or an active state, or a disabled state; the lighting control unit 904 includes:

[0219] The direct conversion unit for lighting brightness is used to directly control the output brightness of the lighting equipment in the monitoring area to convert it into the lighting brightness when the motion state of the new object is a stationary state or a failure state.

[0220] The lighting equipment location information acquisition unit is used to acquire the location information of the target lighting equipment closest to the person in the monitoring area when the new object is in an active state and the new object is a person entering the monitoring area.

[0221] The path planning unit is used to determine the personnel coordinate information based on the radar point cloud data, and to perform path planning based on the personnel coordinate information to obtain the predicted path of the personnel.

[0222] The shortest straight-line distance calculation unit is used to calculate the shortest straight-line distance between the person and the target lighting device in real time based on the location information and the predicted path of the person.

[0223] The pre-start time calculation unit is used to take into account response delay and calculate the pre-start time of the target lighting device based on the shortest straight distance and the movement speed information.

[0224] A standby monitoring startup unit is used to enter a standby monitoring state when the pre-start time is greater than a preset pre-start threshold.

[0225] A pre-start execution unit is used to control the output brightness of the target lighting device to be converted into the lighting brightness in advance when the pre-start time is less than or equal to a preset pre-start threshold.

[0226] In one alternative embodiment, the device further includes:

[0227] The historical dataset acquisition unit is used to acquire the lighting control dataset of the monitoring area within a preset historical time period. The lighting control dataset includes pedestrian traffic data and lighting control data for different time periods throughout the day in the monitoring area.

[0228] The lighting demand prediction unit is used to input the lighting control dataset into a pre-trained LSTM prediction model to predict lighting demand and obtain the probability distribution of lighting demand in the monitored area over a future period of time.

[0229] As the device embodiment is basically similar to the method embodiment, it is described in a relatively simple way. For relevant details, please refer to the description of the method embodiment above.

[0230] This invention also provides an electronic device, which includes a processor and a memory:

[0231] The memory is used to store program code and transfer the program code to the processor;

[0232] The processor is used to execute the dynamic lighting control method of any embodiment of the present invention according to the instructions in the program code.

[0233] This invention also provides a computer-readable storage medium for storing program code for executing the dynamic lighting control method of any embodiment of the invention.

[0234] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0235] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0236] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0237] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0238] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0239] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic lighting control method, characterized in that, include: When a new object is detected in the monitoring area, the current ambient light intensity of the monitoring area, the motion speed information of the new object, radar point cloud data and infrared thermal imaging data are obtained. Temperature information is obtained by performing temperature calculations based on spatiotemporal registration using the radar point cloud data and the infrared thermal imaging data. By combining the temperature information and the speed information, the motion state of the new object is determined through dual verification. Based on the motion state and the ambient light intensity, adaptive dimming is performed to obtain the illumination brightness, and the output brightness of the lighting equipment in the monitoring area is controlled to be converted into the illumination brightness; The motion state of the new object is either a stationary state, an active state, or a disabled state. The process of controlling the output brightness of the lighting equipment in the monitoring area to convert it into the lighting brightness includes: When the motion state of the new object is stationary or in a failed state, the output brightness of the lighting equipment in the monitoring area is directly controlled to be converted into the lighting brightness. When the new object is in an active state, the new object is a person entering the monitoring area. At this time, the location information of the target lighting device closest to the person in the monitoring area is obtained. Based on the radar point cloud data, the personnel coordinate information is determined, and path planning is performed based on the personnel coordinate information to obtain the predicted path of the personnel. Based on the location information and the predicted path of the personnel, the shortest straight-line distance between the personnel and the target lighting equipment is calculated in real time. Taking into account response delay, the pre-start time of the target lighting device is calculated based on the shortest straight distance and the movement speed information; When the pre-start time exceeds the preset pre-start threshold, the system enters a monitoring standby state. When the pre-start time is less than or equal to the preset pre-start threshold, the output brightness of the target lighting device is converted to the lighting brightness in advance, or the output brightness of the target lighting device is first converted to a preset percentage brightness value of the lighting brightness in advance, and then the output brightness is converted to the lighting brightness when the person is about to arrive at the target lighting device.

2. The dynamic lighting control method according to claim 1, characterized in that, The infrared thermal imaging data includes thermal imaging temperature matrix information; the temperature calculation based on the radar point cloud data and the infrared thermal imaging data, performed based on spatiotemporal registration, to obtain temperature information includes: Based on the radar point cloud data, coordinate transformation is performed to obtain thermal imaging plane coordinate information; ICP registration is performed based on the thermal imaging temperature matrix information and the thermal imaging plane coordinate information to obtain coordinate registration information; Based on the coordinate registration information, the temperature information of the new object is obtained through bilinear interpolation.

3. The dynamic lighting control method according to claim 1, characterized in that, The step of combining the temperature information and the speed information to determine the motion state of the new object through dual verification includes: Temperature zone verification is performed based on the temperature information; Determine the speed range based on the aforementioned motion speed information; Based on the combined results of temperature range verification and velocity range judgment, the motion state of the new object is determined.

4. The dynamic lighting control method according to claim 3, characterized in that, The combined temperature range verification results and velocity range judgment results determine the motion state of the new object, including: When the temperature zone verification result indicates that the temperature of the new object is within the preset temperature range, and the speed zone judgment result indicates that the current speed of the new object is zero, the new object is determined to be a person entering the monitoring area, and the person's movement state is a stationary state. When the temperature zone verification result indicates that the temperature of the new object is within the preset temperature range, and the speed zone judgment result indicates that the current movement speed of the new object is within the preset speed range, the new object is determined to be a person entering the monitoring area, and the movement state of the person is an active state. When the temperature zone verification result indicates that the temperature of the new object is outside the preset temperature zone, or when the speed zone judgment result indicates that the current speed of the new object is neither zero nor within the preset speed zone, the motion state of the new object is determined to be in a failure state, and the failure state indicates that there is currently no one in the monitoring area.

5. The dynamic lighting control method according to claim 1, characterized in that, The motion state of the new object is either a stationary state, an active state, or a disabled state. The method of adaptive dimming based on the motion state and the ambient light intensity to obtain illumination brightness includes: When the new object is in an active state, the compensation brightness is calculated based on the ambient light intensity, and the compensation brightness is used as the illumination brightness. When the motion state of the new object is stationary, the preset base brightness is used as the illumination brightness; When the motion state of the new object is in a failed state, the value of the lighting brightness is set to zero to control the lighting to be turned off.

6. The dynamic lighting control method according to any one of claims 1 to 5, characterized in that, Also includes: Obtain the lighting control dataset of the monitoring area within a preset historical time period. The lighting control dataset includes pedestrian traffic data and lighting control data for different time periods throughout the day in the monitoring area. The lighting control dataset is input into a pre-trained LSTM prediction model to predict lighting demand and obtain the probability distribution of lighting demand in the monitored area over a future period.

7. A dynamic lighting control device, characterized in that, include: The data acquisition unit is used to acquire the current ambient light intensity of the monitoring area, the motion speed information of the new object, radar point cloud data and infrared thermal imaging data when a new object is detected in the monitoring area. The temperature information calculation unit is used to perform temperature calculation based on the radar point cloud data and the infrared thermal imaging data to obtain temperature information; A motion state determination unit is used to determine the motion state of the new object by combining the temperature information and the motion speed information through dual verification. The lighting control unit is used to perform environmental adaptive dimming based on the motion state and the ambient light intensity to obtain the lighting brightness, and to control the output brightness of the lighting equipment in the monitoring area to be converted into the lighting brightness; The motion state of the new object is either a stationary state, an active state, or a disabled state. The lighting control unit includes: The direct conversion unit for lighting brightness is used to directly control the output brightness of the lighting equipment in the monitoring area to convert it into the lighting brightness when the motion state of the new object is a stationary state or a failure state. The lighting equipment location information acquisition unit is used to acquire the location information of the target lighting equipment closest to the person in the monitoring area when the new object is in an active state and the new object is a person entering the monitoring area. The path planning unit is used to determine the personnel coordinate information based on the radar point cloud data, and to perform path planning based on the personnel coordinate information to obtain the predicted path of the personnel. The shortest straight-line distance calculation unit is used to calculate the shortest straight-line distance between the person and the target lighting device in real time based on the location information and the predicted path of the person. The pre-start time calculation unit is used to take into account response delay and calculate the pre-start time of the target lighting device based on the shortest straight distance and the movement speed information. A standby monitoring startup unit is used to enter a standby monitoring state when the pre-start time is greater than a preset pre-start threshold. The pre-start execution unit is used to control the output brightness of the target lighting device to be converted to the lighting brightness in advance when the pre-start time is less than or equal to a preset pre-start threshold, or to first control the output brightness of the target lighting device to be converted to a preset percentage brightness value of the lighting brightness in advance, and then control the output brightness to be converted to the lighting brightness when the person is about to arrive at the target lighting device.

8. An electronic device, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the dynamic lighting control method according to any one of claims 1-6 according to the instructions in the program code.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the dynamic lighting control method according to any one of claims 1-6.

Images