Intelligent lighting control system

Abstract

The invention provides an intelligent lighting control system, which belongs to the technical field of intelligent lighting control and comprises a management layer, a communication layer and an equipment layer, the system state is monitored and managed in real time, and any computer in a local area network of a monitoring center can be monitored and managed in real time according to authority. The monitoring and management comprises time management, scene sequential logic association management, equipment operation data monitoring, energy consumption dynamic monitoring historical data record query, interface map custom import, user management and authority setting. Through a sensor linkage algorithm, real-time adjustment of lighting parameters is achieved, user experience is improved, potential faults are found in advance, non-planned downtime is shortened, maintenance cost is reduced, lighting strategies are adjusted according to actual requirements, energy consumption cost is reduced, multi-protocol interfaces are supported, integration with systems such as security and protection and production management is facilitated, and application scenes are expanded.

Description

Technical Field

[0001] This invention relates to the field of intelligent lighting control technology, and more particularly to an intelligent lighting control system. Background Technology

[0002] With the popularization of LED lighting technology, the controllability of lamps has been significantly enhanced, but existing intelligent lighting control equipment has the following problems: (1) The control method is simple, relying on manual or simple timetable control, and cannot be dynamically adjusted according to environmental changes; (2) The fault response is lagging, lacking real-time monitoring and early warning mechanisms, resulting in low efficiency in fault detection and handling; (3) Energy consumption management is crude, failing to achieve refined dimming based on actual needs, resulting in energy waste; (4) The system has poor compatibility, making it difficult to integrate with third-party systems (such as security and production management), limiting intelligent application scenarios.

[0003] Currently, lighting in many factory areas is controlled individually or manually, which cannot achieve overall control of the entire factory area. This application is based on the design of a lighting control device for the second phase (2×660MW) expansion project of Beihai Power Plant of Guangxi Investment Group Beihai Power Generation Co., Ltd. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent lighting control system that solves the technical problems of existing lighting equipment, such as single control method, slow fault response, crude energy management and poor system compatibility.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The intelligent lighting control system comprises a management layer, a communication layer, and an equipment layer. It provides real-time monitoring and management of the system status. Any computer within the monitoring center's local area network can monitor and manage the system in real time according to its permissions. Monitoring and management include time management, scene sequence logic association management, equipment operation data monitoring, energy consumption dynamic monitoring historical data record query, custom map import, user management, and permission settings. The communication layer and the field equipment layer use communication methods for data acquisition and control. The switches use products from brands such as Huawei, TP-Link, and H3C. The intelligent gateway is located in the lighting distribution box, and the remaining communication layer equipment is located in the electrical engineer's office. The intelligent gateway in the equipment layer communicates wirelessly with the loop controller / lamp controller using a star communication architecture. The intelligent gateway can receive control from the backend host via a local area network or a wide area network.

[0006] Furthermore, the management system includes a central control room touchscreen and a server. The central control room touchscreen is connected to the server, and the server is connected to the communication terminal. The factory lighting management personnel can view and manage photos of the entire factory area through the central control room touchscreen.

[0007] Furthermore, the communication terminal includes a switch, a main optical terminal, a splitter optical terminal, and a super gateway. The switch is connected to the management terminal, the switch is connected to the splitter optical terminal via the main optical terminal, the splitter optical terminal is connected to the super gateway, and the super gateway is wirelessly connected to several automatic lighting control circuit devices.

[0008] Furthermore, all automatic lighting control circuit devices are connected to the mains power distribution box, which supplies power to the lighting fixtures on the automatic lighting control circuit device. The automatic lighting control circuit device includes a circuit control box, several lighting fixtures, a light sensor, a human motion sensor, and a control display screen. The circuit control box is connected to several lighting fixtures for control, and the light sensor, human motion sensor, and control display screen are all connected to the circuit control box. Furthermore, the server is equipped with an intelligent control system that runs on the background monitoring host to realize algorithmic control and data processing of the lighting system. The intelligent control system embeds an intelligent control algorithm module, which is used to automatically dim the intelligent lamps based on the schedule and sensor linkage, predict and adaptively repair intelligent lamp faults, and optimize the energy consumption and dynamically save energy of the intelligent lamps. Furthermore, the intelligent control algorithm module includes a time-based dimming submodule, a sensor linkage submodule, a fault prediction submodule, and an energy consumption optimization submodule. The time-based dimming submodule is used to automatically adjust the brightness of the lamps according to the user's preset time schedule and latitude and longitude calculations. The sensor linkage submodule is used to receive data from human body sensors and illuminance sensors in real time, dynamically adjust lighting parameters, and achieve full power range adjustment from 0 to 100%. The fault prediction submodule is used to predict single lamp / circuit faults by analyzing historical fault data and real-time operating status, and trigger an alarm mechanism. The energy consumption optimization submodule is used to optimize lighting strategies and reduce energy consumption costs based on regional power demand and light intensity.

[0009] Furthermore, the collective process for calculating latitude and longitude is as follows: Obtain the current time, real-time illuminance, and local latitude and longitude. Based on the latitude, longitude, and date, obtain the precise sunrise and sunset times for the day using astronomical algorithms or APIs. Based on the calculated sunrise and sunset times, dynamically divide the time into three key periods: sunrise transition period ([sunrise-30 minutes, sunrise+30 minutes]), daytime core period ([sunrise+30 minutes, sunset-30 minutes]), and sunset transition period ([sunset-30 minutes, sunset+30 minutes]). Brightness decisions are made according to the following priority, from highest to lowest: Priority 1: Light Sensing Priority, i.e., daytime and transition periods. Rule: During the core daytime period and sunrise / sunset transition period, as long as the real-time illuminance is <50 lux, regardless of the schedule setting, the system will force an output of 100% brightness, which is the cloudy / insufficient light mode. Priority 2: Schedule strategy, i.e. nighttime. Rule: During nighttime hours, since natural light is very weak or has disappeared, the system sets the brightness entirely according to the preset schedule strategy, without referring to the light sensor value. Priority 3: Smooth transition, which is a transition period with sufficient light. Rule: During the sunrise / sunset transition period, if the real-time illuminance is ≥50 lux, the system will not trigger light priority, but will instead perform a linear smooth transition between the two brightness values ​​set in the schedule to avoid sudden changes in light.

[0010] Furthermore, the sensor linkage submodule's sensor linkage control process involves: receiving data from the human body sensor and illuminance sensor in real time, dynamically adjusting lighting parameters to achieve energy-saving effects by turning on lights when people are present and turning them off when they leave. First, initialization and data acquisition: at the beginning of each control cycle, the system reads the current time, real-time illumination value, radar signal, and calculates sunrise and sunset. The current time is used for subsequent comparison with the schedule strategy. The real-time illumination value is obtained from the light sensor and is used for light priority judgment. The radar signal is used to determine whether there is human activity in the target area. The sunrise and sunset calculation is used by the system to calculate the sunrise and sunset times of the day based on the date and preset geographical location, thereby dynamically defining the start and end points of day and night. Then, a three-level priority judgment is performed. The first priority is the light priority judgment. First, check whether the real-time light value is higher than the preset threshold. If it is higher than the threshold, it means that the ambient light is bright enough. Then, immediately adjust the light to the dimmest or turn it off directly, disable the radar sensor to save energy, and directly end the current control cycle without executing subsequent logic. This level has the highest priority, ensuring that energy is never wasted by turning on the lights when there is sufficient light during the day. The second priority is the human presence detection. This entry condition only occurs when the light priority judgment fails (i.e., insufficient ambient light). This logic activates the radar, which begins monitoring human activity within the area. The radar continuously scans; if a person is detected, the lights immediately turn on and an idle timer is reset to zero. If no person is detected, the system checks if the idle timer has exceeded a preset duration. If not, the lights remain in their current state, and detection continues. If the timer has expired, it's assumed the person has left, and the lights are dimmed or turned off. This achieves the key to high efficiency, energy saving, and improved user experience: lights turn on when someone is present and turn off when they leave. The third priority is the integration of the schedule strategy. This strategy does not run independently, but integrates and modifies the output of the first two logics. It compares the current time with the calculated sunrise and sunset times to distinguish between daytime and nighttime periods. During the daytime, even if the presence of a human body is detected and the lights are turned on, the brightness will be adjusted with reference to the daytime mode brightness configuration. During the nighttime, when the lights are turned on, the brightness configuration of the nighttime mode will be referenced. While meeting the needs of human body sensing, the brightness of the lights is made to better match the natural light intensity and scene requirements of different times, making the lighting strategy more refined and humanized.

[0011] Furthermore, the time-overlay calculation process of the time-adjusting submodule is as follows: It reads and sets system parameters: light threshold (light_threshold), radar timeout (radar_timeout), and day / night brightness configuration; initializes the hardware; starts the light sensor, radar sensor, and light controller; initializes global variables: timer = None, radar_signal = False, last_detection = 0; starts the main control loop; obtains the current system time; reads the current ambient light value (light_value) from the light sensor; reads the current human detection signal from the radar sensor; when radar_signal is True, someone is present; when it is False, no one is present; checks if light_value is greater than the predefined light threshold; if so, the environment is bright enough; calls set_light_state or sets it to the minimum brightness; cancels the existing timer; checks if radar_signal detects a person; if so, calls the reset_timer() function, which cancels the old timer and starts a new timer with a duration of radar_timeout seconds; and records last_detection. =time.time() marks the light status as needing to be turned on, and continues execution. If no one is present, it waits, and the timer continues counting down. Based on the current time, it determines the current time period, i.e., day / night / transition period. Based on the obtained light on / off requirements and time period information, it determines the final brightness. Depending on whether it is day or night, it reads the target brightness when someone is present from the corresponding brightness configuration and calls set_light_state. If no one is present and the timer has not expired, it maintains the current light state, and the brightness is not changed in this loop. If no one is present and the timer expires, it executes the timeout_callback() function to handle the timer start. When the timer expires, this function is automatically called. Inside the function, it checks if not radar_signal: to confirm that no one is present, and then calls set_light_stat to dim or turn off the light.

[0012] Furthermore, the specific process of fault prediction performed by the fault prediction submodule is as follows: Real-time parameter monitoring collects the following seven types of raw data from lighting equipment: online status, whether the equipment can communicate, input and output voltage, current and power, power factor, dimming value, and illuminance value. Data cleaning processes invalid data caused by communication packet loss, outlier filtering removes extreme values ​​from sensor false alarms, and normalization processes standardize parameters such as voltage and current to facilitate comparison. The preprocessed data is analyzed to extract key features for diagnostic purposes: trend analysis (observing current and power trends), harmonic detection (analyzing power factor to assess power quality), waveform analysis (checking for voltage and current waveform distortion), and spatial mapping (correlation between dimming values ​​and actual illuminance values). The diagnostic decision-making process begins, with the diagnostic engine prioritizing judgments from simple to complex and from explicit to implicit: Basic status check: Determines if the device's online status is False. If yes, immediately diagnoses an offline fault (device cannot communicate), and the process ends. If no, the device is online, proceeding to the next diagnostic step. Power supply fault diagnosis: Determines if the input voltage is normal but the output voltage is abnormal. If yes, diagnoses a driver power supply fault (input normal but output abnormal), and the process ends. If no, the power supply output is normal, and proceeding to the next diagnostic step. Aluminum substrate fault diagnosis: Determines if both input and output voltages are normal, but the current parameter is abnormal. If yes, diagnoses an LED aluminum substrate fault (input and output normal but current abnormal), and the process ends. If no... The current is also normal, proceeding to the next diagnostic step: Light source fault diagnosis. Voltage, current, and dimming values ​​are all normal, but is the illuminance value measured by the sensor abnormally low? If so, a light source fault is identified (electrical parameters are normal, but illuminance is abnormal), and the process ends. If not, all basic electrical and optical parameters are normal, and AI model-assisted diagnosis is used. When all previous rules cannot provide a clear diagnosis, the AI ​​model is activated. The pre-processed and feature-extracted complete data is input into the trained AI prediction model. Does the model's predicted value exceed a preset fault threshold? If so, an unknown fault type is identified. The diagnostic clues provided by the model help discover potential, complex, or newly emerging fault modes. If not, the current equipment status is determined to be healthy. The present invention, by adopting the above-described technical solution, has the following beneficial effects: This invention uses a sensor-linked algorithm to achieve real-time adjustment of lighting parameters, improve user experience, detect potential faults in advance, reduce unplanned downtime, lower maintenance costs, adjust lighting strategies according to actual needs, reduce energy consumption costs, support multiple protocol interfaces, facilitate integration with security, production management and other systems, and expand application scenarios. Attached Figure Description

[0013] Figure 1 This is a distributed network structure diagram of the system of the present invention; Figure 2 This is the overall system topology diagram of the present invention.

[0014] Figure 3 This is a diagram of the system interface of the present invention.

[0015] Figure 4 This is a diagram of the device management interface of the present invention.

[0016] Figure 5 This is a diagram of the scene management interface of the present invention.

[0017] Figure 6 This is a diagram of the energy consumption data interface of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and preferred embodiments. However, it should be noted that many details listed in the specification are merely to provide the reader with a thorough understanding of one or more aspects of the invention, and these aspects of the invention can be implemented even without these specific details.

[0019] like Figure 1 As shown, the intelligent lighting control system includes a management layer, a communication layer, and an equipment layer. It monitors and manages the system status in real time. Any computer within the monitoring center's local area network can monitor and manage the system in real time according to its permissions. The monitoring and management includes time management, scene sequence logic association management, equipment operation data monitoring, energy consumption dynamic monitoring historical data record query, interface map customization import, user management, and permission settings. The communication layer and the field equipment layer use communication methods for data acquisition and control. The switches use products from brands such as Huawei, TPlink, and H3C. The intelligent gateway is located in the lighting distribution box, and the remaining communication layer equipment is located in the electrical engineer's room. The intelligent gateway in the equipment layer communicates with the loop controller / lamp controller via wireless signals, using a star communication architecture. The intelligent gateway can receive control from the backend host through a local area network or a wide area network.

[0020] In embodiments of the present invention, such as Figure 2 As shown, the management layer includes a central control room touch screen and a server. The central control room touch screen is connected to the server, and the server is connected to the communication terminal. The factory lighting management personnel can view and manage photos of the entire factory area through the central control room touch screen.

[0021] In embodiments of the present invention, such as Figure 2As shown, the communication terminal includes a switch, a main optical terminal, a splitter optical terminal, and a super gateway. The switch is connected to the management terminal, the switch is connected to the splitter optical terminal via the main optical terminal, the splitter optical terminal is connected to the super gateway, and the super gateway is wirelessly connected to several automatic lighting control circuit devices.

[0022] In embodiments of the present invention, such as Figure 2 As shown, all automatic lighting control circuit devices are connected to the mains power distribution box. The mains power distribution box supplies power to the lighting fixtures on the automatic lighting control circuit devices. The automatic lighting control circuit devices include a circuit control box, several lighting fixtures, a light sensor, a human motion sensor, and a control display screen. The circuit control box is connected to several lighting fixtures for control, and the light sensor, human motion sensor, and control display screen are all connected to the circuit control box. In this embodiment of the invention, an intelligent control system is installed in the server and runs on the background monitoring host to realize the algorithm control and data processing of the lighting system. The intelligent control system is embedded with an intelligent control algorithm module, which is used to automatically dim the intelligent lamps based on the time schedule and sensor linkage, predict and adaptively repair the faults of the intelligent lamps, and optimize the energy consumption and dynamically save energy of the intelligent lamps. In this embodiment of the invention, the intelligent control algorithm module includes a time-based dimming submodule, a sensor linkage submodule, a fault prediction submodule, and an energy consumption optimization submodule. The time-based dimming submodule is used to automatically adjust the brightness of the lamps according to the user-preset time schedule and in combination with latitude and longitude calculations. The sensor linkage submodule is used to receive data from human body sensors and illuminance sensors in real time, dynamically adjust lighting parameters, and achieve full power range adjustment from 0 to 100%. The fault prediction submodule is used to predict single lamp / circuit faults by analyzing historical fault data and real-time operating status, and trigger an alarm mechanism. The energy consumption optimization submodule is used to optimize lighting strategies and reduce energy consumption costs based on regional power demand and light intensity.

[0023] In this embodiment of the invention, the collective process for calculating latitude and longitude is as follows: Obtain the current time, real-time illuminance, and local latitude and longitude; based on the latitude, longitude, and date, obtain the precise sunrise and sunset times for the day using an astronomical algorithm or API; based on the calculated sunrise and sunset times, dynamically divide the time into three key periods: sunrise transition period ([sunrise-30 minutes, sunrise+30 minutes]), daytime core period ([sunrise+30 minutes, sunset-30 minutes]), and sunset transition period ([sunset-30 minutes, sunset+30 minutes]). Brightness decisions are made according to the following priority, from highest to lowest: Priority 1: Light Sensing Priority, i.e., daytime and transition periods. Rule: During the core daytime period and sunrise / sunset transition period, as long as the real-time illuminance is <50 lux, regardless of the schedule setting, the system will force an output of 100% brightness, which is the cloudy / insufficient light mode. Priority 2: Schedule strategy, i.e. nighttime. Rule: During nighttime hours, since natural light is very weak or has disappeared, the system sets the brightness entirely according to the preset schedule strategy, without referring to the light sensor value. Priority 3: Smooth transition, which is a transition period with sufficient light. Rule: During the sunrise / sunset transition period, if the real-time illuminance is ≥50 lux, the system will not trigger light priority, but will instead perform a linear smooth transition between the two brightness values ​​set in the schedule to avoid sudden changes in light.

[0024] In this embodiment of the invention, the sensor linkage control process of the sensor linkage submodule is as follows: real-time reception of data from the human body sensor and the illuminance sensor, dynamic adjustment of lighting parameters, and realization of energy-saving effect of lights turning on when people are present and turning off when people leave. First, initialization and data acquisition: at the beginning of each control cycle, the system reads the current time, real-time illumination value, radar signal, and calculates sunrise and sunset. The current time is used for subsequent comparison with the schedule strategy. The real-time illumination value is obtained from the light sensor and is used for light priority judgment. The radar signal is used to determine whether there is human activity in the target area. The sunrise and sunset calculation is used by the system to calculate the sunrise and sunset times of the day based on the date and preset geographical location, thereby dynamically defining the start and end points of day and night. Then, a three-level priority judgment is performed. The first priority is the light priority judgment. First, check whether the real-time light value is higher than the preset threshold. If it is higher than the threshold, it means that the ambient light is bright enough. Then, immediately adjust the light to the dimmest or turn it off directly, disable the radar sensor to save energy, and directly end the current control cycle without executing subsequent logic. This level has the highest priority, ensuring that energy is never wasted by turning on the lights when there is sufficient light during the day. The second priority is the human presence detection. This entry condition only occurs when the light priority judgment fails (i.e., insufficient ambient light). This logic activates the radar, which begins monitoring human activity within the area. The radar continuously scans; if a person is detected, the lights immediately turn on and an idle timer is reset to zero. If no person is detected, the system checks if the idle timer has exceeded a preset duration. If not, the lights remain in their current state, and detection continues. If the timer has expired, it's assumed the person has left, and the lights are dimmed or turned off. This achieves the key to high efficiency, energy saving, and improved user experience: lights turn on when someone is present and turn off when they leave. The third priority is the integration of the schedule strategy. This strategy does not run independently, but integrates and modifies the output of the first two logics. It compares the current time with the calculated sunrise and sunset times to distinguish between daytime and nighttime periods. During the daytime, even if the presence of a human body is detected and the lights are turned on, the brightness will be adjusted with reference to the daytime mode brightness configuration. During the nighttime, when the lights are turned on, the brightness configuration of the nighttime mode will be referenced. While meeting the needs of human body sensing, the brightness of the lights is made to better match the natural light intensity and scene requirements of different times, making the lighting strategy more refined and humanized.

[0025] In this embodiment of the invention, the time-overlay operation of the time-adjustment submodule is as follows: System parameters are read and set: light threshold (light_threshold), radar timeout (radar_timeout), and day / night brightness configuration. Hardware is initialized, and the light sensor, radar sensor, and light controller are started. Global variables are initialized: timer = None, radar_signal = False, last_detection = 0. The main control loop is started, the current system time is obtained, the current ambient light value (light_value) is read from the light sensor, and the current human detection signal is read from the radar sensor. If radar_signal is True, someone is present; if False, no one is present. It is determined whether light_value is greater than the predefined light threshold. If so, the environment is sufficiently bright. `set_light_state` is called or set to the minimum brightness. The existing timer is canceled. It is determined whether radar_signal detects a person. If someone is present, the `reset_timer()` function is called. Internally, the old timer is canceled, and a new timer with a duration of radar_timeout seconds is started. `last_detection =` is recorded. `time.time()` marks the light status as needing to be turned on and continues execution. If no one is present, it waits while the timer continues counting down. Based on the current time, it determines the current time period (day / night / transition period). Based on the obtained light on / off requirements and time period information, it determines the final brightness. Depending on whether it is day or night, it reads the target brightness for when someone is present from the corresponding brightness configuration and calls `set_light_state`. If no one is present and the timer has not expired, it maintains the current light state and does not change the brightness in this loop. If no one is present and the timer has expired, it executes the `timeout_callback()` function to handle the timer start. When the timer expires, this function is automatically called. Inside the function, it checks `if not radar_signal:` to confirm that no one is present, and then calls `set_light_stat` to dim or turn off the light.

[0026] In this embodiment of the invention, the specific process of fault prediction by the fault prediction submodule is as follows: Real-time parameter monitoring collects the following seven types of raw data from lighting equipment: online status, whether the equipment can communicate, input and output voltage, current and power, power factor, dimming value, and illuminance value. Data cleaning processes invalid data caused by communication packet loss, outlier filtering removes extreme values ​​from sensor false alarms, and normalization processes standardize parameters such as voltage and current to facilitate comparison. The preprocessed data is analyzed to extract key features for diagnostic purposes: trend analysis (observing current and power trends), harmonic detection (analyzing power factor to assess power quality), waveform analysis (checking for voltage and current waveform distortion), and spatial mapping (correlation between dimming values ​​and actual illuminance values). The diagnostic decision-making process begins, with the diagnostic engine prioritizing judgments from simple to complex and from explicit to implicit: Basic status check: Determines if the device's online status is False. If yes, immediately diagnoses an offline fault (device cannot communicate), and the process ends. If no, the device is online, proceeding to the next diagnostic step. Power supply fault diagnosis: Determines if the input voltage is normal but the output voltage is abnormal. If yes, diagnoses a driver power supply fault (input normal but output abnormal), and the process ends. If no, the power supply output is normal, and proceeding to the next diagnostic step. Aluminum substrate fault diagnosis: Determines if both input and output voltages are normal, but the current parameter is abnormal. If yes, diagnoses an LED aluminum substrate fault (input and output normal but current abnormal), and the process ends. If no... The current is also normal, proceeding to the next diagnostic step: Light source fault diagnosis. Voltage, current, and dimming values ​​are all normal, but is the illuminance value measured by the sensor abnormally low? If so, a light source fault is identified (electrical parameters are normal, but illuminance is abnormal), and the process ends. If not, all basic electrical and optical parameters are normal, and AI model-assisted diagnosis is used. When all previous rules cannot provide a clear diagnosis, the AI ​​model is activated. The pre-processed and feature-extracted complete data is input into the trained AI prediction model. Does the model's predicted value exceed a preset fault threshold? If so, an unknown fault type is identified. The diagnostic clues provided by the model help discover potential, complex, or newly emerging fault modes. If not, the current equipment status is determined to be healthy. The specific process of dynamically adjusting the lighting is as follows: Call the sensor network to obtain the current environmental data and input data. sensor_data['natural_light'] is the natural light illuminance value measured by the illuminance sensor from the window. sensor_data['artificial_light'] is the illuminance value measured by the illuminance sensor generated by the current lamp itself, or the value deduced from the current brightness of the lamp under the known lamp efficiency. time_of_day is the current time, used to calculate the color temperature. weather is the current weather condition, such as sunny or rainy, obtained from the meteorological API. Evaluate the current total illuminance and calculate it with weights: Calculate the total illuminance of the current working surface according to the formula total_illumination = natural_light * 0.7 + artificial_light * 0.3. The weights simulate the subjective perception weights of the human eye for natural light and artificial light. Adaptive dimming decision: Compare the calculated total illuminance with the target value and make a dimming decision. Judge whether it is insufficient: if total_illumination < TARGET_ILLUMINANCE, if so, call the function adjust_brightness(up=True, ratio=1.2). This function will send an instruction to the lamp drive system to increase the brightness to 1.2 times the current level. If not, enter the next judgment to determine whether it is too bright. elif total_illumination > TARGET_ILLUMINANCE, if so, call adjust_brightness(up=False, ratio=0.8) to instruct the lamp to reduce the brightness to 0.8 times the current level. If not, the total illuminance is already within the target range, and the brightness is not adjusted in this loop. Color temperature coordinated adjustment: While adjusting the brightness, synchronously optimize the color of the light to make it more in line with the natural rhythm. Calculate the optimal color temperature: Call optimal_cct = calculate_optimal_cct(time_of_day, weather). The function matches the preset color temperature curve according to time_of_day, uses a high color temperature in the morning to boost spirit and a low color temperature in the evening to promote relaxation, and makes fine-tuning according to weather. Call set_cct(optimal_cct) to send the calculated optimal color temperature value to the intelligent lamp that supports color temperature adjustment.

[0027] The main optical transceiver uses the TL-FC382A-3 model, which is based on wavelength division multiplexing (WDM) technology and supports single-mode single-fiber transmission. The splitter uses the TL-FC314B-3 model, which employs WDM technology to convert electrical and optical signals and transmit them over long distances via optical fiber.

[0028] In this embodiment of the invention, all automatic lighting control circuit devices are connected to the mains power distribution box, which supplies power to the lighting fixtures on the automatic lighting control circuit devices.

[0029] In this embodiment of the invention, the automatic lighting control device includes a circuit control box, several lighting fixtures, a light sensor, a human motion sensor, and a control display screen. The circuit control box is connected to the several lighting fixtures, and the light sensor, the human motion sensor, and the control display screen are all connected to the circuit control box.

[0030] In this embodiment of the invention, the automatic lighting control system further includes a six-button scene panel. The six-button scene panel is fixedly installed via mounting holes and connected to the circuit control box. The six-button scene panel uses product model YBIC-T401. By setting corresponding scene modes through the system, local control of the lighting fixtures can be achieved, and flexible switching can be made according to different needs.

[0031] The specific equipment and components of this application are shown in Table 1.

[0032] Table 1 is a list of components and equipment. Example 1: Turbine room control mode: human body induction + time control + light induction + remote control + single lamp control + local control, specific functions are shown in Table 2.

[0033] Table 2 describes the functions of the turbine room. Example 2: The boiler body, electrostatic precipitator and induced draft fan area, central control building, air compressor room and electrostatic precipitator control building, coal conveying system, desulfurization and denitrification area, and auxiliary workshops are all included. Specific functions are shown in Table 3. Control modes: human body induction + time control + light sensing + remote control + loop control + local control.

[0034] Table 3 describes the functional performance of Example 2. Example 3: The specific functions of the factory area road lighting are shown in Table 4. Control modes: human body sensing + time control + light sensing + remote control + individual lamp control + local control.

[0035] Table 4 provides a functional description of the factory area road lighting. Matters not covered in this invention are common knowledge.

[0036] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An intelligent lighting control system, characterized in that: The system comprises a management layer, a communication layer, and a device layer, enabling real-time monitoring and management of the system status. Any computer within the monitoring center's local area network can monitor and manage the system in real time according to its permissions. Monitoring and management include time management, scene sequence logic association management, equipment operation data monitoring, energy consumption dynamic monitoring historical data record query, custom map import, user management, and permission settings. The communication layer and the field device layer use communication methods for data acquisition and control. The switches use products from brands such as Huawei, TP-Link, and H3C. The intelligent gateway is located in the lighting distribution box, while the remaining communication layer devices are located in the electrical engineer's office. The intelligent gateway in the device layer communicates wirelessly with the circuit controller / lamp controller using a star communication architecture. The intelligent gateway can be controlled by the backend host via a local area network or a wide area network.

2. The intelligent lighting control system according to claim 1, characterized in that: The management system includes a central control room touchscreen and a server. The central control room touchscreen is connected to the server, and the server is connected to the communication terminal. The factory lighting management personnel can view and manage photos of the entire factory area through the central control room touchscreen.

3. The intelligent lighting control system according to claim 1, characterized in that: The communication terminal includes a switch, a main optical terminal, a splitter optical terminal, and a super gateway. The switch is connected to the management terminal, the switch is connected to the splitter optical terminal via the main optical terminal, the splitter optical terminal is connected to the super gateway, and the super gateway is wirelessly connected to several automatic lighting control circuit devices.

4. The intelligent lighting control system according to claim 1, characterized in that: All automatic lighting control circuit devices are connected to the mains power distribution box. The mains power distribution box supplies power to the lighting fixtures on the automatic lighting control circuit device. The automatic lighting control circuit device includes a circuit control box, several lighting fixtures, a light sensor, a human motion sensor, and a control display screen. The circuit control box is connected to several lighting fixtures for control. The light sensor, human motion sensor, and control display screen are all connected to the circuit control box.

5. The intelligent lighting control system according to claim 2, characterized in that: The server is equipped with an intelligent control system that runs on the background monitoring host. It realizes algorithmic control and data processing of the lighting system. The intelligent control system embeds an intelligent control algorithm module, which is used to automatically dim the intelligent lamps based on the schedule and sensor linkage, predict and adaptively repair intelligent lamp faults, and optimize the energy consumption and dynamically save energy of the intelligent lamps.

6. The intelligent lighting control system according to claim 2, characterized in that: The intelligent control algorithm module includes a time-based dimming submodule, a sensor linkage submodule, a fault prediction submodule, and an energy consumption optimization submodule. The time-based dimming submodule automatically adjusts the brightness of the lamps according to the user's preset time schedule and latitude and longitude calculations. The sensor linkage submodule receives data from human body sensors and illuminance sensors in real time and dynamically adjusts lighting parameters to achieve full power range adjustment from 0 to 100%. The fault prediction submodule predicts single lamp / circuit faults by analyzing historical fault data and real-time operating status and triggers an alarm mechanism. The energy consumption optimization submodule optimizes lighting strategies and reduces energy costs based on regional power demand and light intensity.

7. The intelligent lighting control system according to claim 6, characterized in that: The overall process for calculating latitude and longitude is as follows: Obtain the current time, real-time illuminance, and local latitude and longitude. Based on the latitude, longitude, and date, obtain the precise sunrise and sunset times for the day using astronomical algorithms or APIs. Based on the calculated sunrise and sunset times, dynamically divide the time into three key periods: sunrise transition period ([sunrise-30 minutes, sunrise+30 minutes]), daytime core period ([sunrise+30 minutes, sunset-30 minutes]), and sunset transition period ([sunset-30 minutes, sunset+30 minutes]). Brightness decisions are made according to the following priority, from highest to lowest: Priority 1: Light Sensing Priority, i.e., daytime and transition periods. Rule: During the core daytime period and sunrise / sunset transition period, as long as the real-time illuminance is <50 lux, regardless of the schedule setting, the system will force an output of 100% brightness, which is the cloudy / insufficient light mode. Priority 2: Schedule strategy, i.e. nighttime. Rule: During nighttime hours, since natural light is very weak or has disappeared, the system sets the brightness entirely according to the preset schedule strategy, without referring to the light sensor value. Priority 3: Smooth transition, which is a transition period with sufficient light. Rule: During the sunrise / sunset transition period, if the real-time illuminance is ≥50 lux, the system will not trigger light priority, but will instead perform a linear smooth transition between the two brightness values ​​set in the schedule to avoid sudden changes in light.

8. The intelligent lighting control system according to claim 2, characterized in that: The sensor linkage submodule's sensor linkage control process involves: receiving data from the human body sensor and illuminance sensor in real time, dynamically adjusting lighting parameters to achieve energy-saving effects such as lights turning on when people are present and turning off when they leave. First, initialization and data acquisition: at the beginning of each control cycle, the system reads the current time, real-time illumination value, radar signal, and calculates sunrise and sunset. The current time is used for subsequent comparison with the schedule strategy. The real-time illumination value is obtained from the light sensor and is used for light priority judgment. The radar signal is used to determine whether there is human activity in the target area. The sunrise and sunset calculation is used by the system to calculate the sunrise and sunset times of the day based on the date and preset geographical location, thereby dynamically defining the start and end points of day and night. Then, a three-level priority judgment is performed. The first priority is the light priority judgment. First, check whether the real-time light value is higher than the preset threshold. If it is higher than the threshold, it means that the ambient light is bright enough. Then, immediately adjust the light to the dimmest or turn it off directly, disable the radar sensor to save energy, and directly end the current control cycle without executing subsequent logic. This level has the highest priority, ensuring that energy is never wasted by turning on the lights when there is sufficient light during the day. The second priority is the human presence detection. This entry condition only occurs when the light priority judgment fails (i.e., insufficient ambient light). This logic activates the radar, which begins monitoring human activity within the area. The radar continuously scans; if a person is detected, the lights immediately turn on and an idle timer is reset to zero. If no person is detected, the system checks if the idle timer has exceeded a preset duration. If not, the lights remain in their current state, and detection continues. If the timer has expired, it's assumed the person has left, and the lights are dimmed or turned off. This achieves the key to high efficiency, energy saving, and improved user experience: lights turn on when someone is present and turn off when they leave. The third priority is the integration of the schedule strategy. This strategy does not run independently, but integrates and modifies the output of the first two logics. It compares the current time with the calculated sunrise and sunset times to distinguish between daytime and nighttime periods. During the daytime, even if the presence of a human body is detected and the lights are turned on, the brightness will be adjusted with reference to the daytime mode brightness configuration. During the nighttime, when the lights are turned on, the brightness configuration of the nighttime mode will be referenced. While meeting the needs of human body sensing, the brightness of the lights is made to better match the natural light intensity and scene requirements of different times, making the lighting strategy more refined and humanized.

9. The intelligent lighting control system according to claim 2, characterized in that: The time-overlay calculation process of the time-adjusting module is as follows: It reads and sets system parameters: light threshold (light_threshold), radar timeout (radar_timeout), and day / night brightness configuration; initializes the hardware; starts the light sensor, radar sensor, and light controller; initializes global variables: timer = None, radar_signal = False, last_detection = 0; starts the main control loop; obtains the current system time; reads the current ambient light value (light_value) from the light sensor; reads the current human detection signal from the radar sensor (radar_signal is True if someone is present, False if no one is present); checks if light_value is greater than the predefined light threshold; if so, the environment is bright enough; calls set_light_state or sets it to the minimum brightness; cancels the existing timer; checks if radar_signal detects a person; if so, calls the reset_timer() function, which cancels the old timer and starts a new timer with a duration of radar_timeout seconds; and records last_detection = ... `time.time()` marks the light status as needing to be turned on and continues execution. If no one is present, it waits while the timer continues counting down. Based on the current time, it determines the current time period (day / night / transition period). Based on the obtained light on / off requirements and time period information, it determines the final brightness. Depending on whether it is day or night, it reads the target brightness for when someone is present from the corresponding brightness configuration and calls `set_light_state`. If no one is present and the timer has not expired, it maintains the current light state and does not change the brightness in this loop. If no one is present and the timer has expired, it executes the `timeout_callback()` function to handle the timer start. When the timer expires, this function is automatically called. Inside the function, it checks `if not radar_signal:` to confirm that no one is present, and then calls `set_light_stat` to dim or turn off the light.

10. The intelligent lighting control system according to claim 2, characterized in that: The specific process of fault prediction by the fault prediction submodule is as follows: Real-time parameter monitoring collects the following seven types of raw data from lighting equipment: online status, whether the equipment can communicate, input and output voltage, current and power, power factor, dimming value, and illuminance value. Data cleaning processes invalid data caused by communication packet loss, outlier filtering removes extreme values ​​from sensor false alarms, and normalization processes standardize parameters such as voltage and current to facilitate comparison. The preprocessed data is analyzed to extract key features for diagnostic purposes: trend analysis (observing current and power trends), harmonic detection (analyzing power factor to assess power quality), waveform analysis (checking for voltage and current waveform distortion), and spatial mapping (correling the relationship between dimming values ​​and actual illuminance values). The diagnostic decision-making process begins, with the diagnostic engine prioritizing judgments from simple to complex and from explicit to implicit: Basic status check: Determines if the device's online status is False. If yes, immediately diagnoses an offline fault (device cannot communicate), and the process ends. If no, the device is online, proceeding to the next diagnostic step. Power supply fault diagnosis: Determines if the input voltage is normal but the output voltage is abnormal. If yes, diagnoses a driver power supply fault (input normal but output abnormal), and the process ends. If no, the power supply output is normal, proceeding to the next diagnostic step. Aluminum substrate fault diagnosis: Determines if both input and output voltages are normal, but the current parameter is abnormal. If yes, diagnoses an LED aluminum substrate fault. If input and output are normal but current is abnormal, the process ends. If not, and current is also normal, proceed to the next diagnostic step: Light source fault diagnosis. Voltage, current, and dimming values ​​are all normal, but is the illuminance value measured by the sensor abnormally low? If so, a light source fault is identified (electrical parameters are normal, but illuminance is abnormal), and the process ends. If not, and all basic electrical and optical parameters are normal, AI model-assisted diagnosis is used. When all previous rules cannot clearly determine the fault, the AI ​​model is activated. The pre-processed and feature-extracted complete data is input into the trained AI prediction model. Does the model's predicted value exceed a preset fault threshold? If so, an unknown fault type is identified. The diagnostic clues provided by the model help discover potential, complex, or newly emerging fault modes. If not, the current equipment status is determined to be healthy.

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