A light-heat-electricity multi-parameter monitoring management method and system for intelligent lighting

Abstract

The present application relates to a kind of LED lamps and lanterns light-heat-electricity multi-parameter monitoring management method and system for intelligent lighting, method includes: the operating data of luminaire is collected, operating data is stored in specific data table, and operating data is input to the fault classification model of multi-parameter coupling and the health degree assessment model of luminaire, obtain the fault result and health degree of each luminaire;Multi-parameter coupling's fault classification model is modeled using the stored fault related data to obtain;The health degree assessment model of luminaire is obtained using the relevant information corresponding to operating data training;When there is abnormality in the fault result of luminaire operating state, send early warning, and guide repair task through health degree.The present application is through comprehensive collection LED luminaire operating data, and through the data prediction luminaire operating state of depth analysis, for optimizing intelligent lighting system operation strategy, simplify the link of fault repair and provide guidance.

Description

Technical Field

[0001] This invention relates to the field of smart lighting technology, and in particular to a method and system for monitoring and managing multiple parameters of light, heat and electricity in LED luminaires for smart lighting. Background Technology

[0002] Smart lighting refers to lighting solutions that utilize technologies such as the Internet of Things (IoT), sensors, and wired / wireless communication to achieve intelligent control and management of lighting systems, aiming to achieve energy conservation, high efficiency, and user-friendliness. With the development of my country's economy and society and the increasing demand for energy conservation and emission reduction, the application areas of smart lighting systems are gradually expanding. However, existing smart lighting systems mostly rely on light sensors and human body sensors for monitoring and adjusting the light intensity of LED lamps. They fail to fully consider data such as lamp brightness, spectrum, power fluctuations, and temperature, neglecting lamp status monitoring. Furthermore, their fault warnings are not accurate enough, failing to deeply analyze historical data to accurately predict potential lamp failures, thus affecting the inspection tasks of staff. Summary of the Invention

[0003] To address the problems existing in the prior art, the purpose of this invention is to provide a method and system for monitoring and managing multiple parameters of LED lighting fixtures (light, heat, and electricity) for smart lighting. This method and system integrates data acquisition, storage, processing and analysis, and visualization modules and systems to comprehensively collect LED lighting fixture operating data and predict the operating status of the fixtures through in-depth data analysis, providing guidance for optimizing the operation strategy of smart lighting systems and simplifying fault diagnosis and repair.

[0004] To achieve the above objectives, the present invention provides the following solution:

[0005] A method for monitoring and managing multiple parameters (light, heat, and electricity) of LED luminaires for smart lighting, comprising:

[0006] The system collects operational data from the lighting fixtures, stores the operational data in a specific data table, and inputs the operational data into a multi-parameter coupled fault classification model and a lighting fixture health assessment model to obtain the fault results and health status of each lighting fixture. The multi-parameter coupled fault classification model is obtained by modeling using the stored fault-related data. The lighting fixture health assessment model is obtained by training using the relevant information corresponding to the operational data.

[0007] When the lamp operating status is abnormal in the fault results, an early warning is issued, and the health status guides the maintenance task.

[0008] Optionally, the operating data includes: luminous intensity of the lamp, spectral distribution of visible light band, temperature of the lamp heat sink, and changes in lamp power.

[0009] Optionally, storing the runtime data in a specific data table includes: compressing the runtime data using a differential coding-based compression algorithm and storing it in the specific data table to reduce storage space usage.

[0010] Optionally, after storing the runtime data in a specific data table, the method further includes:

[0011] Basic information about the lighting fixtures is stored in a specific database; the basic information includes: model, production date, and rated power.

[0012] The fault-related data is stored separately; the fault-related data includes: fault occurrence time, fault type, and operating parameters before the fault.

[0013] Optionally, issuing an early warning when the lamp's operating status is abnormal in the fault results includes:

[0014] Based on the fault classification model, the operating data is analyzed. When the luminous intensity of the lamp exceeds the luminous intensity warning threshold, it indicates an abnormality in the luminous intensity of the lamp, and a luminous intensity abnormality warning is issued. When the spectral distribution of the visible light band deviates from the spectral warning threshold, it indicates an abnormality in the spectral distribution of the visible light band, and a spectral deviation warning is issued. When the temperature of the lamp heat sink exceeds the temperature warning threshold within the first target time period, it indicates an abnormality in the temperature of the lamp heat sink, and a high temperature warning is issued. When the power change of the lamp exceeds the power warning threshold within the first target time period, it indicates an abnormality in the power change of the lamp, and a power abnormality warning is issued.

[0015] Optionally, guiding maintenance tasks based on the health status includes:

[0016] The lighting fixture health assessment model comprehensively analyzes the parameters corresponding to the operating data, such as brightness attenuation rate, spectral shift, temperature rise, and power fluctuation amplitude, to obtain the health status. The health status is then visualized to guide the maintenance tasks.

[0017] Optionally, the method further includes:

[0018] A visual interface is set up to display the operating data of the lamps in the form of charts. At the same time, by inputting basic information about the lamps, users can query and display the operating data curves and related statistical information of the current lamps within a specific time period.

[0019] Optionally, the method further includes:

[0020] The user terminal software interface displays the operating data of the lamps and uses indicator lights of different colors to indicate the operating status of the lamps. At the same time, by entering the lamp number and time information, users can query and display the operating data curve and related statistical information of the current lamp within a specific time period.

[0021] To achieve the above objectives, the present invention also provides a multi-parameter monitoring and management system for LED lighting fixtures (light, heat, and electricity) for smart lighting, comprising:

[0022] The data acquisition module is used to collect the operating data of the lighting fixtures;

[0023] The data storage module is used to store the runtime data in a specific data table;

[0024] The data processing and analysis module is used to input the operating data into a multi-parameter coupled fault classification model and a lighting fixture health assessment model to obtain the fault results and health status of each lighting fixture; the multi-parameter coupled fault classification model is obtained by modeling using stored fault-related data; the lighting fixture health assessment model is obtained by training using relevant information corresponding to the operating data.

[0025] When the lamp operating status is abnormal in the fault results, an early warning is issued, and the health status guides the maintenance task.

[0026] Optionally, the system also includes:

[0027] The data visualization module is used to set up a visualization interface to display the operating data of the lamps in the form of charts. At the same time, by inputting basic information about the lamps, it can query and display the operating data curves and related statistical information of the current lamps within a specific time period.

[0028] The user terminal module is used to set up the user terminal software interface to display the operating data of the lamps and to use different colored indicator lights to indicate the operating status of the lamps. At the same time, by inputting the lamp number and time information, users can query and display the operating data curve and related statistical information of the current lamp within a specific time period.

[0029] The beneficial effects of this invention are as follows:

[0030] This invention uses brightness sensors, spectral sensors, temperature sensors, and power sensors to monitor multiple status parameters of LED lamps in real time during operation. By analyzing historical operating data, it summarizes and judges the operating status of the lamps and realizes fault early warning. The advantage of this invention is that it constructs a "light-heat-electric multi-parameter collaborative monitoring system", realizing the leap from "local monitoring" to "full life cycle status perception". It can also train a fault classification model based on historical operating data, construct a lamp health assessment model, and monitor the health of each lamp. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments 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.

[0032] Figure 1 This is a flowchart of a multi-parameter monitoring and management method for LED lighting fixtures used in smart lighting, according to an embodiment of the present invention.

[0033] Figure 2 This is a schematic diagram of a multi-parameter monitoring and management system for LED lighting fixtures used in smart lighting, according to an embodiment of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] like Figure 1 As shown in the figure, this embodiment discloses a multi-parameter monitoring and management method for LED lamps in smart lighting, including: collecting the operating data of the lamps, storing the operating data in a specific data table, and inputting the operating data into a multi-parameter coupled fault classification model and a lamp health assessment model to obtain the fault results and health status of each lamp; the multi-parameter coupled fault classification model is obtained by modeling using the stored fault-related data; the lamp health assessment model is obtained by training using the relevant information corresponding to the operating data; when there is an abnormality in the operating status of the lamp in the fault results, an early warning is issued, and the health status guides the maintenance task.

[0037] Specifically, this embodiment discloses a method for monitoring and managing multiple parameters of LED luminaires (light, heat, and electricity) for smart lighting, including the following:

[0038] Data is collected by installing multiple sensors inside or near the luminaire. A brightness sensor collects the luminous intensity in real time, a spectral sensor collects the spectral distribution of visible light, a temperature sensor monitors the temperature of the luminaire's heatsink, and a power sensor records changes in the luminaire's power. The collected data is transmitted to a data storage module using a combination of wired and wireless methods.

[0039] Specifically, the brightness sensor uses a high-precision photodiode as the sensing element, with a measurement range of 0-10000 lux and a resolution of 5 lux, enabling real-time and accurate measurement of the luminous brightness of different lamps. The spectral sensor, based on optical spectral dispersion and photoelectric conversion technology, monitors the spectral distribution of the 400-760nm visible light band and calculates the color coordinate offset (accuracy ±0.001). The temperature sensor, designed based on the thermocouple principle, has a measurement range of -50℃ to 200℃ and an accuracy of ±0.5℃, primarily used to monitor the temperature of the lamp's heat dissipation components. The power sensor measures power through the Hall effect principle, with a measurement range of 0-500W and a resolution of 0.1W, used to detect the power consumption of the lamp. Furthermore, different data acquisition frequencies are set according to the lamp's usage scenario and importance. For example, in important commercial lighting scenarios, the data acquisition frequency can be set to once per second; in some non-critical general lighting areas, the acquisition frequency can be once every 5 seconds. Using power as the core trigger parameter, the system automatically increases the sampling frequency of brightness, spectrum, and temperature when power fluctuations exceed 5% of the rated value, and decreases the sampling frequency when power stabilizes, balancing monitoring accuracy and energy consumption. Combining wired Ethernet transmission and ZigBee wireless transmission technologies, the system transmits the collected data to the data storage module in real time.

[0040] Different types of data are stored separately. Basic information about the lighting fixtures (such as model, production date, rated power, etc.) is stored in a specific database; real-time operating data (such as brightness, spectrum, temperature, power, etc. that change over time) is stored in a specific data table according to time series; and fault-related data (such as fault occurrence time, fault type, operating parameters before the fault, etc.) is stored separately.

[0041] Specifically, this includes hardware and software modules. The hardware module is equipped with a high-performance server, using a combination of large-capacity solid-state drives (SSDs) and high-capacity hard disk drives (HDDs). The SSDs are used to store system operating programs and frequently accessed data, while the HDDs are used for long-term storage of large amounts of lighting fixture operating data. The software module is data management software, capable of data reception, storage, and analysis. It can receive data from sensors in real time and classify and store it according to predetermined rules. For large amounts of real-time operating data, a differential coding-based compression algorithm is used for compression storage, ensuring data integrity while reducing storage space usage.

[0042] Data Processing and Analysis Module: This module processes and analyzes different types of data in the data storage module to provide guidance for the control of lighting fixture operation status. It models historical data of lighting fixture operating parameters (including normal and fault states) and trains a multi-parameter coupled fault classification model; it sets different fault warning thresholds and issues warnings when the lighting fixture's operating status is abnormal; and it constructs a lighting fixture health assessment model to monitor the health of each lighting fixture to guide maintenance tasks.

[0043] Specifically, time series analysis (such as LSTM and Prophet) is used to model historical LED lighting brightness, spectrum, temperature, and power data. The system captures fault occurrence times in real time and labels fault types (such as abnormal brightness, spectral shift, etc.). Simultaneously, historical operating parameters (brightness, spectrum, temperature, power, etc.) within a preset time period (e.g., 30 minutes) before the fault occur are extracted. Fault information is correlated with parameters for the corresponding time period to form a specialized fault dataset, which is then used to train a multi-parameter coupled fault classification model. Brightness, spectrum, temperature, and power warning thresholds are set. For example, when the lighting fixture's heat dissipation temperature exceeds 80℃ for 10 consecutive minutes (adjustable according to fixture specifications), a high-temperature warning is issued. When power fluctuations exceed 20% of the rated power and persist for 5 minutes, a potential circuit fault is identified, and a power anomaly warning is issued. An abnormal spectral distribution curve is detected. Even if the power and brightness do not exceed the threshold, a "spectral shift warning" is triggered. A luminaire health assessment model is constructed, and benchmark values ​​(based on the luminaire's rated parameters and initial operating data), warning thresholds, and fault thresholds are set for parameters such as brightness decay rate, spectral shift, temperature rise, and power fluctuation amplitude. The actual measured values ​​of each parameter are converted into standardized values ​​in the 0-1 range using a standardized formula. Then, the weight of each parameter is determined using the analytic hierarchy process (e.g., brightness decay rate 0.35, spectral shift 0.25, temperature rise 0.25, power fluctuation amplitude 0.15, which can be dynamically adjusted according to the application scenario). A health score of 0-100 is calculated using a weighted summation formula, and the health score of each luminaire is output to the data visualization module. When the score is lower than the set threshold (e.g., 80 points), the system automatically generates a maintenance work order to guide the inspection work.

[0044] The key operating parameters of the lamps are displayed in real time through a visual interface. For example, the current brightness, temperature and power values ​​of the lamps are displayed in the form of charts (such as bar charts, line charts, etc.). At the same time, by entering information such as lamp number and time period, the operating data curves and related statistical information of the lamps within a specific time period can be quickly queried and displayed.

[0045] Specifically, the user terminal software interface displays key operating parameters of the lighting fixtures in real time, such as the current brightness, temperature, and power values. Different colored indicator lights represent the operating status of the fixtures (green for normal, yellow for warning, and red for fault). Users can also input the fixture number, time period, and other information into the terminal software to quickly query and view the fixture's operating data curves and related statistics for that specific time period.

[0046] like Figure 2 As shown in the figure, this embodiment also discloses a multi-parameter monitoring and management system for LED lamps used in smart lighting, including: a data acquisition module for collecting operating data of the lamps; a data storage module for storing operating data in a specific data table; and a data processing and analysis module for inputting the operating data into a multi-parameter coupled fault classification model and a lamp health assessment model to obtain the fault results and health status of each lamp. The multi-parameter coupled fault classification model is obtained by modeling using stored fault-related data; the lamp health assessment model is obtained by training using relevant information corresponding to the operating data; when there is an abnormality in the lamp operating status in the fault results, an early warning is issued, and maintenance tasks are guided by the health status.

[0047] Specifically, this embodiment also discloses an LED lamp light-heat-electric multi-parameter monitoring and management system for smart lighting, including: a sensor module, a data transmission module, a data processing module and a user terminal module.

[0048] The sensor module includes various sensors such as brightness sensors, temperature sensors, and power sensors, used to collect operating data of the lighting fixtures.

[0049] The data transmission module includes wired and wireless transmission methods for transmitting the collected data to the data processing center.

[0050] The data processing module includes a high-performance server and running data management software for data storage, analysis, and processing.

[0051] The user terminal module connects to the data processing module via a network for data visualization and remote monitoring.

[0052] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for monitoring and managing multiple parameters (light, heat, and electricity) of LED luminaires for smart lighting, characterized in that, include: The system collects operational data from the lighting fixtures, stores the operational data in a specific data table, and inputs the operational data into a multi-parameter coupled fault classification model and a lighting fixture health assessment model to obtain the fault results and health status of each lighting fixture. The multi-parameter coupled fault classification model is obtained by modeling using the stored fault-related data. The lighting health assessment model is obtained by training using the relevant information corresponding to the operational data; When the lamp operating status is abnormal in the fault results, an early warning is issued, and the health status guides the maintenance task.

2. The method for monitoring and managing multiple parameters (light, heat, and electricity) of LED luminaires for smart lighting according to claim 1, characterized in that, The operational data includes: luminous intensity of the lamp, spectral distribution of visible light band, temperature of the lamp heat sink, and changes in lamp power.

3. The method for monitoring and managing multiple parameters (light, heat, and electricity) of LED luminaires for smart lighting according to claim 1, characterized in that, Storing the runtime data in a specific data table includes: compressing the runtime data using a differential coding-based compression algorithm and storing it in the specific data table to reduce storage space usage.

4. The method for monitoring and managing multiple parameters (light, heat, and electricity) of LED luminaires for smart lighting according to claim 1, characterized in that, After storing the runtime data in a specific data table, the following is also included: Basic information about the lighting fixtures is stored in a specific database; the basic information includes: model, production date, and rated power. The fault-related data is stored separately; the fault-related data includes: fault occurrence time, fault type, and operating parameters before the fault.

5. The method for monitoring and managing multiple parameters (light, heat, and electricity) of LED luminaires for smart lighting according to claim 2, characterized in that, When the lighting fixtures exhibit abnormal operating status in the aforementioned fault results, an early warning is issued, including: Based on the fault classification model, the operating data is analyzed. When the luminous intensity of the lamp exceeds the luminous intensity warning threshold, it indicates an abnormality in the luminous intensity of the lamp, and a luminous intensity abnormality warning is issued. When the spectral distribution of the visible light band deviates from the spectral warning threshold, it indicates an abnormality in the spectral distribution of the visible light band, and a spectral deviation warning is issued. When the temperature of the lamp heat sink exceeds the temperature warning threshold within the first target time period, it indicates an abnormality in the temperature of the lamp heat sink, and a high temperature warning is issued. When the power change of the lamp exceeds the power warning threshold within the first target time period, it indicates an abnormality in the power change of the lamp, and a power abnormality warning is issued.

6. The method for monitoring and managing multiple parameters (light, heat, and electricity) of LED luminaires for smart lighting according to claim 1, characterized in that, Using the aforementioned health status to guide maintenance tasks includes: The lighting fixture health assessment model comprehensively analyzes the parameters corresponding to the operating data, such as brightness attenuation rate, spectral shift, temperature rise, and power fluctuation amplitude, to obtain the health status. The health status is then visualized to guide the maintenance tasks.

7. The method for monitoring and managing multiple parameters (light, heat, and electricity) of LED luminaires for smart lighting according to claim 1, characterized in that, The method also includes: A visual interface is set up to display the operating data of the lamps in the form of charts. At the same time, by inputting basic information about the lamps, users can query and display the operating data curves and related statistical information of the current lamps within a specific time period.

8. The method for monitoring and managing multiple parameters (light, heat, and electricity) of LED luminaires for smart lighting according to claim 1, characterized in that, The method also includes: The user terminal software interface displays the operating data of the lamps and uses indicator lights of different colors to indicate the operating status of the lamps. At the same time, by entering the lamp number and time information, users can query and display the operating data curve and related statistical information of the current lamp within a specific time period.

9. A multi-parameter monitoring and management system for LED lighting fixtures based on light, heat, and electricity, implemented according to any one of claims 1-8, characterized in that, include: The data acquisition module is used to collect the operating data of the lighting fixtures; The data storage module is used to store the runtime data in a specific data table; The data processing and analysis module is used to input the operating data into a multi-parameter coupled fault classification model and a lamp health assessment model to obtain the fault results and health status of each lamp; the multi-parameter coupled fault classification model is obtained by modeling using stored fault-related data; The lighting health assessment model is obtained by training using the relevant information corresponding to the operational data; When the lamp operating status is abnormal in the fault results, an early warning is issued, and the health status guides the maintenance task.

10. The LED luminaire light-heat-electric multi-parameter monitoring and management system for smart lighting according to claim 9, characterized in that, The system also includes: The data visualization module is used to set up a visualization interface to display the operating data of the lamps in the form of charts. At the same time, by inputting basic information about the lamps, it can query and display the operating data curves and related statistical information of the current lamps within a specific time period. The user terminal module is used to set up the user terminal software interface to display the operating data of the lamps and to use different colored indicator lights to indicate the operating status of the lamps. At the same time, by inputting the lamp number and time information, users can query and display the operating data curve and related statistical information of the current lamp within a specific time period.

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