A light box full life cycle intelligent energy-saving management and control system and method

Abstract

The application discloses a kind of intelligent energy-saving management and control systems and methods of light box full life cycle, it is related to lighting control technical field.System includes lighting control device, edge gateway equipment and remote management platform.Lighting control device is connected in series with light box LED light source, integrates microcontroller, Bluetooth Mesh unit, double-path PWM dimming circuit and power measurement unit, for executing dimming, measurement power and local wireless network is formed.Edge gateway equipment accesses the network, converges data and communicates with remote platform.Remote management platform is configured with scene strategy engine and energy consumption analysis module, the former generates differentiated dimming instruction based on operation timetable, position signal and environmental parameter, and the latter generates single lamp level energy consumption statistics based on real-time power data.Method corresponding realizes the deployment networking of device, strategy generation and instruction issuing, data acquisition reporting and energy consumption statistical analysis and other coordination procedures.The application realizes the fine, intelligent energy-saving management and control of advertising light box.

Description

Technical Field

[0001] This invention relates to the field of lighting control technology, and in particular to an intelligent energy-saving management system and method for the entire life cycle of a light box. Background Technology

[0002] Advertising light boxes are widely used in public transportation, commercial centers, and other locations, resulting in significant energy consumption due to their long-term operation. Currently, energy efficiency management of these advertising light boxes mainly faces the following challenges: First, the energy consumption control methods are crude. The vast majority of advertising light boxes use simple on / off control or fixed brightness modes, and their lighting time usually far exceeds actual needs, resulting in a large amount of wasted electricity.

[0003] Secondly, the operation and maintenance efficiency is low. For large-scale deployed advertising light boxes, the monitoring of their working status mainly relies on regular manual inspections. This method is labor-intensive, slow in response, and difficult to achieve full coverage and real-time performance. In addition, because traditional light box circuits lack independent metering design, it is usually impossible to accurately obtain real-time energy consumption data for individual light boxes, resulting in a lack of accurate data foundation for quantitative assessment of energy-saving effects, electricity cost allocation, and preventive maintenance.

[0004] Finally, the system's scalability and intelligence level are limited. Traditional lightbox control systems typically have single functions, lack effective collaboration and information exchange between devices, make it difficult to easily connect new sensing devices, and also cannot achieve data fusion and linkage with upper-level business operation systems.

[0005] Therefore, how to achieve refined, intelligent, and quantifiable energy-saving management of large-scale advertising light boxes, reduce operation and maintenance costs, and improve system scalability is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] The main objective of this invention is to provide an intelligent energy-saving management and control system and method for the entire life cycle of light boxes, aiming to solve the technical problems of serious energy waste caused by the crude control methods of current advertising light boxes, low operation and maintenance efficiency caused by reliance on manual inspections, and insufficient system scalability that makes it difficult to achieve intelligent collaborative control.

[0007] To achieve the above objectives, the first aspect of this application provides an intelligent energy-saving management and control system for the entire life cycle of a light box, comprising: The lighting control device is connected in series between the DC power supply and the LED light source of the advertising light box. It is equipped with a microcontroller, a Bluetooth Mesh communication unit, a dual-channel PWM dimming drive circuit, and a power metering unit. The input terminal of the dual-channel PWM dimming drive circuit is connected to the DC power supply, and the output terminal is connected to the LED light source. The power metering unit is used to collect the current and voltage signals of the dual output circuits and calculate real-time power data. The lighting control device is used to build a local control network based on the Bluetooth Mesh protocol. The edge gateway device is equipped with a microcontroller, multiple Bluetooth Mesh interfaces and a wide area network communication module. The edge gateway device is used to access the local control network to aggregate the operating data of each of the lighting control devices and connect to the remote management platform via wired or wireless means. The remote management platform is equipped with a scene strategy engine and an energy consumption analysis module; the scene strategy engine is used to generate differentiated dimming instructions based on the operation schedule, location signals and environmental parameters; the energy consumption analysis module is used to generate single-lamp-level energy consumption statistics based on the real-time power data.

[0008] Secondly, a method for intelligent energy-saving management and control of the entire life cycle of a lightbox is provided, applied to the system described in the first aspect; the method includes the following steps executed collaboratively by various components within the system: The steps performed by the remote management platform are as follows: Grouping configuration is performed based on the location information of the advertising light boxes; Based on the operating schedule, location signal, and environmental parameters, generate differentiated dimming instructions; The differentiated dimming command is issued; The steps performed by the edge gateway device: Receive the dimming command from the remote management platform and forward it to the target lighting control device via the Bluetooth Mesh local control network; The steps performed by the lighting control device are as follows: It receives and executes the dimming command, and dims the LED light source through its dual-channel PWM dimming drive circuit; Its power metering unit collects current and voltage signals from the dual output circuits, calculates real-time power data and electrical parameters, and reports them. Further steps performed by the remote management platform: Energy consumption statistics for a single lamp are generated based on the reported real-time power data.

[0009] This application constructs a three-tiered IoT management architecture by deploying independently dimmable lighting control devices, data-aggregating edge gateways, and a remote management platform with a policy engine. Based on operating schedules, location, and environmental parameters, the system can automatically generate and issue differentiated dimming commands, enabling refined and scenario-based energy-saving control of large-scale advertising light boxes. Simultaneously, by collecting and reporting power data in real time, the platform can automatically generate single-lamp-level energy consumption statistics and equipment status monitoring. This significantly reduces ineffective lighting energy consumption while greatly improving the automation and intelligence level of operation and maintenance management, solving the core problems of traditional light box control being crude and inefficient in operation and maintenance. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] in: Figure 1 This is a schematic diagram of the structure of a lightbox full life cycle intelligent energy-saving management and control system provided in an embodiment of this application; Figure 2 This application provides an architectural diagram of an intelligent energy-saving management and control system for the entire lifecycle of a light box. Figure 3 A flowchart illustrating another intelligent energy-saving management method for the entire lifecycle of a light box, provided in an embodiment of this application; Figure 4 This application provides an overall architecture for a software-defined digital station. Detailed Implementation

[0012] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0013] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0014] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0015] The embodiments of this application are described below with reference to the accompanying drawings.

[0016] Figure 1 This is a structural schematic diagram of a light box full life cycle intelligent energy-saving management and control system provided in an embodiment of this application.

[0017] like Figure 1 As shown, the system 100 includes: The lighting control device 110 is connected in series between the DC power supply and the LED light source of the advertising light box. It is equipped with a microcontroller, a Bluetooth Mesh communication unit, a dual-channel PWM dimming drive circuit, and a power metering unit. The input terminal of the dual-channel PWM dimming drive circuit is connected to the DC power supply, and the output terminal is connected to the LED light source. The power metering unit is used to collect the current and voltage signals of the dual output circuits and calculate real-time power data. The lighting control device is used to build a local control network based on the Bluetooth Mesh protocol. Edge gateway device 120 is equipped with a microcontroller, multiple Bluetooth Mesh interfaces and a wide area network communication module. The edge gateway device is used to access the local control network to collect the operating data of each of the lighting control devices and connect to the remote management platform via wired or wireless means. The aforementioned remote management platform 130 is equipped with a scene strategy engine and an energy consumption analysis module; the aforementioned scene strategy engine is used to generate differentiated dimming instructions based on the operation schedule, location signal and environmental parameters; the aforementioned energy consumption analysis module is used to generate single-lamp level energy consumption statistics based on the aforementioned real-time power data.

[0018] Please refer to this first. Figure 4 , Figure 4This application provides an overall architecture for a software-defined digital station. It employs a two-tier architecture: an "IoT hardware-based spatial structure" and "people, objects, and process data-driven services." By optimizing the fusion of data, operational data, sensor data, and location data, it achieves digital and intelligent management of the station.

[0019] The intelligent energy-saving management and control system for the entire lifecycle of the lightbox proposed in this application can serve as a core component of a software-defined digital station. For example... Figure 4 As shown, the digital station collects multi-source data through a sensor data layer (including smart lighting, temperature and humidity, CCTV, turnstiles, etc.). After processing by the optimization data layer and the operation data layer, the data is provided to the top-level service layer (lighting service, energy management, asset management, etc.) to support decision-making. Specifically, the lighting service module connects to the lighting control devices of each advertising lightbox via a Bluetooth Mesh network, enabling energy consumption monitoring, intelligent dimming, and fault alarms. Its energy-saving effect can be visualized through digital twin technology, supporting the station's overall energy management and digital value-added operation.

[0020] Specifically, the intelligent energy-saving management and control system 100 for the entire lifecycle of light boxes can be composed of three layers: terminal, edge, and cloud. The terminal layer includes lighting control devices 110 deployed in each advertising light box, responsible for data acquisition and execution control; the edge layer includes edge gateway devices 120 deployed at the site center, responsible for local network aggregation and protocol conversion; the cloud layer includes a remote management platform 130, responsible for global policy formulation and data analysis. The layers are interconnected through standardized interfaces: the terminal layer and the edge layer use the Bluetooth Mesh protocol to build a local control network; the edge layer and the cloud layer use the MQTT protocol over Ethernet or 4G networks to establish a wide-area connection.

[0021] The lighting control device 100 in this embodiment is a hardware module installed inside each advertising light box. The core of the lighting control device 100 may include a microcontroller, a Bluetooth Mesh communication unit, a dual-channel PWM (Pulse Width Modulation) dimming drive circuit, and a power metering unit. The lighting control device 100 can be connected in series between the DC power supply of the light box and its internal LED light source (such as LED strips). The dual-channel PWM dimming drive circuit supports independent dimming control of the two LED loads; the power metering unit collects the current and voltage signals from the dual output circuits, and the microcontroller calculates the real-time power data of the light box. All lighting control devices 100 can automatically form a wireless self-organizing network covering the area (such as a subway station) via the Bluetooth Mesh protocol.

[0022] In this embodiment, the microcontroller may be a processor that supports the Bluetooth Low Energy Mesh protocol, such as a chip based on the RISC-V architecture.

[0023] The pulse width modulation (PWM) mentioned in this application embodiment is a technique for regulating power by periodically switching current. Specifically, PWM regulates the average output voltage by controlling the switching time (i.e., duty cycle) of the switch, thereby affecting the brightness of the light box. Duty cycle refers to the proportion of the time the switch is in the "on" state during the entire cycle.

[0024] Duty cycle: The percentage of time a switching signal remains high in each cycle, usually expressed as a percentage.

[0025] 0% duty cycle: The switch is always off, which is equivalent to 0% brightness.

[0026] 100% duty cycle: The switch is always on, which is equivalent to 100% brightness.

[0027] 50% duty cycle: The switch is on for half the time and off for the other half, which is equivalent to 50% brightness.

[0028] This method allows for smooth adjustment of the lightbox brightness without causing voltage surges or heat loss, thereby improving energy efficiency and extending equipment life. For example, in the embodiments of this application, the duty cycle can be adjusted in 1% increments between 0-100% to achieve smooth dimming.

[0029] Specifically, in this embodiment, the basic principle of PWM control is to control the output power by changing the switching frequency. For example, the frequency of the PWM signal is set to a certain value (e.g., 20kHz), and the average power flowing into the LED light box is controlled by adjusting the ratio of "on" and "off" times in each cycle.

[0030] The specific adjustment formula is as follows: in, V avg It is the average output voltage obtained through PWM control; V max It is the maximum voltage of the power supply; T on It is the time when the signal is at a high level (on). T period It is the time of the entire PWM cycle.

[0031] Suitable hardware components, such as the LEDPOWER600W control board, can be used to generate and control PWM signals, thereby precisely adjusting the power of the light box.

[0032] In one specific implementation, the power metering unit may include a voltage monitoring module and a current monitoring module. The voltage monitoring module is connected in parallel to the output terminal of the dual-channel PWM dimming drive circuit to acquire the output voltage signal in real time; the current monitoring module is connected in series to the negative path of the dual-channel output circuit to acquire the output current signal in real time.

[0033] The above microcontroller is based on the power calculation formula P = V out × I out The real-time power of the two outputs is calculated separately and summed to obtain the total real-time power of the light box. After each brightness adjustment by the PWM dimming drive circuit, the microcontroller immediately recalculates the actual power consumption and reports the real-time power data to the edge gateway device through the Bluetooth Mesh communication unit, forming a closed-loop data stream for energy consumption monitoring.

[0034] In this way, PWM control enables highly precise and linear brightness adjustment, ensuring appropriate brightness in different application scenarios and avoiding unnecessary energy consumption. Furthermore, by combining parameters such as time of day, pedestrian traffic data, and ambient light intensity, the lightbox brightness can be automatically adjusted to ensure optimal advertising display effects under different times and environmental conditions, while simultaneously optimizing energy efficiency.

[0035] The energy consumption analysis module in this embodiment can also be used to generate equipment operation status assessment information based on the cumulative running time, average power load and historical power fluctuation trend of the lighting control device; when abnormal attenuation of the power curve is detected or the cumulative running time exceeds the preset maintenance threshold, a preventive maintenance reminder is triggered.

[0036] Specifically, the energy consumption analysis module also has an operational status assessment function. This module continuously tracks the cumulative runtime, average power load, and historical power fluctuation curves of each lighting control device. When it detects an abnormal attenuation trend in the power curve of a certain light box, or when the cumulative runtime exceeds the preset maintenance cycle threshold, it automatically generates a preventive maintenance reminder and pushes it to the operation and maintenance terminal, realizing the transformation from passive fault repair to proactive preventive maintenance.

[0037] In one optional implementation, a lifespan management module can be added, focusing on equipment lifespan management. Further optionally, the lifespan management module may include: The lifespan prediction submodule combines the theoretical lifespan, cumulative operating time, average brightness load, and historical power fluctuation data of the LED light source with machine learning models (such as LSTM) to predict the remaining lifespan and trigger replacement warnings in advance.

[0038] Optionally, a light decay detection interface can be added to the lighting control device to connect a light sensor to collect the actual luminous brightness of the light box. The light decay rate can be calculated by comparing it with the initial brightness, which can directly assess the aging degree of the LED light source.

[0039] The lifespan management module is also used to automatically adjust the dimming strategy (such as reducing the maximum brightness) when the system predicts that the light box is nearing the end of its lifespan, so as to extend the remaining lifespan as much as possible while ensuring the basic advertising effect. At the same time, it can push maintenance work orders to remind replacement.

[0040] Optionally, in addition to the Bluetooth Mesh communication unit, the lighting control device 100 can also be configured with a 4G communication module interface (a 4G module can be plugged in in the actual product). When the device detects a persistent connection failure with the local Bluetooth Mesh network, it can automatically switch to establishing a remote direct connection with the remote management platform via the 4G network, receive emergency commands and report critical status, serving as a backup communication channel when the local network fails.

[0041] In this embodiment, the edge gateway device 120 is configured with both an Ethernet interface and a 4G communication module. Under normal circumstances, it prioritizes using a stable wired Ethernet connection to the remote management platform; when a wired network interruption is detected, it automatically and seamlessly switches to the 4G network to maintain communication, ensuring uninterrupted control commands and data reporting.

[0042] The remote management platform 130 can be understood as a software system. Operators can use its management interface to digitally manage all advertising light boxes within the system. Basic management functions may include: Group configuration: The light boxes can be divided into different control groups based on their physical location (e.g., “Line 1 Station Hall”, “Exit A Passage”) or business logic.

[0043] Scene strategy configuration: Multiple scene modes (such as "peak mode", "off-peak mode", "low-peak mode" and "maintenance mode") can be configured for different groups or individual light boxes, and a corresponding target brightness value can be set for each mode.

[0044] Status monitoring and alarms: The platform receives and displays real-time information such as the online status, current brightness, and power data of each lighting control device. It automatically generates fault alarms when it detects offline equipment, communication interruptions, or abnormal electrical parameters.

[0045] Energy consumption statistics: Based on the real-time power data reported by each light box, the platform automatically generates and displays energy consumption statistics for each light.

[0046] Specifically, the scene strategy engine can dynamically calculate the target brightness based on preset time period weights and area weights. For example, the time period weights include: 1.2 for morning and evening peak hours, 0.8 for nighttime hours, and 1.0 for off-peak hours. The area weights include: 1.5 for the entrance area, 1.2 for the platform area, 1.0 for the ticket sales area, and 0.8 for the exit area.

[0047] The target brightness is calculated as follows: Target brightness = Base brightness × Time period weight × Region weight, with an upper limit of 100%.

[0048] Furthermore, the scene strategy engine not only adjusts brightness based on time of day, but also applies differentiated area weights according to the pedestrian traffic characteristics of the physical area where the lightbox is located. For example, the preset area weights are: entrance area 1.5 (high traffic), platform area 1.2 (medium to high traffic), ticket area 1.0 (medium traffic), and exit area 0.8 (low traffic).

[0049] The comprehensive calculation formula for target brightness is: Target brightness = Base brightness × Time period weight × Region weight, with an upper limit of 100%.

[0050] For example, during the morning peak hours (time period weight 1.2), the target brightness of the entrance area (area weight 1.5) is 180% of the baseline value, but it is kept fully bright due to the 100% upper limit; during the nighttime hours (time period weight 0.8), the target brightness of the exit passage (area weight 0.8) is 64% of the baseline value, achieving deep energy saving.

[0051] Figure 2 This is a schematic diagram of the architecture of a smart energy-saving management and control system for the entire life cycle of a lightbox, provided as an embodiment of this application. Figure 2 As shown, the system adopts a three-layer IoT architecture of "cloud layer - edge layer - terminal layer". The functions and components of each layer include: The cloud layer (remote management platform) can include four core modules: a scene strategy engine (generating dimming strategies and timing instructions), an energy consumption analysis module (statistically analyzing the energy consumption of a single lamp / area), an energy consumption prediction engine (predicting energy consumption trends based on historical data), and a dynamic optimization module (combining real-time data to optimize strategies); it interacts with the edge layer via Ethernet / 4G communication to issue instructions and receive data.

[0052] Edge layer (edge ​​gateway device): may include multiple BT (Bluetooth) interfaces (such as BT interfaces 1~3, for accessing Bluetooth Mesh networks) and wide area network modules (such as 4G / Ethernet modules, for communicating with the cloud); its function is to aggregate Bluetooth Mesh network data from the terminal layer and forward it to the cloud; at the same time, it forwards cloud commands to the terminal layer.

[0053] Terminal layer (lighting control device + light box): may include multiple lighting control devices (such as the lighting control devices of light boxes 1 to 4), each device has a built-in microcontroller, Bluetooth Mesh communication unit, dual-channel PWM dimming drive circuit, and power metering unit (refer to the original application technical solution); communicates with the edge layer through Bluetooth Mesh network, executes dimming commands issued by the cloud / edge, and collects and reports real-time power and environmental data.

[0054] The three-layer architecture in this application embodiment can achieve full closed-loop management of the method in this application through Bluetooth Mesh (terminal-edge) combined with Ethernet / 4G communication links, supporting intelligent energy-saving operation of the light box throughout its entire life cycle.

[0055] In one alternative implementation, based on the aforementioned system, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating another intelligent energy-saving management method for the entire lifecycle of a lightbox provided in this application embodiment. Specifically, the method can be executed cyclically with a control cycle of 5 minutes, and includes the following steps: Initiate Control Cycle: The process that triggers a single control cycle; Real-time data collection: Synchronously collect three types of real-time operating parameters: power of the light box, ambient light, and pedestrian flow; LSTM Long-Term Prediction: Utilizes a Long Short-Term Memory (LSTM) network model to predict long-term (e.g., time period, date dimension) brightness demand trends and generate a basic brightness curve; Linear regression short-term forecasting: Based on the linear regression algorithm, the power demand in the short term (such as at the minute level) is accurately calculated to obtain the accurate power demand; Prediction deviation judgment: Compare the deviation between "power demand predicted by linear regression in the short term" and "power corresponding to the baseline brightness predicted by LSTM in the long term" to determine whether it exceeds the 10% threshold. If the deviation is ≤10%, proceed directly to the next step; If the deviation is greater than 10%, the particle swarm optimization algorithm is triggered to iterate and optimize the brightness control strategy (such as brightness values ​​and power allocation schemes for each time period). Scene strategy engine generates instructions: Regardless of whether it has undergone particle swarm optimization, the scene strategy engine will combine the prediction results (or the optimized strategy) to generate time-series dimming instructions to clarify the brightness adjustment target at each time point / period. Issue and execute commands: Send dimming commands to the terminal light box, execute the dimming operation, and monitor the actual power consumption in real time; Store measured data: Store measured power, brightness, environmental parameters and other data after dimming for subsequent iterative training of AI models (such as LSTM, linear regression models) to continuously optimize prediction accuracy; Cycle End and Recurrence: The current 5-minute cycle ends, and the system waits for the next cycle to start, repeating the above process.

[0056] Example 2: This example can enable advanced software modules on the system architecture and basic functions of Example 1 to achieve predictive, adaptive, and highly reliable intelligent control.

[0057] In an optional embodiment, the lighting control device 110 is further equipped with a sensor interface and a local storage unit. The aforementioned sensor interface is used to connect to a door magnetic detection device or a train detection sensor; The aforementioned scenario strategy engine is specifically used to execute a maintenance mode dimming strategy in response to the trigger signal of the aforementioned door magnetic detection device, or to execute dimming control during the train approach period and recovery control after the train leaves in response to the trigger signal of the aforementioned train detection sensor. The aforementioned local storage unit is used to maintain the execution of the local dimming strategy when communication is interrupted.

[0058] In this embodiment of the application, for the subway station scenario, "train" can refer to the subway; for the train station or bus station scenario, "train" corresponds to train, high-speed rail, bus, etc., and there is no limitation here.

[0059] Specifically, the lighting control device 110 is equipped with a sensor interface connected to a door magnetic detection device or a train detection sensor. When maintenance personnel open the light box door, the door magnetic detection device is triggered, and the scene strategy engine automatically adjusts the brightness of the light box to maintenance mode (e.g., 10% brightness) to ensure safety and facilitate observation of light strip damage. In the track area, a train proximity sensor is connected to the light box. When the sensor detects a train entering the station, the platform automatically dims the obscured light box to a preset low brightness; after the train leaves the station, it automatically restores its original brightness, eliminating light pollution for passengers inside the train.

[0060] In one optional implementation, the remote management platform 130 is further configured with an energy consumption prediction engine and a dynamic optimization engine. The aforementioned energy consumption prediction engine is used to predict energy consumption demand in future periods based on historical energy consumption data, target brightness, ambient light intensity, time period weight, and holiday adjustment coefficient, using a linear regression model or long short-term memory network. The aforementioned dynamic optimization engine is used to generate the optimal brightness control strategy based on genetic algorithms or particle swarm optimization algorithms, with the fitness function being the maximization of the ratio of advertising effectiveness to energy consumption.

[0061] The dynamic optimization engine in this embodiment is configured with algorithm adaptation logic, which can be used to select the optimization algorithm according to the size of the lightbox cluster: when the cluster size is less than a preset threshold, a genetic algorithm is used to reduce the amount of computation; when the cluster size is greater than the preset threshold, a parallelized particle swarm optimization algorithm is used to improve computational efficiency.

[0062] In this embodiment of the application, the energy consumption requirements of the light box can be predicted using a linear regression model and historical data (brightness, ambient light intensity, pedestrian traffic, time period information, etc.).

[0063] In one alternative implementation, the energy consumption prediction engine is configured with a two-layer prediction mechanism, including a long-term trend prediction layer and a short-term real-time prediction layer.

[0064] Specifically, the long-term trend prediction layer can use historical time-series data (such as the past 7 to 30 days), including daily energy consumption curves, changes in ambient light intensity, and fluctuations in population density, to predict energy demand for the next few hours to days using Long Short-Term Memory (LSTM) networks or Autoregressive Integral Moving Average (ARIMA) models.

[0065] The aforementioned LSTM model comprises an input gate, a forget gate, and an output gate: the input gate controls the proportion of historical time-series data input, the forget gate determines the degree of retention of historical information, and the output gate adjusts the output weights of the prediction results. Through this gating mechanism, the model can learn and remember the long-term dependencies of the lightbox's energy consumption and predict energy consumption trends over the next 3 to 6 hours.

[0066] Alternatively, an ARIMA model can be used, for example, with parameters order=(5,1,0), based on the autoregressive terms, first-order differences and zero-order moving averages of the past 5 time points, to predict passenger flow and energy consumption data for the next 3 time steps.

[0067] For the short-term real-time prediction layer, the precise power demand for a future period can be calculated using a linear regression model based on the current target brightness (L), ambient light intensity (IL), time period weight (T), holiday adjustment coefficient (W), and real-time subway passenger flow data.

[0068] In this embodiment, the basic scheduling strategy generated by the long-term prediction layer can adjust the target brightness benchmark value in advance. For example, if it is predicted that the passenger flow period will enter a low period in the next 3 hours, the benchmark brightness will be gradually reduced 30 minutes in advance. The short-term real-time prediction layer makes fine corrections on this basis to form a collaborative control mechanism.

[0069] Specifically, data input and real-time prediction: The energy consumption prediction engine receives multiple data sources in real time, including: real-time power and target brightness (L) reported by the lighting control device 110, ambient light intensity (IL) collected by the light sensor, preset time period weights (T) (e.g., 1.2 for morning peak and 0.8 for night), holiday adjustment coefficients (W), and real-time subway passenger flow data obtained through platform data interfaces (e.g., REST API).

[0070] This application embodiment can enable the energy consumption prediction engine and dynamic optimization engine in the remote management platform 130.

[0071] In one alternative implementation, the energy consumption prediction engine feeds this data into a linear regression model pre-trained based on historical data, the model being in the form of: P_predicted = β 0+ β 1·L + β 2·IL + β 3·T + β 4·W, The predicted power value (P_predicted) for the next control cycle (the next 15 minutes) is calculated, and the deviation between the real-time power and the predicted power (ΔP) is further calculated.

[0072] Multi-objective optimization decision-making: The dynamic optimization engine can use ΔP, T, and W as environmental input parameters. Its optimization objective is defined as maximizing the ratio of advertising display effect to real-time energy consumption (i.e., Fitness = Total Effectiveness / Total Energy Consumption), where advertising display effect is positively correlated with brightness. The engine mentioned in this embodiment runs the Particle Swarm Optimization (PSO) algorithm, which encodes each possible brightness combination scheme of the entire site's light boxes as a "particle." Through iterative search, it outputs the brightness strategy vector that optimizes the fitness function, and this vector is specifically targeted at different partitions or groups.

[0073] Optionally, the embodiments of this application may also employ a genetic algorithm (GA), as detailed below: Initialize the population: Randomly generate several brightness control strategies (e.g., brightness adjustment schemes for different time periods) as the initial population.

[0074] Fitness assessment: Evaluate the energy efficiency of each control strategy; the calculation formula is as follows: Fitness=TotalEnergy / ConsumptionTotalEffectiveness, Among them, Total Effectiveness refers to the effect of matching the advertising effect with the brightness, and Total EnergyConsumption refers to the energy consumption under this strategy.

[0075] Selection, crossover, and mutation: Through the operation of genetic algorithms, strategies with high fitness are selected for crossover and mutation, thereby continuously optimizing energy efficiency.

[0076] Strategy execution and real-time feedback correction: The scene strategy engine combines the optimized brightness strategy vector with the preset peak, off-peak, low-peak, and maintenance scene modes to generate a time-sequential dimming instruction sequence.

[0077] In one alternative implementation, before execution, the engine can fine-tune the target brightness (B_target) in the instruction in real time based on the following formula, which can be understood as adjusting the brightness of the lightbox according to the power change: B_adjusted = B_target × (1 - α ·ΔP), Where B_adjusted is the final brightness value sent. α The preset adjustment coefficient is ΔP, which is the latest power deviation. After execution, the lighting control device 110 reports the new data, forming a closed-loop feedback.

[0078] Optionally, during periods of low foot traffic or no foot traffic (such as at night or during off-peak hours), the lightbox enters a low-energy mode. The system automatically adjusts the brightness to a minimum (e.g., 20%-30%) using algorithms to reduce energy consumption while maintaining basic visibility of the advertising content.

[0079] Further, optionally, the brightness adjustment in the embodiments of this application may also include, but is not limited to: Time-of-use adjustment: The system automatically adjusts the brightness of the lightbox based on time-of-use information (such as peak hours, off-peak hours, holidays, etc.). Different time periods correspond to different brightness adjustment strategies, such as increasing brightness during peak hours and decreasing brightness during off-peak hours to reduce energy consumption.

[0080] Holiday scheduling: During holidays or major events, the system can adjust brightness to a higher level based on predicted foot traffic and advertising demand to maximize advertising effectiveness. After the holidays, the system will automatically switch back to normal energy efficiency mode.

[0081] In an optional implementation, the remote management platform 130 is also equipped with a fault diagnosis model, which is used to identify LED light source aging and circuit contact failure types based on real-time power data, dimming feedback signals and historical operating curves reported by the lighting control device. The aforementioned scenario strategy engine is also used to automatically adjust the output power of the corresponding lighting control device or execute a preset temporary compensation strategy when a minor fault is identified.

[0082] In an optional implementation, the aforementioned scene strategy engine is further configured to: when generating dimming instructions, also access monitoring data from temperature sensors, humidity sensors, and / or air quality sensors; and perform weighted correction on the calculated base brightness value based on preset environmental factor correction rules, wherein the environmental factor correction rules include: When the temperature is above the first threshold, the humidity is above the second threshold, or the air quality index is below the third threshold, a correction factor for reducing brightness is applied.

[0083] In this embodiment, temperature, humidity, and air quality sensors can be installed in practical application scenarios to monitor system environmental parameters. Furthermore, when generating dimming commands, the scene strategy engine can also access monitoring data from these sensors. The aforementioned thresholds can be set and adjusted as needed. For example, the platform can preset environmental factor correction rules, such as applying a brightness reduction correction coefficient (e.g., 0.9) when the temperature is above 30°C, the air quality index is below 50 (excellent), or the humidity is above 80%. This correction coefficient will be weighted with the base brightness calculated based on passenger flow and algorithms to achieve multi-environmental adaptability.

[0084] In one specific implementation, the scene policy engine can calculate a comprehensive environmental factor: Comprehensive environmental factors = Temperature correction factor × Humidity correction factor × Air quality correction factor The comprehensive environmental factor is then multiplied by the baseline brightness calculated based on passenger flow and algorithms to obtain the final target brightness, thereby achieving adaptive energy-saving control across multiple environmental dimensions.

[0085] In an optional implementation, the energy consumption analysis module is further configured with a data calibration unit, which is used to fuse the real-time power data of the lighting control device 110 with the external electricity metering data based on the Kalman filter algorithm, and correct the power calculation model through an adaptive correction coefficient to make the single-lamp level energy consumption statistics consistent with the total metering data.

[0086] To ensure the authority of single-lamp energy consumption data, in this embodiment of the application, the data calibration unit in the energy consumption analysis module can be used to periodically (e.g., daily) compare the sum of real-time power data reported by all lighting control devices 110 in the system with the metering reading of the external main power meter installed in the power distribution room.

[0087] Optionally, the data calibration unit can use a Kalman filter algorithm to fuse and filter the two sets of data, dynamically calculate and apply an adaptive correction coefficient to the power calculation model of each light box. This process is continuous, ensuring that the energy consumption statistics of the single lamp level generated by the system are consistent with the total meter readings in the long term, providing a reliable basis for energy saving accounting and cost allocation.

[0088] For example, a data calibration unit can perform the following steps on a monthly cycle: Obtain the power readings (control_board_power) reported by each lighting control device and the meter readings (meter_power) of the external main power meter. Calculate the error between the two: error = control_board_power - meter_power; Calculate the correction factor: correction_factor = 1 + (error / meter_power); The correction coefficients are stored and applied to the subsequent power calculation model, so that the control board measurement results converge to the meter data.

[0089] Furthermore, dynamic adaptive adjustment can be performed: during each power measurement, the calibration unit learns online based on the latest meter data and fine-tunes the correction coefficients in real time to achieve continuous optimization of calibration parameters.

[0090] Optionally, the calibration unit runs a Kalman filter algorithm, fusing real-time power data from the lighting control device with external meter readings. Specifically, this can be achieved by calculating the Kalman gain K_k = P_k⁻¹ / (P_k⁻¹ + R), updating the estimated value x_hat_k = x_hat_k⁻¹ + K_k × (z_k - x_hat_k⁻¹), and the error covariance P_k = (1 - K_k) × P_k⁻¹ + Q. Adaptive correction coefficients are then dynamically calculated and applied to ensure that the energy consumption statistics for individual lamps remain consistent with the total meter readings over the long term. Where: z_k represents the current observation (e.g., the meter's energy consumption reading), x_hat_k represents the previous estimate (e.g., the previous energy consumption of the control board), P_k represents the previous error covariance, Q represents the process noise covariance, and R represents the measurement noise covariance.

[0091] Example 3: This example can further combine the intelligent recommendation function of advertising slots with the energy management system and advertising business on the system architecture and basic functions of Example 1 or 2.

[0092] In one implementation, the remote management platform 130 is further configured with an intelligent ad placement recommendation module, used for: Data aggregation: Link and aggregate the historical and real-time data of each of the above advertising light boxes, including: single-lamp level energy consumption and energy-saving efficiency data generated by the above energy consumption analysis module, corresponding location traffic statistics obtained from external systems, physical location attributes of advertising spaces, and advertising content and scheduling information that is currently playing. Value assessment modeling: Based on aggregated data, a comprehensive value assessment model for ad placements is constructed. This model takes at least the potential traffic exposure per unit of energy consumption as one of the core assessment dimensions. Intelligent Recommendation and Report Generation: In response to advertisers' query requests, based on the above evaluation model, the system selects the top-ranked lightbox locations from currently available or soon-to-expire ad slots and recommends them to advertisers; it also generates a fusion performance analysis report for the ads that have been placed, including but not limited to actual energy consumption costs, number of impressions, and energy-saving contribution.

[0093] Specifically, the ad placement intelligent recommendation module can perform the following intelligent steps: Data aggregation: The module can automatically associate and aggregate three core types of data: 1. Energy consumption and energy-saving efficiency data for each light box at the single lamp level generated by the energy consumption analysis module (e.g., energy consumption per unit time, percentage of energy saving compared to the baseline). 2. Historical and real-time passenger flow statistics for the corresponding advertising locations provided by external systems (such as subway passenger flow statistics systems); 3. The physical location attributes of the advertising space (such as the line it belongs to, the station hall / platform, and whether it is near the elevator) and the content and schedule of the advertisements currently playing.

[0094] Furthermore, the intelligent ad placement recommendation module in this embodiment can achieve the following functions: The system associates and aggregates historical and real-time data from each advertising lightbox, including: single-light energy consumption and energy-saving efficiency data generated by the energy consumption analysis module, corresponding location traffic statistics obtained from external systems, physical location attributes of the advertising space (route, station hall / platform, whether it is near the elevator), and the currently playing advertising content and scheduling information. A comprehensive value assessment model for advertising spaces is built based on aggregated data. The core assessment dimensions of this model include: location traffic level, historical advertising performance data, surrounding commercial environment resources, and energy efficiency of the light box. In response to advertisers' queries, the system selects and recommends top-ranked ad slots based on an evaluation model from currently available ad placements. It also generates integrated performance analysis reports for already placed ads, including actual impressions, display duration, corresponding energy costs, and explanations of energy efficiency improvements resulting from the system's intelligent energy-saving features.

[0095] In one specific implementation, the remote management platform 130 in this application embodiment is also configured with an advertiser self-service interface. Advertisers can view the operational status of their advertisements in real time through a dedicated web interface or mobile APP, including: the actual display time of the advertisement screen, the current brightness status, the traffic statistics of the corresponding location, and the online status of the lightbox. The platform can display the cumulative exposure of the advertisement, the display duration trend, and the fault interruption record in the form of charts, realizing transparent monitoring of the advertising effect.

[0096] The remote management platform 130 can establish a unique identifier for each advertising lightbox, digitally binding it to the currently displayed advertising content, and recording the advertisement's launch time, planned removal time, and actual display duration. Each time the advertising content changes, the system automatically records the change history, forming a complete advertising schedule file.

[0097] In practical applications, the entire IoT network system can be divided into two parts: the underlying IoT layer and the cloud platform. The lighting network, serving as the underlying IoT layer, uses wireless controllers as carriers and can later serve as a physical pathway to further integrate temperature and humidity sensors, vehicle sensors, air conditioning shading controllers, and more. The network is entirely wireless, based on a self-developed BLE Mesh self-organizing network protocol. Data and commands collected by the lighting network are reported to the cloud platform through the IoT gateway. This network layer connects to the internet via Ethernet cable or 4G, using the MQTT IoT protocol. The IoT platform, as the foundation of the cloud platform, provides REST APIs and SDKs, upon which specific client applications can be integrated, including functions such as energy saving, indoor navigation, and intelligent security.

[0098] The cloud platform's main functions include operation configuration for IoT controllers; data information management, such as the management of light box status, energy consumption, and alarms. Specific functions are as follows: Multiple default scene modes can be configured according to the scenario, and quick configuration is supported locally and remotely. Each scene mode allows users to define their own brightness, turning-on speed and turning-off speed. Supports timed automatic control, allowing lights to be turned on or off automatically or scene modes to be switched at set times; The lighting control uses smooth and gradual adjustment technology, making the adjustment process gentle and comfortable. The adjustment range is 0~100%, with a minimum adjustment granularity of 1%. The lighting fixtures can be zoned, grouped, and numbered. Within the same zone, the number of individual numbered lights can be no less than [number] controllers, and the number of zones can be no less than 200 groups. It can control and query each light, and can control by zone or by group. Even in the event of a communication failure, the lighting equipment will still maintain its normal lighting function; Supports mobile phone control of lights. You can adjust the lights or switch lighting scenes locally or remotely via your mobile phone (you need to enter the correct password). When paired with a gateway, it can monitor the online status, power-on time, lighting time, and current brightness of each light, and issue alarms for problematic lights. It supports unified management, and the status of each light box can be managed and controlled uniformly on the cloud management platform.

[0099] Supports automatic execution of relevant scenarios when a vehicle enters the station, such as dimming the light box before entering the station and brightening the light box after leaving the station. It supports automatically executing relevant scenarios when the light box is opened for maintenance, such as dimming the light box when it is opened, which facilitates inspection and allows for clear observation of damaged light strips.

[0100] Based on the system architecture of the above embodiments, a lightbox renovation implementation plan based on on-site survey is further introduced.

[0101] This embodiment is based on a field survey of traditional advertising light boxes in subway stations, illustrating how to transform existing light boxes into intelligent energy-saving management and control systems, ensuring that the transformation process makes full use of existing equipment and reduces implementation costs.

[0102] The complete system may contain the following devices: New equipment: The central control gateway (BT+4G) integrates functions such as LAN / WIFI / 4G communication, edge computing, IoT transmission, positioning and navigation, and device management. One gateway is deployed at each layer to serve as the central hub for system communication and data aggregation.

[0103] The dimmer drives LED light strips and supports SCR dimming, switch control, power metering, dimming control, and external sensor signal input to achieve dynamic brightness adjustment and energy consumption monitoring.

[0104] The distance sensor detects the vehicle's entry and exit status and, in conjunction with the central control gateway, connects to the network to dynamically adjust the light box's illuminance under the gateway's control (such as increasing brightness when a vehicle approaches).

[0105] Consumables, including wires, fasteners, connectors, etc. required for construction, are used for system installation and equipment interconnection.

[0106] Reusing old equipment: LED light strips utilize existing LED light source components within advertising light boxes and support external dimmer control, serving as lighting execution terminals.

[0107] LED power driver, which is a driver power supply for LED light strips, is responsible for converting AC power to DC power for LED use, and can be used with a dimmer to achieve brightness adjustment.

[0108] Air circuit breakers are power distribution protection devices used for circuit safety isolation and overload / short circuit protection to ensure the safety of system power supply.

[0109] The system management and control functions will be further introduced.

[0110] An IoT lighting system can consist of three parts: a data dashboard, intelligent lighting control, and settings (parameter coefficient settings). It can be controlled via a platform or a mobile phone.

[0111] 1. Data Dashboard The data dashboard displays the current project's total energy consumption, total electricity cost, energy saving percentage, energy saving, electricity cost savings, carbon emission reduction, number of connected devices, cumulative lighting duration, and number of abnormal alarms.

[0112] Alarm work orders can be divided into offline alarms and fault alarms. They serve as reminders on the data dashboard interface. You can click "View More" to jump to the "Fault Management System" module.

[0113] The system displays statistical line charts and energy consumption percentage charts by filtering by time period. It also offers report export, with the exported time dimension depending on the selected time period and the time unit chosen during export.

[0114] The percentage of energy savings is displayed based on the selected time period, with the time dimension fixed in days.

[0115] The system can provide two types of data: maintenance statistics and maintenance status display, and also provides report export function.

[0116] Environmental monitoring provides information on indoor conditions, sensor values, and whether the conditions are normal.

[0117] Abnormal alarms and offline alarms only serve as reminders; specific information can be viewed within the "Fault Management System" module.

[0118] 2. Intelligent lighting control It can quickly switch scenes or control the brightness and color temperature of lighting equipment through spatial control and group control. For example, it can provide the following capabilities: Space scene control: Provides predefined functional scenes for specific spaces, with one-click control.

[0119] Grouped device control: Control is performed according to device type (can span spatial locations), enabling one-click quick management and control based on predefined scenarios.

[0120] 3. Settings (energy consumption parameter settings, carbon emission coefficient settings) In specific implementations, a system setting approach can be chosen. This parameter can be set to the energy consumption display setting before the modification; only one of the two settings will be effective.

[0121] Original building energy consumption parameters: Energy consumption before renovation is comprehensively calculated based on dimensions such as building structure, lighting power, number of lighting fixtures, and working hours; You can set the electricity cost, the power of the lighting equipment, the number of devices, and the daily working hours.

[0122] Monthly average energy consumption: Set the energy consumption before the renovation based on the monthly average energy consumption. The monthly average energy consumption before the renovation can be set.

[0123] Considering the site conditions and in conjunction with the digital energy management system, and given the large number, high density, and standardized nature of the 12 light boxes, the following improvement plan is customized for the 12 light boxes: An IoT dimmer is connected in series between each DC power supply and LED strip, and two are needed for each 12-seal light box on average. One IoT gateway is deployed in each station hall and rail transit area, and more can be added as needed in larger stations. Each subway station deploys a PoE switch to power and communicate with the IoT gateway.

[0124] Overall, considering the large number, dense distribution, and uniform specifications of the twelve-seal light boxes, this solution allows for the development of specific equipment deployment strategies. Inside each light box, an IoT dimmer is connected in series between the DC power supply and the LED strips. Since each group of LED strips is independently powered, an average of two IoT dimmers are needed per twelve-seal light box for comprehensive control. These IoT dimmers, as the core of the lighting control device, employ a dual-path pulse-width modulation dimming drive design, with each path supporting 300 watts of output power, totaling 600 watts, fully meeting the independent control requirements of two 200-watt power supplies. The device integrates power metering functionality, collecting the current and voltage signals from the two output paths through high-precision sampling resistors and operational amplifier circuits. The microcontroller calculates the light box's energy consumption data in real time using a power calculation formula. All lighting control devices automatically form a wireless self-organizing network covering the area via Bluetooth Mesh protocol, enabling data relay and collaborative communication between devices.

[0125] At the station network architecture level, one IoT gateway is deployed as an edge computing node in each station hall and track area. For larger stations, the deployment density can be increased as needed to eliminate signal blind spots. Each IoT gateway is equipped with multiple Bluetooth Mesh interfaces for accessing the terminal network, and also features an Ethernet interface and a 4G mobile communication module to form a dual uplink redundancy design. Under normal circumstances, it prioritizes communication with the remote management platform using a stable wired network. Once a wired link interruption is detected, it automatically and seamlessly switches to the wireless channel to ensure the continuity of control commands and data reporting. Switches supporting Power over Ethernet are deployed within the station to provide power and data transmission support for the IoT gateways. In addition, each lighting control device also has a reserved 4G mobile communication module interface, which can automatically switch to remote direct connection mode when the local Bluetooth Mesh network fails, receiving emergency commands and reporting critical statuses.

[0126] The remote management platform is deployed on a cloud server, and operators access the management interface through a browser to complete system configuration. The platform supports dividing light boxes into different control groups based on physical location, such as the east side of the station hall, platform 1, and Exit A passage, facilitating zoned management. Tailored to the characteristics of subway operations, the platform has preset multiple scene modes: peak mode uses full power output during busy operating hours, off-peak mode moderately reduces brightness during normal passenger flow periods, low-peak mode significantly saves energy at night or during non-operating periods, and maintenance mode provides safe lighting during maintenance work. The system supports configuring timed switching plans for weekdays and weekends / holidays to achieve automated operation.

[0127] Regarding sensor linkage, door magnetic detection devices are installed on light boxes that require regular maintenance. When maintenance personnel open the light box door, the brightness is automatically dimmed to a safe level, ensuring personnel safety and facilitating the observation of light strip damage. Light boxes in the track area are connected to train proximity sensors. When a train is detected entering the station, the brightness of the obscured light box is automatically reduced to minimize light pollution, and the original lighting level is restored after the train leaves.

[0128] The upgrade plan fully considers the utilization of existing equipment. The original LED light sources and DC power supplies are retained, and only an IoT dimmer is connected in series to achieve intelligent upgrades. The system adopts a standardized interface design, with the light source, power supply, and intelligent control module being independent of each other, facilitating individual replacement and maintenance in the future. Similar components from different light boxes can be used interchangeably, significantly reducing the total lifecycle maintenance cost.

[0129] After implementation, the system possesses multi-dimensional intelligent management and control capabilities. It can automatically adjust lighting strategies based on the characteristics of different time periods and regions, achieving refined energy management; it replaces manual on-site inspections with automatic inspections through a digital platform, automatically reports faults and generates maintenance lists, improving operation and maintenance efficiency; most importantly, it achieves precise energy consumption accounting at the single lamp level, solving the problem of chaotic electricity data caused by the lack of independent metering in early circuit designs, and providing a reliable basis for energy-saving effect evaluation and cost allocation.

[0130] Based on the description of the foregoing method embodiments, this application also provides a method for intelligent energy-saving management and control of the entire life cycle of a light box. This method can be applied to the system described in any of the foregoing embodiments; the method includes the following steps executed collaboratively by the various components within the system: The steps performed by the aforementioned remote management platform are as follows: Grouping configuration is performed based on the location information of the advertising light boxes; Based on the operating schedule, location signal, and environmental parameters, generate differentiated dimming instructions; Issue the aforementioned differentiated dimming instructions; The steps performed by the aforementioned edge gateway device: Receive the dimming command from the aforementioned remote management platform and forward it to the target lighting control device via the Bluetooth Mesh local control network; The steps performed by the above-mentioned lighting control device are as follows: It receives and executes the dimming command mentioned above, and dims the LED light source through its dual-channel PWM dimming drive circuit; Its power metering unit collects current and voltage signals from the dual output circuits, calculates real-time power data and electrical parameters, and reports them. Further steps performed by the aforementioned remote management platform: Energy consumption statistics for a single lamp are generated based on the reported real-time power data.

[0131] It is understood that the relevant content of each module in the above system involved in the method embodiment has been described in detail above. For details, please refer to the content of the system embodiment, which will not be repeated here.

[0132] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0133] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The embodiments described above are merely examples of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

Claims

1. A smart energy-saving management and control system for the entire life cycle of a lightbox, characterized in that, include: The lighting control device is connected in series between the DC power supply and the LED light source of the advertising light box. It is equipped with a microcontroller, a Bluetooth Mesh communication unit, a dual-channel PWM dimming drive circuit, and a power metering unit. The input terminal of the dual-channel PWM dimming drive circuit is connected to the DC power supply, and the output terminal is connected to the LED light source. The power metering unit is used to collect the current and voltage signals of the dual output circuits and calculate real-time power data. The lighting control device is used to build a local control network based on the Bluetooth Mesh protocol. The edge gateway device is equipped with a microcontroller, multiple Bluetooth Mesh interfaces and a wide area network communication module. The edge gateway device is used to access the local control network to aggregate the operating data of each of the lighting control devices and connect to the remote management platform via wired or wireless means. The remote management platform is equipped with a scenario strategy engine and an energy consumption analysis module; The scene strategy engine is used to generate differentiated dimming instructions based on the operating schedule, location signal and environmental parameters; The energy consumption analysis module is used to generate single-lamp-level energy consumption statistics based on the real-time power data.

2. The intelligent energy-saving management and control system for the entire life cycle of light boxes according to claim 1, characterized in that, The dual-channel PWM dimming drive circuit adopts a DC-DC topology, the frequency of the PWM dimming signal is set to above 20kHz, the duty cycle adjustment range is 0-100%, and the adjustment granularity is 1%.

3. The intelligent energy-saving management and control system for the entire life cycle of light boxes according to claim 1, characterized in that, The lighting control device is also equipped with a sensor interface and a local storage unit; The sensor interface is used to connect to a door magnetic detection device or a train detection sensor. The scene strategy engine is specifically used to execute a maintenance mode dimming strategy in response to the trigger signal of the door magnetic detection device, or to execute dimming control during the train approach period and recovery control after the train leaves in response to the trigger signal of the train detection sensor. The local storage unit is used to maintain the execution of the local dimming strategy when communication is interrupted.

4. The intelligent energy-saving management and control system for the entire life cycle of light boxes according to claim 1, characterized in that, The remote management platform is also equipped with an energy consumption prediction engine and a dynamic optimization engine; The energy consumption prediction engine is used to predict the energy consumption demand for future periods based on historical energy consumption data, target brightness, ambient light intensity, time period weight, and holiday adjustment coefficient, using a linear regression model or long short-term memory network. The dynamic optimization engine is used to generate the optimal brightness control strategy based on a genetic algorithm or a particle swarm optimization algorithm, with the fitness function being the maximization of the ratio of advertising effectiveness to energy consumption.

5. The intelligent energy-saving management and control system for the entire life cycle of light boxes according to claim 4, characterized in that, The energy consumption prediction engine, the dynamic optimization engine, and the scenario strategy engine are specifically used to operate according to the following collaborative control process: Data input and real-time prediction: The energy consumption prediction engine receives real-time power data reported by the lighting control device and collected by the power metering unit, real-time subway passenger flow data entering and exiting the station, and target brightness and ambient light intensity parameters through the data interface; the obtained data is input into the trained linear regression model to obtain the predicted power value for the next control cycle, and the deviation between the real-time power and the predicted power is calculated. Multi-objective optimization decision: The dynamic optimization engine uses the current time period weight, date type adjustment coefficient and the deviation as environmental input parameters to construct the fitness function; the dynamic optimization engine runs the particle swarm optimization algorithm, where each particle represents a set of brightness parameter combinations for different partitions or groups of light boxes, and iteratively searches for optimization to output a brightness strategy vector that makes the fitness function optimal; Strategy execution and closed-loop feedback: The scene strategy engine combines the output brightness strategy vector with preset peak, off-peak, low-peak, and maintenance scene modes, and converts it into a dimming command sequence with time-series attributes for issuance; After the lighting control device executes the command, its power metering unit collects the current and voltage signals of the dual output circuit, calculates real-time power data and reports it, forming a closed-loop feedback input for the next control cycle.

6. The intelligent energy-saving management and control system for the entire life cycle of light boxes according to claim 5, characterized in that, The scene strategy engine is specifically used to perform real-time feedback correction of the target brightness based on the following formula: B_adjusted = B_target × (1 - α·ΔP) B_adjusted is the adjusted brightness, B_target is the target brightness, α is the adjustment coefficient, and ΔP is the deviation between the real-time power consumption and the predicted power.

7. The intelligent energy-saving management and control system for the entire life cycle of light boxes according to claim 4, characterized in that, The scene strategy engine is also used to: when generating dimming instructions, access monitoring data from temperature sensors, humidity sensors and / or air quality sensors; The calculated baseline brightness value is then weighted and corrected based on a preset environmental factor correction rule, wherein the environmental factor correction rule includes: When the temperature is above the first threshold, the humidity is above the second threshold, or the air quality index is below the third threshold, a correction factor for reducing brightness is applied.

8. The intelligent energy-saving management and control system for the entire life cycle of a lightbox according to claim 1 or 2, characterized in that, The energy consumption analysis module is also equipped with a data calibration unit, which is used to fuse the real-time power data of the lighting control device with the metering data of the external electricity meter based on the Kalman filter algorithm, and correct the power calculation model through an adaptive correction coefficient to make the energy consumption statistics of the single lamp level consistent with the metering data of the total meter.

9. The intelligent energy-saving management and control system for the entire life cycle of light boxes according to claim 1, characterized in that, The remote management platform is also equipped with an intelligent ad placement recommendation module, which is used for: The historical and real-time data of each of the advertising light boxes are associated and aggregated, including: single-lamp level energy consumption and energy-saving efficiency data generated by the energy consumption analysis module, corresponding location traffic statistics obtained from external systems, physical location attributes of the advertising space, and the advertising content and scheduling information currently being played. Based on aggregated data, a comprehensive value assessment model for ad placements is constructed. This model takes at least one core assessment dimension: potential foot traffic exposure per unit of energy consumption. In response to the advertiser's query request, based on the evaluation model, the system selects the top-ranked lightbox locations from the currently available or soon-to-expire ad slots and recommends them to the advertiser; it also generates a fusion effect analysis report for the placed ads, which includes, but is not limited to, actual energy consumption costs, number of impressions, and energy-saving contribution.

10. A method for intelligent energy-saving management and control of a lightbox throughout its entire lifecycle, characterized in that, Applied to the system as described in any one of claims 1-9; the method includes the following steps performed collaboratively by the components within the system: The steps performed by the remote management platform are as follows: Grouping configuration is performed based on the location information of the advertising light boxes; Based on the operating schedule, location signal, and environmental parameters, generate differentiated dimming instructions; The differentiated dimming command is issued; The steps performed by the edge gateway device: Receive the dimming command from the remote management platform and forward it to the target lighting control device via the Bluetooth Mesh local control network; The steps performed by the lighting control device are as follows: It receives and executes the dimming command, and dims the LED light source through its dual-channel PWM dimming drive circuit; Its power metering unit collects current and voltage signals from the dual output circuits, calculates real-time power data and electrical parameters, and reports them. Further steps performed by the remote management platform: Energy consumption statistics for a single lamp are generated based on the reported real-time power data.

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