A classroom intelligent lighting system based on indoor micro-light collection

Abstract

The application discloses a classroom intelligent lighting system based on indoor dim light collection, which comprises three subsystems, namely, an energy collection subsystem, an environment monitoring subsystem, a light control subsystem and a data processing unit. The energy collection subsystem comprises an amorphous silicon solar cell, a power management module and an energy storage module, and supplies power for monitoring nodes of the environment monitoring subsystem; the environment monitoring subsystem comprises a plurality of wireless monitoring nodes, a coordinator and a server; and the light control subsystem comprises a plurality of light control nodes composed of a control module and a communication module, a control panel and an electric power detection sensor. The application is developed from the aspects of eye health, energy saving and environmental protection, monitors indoor lighting, and establishes a dynamic model of indoor light environment by taking the collection and transmission of more data as a target and energy balance as a constraint point, so that the indoor light environment is automatically adjusted according to human health indexes and energy saving requirements, the eye muscles are relaxed, the eye fatigue is reduced, and a comfortable environment is created.

Description

Technical Field

[0001] This invention relates to the field of smart classroom lighting systems, and more specifically to a smart classroom lighting system based on indoor low-light acquisition. Background Technology

[0002] The National Standard for Hygienic Requirements for Myopia Prevention and Control of School Supplies for Children and Adolescents (GB 40070-2021), issued in 2021, clearly states that the correlated color temperature of ordinary classroom lighting fixtures should be no less than 3300K and no more than 5300K. Moreover, light with a color temperature exceeding 5000K tends to be bluish, which may cause damage to the human eye. Therefore, it is very necessary to control the color temperature of classroom lighting fixtures.

[0003] According to relevant statistics, the number of nearsighted teenagers in my country is soaring, and classrooms are the places where teenagers use their eyes the most during their daily life and study. Since the National Health Commission issued the "Guidelines for the Prevention and Control of Myopia" in 2018, various regions have responded and required that "all classroom lighting renovations be completed by 2023".

[0004] Existing classroom lighting control systems are poorly designed for energy conservation and emission reduction, often leaving lights on even when people leave, resulting in energy waste. Furthermore, many classroom lighting control systems offer overall control, lacking precise, independent point-to-point control. Some systems ignore the impact of sunlight on the indoor lighting environment, making smart dimming in classrooms insufficiently "smart." This system establishes a classroom illuminance model addressing uneven illuminance and eye health standards. It then approximates this model using a control programming algorithm for a multi-input multi-output stochastic nonlinear time-varying system to achieve healthy classroom lighting requirements.

[0005] Classroom lighting upgrades require real-time monitoring to achieve dynamic control of the lighting environment. A self-powered wireless sensor network can effectively address this issue, reducing maintenance costs associated with replacing batteries at each node, making the entire system more environmentally friendly, preventing errors from manual data entry, improving data collection accuracy, and enabling more precise and real-time classroom lighting control. However, current self-powered wireless sensor systems often fail to achieve a proper balance between data acquisition rate and energy collection efficiency. When energy collection efficiency is high, more data should be collected; when efficiency is low, the amount of data collected should be reduced to maintain overall system energy balance. This prevents nodes from disconnecting due to low energy levels, or energy storage devices from wasting energy due to insufficient data collection during periods of high efficiency.

[0006] By changing the acquisition cycle in real time, the algorithm can increase the amount of data collected while balancing energy and ensuring the normal operation of the system, thereby improving energy utilization. At the same time, more data can make the modeling of the classroom light environment more accurate, better control the classroom light environment, prevent myopia and improve learning efficiency, and create a healthy and comfortable teaching environment for teachers and students. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the present invention aims to provide a smart classroom lighting system based on indoor low-light acquisition, comprising the following subsystems: an energy acquisition subsystem, an environmental monitoring subsystem, a lighting control subsystem, and a data processing unit;

[0008] The energy harvesting subsystem supplies power to the environmental monitoring subsystem;

[0009] The environmental monitoring subsystem includes several monitoring nodes and a first coordinator, and the monitoring nodes are connected to the first coordinator via wireless communication.

[0010] The lighting control subsystem includes several lighting control nodes, a control panel, an electrical power detection sensor, and a second coordinator;

[0011] The data collected by the environmental monitoring subsystem is sent to the data processing unit through the first coordinator, and the control signal generated by the data processing unit is sent to the lighting control subsystem through the second coordinator.

[0012] The monitoring nodes are divided into four groups: the first group of monitoring nodes is arranged on the horizontal surface of the desks, called group A; the second group of monitoring nodes is arranged in front of the blackboard, called group B; the third group of monitoring nodes is distributed at the windows, called group C; and the fourth group of monitoring nodes is distributed in the upper middle position of the interior wall of the classroom, called group D.

[0013] Group A is divided into the group closest to the window and the group furthest from the window;

[0014] The energy harvesting subsystem includes an amorphous silicon solar cell, a power management chip BQ25505, and an energy storage module. The amorphous silicon solar cell is connected to the VIN_DC pin of the power management chip BQ25505 as an input, and the VBAT_SEC pin of the power management chip BQ25505 is connected to the energy storage module.

[0015] The power management chip BQ25505 includes a maximum power point tracking (MPPT) circuit, a boost control circuit, a nano power management circuit, and a cold start circuit. It features a cold start function under low illumination, a maximum power point tracking (MPPT) function achieved by periodically collecting the open-circuit voltage of the solar cell and setting a voltage scaling factor, a voltage regulation function, an energy storage / charging function, and a load enable circuit.

[0016] The external circuitry of the power management chip BQ25505 includes the chip FDC6312P;

[0017] The power management chip BQ25505 has an intelligent load shutdown function, enabling the load to operate continuously under normal voltage. The load is connected via a chip FDC6312P. The FDC6312P chip internally includes two PMOS transistors, with their source (S) terminals interconnected. The two gate (G) terminals of the two PMOS transistors are connected to the power management chip BQ25505. Pin connection; the control panel accepts touch screen input information and sends input lighting control signals to the data processing unit, the lighting control signals including illuminance information, color temperature information, and time information;

[0018] The second coordinator is used to send control signals to the lighting control node;

[0019] The power detection sensor is connected to the projector via a wired connection and sends a signal indicating the projector's usage status to the data processing unit; the power sensor controls the lamps to turn off by detecting the power consumption of the projector in the classroom.

[0020] Each of the lighting control nodes includes a DC constant voltage module, a constant current drive module, a lamp, a human body detection device, and a Zigbee communication module;

[0021] The DC constant voltage module converts 220V AC power into a low DC level to power the constant current drive module, lamps, human body detection sensor and Zigbee communication module.

[0022] The constant current drive module receives the control signal for dimming sent by the second coordinator through the Zigbee communication module and drives the lamp.

[0023] The human detection device includes an infrared sensor unit, which collects information on whether there are people in the area where the current lighting control node is located, and sends it to the second coordinator through a Zigbee communication module. The second coordinator controls the light intensity of the lamps based on whether there are people present.

[0024] The lighting control nodes are divided into two groups: one group is located at the top of the classroom and is called the student group; the other group is located at the top of the blackboard and is called the podium group.

[0025] This invention also provides a control method for a smart classroom lighting system based on indoor low-light harvesting, wherein the control method for the energy harvesting subsystem includes the following steps:

[0026] The solar cell receives indoor light input energy and feeds it to the power management chip BQ25505;

[0027] When the output voltage of the power management chip BQ25505 is lower than 1.8V, the power management chip BQ25505 is in cold start mode, controlling the solar cell input voltage to 330mV;

[0028] When the output voltage of the power management chip BQ25505 is greater than or equal to 1.8V, the power management chip BQ25505 enters the boost charging mode, and the maximum power point tracking (MPPT) circuit starts to work. By using the voltage proportional coefficient method, the open-circuit voltage of the solar cell is periodically disconnected and collected. The input voltage is controlled to be the open-circuit voltage multiplied by the proportional coefficient, which is the ratio of the maximum power point voltage to the open-circuit voltage, so that the solar cell works at the maximum power point.

[0029] When the output voltage of the power management chip BQ25505 is greater than or equal to 2.7V, charging of the energy storage module is stopped to prevent overcharging.

[0030] When the output of the power management chip BQ25505 is greater than or equal to 2.6V, the gate of the PMOS is at a low level, turning on the energy storage module and the load, and the load begins to work normally; when the voltage drops to 2.3V, the load is turned off.

[0031] The sampling period control method for monitoring nodes in the environmental monitoring subsystem 2 includes the following steps:

[0032] The efficiency of the energy harvesting subsystem in converting light energy into electrical energy was obtained under different light intensities within the range of 100 to 1000 Lux indoor light intensity.

[0033] Then, based on the data acquisition and transmission power consumption and static power consumption of the monitoring nodes, the energy consumption of each monitoring node under different illumination conditions is calculated, and a greedy algorithm is selected to find the optimal acquisition and transmission time interval as the timer period.

[0034] The algorithm needs to satisfy the condition that the energy E collected within the timer period is... collection It is higher than the energy consumed, E. consumption The following formula must be satisfied:

[0035] E collection ≥E consumption

[0036] The collected energy includes the battery's remaining capacity E at that moment. battery The average energy harvesting efficiency under different illuminance levels within that period. The sum of the products of the period T and the energy consumed, including the static power consumption W during that period. leakage The power consumption W for collecting and sending data once data As shown in the following formula:

[0037]

[0038] Each cycle requires calculating the duration of the next cycle;

[0039] Average energy harvesting efficiency over the period Specifically, the solar cell's data collection and conversion efficiency η needs to be considered. pv And the efficiency η of the power management module PMU Battery inventory E battery The self-discharge power loss P of the energy storage module must be considered. self_discharge The efficiency of the battery management module must take into account the MPPT error loss η. MPPT Control circuit loss η control The specific formula is shown below:

[0040]

[0041] E battery =E′ battery -P self_discharge *T

[0042] η PMU =η MPPT *η control

[0043] Among them, E′ battery The number of batteries remaining at the end of the previous cycle;

[0044] The method for controlling illuminance using the lighting control subsystem includes the following sub-steps:

[0045] Sub-step (1) Collect data and train a neural network as the system model:

[0046] x + =F(x,u)

[0047] x = [x inner ,x outer [x] is the illuminance vector, where x inner x represents the illuminance measured by the monitoring node located inside the classroom. outer This represents the natural illuminance measured by the monitoring node located in the window;

[0048] u is the control current vector that controls the illuminance of the lamp, x + Let x be the new illuminance vector obtained under the influence of a certain x and u.

[0049] Based on this model, the predicted system state trajectory is as follows:

[0050] x u(0)=x(n) x u (k+1)=F(x u (k),u(k))k=1,2,……,N-1

[0051] Where, x u (k) represents the illuminance after the combined effect of x and u; n represents time, with time n as the initial state, and u(k) can all be a constant;

[0052] Sub-step (2): Establish the optimal control problem

[0053]

[0054] Where l(x) u (k))=||x u (k)-X ref || 2 Used to ensure that the optimized illuminance X approaches that of healthy eye use ref Meanwhile, Th||δu(k)|| 2 It is a regularization term used to ensure that the adjustment of the contrast is smooth and does not fluctuate drastically, thus preventing the lamps from flickering.

[0055] By solving the above optimization problem using sequential quadratic programming, the optimal control current vector sequence can be obtained as follows:

[0056] u * (0),u * (1),...,u * (N-1)

[0057] Among them, u * represents the optimal control current vector, and N represents the number of adjustments;

[0058] Sub-step (3) applies the first term of the optimal control voltage vector sequence to adjust the illuminance of the lamps;

[0059] Next, determine whether each monitoring node has transmitted the illuminance status data to the data processing module through the first coordinator; if the illuminance status data has been transmitted to the data processing module through the first coordinator, obtain x(n+1), proceed to sub-step (1), and start a new round of iteration;

[0060] Where x(n+1) is the next state for x(n), and x(n) is the illuminance vector at time n; transitioning to sub-step (1) means x(n+1) = x u (1) That is, the prediction result of the previous moment is used as the initial illuminance state at this moment; the new round of iteration refers to changing the initial illuminance state, replacing the illuminance state data of the previous moment with the newly collected illuminance state data, performing the next round of prediction, and solving the optimal control current vector sequence.

[0061] If the illuminance status data of the monitoring node is not transmitted to the data processing module through the first coordinator, then for the illuminance status data x that has not been transmitted... i (n+1), replace the previously unprovided illuminance state data x with the predicted value (n+1) of the i-th illuminance state data. i (n+1), then proceed to sub-step (1) to begin a new round of iteration;

[0062] Where, x i (n+1) represents the illuminance status data for the i-th time that was not passed to the data processing module;

[0063] If none of the illumination status data of the monitoring node is transmitted, then the predictive control current vector sequence u is directly used at the next sampling time. * (1),...,u * (N-1) until a monitoring node transmits data to the data processing module, then proceed to sub-step (1) to start a new round of iteration.

[0064] Color temperature affects students' mood, health, sleep, and learning status. Based on students' daily routines, learning efficiency, and national eye care standards at different times, color temperature is controlled in four time periods: morning high-efficiency learning state, afternoon high-efficiency learning state, lunch break state, and transition state. This ensures the quality of lunch break sleep, improves students' learning efficiency, and helps them achieve a healthy and regular learning state.

[0065] Preferably, the color temperature provided by the lamp is controlled as follows: from 6:00 AM to 8:00 AM, the color temperature provided by the lamp is controlled to rise from 3300K to 5300K; from 8:00 AM to 10:00 AM, the color temperature is maintained at 5300K; from 12:00 PM to 1:00 PM, the color temperature is controlled below 3300K; from 1:00 PM to 3:00 PM, the color temperature rises to 5300K; from 3:00 PM to 6:00 PM, the color temperature is controlled at 5300K; from 6:00 PM to 8:00 PM, the color temperature slowly decreases to 3300K; and after 8:00 PM, the color temperature gradually decreases until the lamp is turned off.

[0066] Based on classroom learning needs, the required illumination conditions for teachers and students vary in different teaching scenarios. According to teaching objectives and the status of teachers and students, the lighting control subsystem is divided into different adjustment modes to facilitate illumination adjustment.

[0067] The lighting control subsystem provides three control modes based on the different illumination requirements of teachers and students in different teaching scenarios, according to the teaching objectives and the status of teachers and students: slideshow mode, self-study mode, and lunch break mode. In slideshow mode, the lectern lights are turned off, and the illumination of the student lights is controlled below 50 lux. In self-study mode, the illumination of the student lights is controlled above 400 lux, and the illumination of the lectern lights is controlled above 500 lux. In lunch break mode, the lighting is set daily. Starting at 12:00 noon, the lectern lights are turned off, and the illumination of the student lights is controlled at around 200 lux. The student lights are turned off between 12:30 and 1:00 p.m. After 1:00 p.m., the lectern lights are turned on, and the student lights slowly recover to above 400 lux in a certain time step.

[0068] Preferably, the energy storage module can be: a 3mAh micro cylindrical rechargeable secondary lithium titanate battery CT04120; a rechargeable solid-state thin-film battery (EFL1K0AF39); or a combination of a supercapacitor and a small-capacity rechargeable lithium battery (CG-320A).

[0069] Different application requirements determine the load function and energy storage requirements. Different loads have different operating voltages, and different energy storage elements have different charging and discharging states and limiting conditions. Therefore, it is necessary to replace different energy storage elements to adapt to the load and meet the application requirements.

[0070] When replacing the energy storage module with one that operates at a different voltage, the output voltage of the BQ25505 needs to be adjusted to the corresponding operating voltage. Then, connect the step-down module (TPS62840) with a programmable output voltage to stabilize the voltage at around 2.3V to power the load.

[0071] Due to limitations in the application environment, amorphous silicon solar cells have advantages over common monocrystalline and polycrystalline silicon solar cells in order to adapt to low indoor light levels and improve photoelectric conversion efficiency. They can also be replaced by perovskite solar cells, III-V semiconductor solar cells, organic solar cells, dye-sensitized solar cells, colloidal quantum dot solar cells, etc. The size is adjusted accordingly based on the conversion efficiency of different solar cells.

[0072] Compared with existing technologies, this invention has the following beneficial effects: This invention is a self-powered real-time closed-loop control system. Based on the collected data and national eye health standards, it can precisely control the lighting effect of each lamp in a relative position, achieving healthy lighting, balanced lighting, freeing up hands, and saving energy and reducing emissions. At the same time, the energy collected from indoor low light supplies power the monitoring nodes, which can eliminate the impact of battery life on the lifespan of the monitoring nodes, reduce manual maintenance costs, and improve system robustness. Furthermore, by increasing the amount of data collected under the premise of balanced energy through algorithms, energy utilization can be improved. More data can also make the modeling of the classroom lighting environment more accurate. The illuminance model is established for uneven illuminance and eye health standards. It is approximated by a control planning algorithm for a multi-input multi-output stochastic nonlinear time-varying system to meet the requirements of healthy classroom lighting and create a healthy and comfortable classroom environment. Attached Figure Description

[0073] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Some specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings in an exemplary and non-limiting manner. The same reference numerals in the drawings indicate the same or similar parts or components. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0074] Figure 1 This is a diagram showing the illuminance distribution during a day's work in an environment far from the window, which is the applicable environment for this invention.

[0075] Figure 2 This is a diagram showing the illuminance distribution near a window during a day's work in the environment to which this invention is applicable.

[0076] Figure 3 This is a block diagram of the intelligent classroom lighting system based on indoor low-light acquisition according to the present invention.

[0077] Figure 4 P for different solar panels under different LED illuminance max (Maximum power per square centimeter) and PCE (photovoltaic conversion efficiency).

[0078] Figure 5 This is a system block diagram of the energy harvesting subsystem of the present invention.

[0079] Figure 6 This describes the workflow of the energy harvesting subsystem of the present invention.

[0080] Figure 7 This is a system block diagram of the environmental monitoring subsystem of the present invention.

[0081] Figure 8 This is a system block diagram of the lighting control subsystem of the present invention.

[0082] Figure 9 This is a flowchart of the control planning algorithm for the multi-input multi-output stochastic nonlinear time-varying system of the lighting control subsystem of the present invention.

[0083] Figure 10 This is a schematic diagram of the shell structure of the Zigbee monitoring node of the present invention. Detailed Implementation

[0084] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0085] Setting up sensor nodes for a smart classroom lighting system based on indoor low-light acquisition requires consideration of constraints, including ease of deployment, range, and compliance with data acquisition requirements. For self-powered wireless sensor nodes, it's also necessary to consider whether the light intensity at each point is sufficient to power the entire node. Simultaneously, the system aims to provide healthy lighting conditions; design standards specify that the average illuminance in the classroom should be no less than 300 Lux, the daylight factor on the desks should be higher than 2%, and the illuminance in front of the blackboard should be no less than 500 lux. Therefore, considering factors such as energy balance, cost savings, modeling requirements, and system efficiency, the sensor nodes are divided into four groups. The first group, designated A, is placed on the horizontal surface of the desks (a recess can be marked in the corner of the desk to place the node), forming a grid structure in the classroom (the location of the nodes will be determined later using a two-dimensional coordinate system). This group monitors the illuminance on the desks. Because the nodes are small and thin, they do not occupy much space and therefore do not affect learning. The second group, designated B, is placed in front of the blackboard (evenly distributed on the wall slightly higher than the blackboard) to monitor the blackboard illuminance. This group is smaller in number. The third group, designated C, is distributed at the windows (horizontally placed on the windowsill) to monitor outdoor light and provide predictive data for modeling. The fourth group, designated D, is distributed in the upper-middle position of the interior walls, where the classroom lighting is weakest. Group A can be further subdivided into groups near and far from the windows, facilitating adjustments by the subsequent lighting control subsystem. When the illuminance meets the upper limit, the classroom lights are turned off, using natural light. The lights are only turned on when the illuminance in Group D falls below a certain threshold, reducing the frequency of light switching.

[0086] For the system to operate efficiently, more data is needed to make the model and subsequent algorithms more accurate. However, acquiring more data requires setting up more sensor nodes to collect data, which increases the number of nodes, leading to higher costs and energy consumption. Therefore, an algorithm needs to be designed to optimize the distribution and number of nodes based on two constraints: energy saving and data volume.

[0087] To achieve precise control, the lighting control nodes of the lighting control subsystem are divided into two groups according to the control range. One group is evenly distributed at the top of the classroom and numbered (corresponding to group A of the sensor node network), called the student group; the other group is distributed in the podium area and evenly placed on top of the blackboard, called the podium group (corresponding to group B of the sensor node network), which facilitates zoned management.

[0088] Based on classroom learning needs, the required illumination conditions for teachers and students vary in different teaching scenarios. According to teaching objectives and the status of teachers and students, the lighting control subsystem is divided into different adjustment modes to facilitate illumination adjustment.

[0089] The lighting control subsystem has three modes selectable from the server or control panel: slideshow mode, self-study mode, and lunch break mode. In slideshow mode, the lectern lights are off, and the student lights are kept below 50 lux. In self-study mode, the student lights are kept above 400 lux, and the lectern lights are kept above 500 lux. The lunch break mode is a daily timed system: starting at 12:00 PM, the lectern lights are off, and the student lights are kept around 200 lux; from 12:30 PM to 1:00 PM, the student lights are off again; after 1:00 PM, the lectern lights are turned on, and the student lights gradually recover to above 400 lux in increments over a certain period.

[0090] The power sensor is used to measure the power consumption of devices such as projectors to identify teacher behavior and thus determine the adjustment mode of the lighting control subsystem.

[0091] The human body detection device controls the lights to turn on and off. The student group's light control node turns on when a human body is detected, and turns off otherwise. The lectern group's light control node automatically sets to slideshow mode when a human body is detected by the human body detection sensor and the power consumption indicates that a projector is being used. When the lectern group's light control node detects a human body by the human body detection sensor and the power consumption indicates that a projector is not being used, it automatically sets to self-study mode. When the lectern group's light control node does not detect a human body by the human body detection sensor and the power consumption indicates that a projector is not being used, it turns off the lights.

[0092] Meanwhile, to balance students' daily healthy routines and eye health standards, improve concentration, and enhance learning and work efficiency, the system also implements timed control of color temperature based on the body's daily melatonin levels. The classroom color temperature control is as follows: from 6:00 AM to 8:00 AM, the color temperature slowly rises from 3300K to 5300K; from 8:00 AM to 10:00 AM, the color temperature remains at 5300K; from 12:00 PM to 1:00 PM, the color temperature is controlled below 3300K; from 1:00 PM to 3:00 PM, the color temperature slowly rises to 5300K; from 3:00 PM to 6:00 PM, the color temperature is controlled at around 5300K; from 6:00 PM to 8:00 PM, the color temperature slowly decreases to 3300K; and after 8:00 PM, the color temperature gradually decreases until the lights are turned off.

[0093] The above color temperature and illuminance controls are for the summer season; for the winter season, each time point is moved forward by 1 hour.

[0094] The present invention will now be further described with reference to the accompanying drawings.

[0095] like Figure 1 and Figure 2 As shown, the illuminance of an indoor environment differs between areas near and away from windows. Uneven distribution of indoor illuminance can affect eye health when studying or working indoors.

[0096] like Figure 3 As shown, a smart classroom lighting system based on indoor low-light acquisition includes three subsystems and a data processing unit: an energy acquisition subsystem, an environmental monitoring subsystem, a lighting control subsystem, and the data processing unit. The energy acquisition subsystem powers the monitoring nodes of the environmental monitoring subsystem, which monitors the classroom light environment and uploads the data for analysis. The lighting control subsystem controls the indoor light environment based on the dimming data analyzed by the environmental monitoring subsystem. The data processing unit includes a server capable of exchanging data with the cloud. The data processing unit processes the data uploaded by the environmental monitoring subsystem, analyzes it, and then distributes the data to the lighting control subsystem. Although this invention is designed for classroom environments, its application can be expanded to any indoor space requiring adjustable lighting, such as a vehicle or a single-node desk lamp.

[0097] To improve the adaptability of solar cells in indoor low-light environments, it is necessary to understand the characteristics of the indoor light environment.

[0098] like Figure 4 As shown, the energy harvesting subsystem receives indoor light (100-1000 lux) as input. Compared to traditional outdoor light, the light intensity is weak and the energy density is low. Therefore, this system does not use common polycrystalline silicon and monocrystalline silicon solar cells, but instead uses amorphous silicon solar cells, which are more suitable for harvesting energy under non-natural light (LED, fluorescent lamp, halogen lamp), resulting in higher conversion efficiency and improved overall system performance.

[0099] like Figure 4 As shown, the P values ​​of different solar panels under different LED illuminance are... max The amorphous silicon solar cell can be replaced with other solar cells suitable for indoor light harvesting, such as perovskite solar cells, III-V semiconductor solar cells, organic solar cells, dye-sensitized solar cells, colloidal quantum dot solar cells, etc., with the size adjusted accordingly based on the conversion efficiency of different solar cells.

[0100] like Figure 5 As shown, the energy harvesting subsystem includes an amorphous silicon solar cell, a power management module, and an energy storage module. The amorphous silicon solar cell has a size of 55mm*56mm. The power management module is a BQ25505 and its external circuits. The BQ25505 internally includes an MPPT circuit, a boost control circuit, a Nano power management circuit, and a cold start circuit. The energy storage module is a miniature cylindrical rechargeable secondary lithium titanate battery CT04120.

[0101] The BQ25505 enables the system to maintain high conversion efficiency in low-light environments, shorten charging time, protect energy storage units and loads, control the load to operate normally and sustainably, and increase system robustness.

[0102] Different application requirements determine the load functions and energy storage requirements. Different loads have different operating voltages, and different energy storage elements have different charge / discharge states and limitations. Therefore, it is necessary to replace different energy storage elements to adapt to the load and meet the application requirements. This invention is designed for classroom indoor environments. In order to maintain the system's energy balance and continuous operation, the energy storage unit must work with energy harvesting to meet the sustainable power supply requirements of ultra-low illumination and no-light environments. At the same time, the nodes of the environmental monitoring subsystem must meet the requirements of miniaturization, aesthetics, and robustness. The required energy storage modules, as well as the overall circuit and solar cells, should be minimized in size.

[0103] The energy storage module can also be replaced with a rechargeable solid-state thin-film battery (EFL1K0AF39) or charged by a combination of a supercapacitor and a small-capacity rechargeable lithium battery (CG-320A). When replacing with an energy storage module with a different operating voltage, the output voltage of the BQ25505 needs to be adjusted to the corresponding operating voltage, and then a step-down module with a programmable output voltage (TPS62840) needs to be connected to stabilize the voltage at around 2.3V to power the load.

[0104] like Figure 6As shown, the control flow of the energy harvesting subsystem is as follows: Indoor light is input, and the solar cell inputs energy to the power management module. When the output voltage of the power management module is below 1.8V, charging is in the cold start phase, and the solar cell input voltage is controlled at 330mV. When it reaches 1.8V, it enters the boost charging mode, and the MPPT (Maximum Power Point Test) starts working. Using a voltage proportionality coefficient method, it periodically collects the open-circuit voltage of the solar cell and controls the input voltage to be the open-circuit voltage multiplied by a proportionality coefficient (typically 0.7–0.8), ensuring the solar cell operates at its maximum power point. The power management module connects to a small-capacity lithium battery (CT04120) as an energy storage module to store energy. Based on the operating range of the CT04120 (1.8V–2.7V), charging stops when the voltage reaches 2.7V to prevent overcharging. The load operating voltage is between 2.0V and 3.6V. The power management module connects to the load through two PMOS transistors connected to their source terminals to control the load to turn off. When the output of the power management module exceeds 2.6V, the gate input is low, turning on the energy storage module and the load, and the load starts to work normally. When the voltage drops to 2.3V, the load is turned off.

[0105] The PMOS connected to the S-terminus is directly constructed from the FDC6312P chip.

[0106] As shown in the table below, the power management module uses power management chips with similar functions, and the selection is based on a comprehensive consideration of factors such as startup power, cold start voltage, quiescent current, output voltage, conversion efficiency, and price.

[0107] Table 1 is a summary table of power management module parameters for indoor light acquisition.

[0108]

[0109] like Figure 7 As shown, the environmental monitoring subsystem's monitoring nodes and the first coordinator communicate via Zigbee networking. The chip is CC2530, and the light sensor is MAX44009. The first coordinator has an LCD display. After the node is powered on, it periodically collects information through a timer and sends data to the first coordinator. The first coordinator then sends the data to the server via WIFI or 4G communication, and finally uploads it to the cloud. The obtained data is modeled and analyzed, converted into corresponding dimming data for each location, and then sent to the second coordinator, which then transmits it to each lighting control node.

[0110] The communication method of the environmental monitoring subsystem needs to meet the requirements of low power consumption and low latency networking. In addition to Zigbee, it can also be NB-IoT, LoRa or Bluetooth wireless communication methods. Bluetooth refers to Bluetooth Low Energy, Bluetooth 4.0 and above.

[0111] To improve energy efficiency, the amount of data collected is increased while achieving balanced energy through algorithms. More data also allows for more accurate modeling of the classroom lighting environment and more intelligent control of the system.

[0112] The timer period is determined by the illuminance collected by the monitoring node, the battery level, and the data acquisition and transmission power consumption and static power consumption of the monitoring node. First, through calculation and experimental verification, the efficiency of the energy harvesting subsystem in converting light energy into electrical energy under different illuminance levels (indoor light intensity approximately 100–1000 Lux) is collected. Then, based on the data acquisition and transmission power consumption and static power consumption of the monitoring node, the energy change of the node under different illuminance levels is calculated. A greedy algorithm or similar method can be used to find the optimal acquisition and transmission time interval as the timer period. The algorithm needs to satisfy the requirement that the energy (E) collected within the period... collection The energy consumed is higher than that consumed (E). consumption The following formula must be satisfied:

[0113] E collection ≥E consumption

[0114] The collected energy includes the battery's remaining capacity at that moment (E). battery ), and the average energy harvesting efficiency under different illuminance levels within that period. The sum of the products of the period (T) and the energy consumed, including the static power consumption (W) during that period. leakage ) and the power consumption (W) of collecting and transmitting data once. data As shown in the following formula:

[0115]

[0116] The amount of light in each cycle changes the battery level, so the duration of the next cycle needs to be calculated for each cycle.

[0117] Average energy harvesting efficiency under different illuminance levels Specifically, the data collection and conversion efficiency (η) of the solar cell needs to be considered. pv ) and the efficiency (η) of the power management module PMU Battery inventory (E) battery The self-discharge power loss (P) of the energy storage module must be considered. self_discharge The efficiency of the battery management module must take into account MPPT error loss (η). MPPT ), control circuit loss (η) control Specifically, as shown in the following formula:

[0118]

[0119] Ebattery =E b ′ attery -P self_discharge *T

[0120] η PMU =η MpPT *η control

[0121] Here E′ battery This refers to the battery inventory at the end of the previous cycle.

[0122] like Figure 8 As shown, the lighting control subsystem includes several lighting control nodes and control panels, as well as power detection sensors, consisting of a DC constant voltage module, a constant current drive module, luminaires, a human body detection sensor, and a Zigbee communication module. The DC constant voltage module converts 220V AC power into a low DC level to power the entire control node. It receives dimming information from the second coordinator via Zigbee and outputs a PWM wave to the constant current drive module, which then drives the luminaires.

[0123] like Figure 9 As shown, in order to intelligently control illuminance and achieve energy-saving, uniform, and healthy lighting requirements, the lighting control subsystem needs to select a control programming algorithm for a multi-input multi-output stochastic nonlinear time-varying system, specifically:

[0124] 1. First, using sensor measurements or simulation, train a neural network as a system model:

[0125] x + =F(x,u)

[0126] x = [x inner ,x outer [x] is the illuminance vector, where x inner It is an indoor illuminance sensor (placed at a specific indoor sampling point), x outer It is a natural light sensor (placed in a location with natural light).

[0127] u is the control current vector that controls the illuminance of the lamp, x + Let be the new illuminance vector obtained under the action of a certain x and u.

[0128] Based on this model, the system state trajectory can be predicted.

[0129] x u (0)=x(n)x u (k+1)=F(x u (k),u(k))k=1,2,...,N-1.

[0130] At time n, the initial state can be u(k), which can be a constant.

[0131] 2. Secondly, establish the optimal control problem.

[0132]

[0133] Where l(x) u (k))=||x u (k)-X ref || 2 Used to ensure that the optimized illuminance X approaches that of healthy eye use ref At the same time, T h ||du(k)|| 2 It is a regularization term used to ensure that the adjustment of the illuminance is smooth and does not fluctuate drastically (to solve the problem of lamp flicker).

[0134] By solving the above optimization problem using sequential quadratic programming, the optimal control current vector sequence can be obtained as follows:

[0135] u * (0),u * (1),...,u * (N-1)

[0136] 3. Finally, apply the first term of the optimal control voltage vector sequence to adjust the illuminance of the lamps. Then check whether each illuminance sensor has transmitted data to the lighting control system node through the second coordinator. If all have received data, obtain x(n+1), go to step 1, and start a new round of iteration. Otherwise, for the state x where no data has been transmitted... i (n+1), replaced by Then switch to step 1 and begin a new round of iteration. If no state is passed in, predictive control u is directly used in the next sampling time. * (1),...,u * (N-1) and so on, until data is transmitted from a sensor, then switch to 1 and start a new round of iteration.

[0137] By using a method that replaces the undetected state with a predicted state, the sensors of the environmental detection node can transmit data in a row when the data is ready.

[0138] To achieve precise control, the lighting control nodes of the lighting control subsystem are divided into two groups according to the control range. One group is evenly distributed at the top of the classroom and numbered (corresponding to group A of the sensor node network), called the student group; the other group is distributed in the podium area and evenly placed on top of the blackboard, called the podium group (corresponding to group B of the sensor node network), which facilitates zoned management.

[0139] Based on classroom learning needs, the required illumination conditions for teachers and students vary in different teaching scenarios. According to teaching objectives and the status of teachers and students, the lighting control subsystem is divided into different adjustment modes to facilitate illumination adjustment.

[0140] The lighting control subsystem has three modes selectable from the server or control panel: slideshow mode, self-study mode, and lunch break mode. In slideshow mode, the lectern lights are off, and the student lights are kept below 50 lux. In self-study mode, the student lights are kept above 400 lux, and the lectern lights are kept above 500 lux. The lunch break mode is a daily timed system: starting at 12:00 PM, the lectern lights are off, and the student lights are kept around 200 lux; from 12:30 PM to 1:00 PM, the student lights are off again; after 1:00 PM, the lectern lights are turned on, and the student lights gradually recover to above 400 lux in increments over a certain period.

[0141] The illuminance control and color temperature control of the lighting control subsystem are based on eye health standards and human body rhythms.

[0142] The power sensor is used to measure the power consumption of devices such as projectors to identify teacher behavior and thus determine the adjustment mode of the lighting control subsystem.

[0143] The human body detection sensor controls the lights to turn on and off. The student group's light control node turns on when a human body is detected, and turns off otherwise. The lectern group's light control node automatically sets to slideshow mode when a human body is detected by the human body detection sensor and the power consumption indicates that a projector is being used. When a human body is detected by the lectern group's light control node and the power consumption indicates that a projector is not being used, the light control node automatically sets to self-study mode. When no human body is detected by the lectern group's light control node and the power consumption indicates that a projector is not being used, the lights turn off.

[0144] When applied to a desk lamp, a robotic arm control system can be incorporated. The robotic arm replaces the lamp's support frame to adjust the distance between the light source and the horizontal tabletop, thereby controlling the light intensity. It can also connect to a mobile app via Bluetooth Low Energy to replace the function of the wall-mounted control panel.

[0145] like Figure 10As shown, the outer shell of each wireless monitoring node in the environmental monitoring subsystem is manufactured using 3D printing technology. It consists of two parts and four screws forming an assembly for assembling the node circuit and solar cell. The shell size is limited by the node circuit size, antenna size, and the position of the photosensitive module. The upper part of the shell is the frame 1, i.e., part 1, which fixes the solar cell, and the lower part is the box 2, i.e., part 2, which holds the node circuit. The bottom frame protrusion 101 in part 1 is for fixing the solar cell from the horizontal plane, and the bottom half frame protrusion 102 in part 1 is for fixing the solar cell from the vertical plane. The four thin plates 201 around the box in part 2 serve as support points for placing the solar cell, and the node circuit is placed inside the box 202 below the four thin plates 201. The four screws fix the frame part 1 and the box part 2, passing through the small holes 103 in the frame part 1 and the small holes 203 in the box part 2 from top to bottom. The two small holes overlap to form the position of the small hole 3 in the assembly.

[0146] The above is a further detailed description of the present invention in conjunction with specific embodiments. It should not be considered that the specific implementation of the present invention is limited to this. For those skilled in the art to which the present invention pertains and related fields, any extensions, operation methods, and data substitutions made based on the technical solution concept of the present invention should fall within the protection scope of the present invention.

[0147] The above description is only a part of the specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A control method for a smart classroom lighting system based on indoor low-light acquisition, characterized in that: The intelligent classroom lighting system based on indoor low-light acquisition includes the following subsystems: energy acquisition subsystem, environmental monitoring subsystem, lighting control subsystem, and data processing unit; The energy harvesting subsystem supplies power to the environmental monitoring subsystem; The environmental monitoring subsystem includes several monitoring nodes and a first coordinator, and the monitoring nodes are connected to the first coordinator via wireless communication. The lighting control subsystem includes several lighting control nodes, a control panel, an electrical power detection sensor, and a second coordinator. The data collected by the environmental monitoring subsystem is sent to the data processing unit through the first coordinator, and the control signal generated by the data processing unit is sent to the lighting control subsystem through the second coordinator. The monitoring nodes are divided into four groups: the first group of monitoring nodes is arranged on the horizontal surface of the desks, called group A; the second group of monitoring nodes is arranged in front of the blackboard, called group B; the third group of monitoring nodes is distributed at the windows, called group C; and the fourth group of monitoring nodes is distributed in the upper middle position of the interior wall of the classroom, called group D. Group A is divided into the group closest to the window and the group furthest from the window; The energy harvesting subsystem includes an amorphous silicon solar cell, a power management chip BQ25505, and an energy storage module. The amorphous silicon solar cell is connected to the VIN_DC pin of the power management chip BQ25505 as an input, and the VBAT_SEC pin of the power management chip BQ25505 is connected to the energy storage module. The power management chip BQ25505 internally includes a maximum power point tracking (MPPT) circuit, a boost control circuit, a nano-level power management (Nano) circuit, and a cold start circuit. The external circuit of the power management chip BQ25505 includes the FDC6312P chip. The power management chip BQ25505 is connected to the load via the chip FDC6312P. The FDC6312P chip internally includes two PMOS transistors, with their source (S) terminals interconnected. The two gate (G) terminals of the two PMOS transistors are connected to the power management chip BQ25505. Pin connections; the control panel accepts touchscreen input information and sends input lighting control signals to the data processing unit, the lighting control signals including illuminance information, color temperature information, and time information; The second coordinator is used to send control signals to the lighting control node; The power detection sensor is connected to the projector via a wired connection and sends a signal indicating the projector's usage status to the data processing unit; the power detection sensor controls the lamps to turn off by detecting the power consumption of the projector in the classroom. Each of the lighting control nodes includes a DC constant voltage module, a constant current drive module, a lamp, a human body detection device, and a Zigbee communication module; The DC constant voltage module converts 220V AC power into a low DC level to power the constant current drive module, lamps, human body detection device and Zigbee communication module. The constant current drive module receives the control signal for dimming sent by the second coordinator through the Zigbee communication module and drives the lamp. The human detection device includes an infrared sensor unit, which collects information on whether there are people in the area where the current lighting control node is located, and sends it to the second coordinator through the Zigbee communication module. The second coordinator controls the light intensity of the lamps based on whether there are people. The lighting control nodes are divided into two groups: one group is located at the top of the classroom and is called the student group; the other group is located at the top of the blackboard and is called the podium group. The light sensor of the monitoring node is MAX44009. The first coordinator is equipped with an LCD display screen. The monitoring node periodically collects data and sends it to the first coordinator, which then sends it to the data processing module to model and analyze the obtained data. The control method for the energy harvesting subsystem includes the following steps: The solar cell receives indoor light input energy and feeds it to the power management chip BQ25505; When the output voltage of the power management chip BQ25505 is lower than 1.8V, the power management chip BQ25505 is in cold start mode, controlling the solar cell input voltage to 330mV; When the output voltage of the power management chip BQ25505 is greater than or equal to 1.8V, the power management chip BQ25505 enters the boost charging mode, and the maximum power point tracking (MPPT) circuit starts to work. By using the voltage proportional coefficient method, the open-circuit voltage of the solar cell is periodically disconnected and collected. The input voltage is controlled to be the open-circuit voltage multiplied by the proportional coefficient, which is the ratio of the maximum power point voltage to the open-circuit voltage, so that the solar cell works at the maximum power point. When the output voltage of the power management chip BQ25505 is greater than or equal to 2.7V, charging of the energy storage module is stopped to prevent overcharging. When the output of the power management chip BQ25505 is greater than or equal to 2.6V, the gate of the PMOS is at a low level, turning on the energy storage module and the load, and the load begins to work normally; when the voltage drops to 2.3V, the load is turned off. The sampling period control method for monitoring nodes in the environmental monitoring subsystem includes the following steps: The efficiency of the energy harvesting subsystem in converting light energy into electrical energy under different indoor light intensities was obtained. Then, based on the data acquisition and transmission power consumption and static power consumption of the monitoring nodes, the energy consumption of each monitoring node under different illumination conditions is calculated, and a greedy algorithm is selected to find the optimal acquisition and transmission time interval as the timer period. The algorithm needs to satisfy the requirement that the energy collected within the timer period... It is higher than the energy consumed. The following formula must be satisfied: ； The collected energy includes the current battery level. The average energy harvesting efficiency under different illuminance levels within that period. and cycle The sum of the products; the energy consumed includes the static power consumption during this cycle. and the power consumption of collecting and sending data once As shown in the following formula: ； Each cycle requires calculating the duration of the next cycle; Average energy harvesting efficiency over the period Specifically, the data collection and conversion efficiency of the solar cells needs to be considered. and the efficiency of the power management module Battery inventory The self-discharge power loss of the energy storage module must be considered. The efficiency of the battery management module must take into account MPPT error losses. Control circuit loss The specific formula is shown below: ； ； ； in, The number of batteries remaining at the end of the previous cycle; The method for controlling illuminance using the lighting control subsystem includes the following sub-steps: Sub-step (1) Collect data and train a neural network as the system model: ； x = [x inner , x outer ] is an illuminance vector, where x inner is representative of the illuminance measured by the monitoring nodes located within the classroom, and x outer is representative of the natural illuminance measured by the monitoring nodes located at the windows; u is a control current vector that controls the luminaire illuminance, x + is the new illuminance vector resulting from the action of x and u. Based on this model, the predicted system state trajectory is as follows: ； in, The illuminance represents the combined effect of x and u; n represents time, with time n being the initial state, and u(k) being a constant. Sub-step (2): Establish the optimal control problem ； in Used to ensure that the optimized illuminance X approaches that of healthy eye use ref Meanwhile, T h || u(k)|| 2 It is a regularization term used to ensure that the adjustment of illuminance is smooth and does not fluctuate drastically, thus preventing lamp flicker. By solving the above optimization problem using sequential quadratic programming, the optimal control current vector sequence can be obtained as follows: u * (0), u * (1), ..., u * (N-1) in, represents the optimal control current vector, and N represents the number of adjustments; Sub-step (3) applies the first term of the optimal control voltage vector sequence to adjust the illuminance of the lamps; Next, determine whether each monitoring node has transmitted the illuminance status data to the data processing module through the first coordinator; if the illuminance status data has been transmitted to the data processing module through the first coordinator, obtain x(n+1), proceed to sub-step (1), and start a new round of iteration; Where x(n+1) is the next state for x(n), and x(n) is the illuminance vector at time n; transitioning to sub-step (1) means x(n+1) = x u (1) That is, the prediction result of the previous moment is used as the initial illuminance state at this moment; the new round of iteration refers to changing the initial illuminance state, replacing the illuminance state data of the previous moment with the newly collected illuminance state data, performing the next round of prediction, and solving the optimal control current vector sequence. If the illuminance status data of the monitoring node is not transmitted to the data processing module through the first coordinator, then for the illuminance status data that has not been transmitted... The predicted value using the illuminance state data of the i-th time. Replace the illuminance status data that was not previously transmitted Then proceed to sub-step (1) to begin a new round of iteration; in, This represents the illuminance status data for the i-th time that was not passed to the data processing module. If none of the illumination status data of the monitoring node is transmitted, then the predictive control current vector sequence u is directly used at the next sampling time. * (1), ..., u * (N-1) until a monitoring node transmits data to the data processing module, then proceed to sub-step (1) to start a new round of iteration.

2. The control method for a smart classroom lighting system based on indoor low-light acquisition as described in claim 1, characterized in that: The lighting control subsystem provides three control modes based on the different illumination requirements of teachers and students in different teaching scenarios, according to the teaching objectives and the status of teachers and students: slideshow mode, self-study mode, and lunch break mode. In slideshow mode, the lectern lights are turned off, and the illumination of the student lights is controlled below 50 lux. In self-study mode, the illumination of the student lights is controlled above 400 lux, and the illumination of the lectern lights is controlled above 500 lux. In lunch break mode, the lighting is set daily. Starting at 12:00 noon, the lectern lights are turned off, and the illumination of the student lights is controlled at 200 lux. The student lights are turned off between 12:30 and 1:00 p.m. After 1:00 p.m., the lectern lights are turned on, and the student lights are slowly restored to above 400 lux in a certain time step.

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