Method and system for realizing energy consumption optimization of lighting system based on big data

Abstract

The application relates to the technical field of intelligent lighting control, and discloses a method and system for realizing energy consumption optimization of a lighting system based on big data, which comprises the following steps: recording micron-level bending displacement of a house window frame under natural light irradiation, establishing light intensity distribution of a house internal space according to the micron-level bending displacement, capturing interference vortex phase between artificial light emitted by an artificial light lamp and natural light, analyzing light field capture efficiency of the house internal space by using the interference vortex phase, generating dimming control parameters of an indoor lighting system according to the light intensity distribution and the light field capture efficiency, generating light deflection parameters corresponding to the house window frame based on the micron-level bending displacement, and generating light path navigation instructions of the artificial light lamp according to the light deflection parameters and the micron-level bending displacement. The application can solve technical bottlenecks such as limited sensing mode, lack of utilization of light field physical characteristics, extensive control strategy, single execution means and insufficient collaborative optimization.

Description

Technical Field

[0001] This invention relates to a method and system for optimizing the energy consumption of a lighting system based on big data, belonging to the field of intelligent lighting control technology. Background Technology

[0002] Big data-driven lighting system energy consumption optimization refers to a systematic closed-loop process that generates multi-dimensional data streams by leveraging the physical response of the building and the fluctuation characteristics of the light field, driving the coordinated execution of dynamic dimming, waveguide refraction control, and artificial optical path navigation to maximize the energy efficiency of both natural and artificial light.

[0003] Currently, existing technologies rely on numerous point-type light sensors installed indoors, such as photoresistors and photodiodes, to measure illuminance. This approach is costly, involves complex wiring, and may affect aesthetics. Alternatively, simulations based on weather data and simple geometric models have limited accuracy and cannot reflect real-time changes such as cloud cover or obstructions. Existing technologies for evaluating lighting efficiency typically only consider illuminance uniformity or simple luminous flux utilization, rarely addressing light fluctuations, such as phase, and its energy capture capacity in space. Coordination between artificial and natural light is mostly limited to brightness superposition. Current lighting technologies rely on simple threshold control. When the natural light intensity is below the set value, the lights are turned on or brightened, and when it is above the set value, the lights are turned off or dimmed. This does not take into account spatial differences and light effects, or it is based on preset scene mode switching, which is not flexible. Existing technologies for controlling light incidence mainly rely on mechanical blinds, motorized curtains or static coatings. Dynamic active control of light path technology is complex and expensive. For example, electrochromic glass has a slow response and high cost. Existing adjustable light fixtures usually rely on preset angles or simple programs, without linkage with the real-time environment, such as changes in natural light and window frame status. Moreover, optimization is usually limited to a single means, such as only adjusting the light or only adjusting the curtains, lacking multi-means collaborative optimization.

[0004] Therefore, existing lighting systems suffer from technical bottlenecks such as limited sensing methods, lack of utilization of the physical characteristics of light fields, crude control strategies, single execution methods, and insufficient collaborative optimization. Summary of the Invention

[0005] This invention provides a method and system for optimizing the energy consumption of lighting systems based on big data. Its main purpose is to solve the technical bottlenecks of limited sensing methods, lack of utilization of the physical characteristics of light fields, crude control strategies, single execution means, and insufficient collaborative optimization.

[0006] To achieve the above objectives, this invention provides a method for optimizing the energy consumption of a lighting system based on big data, comprising:

[0007] An indoor lighting system is defined, wherein the indoor lighting system includes window frames, artificial light fixtures, and the interior space of the building.

[0008] Record the micron-level bending displacement of the house window frame under natural light, and establish the light intensity distribution of the house interior space based on the micron-level bending displacement;

[0009] The interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light is captured, and the light field capture efficiency of the interior space of the building is analyzed using the interference vortex phase.

[0010] Based on the light intensity distribution and the light field capture efficiency, dimming control parameters for the indoor lighting system are generated, and light deflection parameters corresponding to the house window frame are generated based on the micron-level bending displacement.

[0011] Based on the light deflection parameters and the micron-level bending displacement, the optical path navigation command of the artificial light fixture is generated, and the energy consumption optimization processing of the indoor lighting system is performed through the dimming control parameters, the light deflection parameters, and the optical path navigation command.

[0012] Optionally, establishing the light intensity distribution of the interior space of the house based on the micrometer-level bending displacement includes:

[0013] The illuminance on the window frame of the house is calculated based on the micron-level bending displacement.

[0014] The illuminance on the window frame of the house is extended into the interior space of the house using an interpolation method to obtain the illuminance distribution.

[0015] Optionally, capturing the interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light includes:

[0016] The artificial light and the natural light are input into the interferometer;

[0017] A photodetector is used to capture the interference pattern produced by the interferometer under the interaction of the artificial light and the natural light.

[0018] Extract phase information from the interferogram;

[0019] The vortex phase field corresponding to the phase information is calculated using the phase-shifting interferometry method.

[0020] The vortex phase field is taken as the interference vortex phase.

[0021] Optionally, the step of using the interference vortex phase analysis to determine the light field capture efficiency of the interior space of the house includes:

[0022] Calculate the phase gradient in the phase of the interference vortex;

[0023] Based on the phase gradient, the light field capture efficiency of the interior space of the building is calculated using the following formula:

[0024]

[0025] in, Indicates the light field capture efficiency. Represents the phase gradient. Represents the differential line element along a closed path.

[0026] Optionally, generating the dimming control parameters of the indoor lighting system based on the illuminance distribution and the light field capture efficiency includes:

[0027] When the illuminance in the light intensity distribution is greater than the preset illuminance, the luminaire health coefficient of the indoor lighting system is determined;

[0028] Based on the luminaire health coefficient, the luminous intensity of the artificial light source in the indoor lighting system is calculated using the following formula:

[0029]

[0030] in, Indicates the light intensity of an artificial light source. This indicates the health factor of the lighting fixtures. This represents the illuminance in the light intensity distribution. Indicates the target illuminance. Indicates the phase gradient;

[0031] Query the dimming percentage and color temperature (CCT) of the luminous intensity of the artificial light source;

[0032] The dimming percentage of the luminaire and the color temperature (CCT) are used as the dimming control parameters of the indoor lighting system.

[0033] When the illuminance in the light intensity distribution is not greater than the preset illuminance, the indoor projection areas corresponding to the light field capture efficiency are arranged in descending order of the light field capture efficiency to obtain the arranged indoor areas.

[0034] Select target luminaires among the artificial light luminaires corresponding to the indoor lighting system whose luminaire health coefficient is greater than a preset health coefficient;

[0035] Query the target lighting fixtures that are closest to the arranged indoor area;

[0036] The power duty cycle of the final luminaire is calculated based on the light field capture efficiency and the luminaire health coefficient.

[0037] Dimming control parameters are generated between the arranged indoor area, the final luminaire, the light field capture efficiency, and the power duty cycle.

[0038] Optionally, determining the luminaire health coefficient of the indoor lighting system includes:

[0039] Collect data on the operating time, color temperature deviation, and drive current fluctuation of indoor lighting systems;

[0040] The working time of the lamp, the color temperature offset, and the driving current fluctuation are input into a preset LSTM model;

[0041] Obtain the luminaire health coefficients output by the LSTM model regarding the luminaire's operating time, color temperature offset, and drive current fluctuation.

[0042] Optionally, generating the light deflection parameters corresponding to the house window frame based on the micrometer-level bending displacement includes:

[0043] Based on the micron-level bending displacement, the waveguide refractive index of the waveguide refractive device corresponding to the house window frame is calculated.

[0044] Calculate the refractive index deviation between the waveguide refractive index and the waveguide base refractive index;

[0045] The deviation between the refractive index of the waveguide foundation and the refractive index is used as the light deflection parameter.

[0046] Optionally, generating the optical path navigation command for the artificial light fixture based on the light deflection parameters and the micrometer-level bending displacement includes:

[0047] Based on the waveguide fundamental refractive index and refractive index deviation in the light deflection parameters, the light deflection angle corresponding to the artificial light fixture is calculated using the following formula:

[0048]

[0049] in, Indicates the angle of light deflection. Indicates refractive index deviation. Indicates the fundamental refractive index of the waveguide. This represents the deflection sensitivity coefficient;

[0050] The optical path navigation command is determined by the window frame area corresponding to the light deflection angle and the micrometer-level bending displacement.

[0051] Optionally, the step of performing energy consumption optimization processing of the indoor lighting system through the dimming control parameters, the light deflection parameters, and the optical path navigation command includes:

[0052] The lighting parameters of the indoor lighting system are adjusted by using the dimming control parameters to obtain the lighting adjustment result;

[0053] The refraction parameters of the waveguide refraction device corresponding to the window frame of the house are adjusted by the light deflection parameters to obtain the refraction adjustment result.

[0054] The lighting direction of the indoor lighting system is adjusted by the optical path navigation command to obtain the direction adjustment result;

[0055] The energy consumption optimization of the indoor lighting system is completed using the lighting adjustment results, the refraction adjustment results, and the direction adjustment results.

[0056] To address the aforementioned problems, this invention also provides a system for optimizing the energy consumption of a lighting system based on big data, the system comprising:

[0057] The system determination module is used to determine the indoor lighting system, wherein the indoor lighting system includes the house window frames, artificial light fixtures and the interior space of the house.

[0058] The illumination establishment module is used to record the micron-level bending displacement of the house window frame under natural light illumination, and establish the illumination intensity distribution of the house interior space based on the micron-level bending displacement;

[0059] The efficiency analysis module is used to capture the interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light, and to analyze the light field capture efficiency of the interior space of the building using the interference vortex phase.

[0060] The parameter generation module is used to generate dimming control parameters for the indoor lighting system based on the light intensity distribution and the light field capture efficiency, and to generate light deflection parameters corresponding to the window frame of the house based on the micron-level bending displacement.

[0061] The energy consumption optimization module is used to generate optical path navigation instructions for the artificial light fixture based on the light deflection parameters and the micron-level bending displacement, and to perform energy consumption optimization processing of the indoor lighting system through the dimming control parameters, the light deflection parameters and the optical path navigation instructions.

[0062] Compared to the problems described in the background art, the embodiments of the present invention use the window frame as a large-area, in-situ, passive sensor, and directly measure the micron-level bending displacement of the window frame under the natural photothermal effect using strain gauges. This eliminates the need for a large number of additional light sensors, reducing costs, simplifying installation, and avoiding shading problems. Furthermore, based on the principle that the degree of bending of the window frame is directly caused by the photothermal effect of sunlight illuminating it, and the strong physical correlation, the mechanical deformation is accurately converted into illuminance through a formula. The window frame covers the entire light-receiving surface, and its deformation measurement can reflect the details of the light distribution on the window frame plane, not just a few points. Through interpolation, the light intensity distribution map of the entire indoor space can be efficiently reconstructed, with an accuracy far exceeding that of simple models. The embodiments of the present invention utilize interferometers to capture... By capturing the interference pattern generated by the superposition of artificial and natural light, and extracting the vortex phase field using phase-shifting interferometry, this directly detects the wave nature and topological structure of the light field. Furthermore, this embodiment calculates the integral of the phase gradient along a closed path using a formula. This light field capture efficiency precisely quantifies the ability of the light field in that region to "bind" or "converge" light energy—a depth information that traditional illuminance measurements cannot provide. This embodiment dynamically calculates the required intensity of the artificial light source in well-lit areas using a formula. The innovation lies in introducing the reciprocal of the phase gradient. In areas with drastic phase changes, typically light field boundaries or areas of intense interference, more cautious or less artificial light supplementation is needed to avoid interfering with the natural light field or causing glare. The luminaire health coefficient ensures accurate output from aging luminaires. In areas with insufficient light, priority is given to areas with high light field capture efficiency for focused supplemental lighting, avoiding energy waste in inefficient areas. The optimal luminaires, close to the target area and in good condition, are selected based on luminaire health status. The precise power duty cycle is calculated, outputting not only the dimming percentage and color temperature (CCT), but also actions performed by specific luminaires at specific power levels for specific areas. This achieves refined intelligent dimming based on spatial differentiation, physical perception, and device status perception. Compared to simple threshold control, it is more energy-efficient, more comfortable, more precise, and offers better device lifespan management. Furthermore, this embodiment of the invention dynamically calculates the required refractive index of the embedded liquid crystal waveguide layer based on the measured bending displacement of the window frame using a formula. The greater the curvature, the higher the refractive index, the stronger the light deflection, and the greater the mechanical deformation of the window frame. By dynamically adjusting the refractive index of the internal optical materials, light deflection control can be achieved without mechanically moving parts. The deformation sensitivity coefficient can be designed and adjusted, providing a low-cost, fast-response, and wear-free method for dynamically controlling the incident light direction and distribution. In conjunction with a dimming system, it enables deep collaborative management of natural and artificial light. This invention uses a formula to calculate the required deflection angle of the artificial light. The product between the waveguide's basic refractive index and the refractive index deviation reflects the influence of refractive index changes on the light direction. The calculated deflection angle is bound to the window frame area that produces the deformation, generating specific optical path navigation instructions. This tells the artificial light system how much deflection angle is needed, and its purpose is to compensate for or coordinate with changes in natural light in which window frame area.This transforms complex lighting control needs into specific, executable instructions, enabling artificial light to actively respond to dynamic changes in natural light across different areas of the window frame, achieving dynamic light tracking or avoidance. Therefore, the energy consumption optimization method and system for lighting systems based on big data provided in this invention can overcome technical bottlenecks such as limitations in sensing methods, lack of utilization of the physical characteristics of light fields, coarse control strategies, single execution methods, and insufficient collaborative optimization. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating a method for optimizing the energy consumption of a lighting system based on big data, provided in an embodiment of the present invention.

[0064] Figure 2 This is a flowchart illustrating the lamp health coefficient of a method for optimizing the energy consumption of a lighting system based on big data, provided in an embodiment of the present invention.

[0065] Figure 3 This is a schematic diagram of a module for implementing the energy consumption optimization system for lighting systems based on big data, provided as an embodiment of the present invention.

[0066] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0067] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0068] This application provides a method for optimizing the energy consumption of a lighting system based on big data. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for optimizing the energy consumption of a lighting system based on big data can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0069] Example 1:

[0070] Reference Figure 1 The diagram shown is a flowchart illustrating a method for optimizing lighting system energy consumption based on big data, according to an embodiment of the present invention. In this embodiment, the method for optimizing lighting system energy consumption based on big data includes:

[0071] S1. Determine the indoor lighting system, wherein the indoor lighting system includes the house window frame, artificial light fixtures and the interior space of the house.

[0072] S2. Record the micron-level bending displacement of the house window frame under natural light, and establish the light intensity distribution of the interior space of the house based on the micron-level bending displacement.

[0073] This invention uses the window frame as a large-area, in-situ, passive sensor, and uses strain gauges to directly measure the micron-level bending displacement of the window frame under the natural light and heat effect. This eliminates the need for a large number of additional light sensors, reduces costs, simplifies installation, and avoids shading problems.

[0074] In one embodiment of the present invention, recording the micron-level bending displacement of the house window frame under natural light irradiation includes: using a strain gauge sensor to record the micron-level bending displacement of the house window frame under natural light irradiation.

[0075] The micron-level bending displacement refers to the deformation (in μm) of the window frame caused by thermal expansion under sunlight. For example, the deformation of the east-side window frame is 12 μm when exposed to sunlight.

[0076] Furthermore, this embodiment of the invention is based on the principle that the curvature of the window frame is directly caused by the photothermal effect of sunlight shining on it, and has a strong physical correlation. The mechanical deformation is accurately converted into illuminance through a formula. The window frame covers the entire light-receiving surface, and its deformation measurement can reflect the details of the light distribution on the window frame plane, rather than just a few points. Through interpolation, the light intensity distribution map of the entire indoor space can be efficiently reconstructed, with an accuracy far exceeding that of simple models.

[0077] In one embodiment of the present invention, establishing the light intensity distribution of the interior space of the house based on the micrometer-level bending displacement includes: calculating the illuminance on the window frame of the house using the following formula based on the micrometer-level bending displacement:

[0078]

[0079] in, Indicates illuminance, Represents a proportionality constant. This represents bending displacement at the micrometer level. This indicates the curvature changes of the house window frame in the x and y directions;

[0080] The illuminance on the window frame of the house is extended into the interior space of the house using an interpolation method to obtain the illuminance distribution.

[0081] in, The photothermal conversion rate of the window frame material was determined experimentally, for example, by measuring under known illumination. To obtain by comparison with a standard lux meter The illuminance refers to the visible light flux received per unit area, measured in lux. The interpolation method refers to the method of extending the discrete illuminance data of the window frame surface to the entire indoor space, such as the bicubic interpolation algorithm. The specific steps are: arranging multiple strain gauge sensors on the window frame surface and measuring discrete points... Calculate each point using a formula The window frame plane is divided into grids, and the illuminance of unmeasured points in the room is calculated using an interpolation algorithm. The illuminance distribution of the window frame is then projected onto the indoor space, which means the illuminance of the indoor area is obtained by interpolation.

[0082] S3. Capture the interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light, and use the interference vortex phase to analyze the light field capture efficiency of the interior space of the house.

[0083] The embodiments of the present invention utilize an interferometer to capture the interference pattern generated by the superposition of artificial light and natural light, and extract the vortex phase field through phase-shifting interferometry, which directly detects the wave nature and topological structure of the light field.

[0084] In one embodiment of the present invention, capturing the interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light includes: inputting the artificial light and the natural light into an interferometer; using a photodetector to capture the interference pattern generated by the interferometer under the interaction of the artificial light and the natural light; extracting phase information from the interference pattern; calculating the vortex phase field corresponding to the phase information using phase-shifting interferometry; and using the vortex phase field as the interference vortex phase.

[0085] The interferometer refers to an optical device that superimposes artificial light and natural light to produce an interference pattern (used to analyze the phase of the light field). The photodetector refers to an instrument that captures the interference pattern output by the interferometer, such as a CCD camera. The interference pattern refers to the bright and dark fringes formed by the superposition of light waves, which contain phase information. The phase information refers to the light wave phase information extracted from the interference pattern, which reflects the wave nature of the light field. The phase information is usually represented in the form of a two-dimensional array, where each element corresponds to the phase value of a pixel in the image. The vortex phase field refers to the spiral structure (topological defect) in the phase distribution, which is calculated by the phase-shifting interferometry.

[0086] Furthermore, in this embodiment of the invention, the integral of the phase gradient along the closed path is calculated by a formula. This light field capture efficiency accurately quantifies the ability of the light field in the region to "bind" or "converge" light energy, which is depth information that traditional illuminance measurements cannot provide.

[0087] In one embodiment of the present invention, the step of analyzing the light field capture efficiency of the interior space of a building using the interferometric vortex phase includes: calculating the phase gradient in the interferometric vortex phase; and calculating the light field capture efficiency of the interior space of the building using the following formula based on the phase gradient:

[0088]

[0089] in, Indicates the light field capture efficiency. Represents the phase gradient. Represents the differential line element along a closed path.

[0090] Wherein, the phase gradient is expressed by the formula Calculations show that The phase at a certain coordinate position in the interference vortex phase, the light field trapping efficiency is used to quantify the ability of light energy to be concentrated in a specific region (0~1), for example, the speech area. =0.92 (high-efficiency capture zone).

[0091] S4. Based on the light intensity distribution and the light field capture efficiency, generate the dimming control parameters of the indoor lighting system, and based on the micron-level bending displacement, generate the light deflection parameters corresponding to the house window frame.

[0092] This invention dynamically calculates the required intensity of artificial light sources in well-lit areas using a formula. The innovation lies in introducing the inverse of the phase gradient. In areas with drastic phase changes, typically light field boundaries or areas of intense interference, more cautious or less artificial light supplementation is needed to avoid interfering with the natural light field or causing glare. The luminaire health coefficient ensures accurate output from aging luminaires. In insufficiently lit areas, areas with high light field capture efficiency are prioritized for focused supplemental lighting, avoiding energy waste in inefficient areas. The optimal luminaires, close to the target area and in good condition, are selected based on luminaire health. Precise power duty cycles are calculated, outputting not only the dimming percentage and color temperature (CCT) but also actions performed by specific luminaires at specific power levels for specific areas. This achieves refined intelligent dimming based on spatial differentiation, physical perception, and equipment status perception. Compared to simple threshold control, this is more energy-efficient, more comfortable, more precise, and provides better equipment lifespan management.

[0093] In one embodiment of the present invention, generating dimming control parameters for the indoor lighting system based on the illuminance distribution and the light field capture efficiency includes: determining a luminaire health coefficient for the indoor lighting system when the illuminance in the illuminance distribution is greater than a preset illuminance; and calculating the artificial light source intensity of the indoor lighting system using the following formula based on the luminaire health coefficient:

[0094]

[0095] in, Indicates the light intensity of an artificial light source. This indicates the health factor of the lighting fixtures. This represents the illuminance in the light intensity distribution. Indicates the target illuminance. Indicates the phase gradient;

[0096] The dimming percentage and color temperature (CCT) of the luminaire corresponding to the light intensity of the artificial light source are queried; the dimming percentage and color temperature (CCT) are used as dimming control parameters of the indoor lighting system; when the illuminance in the light intensity distribution is not greater than the preset illuminance, the indoor projection areas corresponding to the light field capture efficiency are arranged in descending order of the light field capture efficiency to obtain the arranged indoor areas; target luminaires with a luminaire health coefficient greater than the preset health coefficient are selected from the artificial light luminaires corresponding to the indoor lighting system; the final luminaire closest to the arranged indoor areas is queried from the target luminaires; the power duty cycle of the final luminaire is calculated based on the light field capture efficiency and the luminaire health coefficient; dimming control parameters are generated between the arranged indoor areas, the final luminaire, the light field capture efficiency, and the power duty cycle.

[0097] The lamp health coefficient refers to the lamp performance degradation index (0~1), where β=1 indicates a brand new lamp. For example, a lamp used for 3 years has β=0.85. The preset illuminance refers to the target illuminance value set by the user. =500 lux, where the dimming percentage refers to the percentage of the lamp's output power, the color temperature (CCT) refers to the color temperature of the light (in K), such as 3500K (warm white), and the power duty cycle refers to the proportion of the lamp's actual output power. It should be noted that the formula... In It has a limited scope of use, for example, 0.1 <| |<5.0rad / m.

[0098] In another embodiment of the present invention, determining the luminaire health coefficient of an indoor lighting system includes: collecting the luminaire operating time, color temperature shift, and drive current fluctuation of the indoor lighting system; inputting the luminaire operating time, color temperature shift, and drive current fluctuation into a preset LSTM model; and obtaining the luminaire health coefficient output by the LSTM model regarding the luminaire operating time, color temperature shift, and drive current fluctuation.

[0099] Wherein, the lamp working time refers to the cumulative lighting time of the lamp from start to stop, the color temperature offset refers to the deviation between the actual luminous color temperature and the nominal color temperature of the lamp, and the drive current fluctuation refers to the oscillation range of the drive current deviating from the stable value.

[0100] See Figure 2 The diagram shown is a flowchart illustrating the lamp health coefficient of a lighting system energy consumption optimization method based on big data, according to an embodiment of the present invention. Figure 2 In this model, the input features include the operating time of the lamp, color temperature offset, and driving current fluctuation. The model is an LSTM time series prediction model, and the output is the predicted value of β.

[0101] For example, the indoor projection areas corresponding to the light field capture efficiency are arranged in descending order of light field capture efficiency, resulting in the following arrangement of indoor areas: according to The regions are sorted in descending order of their values. For example, given the input data "zoneA": η=0.85, "zoneB": η=0.62, the sorted result would be: ["zoneA", "zoneB"]. This is because zoneA's... The value (0.85) is greater than zone B. The process of selecting target lamps with a health coefficient greater than a preset health coefficient from the artificial light fixtures corresponding to the indoor lighting system (value 0.62) is as follows: From all the lighting fixtures, select lamps with a health coefficient (β) greater than 0.9, considering these lamps to be healthy and functioning normally. For example, assuming there are several lighting fixtures, the healthy lamps obtained after selection are: ["LED_01", "LED_03"], that is, the lamps numbered LED_01 and LED_03 meet the health condition. Further, the process of querying the target lamps that are closest to the arranged indoor area is as follows: According to the preset area-device matching rules, assign lamps to each area, traverse the sorted area list, for each area, find the healthy lamp closest to that area, assign the found lamp to the current area, and set the projection weight of the lamp to its corresponding area. Furthermore, the calculation of the final power duty cycle of the luminaire is as follows: calculate the product of the light field capture efficiency and the luminaire health coefficient, and then multiply by 100% to obtain the power duty cycle.

[0102] Table 1 shows the corresponding relationships between artificial light source luminous intensity, luminaire dimming percentage, and color temperature (CCT):

[0103]

[0104] Table 2 shows the dimming control parameters when the illuminance in the light intensity distribution is not greater than the preset illuminance:

[0105]

[0106] Furthermore, based on the measured bending displacement of the window frame, the present invention dynamically calculates the refractive index required for the embedded liquid crystal waveguide layer using a formula. The greater the curvature, the higher the refractive index, and the stronger the light deflection. The mechanical deformation of the window frame adjusts the refractive index of the internal optical material in real time, thereby achieving light deflection control without mechanical moving parts. The deformation sensitivity coefficient can be designed to adjust the sensitivity, thus providing a low-cost, fast-response, and mechanically wear-free method for dynamically controlling the incident direction and distribution of light. In conjunction with a dimming system, it enables deep collaborative management of natural light and artificial light.

[0107] In one embodiment of the present invention, generating the light deflection parameters corresponding to the house window frame based on the micrometer-level bending displacement includes: calculating the waveguide refractive index of the waveguide refraction device corresponding to the house window frame using the following formula based on the micrometer-level bending displacement:

[0108]

[0109] in, Indicates the refractive index of the waveguide. Indicates the fundamental refractive index of the waveguide. This represents bending displacement at the micrometer level. This represents the curvature changes of the house window frame in the x and y directions. Indicates the deformation sensitivity coefficient;

[0110] Calculate the refractive index deviation between the waveguide refractive index and the waveguide base refractive index; use the refractive index deviation between the waveguide base refractive index and the refractive index as a light deflection parameter.

[0111] Wherein, the deformation sensitivity coefficient refers to the intensity of the adjustment of the refractive index by the deformation of the window frame (unit: μm²). For example, a=0.28 for high-sensitivity materials can be obtained by fitting the slope of the stress-refractive index curve. The waveguide base refractive index refers to the initial refractive index of the liquid crystal layer when there is no light. The waveguide refractive index refers to the dynamic light refractive index controlled by the bending of the window frame. The waveguide refraction device refers to the liquid crystal layer embedded in the window frame, which achieves light deflection by adjusting the refractive index. The refractive index deviation refers to the difference between the waveguide refractive index and the waveguide base refractive index.

[0112] in, The physical meaning of the formula is: the greater the curvature, the higher the refractive index, and the stronger the light refraction. This design reduces the dependence on artificial lighting and achieves energy-saving goals by dynamically adjusting the refractive index of the window frame and changing the propagation path of incident natural light (non-artificial light) (for example, directing sunlight into the depths of the room).

[0113] S5. Based on the light deflection parameters and the micron-level bending displacement, generate the optical path navigation command for the artificial light fixture, and perform energy consumption optimization processing of the indoor lighting system through the dimming control parameters, the light deflection parameters, and the optical path navigation command.

[0114] This invention utilizes a formula to calculate the angle that artificial light needs to be deflected. The product between the waveguide's basic refractive index and the refractive index deviation reflects the influence of refractive index changes on the direction of light. The calculated light deflection angle is bound to the window frame area that produces the deformation, generating specific optical path navigation instructions. This tells the artificial light system how much deflection angle is needed, and the purpose is to compensate for or coordinate with the changes in natural light in which window frame area. In this way, complex light control requirements can be transformed into specific, executable instructions, enabling artificial light to actively respond to the dynamic changes of natural light in different areas of the window frame, achieving dynamic light tracking or light avoidance.

[0115] In one embodiment of the present invention, generating the optical path navigation command for the artificial light fixture based on the light deflection parameters and the micrometer-level bending displacement includes: calculating the light deflection angle corresponding to the artificial light fixture using the following formula based on the waveguide fundamental refractive index and refractive index deviation in the light deflection parameters:

[0116]

[0117] in, Indicates the angle of light deflection. Indicates refractive index deviation. Indicates the fundamental refractive index of the waveguide. This represents the deflection sensitivity coefficient;

[0118] The optical path navigation command is determined by the window frame area corresponding to the light deflection angle and the micrometer-level bending displacement.

[0119] The light deflection angle refers to the deflection angle of the artificial light emitted by the artificial light fixture. The deflection sensitivity coefficient is obtained as follows: under controlled lighting conditions, the actual light deflection angle corresponding to the actual deformation of the window frame is measured, and the result is obtained through actual measurement... , , Using parameters to backfit to obtain .

[0120] Table 3 of the optical path navigation instructions is as follows:

[0121]

[0122] In one embodiment of the present invention, the energy consumption optimization process of the indoor lighting system performed by means of the dimming control parameters, the light deflection parameters, and the optical path navigation command includes: adjusting the lighting parameters of the luminaires of the indoor lighting system by means of the dimming control parameters to obtain a lighting adjustment result; adjusting the refraction parameters of the waveguide refraction device corresponding to the window frame of the house by means of the light deflection parameters to obtain a refraction adjustment result; adjusting the lighting direction of the luminaires of the indoor lighting system by means of the optical path navigation command to obtain a direction adjustment result; and completing the energy consumption optimization process of the indoor lighting system by means of the lighting adjustment result, the refraction adjustment result, and the direction adjustment result.

[0123] Compared to the problems described in the background art, the embodiments of the present invention use the window frame as a large-area, in-situ, passive sensor, and directly measure the micron-level bending displacement of the window frame under the natural photothermal effect using strain gauges. This eliminates the need for a large number of additional light sensors, reducing costs, simplifying installation, and avoiding shading problems. Furthermore, based on the principle that the degree of bending of the window frame is directly caused by the photothermal effect of sunlight illuminating it, and the strong physical correlation, the mechanical deformation is accurately converted into illuminance through a formula. The window frame covers the entire light-receiving surface, and its deformation measurement can reflect the details of the light distribution on the window frame plane, not just a few points. Through interpolation, the light intensity distribution map of the entire indoor space can be efficiently reconstructed, with an accuracy far exceeding that of simple models. The embodiments of the present invention utilize interferometers to capture... By capturing the interference pattern generated by the superposition of artificial and natural light, and extracting the vortex phase field using phase-shifting interferometry, this directly detects the wave nature and topological structure of the light field. Furthermore, this embodiment calculates the integral of the phase gradient along a closed path using a formula. This light field capture efficiency precisely quantifies the ability of the light field in that region to "bind" or "converge" light energy—a depth information that traditional illuminance measurements cannot provide. This embodiment dynamically calculates the required intensity of the artificial light source in well-lit areas using a formula. The innovation lies in introducing the reciprocal of the phase gradient. In areas with drastic phase changes, typically light field boundaries or areas of intense interference, more cautious or less artificial light supplementation is needed to avoid interfering with the natural light field or causing glare. The luminaire health coefficient ensures accurate output from aging luminaires. In areas with insufficient light, priority is given to areas with high light field capture efficiency for focused supplemental lighting, avoiding energy waste in inefficient areas. The optimal luminaires, close to the target area and in good condition, are selected based on luminaire health status. The precise power duty cycle is calculated, outputting not only the dimming percentage and color temperature (CCT), but also actions performed by specific luminaires at specific power levels for specific areas. This achieves refined intelligent dimming based on spatial differentiation, physical perception, and device status perception. Compared to simple threshold control, it is more energy-efficient, more comfortable, more precise, and offers better device lifespan management. Furthermore, this embodiment of the invention dynamically calculates the required refractive index of the embedded liquid crystal waveguide layer based on the measured bending displacement of the window frame using a formula. The greater the curvature, the higher the refractive index, the stronger the light deflection, and the greater the mechanical deformation of the window frame. By dynamically adjusting the refractive index of the internal optical materials, light deflection control can be achieved without mechanically moving parts. The deformation sensitivity coefficient can be designed and adjusted, providing a low-cost, fast-response, and wear-free method for dynamically controlling the incident light direction and distribution. In conjunction with a dimming system, it enables deep collaborative management of natural and artificial light. This invention uses a formula to calculate the required deflection angle of the artificial light. The product between the waveguide's basic refractive index and the refractive index deviation reflects the influence of refractive index changes on the light direction. The calculated deflection angle is bound to the window frame area that produces the deformation, generating specific optical path navigation instructions. This tells the artificial light system how much deflection angle is needed, and its purpose is to compensate for or coordinate with changes in natural light in which window frame area.This transforms complex lighting control needs into specific, executable instructions, enabling artificial light to actively respond to dynamic changes in natural light across different areas of the window frame, achieving dynamic light tracking or avoidance. Therefore, the energy consumption optimization method and system for lighting systems based on big data provided in this invention can overcome technical bottlenecks such as limitations in sensing methods, lack of utilization of the physical characteristics of light fields, coarse control strategies, single execution methods, and insufficient collaborative optimization.

[0124] Example 2:

[0125] like Figure 3 The diagram shown is a functional module diagram of a lighting system energy consumption optimization system based on big data according to the present invention.

[0126] The energy consumption optimization system 300 for lighting systems based on big data described in this invention can be installed in electronic devices. Depending on the functions implemented, the energy consumption optimization system for lighting systems based on big data may include a system determination module 301, a lighting establishment module 302, an efficiency analysis module 303, a parameter generation module 304, and an energy consumption optimization module 305. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0127] In this embodiment of the invention, the functions of each module / unit are as follows:

[0128] The system determination module 301 is used to determine the indoor lighting system, wherein the indoor lighting system includes the house window frame, artificial light fixtures and the interior space of the house.

[0129] The illumination establishment module 302 is used to record the micron-level bending displacement of the house window frame under natural light illumination, and establish the illumination intensity distribution of the interior space of the house based on the micron-level bending displacement.

[0130] The efficiency analysis module 303 is used to capture the interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light, and to analyze the light field capture efficiency of the interior space of the house using the interference vortex phase.

[0131] The parameter generation module 304 is used to generate dimming control parameters for the indoor lighting system based on the light intensity distribution and the light field capture efficiency, and to generate light deflection parameters corresponding to the window frame of the house based on the micron-level bending displacement.

[0132] The energy consumption optimization module 305 is used to generate optical path navigation instructions for the artificial light fixture based on the light deflection parameters and the micron-level bending displacement, and to perform energy consumption optimization processing of the indoor lighting system through the dimming control parameters, the light deflection parameters and the optical path navigation instructions.

[0133] In detail, the modules in the big data-based lighting system energy consumption optimization system 300 described in this embodiment of the invention employ the same methods as described above. Figure 1 The method described above uses the same techniques as the big data-based method for optimizing lighting system energy consumption, and can produce the same technical effects, so it will not be elaborated here.

[0134] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the energy consumption of a lighting system based on big data, characterized in that, The method includes: An indoor lighting system is defined, wherein the indoor lighting system includes window frames, artificial light fixtures, and the interior space of the building; Record the micron-level bending displacement of the house window frame under natural light, and establish the light intensity distribution of the house interior space based on the micron-level bending displacement; The interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light is captured, and the light field capture efficiency of the interior space of the building is analyzed using the interference vortex phase. Based on the light intensity distribution and the light field capture efficiency, dimming control parameters for the indoor lighting system are generated, and light deflection parameters corresponding to the house window frame are generated based on the micron-level bending displacement. Based on the light deflection parameters and the micron-level bending displacement, the optical path navigation command of the artificial light fixture is generated, and the energy consumption optimization processing of the indoor lighting system is performed through the dimming control parameters, the light deflection parameters, and the optical path navigation command.

2. The method for optimizing lighting system energy consumption based on big data as described in claim 1, characterized in that, The step of establishing the light intensity distribution of the interior space of the house based on the micron-level bending displacement includes: The illuminance on the window frame of the house is calculated based on the micron-level bending displacement. The illuminance on the window frame of the house is extended into the interior space of the house using an interpolation method to obtain the illuminance distribution.

3. The method for optimizing lighting system energy consumption based on big data as described in claim 1, characterized in that, The method of capturing the interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light includes: The artificial light and the natural light are input into the interferometer; A photodetector is used to capture the interference pattern produced by the interferometer under the interaction of the artificial light and the natural light. Extract phase information from the interferogram; The vortex phase field corresponding to the phase information is calculated using the phase-shifting interferometry method. The vortex phase field is taken as the interference vortex phase.

4. The method for optimizing lighting system energy consumption based on big data as described in claim 1, characterized in that, The method of using the interference vortex phase analysis to determine the light field capture efficiency of the interior space of the building includes: Calculate the phase gradient in the phase of the interference vortex; Based on the phase gradient, the light field capture efficiency of the interior space of the building is calculated using the following formula: in, Indicates the light field capture efficiency. Represents the phase gradient. Represents the differential line element along a closed path.

5. The method for optimizing lighting system energy consumption based on big data as described in claim 1, characterized in that, The step of generating dimming control parameters for the indoor lighting system based on the light intensity distribution and the light field capture efficiency includes: When the illuminance in the light intensity distribution is greater than the preset illuminance, the luminaire health coefficient of the indoor lighting system is determined; Based on the luminaire health coefficient, the luminous intensity of the artificial light source in the indoor lighting system is calculated using the following formula: in, Indicates the light intensity of an artificial light source. This indicates the health factor of the lighting fixtures. This represents the illuminance in the light intensity distribution. Indicates the target illuminance. Indicates the phase gradient; Query the dimming percentage and color temperature (CCT) of the luminous intensity of the artificial light source; The dimming percentage of the luminaire and the color temperature (CCT) are used as the dimming control parameters of the indoor lighting system. When the illuminance in the light intensity distribution is not greater than the preset illuminance, the indoor projection areas corresponding to the light field capture efficiency are arranged in descending order of the light field capture efficiency to obtain the arranged indoor areas. Select target luminaires among the artificial light luminaires corresponding to the indoor lighting system whose luminaire health coefficient is greater than a preset health coefficient; Query the target lighting fixtures that are closest to the arranged indoor area; The power duty cycle of the final luminaire is calculated based on the light field capture efficiency and the luminaire health coefficient. Dimming control parameters are generated between the arranged indoor area, the final luminaire, the light field capture efficiency, and the power duty cycle.

6. The method for optimizing lighting system energy consumption based on big data as described in claim 5, characterized in that, Determining the luminaire health coefficient of the indoor lighting system includes: Collect data on the operating time, color temperature deviation, and drive current fluctuation of indoor lighting systems; The working time of the lamp, the color temperature offset, and the driving current fluctuation are input into a preset LSTM model; Obtain the luminaire health coefficients output by the LSTM model regarding the luminaire's operating time, color temperature offset, and drive current fluctuation.

7. The method for optimizing lighting system energy consumption based on big data as described in claim 1, characterized in that, The process of generating the light deflection parameters corresponding to the house window frame based on the micrometer-level bending displacement includes: Based on the micron-level bending displacement, the waveguide refractive index of the waveguide refractive device corresponding to the house window frame is calculated. Calculate the refractive index deviation between the waveguide refractive index and the waveguide base refractive index; The deviation between the refractive index of the waveguide foundation and the refractive index is used as the light deflection parameter.

8. The method for optimizing lighting system energy consumption based on big data as described in claim 1, characterized in that, The step of generating optical path navigation commands for the artificial light fixture based on the light deflection parameters and the micrometer-level bending displacement includes: Based on the waveguide fundamental refractive index and refractive index deviation in the light deflection parameters, the light deflection angle corresponding to the artificial light fixture is calculated using the following formula: in, Indicates the angle of light deflection. Indicates refractive index deviation. Indicates the fundamental refractive index of the waveguide. This represents the deflection sensitivity coefficient; The optical path navigation command is determined by the window frame area corresponding to the light deflection angle and the micrometer-level bending displacement.

9. The method for optimizing lighting system energy consumption based on big data as described in claim 1, characterized in that, The energy consumption optimization process of the indoor lighting system, performed using the dimming control parameters, the light deflection parameters, and the optical path navigation commands, includes: The lighting parameters of the indoor lighting system are adjusted by using the dimming control parameters to obtain the lighting adjustment result; The refraction parameters of the waveguide refraction device corresponding to the window frame of the house are adjusted by the light deflection parameters to obtain the refraction adjustment result. The lighting direction of the indoor lighting system is adjusted by the optical path navigation command to obtain the direction adjustment result; The energy consumption optimization of the indoor lighting system is completed using the lighting adjustment results, the refraction adjustment results, and the direction adjustment results.

10. A system for optimizing the energy consumption of a lighting system based on big data, characterized in that, The system includes: The system determination module is used to determine the indoor lighting system, wherein the indoor lighting system includes the building window frame, artificial light fixtures and the interior space of the building; The illumination establishment module is used to record the micron-level bending displacement of the house window frame under natural light illumination, and establish the illumination intensity distribution of the house interior space based on the micron-level bending displacement; The efficiency analysis module is used to capture the interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light, and to analyze the light field capture efficiency of the interior space of the building using the interference vortex phase. The parameter generation module is used to generate dimming control parameters for the indoor lighting system based on the light intensity distribution and the light field capture efficiency, and to generate light deflection parameters corresponding to the window frame of the house based on the micron-level bending displacement. The energy consumption optimization module is used to generate optical path navigation instructions for the artificial light fixture based on the light deflection parameters and the micron-level bending displacement, and to perform energy consumption optimization processing of the indoor lighting system through the dimming control parameters, the light deflection parameters and the optical path navigation instructions.

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