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

Abstract

The invention relates to the technical field of intelligent lighting control, and discloses a method and system for optimizing energy consumption of a lighting system based on big data, and the method comprises the steps: recording the micron-order bending displacement of a window frame of a house under the irradiation of natural light, and building the illumination intensity distribution of the internal space of the house according to the micron-order bending displacement; capturing an interference vortex phase between the artificial light emitted by the artificial light lamp and the natural light, and analyzing the light field capturing efficiency of the internal space of the house by using the interference vortex phase; according to the illumination intensity distribution and the light field capture efficiency, dimming control parameters of an indoor illumination system are generated, and light deflection parameters corresponding to the house window frame are generated based on the micron-order bending displacement; and generating a light path navigation instruction of the artificial light lamp according to the light deflection parameter and the micron-sized bending displacement. According to the method, the technical bottlenecks of limited sensing modes, lack of utilization of physical characteristics of the light field, extensive control strategies, single execution means and insufficient collaborative optimization can be solved.

Description

Technical Field

[0001] The present invention relates to a method and system for optimizing energy consumption of a lighting system based on big data, and belongs to the technical field of intelligent lighting control. Background Art

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

[0003] Currently, existing technologies rely on a large number of point-type light sensors installed indoors, such as photoresistors and photodiodes, to measure illuminance, which are costly, require complex wiring, and may affect aesthetics. Alternatively, existing technologies use simulation calculations based on weather data and simple geometric models, which have limited accuracy and cannot reflect actual changes in real time, such as cloud cover and occlusion. Existing technologies for evaluating lighting efficiency usually only consider illuminance uniformity or simple luminous flux utilization, and rarely pay attention to the volatility of light, such as phase, and its energy capture ability in space. The coordination of artificial light and natural light mostly stays at the level of brightness superposition. Existing lighting technologies are simple threshold control. When the natural light intensity is lower than the set value, the light is turned on or brightened; when it is higher, the light is turned off or dimmed, without considering spatial differences and lighting effects, or switching based on preset scene modes, with poor flexibility. The existing technology for controlling light incidence mainly relies on mechanical blinds, electric curtains or static coatings. The technology for dynamically and actively regulating the light path is complex and expensive. For example, electrochromic glass has slow response and high cost. The direction of adjustable lamps in existing technology usually relies on preset angles or simple programs, and is not linked to real-time environments, such as natural light changes, window frame status, etc., and optimization is usually limited to a single means, such as only dimming or only adjusting curtains, and lacks multi-means collaborative optimization.

[0004] Therefore, the existing lighting system has technical bottlenecks such as limited perception methods, lack of utilization of the physical characteristics of the light field, extensive control strategies, single execution means and insufficient collaborative optimization. Summary of the Invention

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

[0006] To achieve the above objectives, the present invention provides a method for optimizing lighting system energy consumption based on big data, comprising: An indoor lighting system is determined, wherein the indoor lighting system includes window frames of a house, artificial light fixtures, and an interior space of the house.

[0007] Recording the micrometer-level bending displacement of the window frame of the house under natural light, and establishing the light intensity distribution of the interior space of the house based on the micrometer-level bending displacement; capturing the interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light, and analyzing the light field capture efficiency of the interior space of the house using the interference vortex phase; generating a dimming control parameter of the indoor lighting system according to the light intensity distribution and the light field capture efficiency, and generating a light deflection parameter corresponding to the window frame of the house based on the micron-level bending displacement; According to the light deflection parameters and the micron-level bending displacement, a light path navigation instruction for the artificial light fixture is generated, and energy consumption optimization processing of the indoor lighting system is performed through the dimming control parameters, the light deflection parameters and the light path navigation instruction.

[0008] Optionally, establishing the light intensity distribution in the interior space of the house according to the micron-level bending displacement includes: Calculating the illuminance on the window frame of the house according to the micron-level bending displacement; The interpolation method is used to extend the illumination on the window frame of the house to the interior space of the house to obtain the illumination intensity distribution.

[0009] Optionally, 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 light detector to capture an interference pattern generated by the interferometer under the action 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 by phase shift interferometry; The vortex phase field is taken as the interference vortex phase.

[0010] Optionally, analyzing the light field capture efficiency of the interior space of the house by using the interference vortex phase includes: calculating a phase gradient in the interfering vortex phase; According to the phase gradient, the light field capture efficiency of the interior space of the house is calculated using the following formula:

[0011] in, represents the light field capture efficiency, represents the phase gradient, Represents a differential line element along a closed path.

[0012] Optionally, generating a dimming control parameter of the indoor lighting system according to the light intensity distribution and the light field capture efficiency includes: When the illuminance in the light intensity distribution is greater than a preset illuminance, determining a lamp health coefficient of the indoor lighting system; According to the lamp health coefficient, the light intensity of the artificial light source of the indoor lighting system is calculated using the following formula:

[0013] in, Indicates the intensity of artificial light source, Indicates the health coefficient of the lamp, represents the illuminance in the light intensity distribution, represents the target illuminance, represents the phase gradient; Query the lamp dimming percentage and color temperature CCT corresponding to the light intensity of the artificial light source; Using the lamp dimming percentage and the color temperature CCT as dimming control parameters of the indoor lighting system; When the illuminance in the light intensity distribution is not greater than the preset illuminance, arranging the indoor projection areas corresponding to the light field capture efficiencies in descending order of the light field capture efficiencies to obtain arranged indoor areas; Selecting target lamps whose lamp health coefficient is greater than a preset health coefficient among the artificial light lamps corresponding to the indoor lighting system; Querying the final lamp among the target lamps that is closest to the arranged indoor area; Calculating a power duty cycle of the final lamp according to the light field capture efficiency and the lamp health coefficient; A dimming control parameter is generated among the arranged indoor area, the final lamp, the light field capture efficiency and the power duty cycle.

[0014] Optionally, determining the health coefficient of lamps in the indoor lighting system includes: Collect information about lamp operating hours, color temperature deviation, and driving current fluctuations in indoor lighting systems; Inputting the lamp operating time, the color temperature offset and the driving current fluctuation into a preset LSTM model; Obtain a lamp health coefficient output by the LSTM model regarding the lamp operating time, the color temperature offset, and the driving current fluctuation.

[0015] Optionally, generating light deflection parameters corresponding to the house window frame based on the micron-level bending displacement includes: Calculating a waveguide refractive index of a waveguide refraction device corresponding to the house window frame based on the micron-level bending displacement; calculating a refractive index deviation between the waveguide refractive index and the waveguide base refractive index; The waveguide base refractive index and the refractive index deviation are used as light deflection parameters.

[0016] Optionally, generating a light path navigation instruction for the artificial light fixture according to the light deflection parameter and the micron-level bending displacement includes: Based on the waveguide base 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:

[0017] in, represents the light deflection angle, represents the refractive index deviation, represents the waveguide base refractive index, represents the deflection sensitivity coefficient; The light path navigation instruction is determined by the window frame area corresponding to the light deflection angle and the micron-level bending displacement.

[0018] Optionally, the performing energy consumption optimization processing of the indoor lighting system by using the dimming control parameter, the light deflection parameter, and the light path navigation instruction includes: Adjusting the lighting parameters of the lamps of the indoor lighting system by using the dimming control parameters to obtain a lighting adjustment result; Adjusting the refraction parameters of the waveguide refraction device corresponding to the house window frame using the light deflection parameters to obtain a refraction adjustment result; Adjusting the lighting direction of the lamps of the indoor lighting system according to the light path navigation instruction to obtain a direction adjustment result; The energy consumption optimization processing of the indoor lighting system is completed through the lighting adjustment result, the refraction adjustment result and the direction adjustment result.

[0019] In order to solve the above problems, the present invention further provides a system for optimizing lighting system energy consumption based on big data, the system comprising: The system determination module is used to determine the indoor lighting system, wherein the indoor lighting system includes the window frames of the house, artificial light fixtures and the interior space of the house.

[0020] a light establishment module, configured to record the micron-level bending displacement of the window frame of the house under natural light, and establish the light intensity distribution of the interior space of the house based on the micron-level bending displacement; an efficiency analysis module, configured to capture an interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light, and analyze the light field capture efficiency of the interior space of the house using the interference vortex phase; a parameter generation module, configured 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; An energy consumption optimization module is used to generate a light path navigation instruction for the artificial light fixture based on the light deflection parameter and the micron-level bending displacement, and perform energy consumption optimization processing of the indoor lighting system through the dimming control parameter, the light deflection parameter and the light path navigation instruction.

[0021] Compared with the problems described in the background technology, the embodiment of the present 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 caused by the natural light and heat effect. This saves a lot of additional light sensors, reduces costs, simplifies installation, and avoids occlusion problems. Furthermore, based on the principle that the degree of bending of the window frame is directly caused by the photothermal effect of the sunlight irradiating it, and has a strong physical correlation, the embodiment of the present invention accurately converts mechanical deformation into illuminance through a formula. The window frame covers the entire lighting surface, and its deformation measurement can reflect the details of the light distribution on the window frame plane, not just a few points. The light intensity distribution map of the entire indoor space can be efficiently reconstructed through interpolation, and the accuracy far exceeds that of a simple model. The embodiment of the present invention uses an interferometer to capture The interference pattern produced by the superposition of artificial light and natural light is captured, and the vortex phase field is extracted by phase-shift interferometry, which directly detects the volatility and topological structure of the light field. Furthermore, the embodiment of the present invention calculates the integral of the phase gradient along a closed path through a formula. This light field capture efficiency accurately quantifies the ability of the light field in the area to "bind" or "converge" light energy, which is depth information that traditional illuminance measurement cannot provide. The embodiment of the present invention dynamically calculates the required intensity of the artificial light source by using a formula in an area with sufficient light. The innovation lies in the introduction of the inverse of the phase gradient. In areas with drastic phase changes, which are usually light field boundaries or areas with drastic interference, more cautious or less artificial light supplementation is required to avoid interfering with the natural light field or causing glare. The lamp health coefficient ensures that the output of aging lamps is accurate. In areas with insufficient lighting, areas with high light field capture efficiency are prioritized for key fill lighting to avoid wasting energy in low-efficiency areas. The optimal lamps that are close to the target area and in good condition are selected based on the health of the lamps, and the accurate power duty cycle is calculated. Not only the dimming percentage and color temperature CCT are output, but also the action performed by a specific lamp at a specific power in a specific area is output, thereby realizing refined intelligent dimming with spatial differentiation, physical perception, and device status perception. Compared with simple threshold control, it is more energy-saving, more comfortable, more accurate, and has better equipment life management. Furthermore, the embodiment of the present invention uses a formula to dynamically calculate the refractive index required for the embedded liquid crystal waveguide layer based on the measured bending displacement of the window frame. The greater the curvature, the higher the refractive index, the stronger the light deflection, and the mechanical deformation of the window frame. The refractive index of the internal optical material is adjusted in real time, thereby realizing light deflection control without mechanical moving parts. The deformation sensitivity coefficient can be designed to adjust the sensitivity, thereby providing a low-cost, fast-response, and mechanical wear-free method for dynamically controlling the incident direction and distribution of light. In conjunction with the dimming system, deep collaborative management of natural light and artificial light is achieved. The embodiment of the present invention uses a formula to calculate the angle at which artificial light needs to be deflected. The product between the basic refractive index of the waveguide and the refractive index deviation reflects the influence of the refractive index change on the direction of the light. The calculated light deflection angle is bound to the window frame area that produces the deformation to generate a specific optical path navigation instruction, which tells the artificial light system how large the deflection angle needs to be. Its purpose is to compensate for or coordinate with the natural light changes in which window frame area.This allows complex light control requirements to be converted into specific, executable instructions, enabling artificial light to actively respond to the dynamic changes in natural light in different areas of the window frame, achieving dynamic light tracking or light avoidance. Therefore, the method and system for optimizing lighting system energy consumption based on big data provided by the embodiments of the present invention can address technical bottlenecks such as limited perception methods, lack of utilization of light field physical characteristics, extensive control strategies, single execution methods, and insufficient collaborative optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of a flow chart of a method for optimizing lighting system energy consumption based on big data provided by one embodiment of the present invention; Figure 2 A schematic diagram of a process flow for calculating the health coefficient of a lamp in a method for optimizing lighting system energy consumption based on big data provided by an embodiment of the present invention; Figure 3 A schematic diagram of modules for implementing the lighting system energy consumption optimization system based on big data provided by an embodiment of the present invention.

[0023] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0024] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0025] The present invention provides a method for optimizing lighting system energy consumption based on big data. This method can be executed by at least one of a server, a terminal, or other electronic device capable of executing the method provided by the present invention. In other words, this method can be executed by software or hardware installed on a terminal or server. The server can include, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0026] Example 1: Reference Figure 1 FIG. 1 is a flow chart of 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: S1. Determine an indoor lighting system, wherein the indoor lighting system includes window frames of a house, artificial light fixtures, and an interior space of the house.

[0027] S2. Recording the micron-level bending displacement of the window frame of the house under natural light, and establishing the light intensity distribution of the interior space of the house based on the micron-level bending displacement.

[0028] In an embodiment of the present invention, the window frame is used as a large-area, in-situ, passive sensor, and strain gauges are used to directly measure the micron-level bending displacement of the window frame caused by the natural light and heat effect. This eliminates the need for a large number of additional light sensors, reduces costs, simplifies installation, and avoids occlusion problems.

[0029] In one embodiment of the present invention, recording the micrometer-level bending displacement of the house window frame under natural light includes: using a strain gauge sensor to record the micrometer-level bending displacement of the house window frame under natural light.

[0030] The micron-level bending displacement refers to the deformation of the window frame caused by thermal expansion due to sunlight (in μm). For example, the east window frame deforms by 12 μm when exposed to sunlight.

[0031] Furthermore, based on the principle that the degree of window frame bending is directly caused by the photothermal effect of sunlight shining on it and has a strong physical correlation, the embodiment of the present invention accurately converts mechanical deformation into illuminance through a formula. The window frame covers the entire lighting 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.

[0032] In one embodiment of the present invention, establishing the light intensity distribution of the interior space of the house based on the micron-level bending displacement includes: calculating the illuminance on the window frame of the house using the following formula based on the micron-level bending displacement:

[0033] in, Indicates the illuminance, represents the proportionality constant, represents the micron-level bending displacement, Represents the curvature change of the house window frame in the x and y directions; The interpolation method is used to extend the illuminance on the window frame of the house to the interior space of the house to obtain the light intensity distribution.

[0034] in, The results are obtained by experimental calibration of the light-to-heat conversion rate of window frame materials, for example, the measurement under known light Compare with the standard illuminance meter to get The illuminance refers to the visible light flux received per unit area, and the unit is lux. The interpolation method refers to a method of expanding the discrete illuminance data on 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, measuring the values ​​of discrete points , calculate the value of each point by the formula , mesh the window frame plane, use the interpolation algorithm to calculate the illuminance of unmeasured points indoors, and project the window frame illumination distribution to the indoor space, that is, interpolate the illuminance of the indoor area in sequence.

[0035] S3. Capturing the interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light, and analyzing the light field capture efficiency of the interior space of the house using the interference vortex phase.

[0036] The embodiment of the present invention uses an interferometer to capture the interference pattern generated by the superposition of artificial light and natural light, and extracts the vortex phase field through phase-shift interferometry, which directly detects the wave nature and topological structure of the light field.

[0037] 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 light detector to capture the interference pattern generated by the interferometer under the action 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 by phase-shift interferometry; and using the vortex phase field as the interference vortex phase.

[0038] Among them, 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 light detector refers to an instrument that captures the interference pattern output by the interferometer, such as a CCD camera. The interference pattern refers to the light and dark stripes formed by the superposition of light waves, which contains phase information. The phase information refers to the phase information of the light wave extracted from the interference pattern, which reflects the volatility 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 point in the image. The vortex phase field refers to the spiral structure (topological defect) in the phase distribution, which is calculated by phase shift interferometry.

[0039] Furthermore, the embodiment of the present invention calculates the integral of the phase gradient along a closed path through a formula. This light field capture efficiency accurately quantifies the ability of the light field in the area to "bind" or "converge" light energy, which is depth information that traditional illumination measurement cannot provide.

[0040] In one embodiment of the present invention, analyzing the light field capture efficiency of the interior space of the house using the interference vortex phase includes: calculating a phase gradient in the interference vortex phase; and calculating the light field capture efficiency of the interior space of the house based on the phase gradient using the following formula:

[0041] in, represents the light field capture efficiency, represents the phase gradient, Represents a differential line element along a closed path.

[0042] Wherein, the phase gradient is given by the formula Calculated, is the phase at a certain coordinate position in the interference vortex phase. The light field capture efficiency is used to quantify the ability of light energy to gather in a specific area (0~1), for example, the lecture area =0.92 (high-efficiency capture area).

[0043] S4. Generate dimming control parameters of the indoor lighting system based on the light intensity distribution and the light field capture efficiency, and generate light deflection parameters corresponding to the house window frame based on the micron-level bending displacement.

[0044] The embodiment of the present invention uses a formula to dynamically calculate the required intensity of artificial light sources in areas with sufficient lighting. The innovation lies in the introduction of the inverse of the phase gradient. In areas with drastic phase changes, usually the boundaries of the light field or areas of severe interference, more cautious or less artificial light supplementation is required to avoid interfering with the natural light field or causing glare. The lamp health coefficient ensures the accurate output of aging lamps. In areas with insufficient lighting, areas with high light field capture efficiency are prioritized for key light supplementation to avoid wasting energy in inefficient areas. The optimal lamp that is close to the target area and in good condition is selected based on the lamp health, and the precise power duty cycle is calculated. Not only the dimming percentage and color temperature CCT are output, but also the action performed by a specific lamp at a specific power in a specific area is output, thereby realizing refined intelligent dimming based on spatial differentiation, physical perception, and device status perception. Compared with simple threshold control, it is more energy-saving, more comfortable, more accurate, and has better equipment life management.

[0045] In one embodiment of the present invention, generating the dimming control parameters of the indoor lighting system based on the light intensity distribution and the light field capture efficiency includes: determining a lamp health coefficient of the indoor lighting system when the illuminance in the light intensity distribution is greater than a preset illuminance; and calculating the light intensity of the artificial light source of the indoor lighting system using the following formula based on the lamp health coefficient:

[0046] in, Indicates the intensity of artificial light source, Indicates the health coefficient of the lamp, represents the illuminance in the light intensity distribution, represents the target illuminance, represents the phase gradient; Query the dimming percentage and color temperature CCT of the lamp corresponding to the light intensity of the artificial light source; use the dimming percentage and the color temperature CCT of the lamp as dimming control parameters of the indoor lighting system; when the illuminance in the light intensity distribution is not greater than the preset illuminance, arrange the indoor projection areas corresponding to the light field capture efficiency in descending order of the light field capture efficiency to obtain an arranged indoor area; screen the target lamps whose lamp health coefficient is greater than the preset health coefficient among the artificial light lamps corresponding to the indoor lighting system; query the final lamp that is closest to the arranged indoor area among the target lamps; calculate the power duty cycle of the final lamp based on the light field capture efficiency and the lamp health coefficient; and generate dimming control parameters among the arranged indoor area, the final lamp, the light field capture efficiency and the power duty cycle.

[0047] The lamp health coefficient refers to the lamp performance attenuation index (0~1), β=1 means brand new, for example, a 3-year-old lamp has β=0.85, and the preset illuminance refers to the target illuminance value set by the user, such as =500lux, the dimming percentage of the lamp refers to the percentage of the lamp output power, the color temperature CCT refers to the color temperature of the light (unit is K), such as 3500K (warm white), and the power duty cycle refers to the proportion of the actual output power of the lamp. It should be noted that the formula in There is a limited range of use, for example, 0.1<| |<5.0rad / m.

[0048] In another embodiment of the present invention, determining the health coefficient of lamps in an indoor lighting system includes: collecting the working time, color temperature offset and driving current fluctuation of the lamps in the indoor lighting system; inputting the lamp working time, the color temperature offset and the driving current fluctuation into a preset LSTM model; and obtaining the lamp health coefficient output by the LSTM model regarding the lamp working time, the color temperature offset and the driving current fluctuation.

[0049] Among them, the lamp working time refers to the cumulative lighting time of the lamp from startup to shutdown, the color temperature offset refers to the deviation between the actual luminous color temperature of the lamp and the nominal color temperature, and the driving current fluctuation refers to the oscillation change amplitude of the driving current deviating from the stable value.

[0050] See Figure 2 FIG. 1 is a flow chart of the lamp health coefficient of a lighting system energy consumption optimization method based on big data provided by an embodiment of the present invention. Figure 2 In

[15] , the input features include the lamp's operating time, 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.

[0051] For example, the indoor projection areas corresponding to the light field capture efficiency are arranged in descending order according to the light field capture efficiency, and the indoor areas are arranged as follows: The values ​​are sorted from high to low. For example, based on the input data "zoneA": η=0.85, "zoneB": η=0.62, the sorted result is: ["zoneA", "zoneB"], because zoneA The value (0.85) is greater than that of zoneB Value (0.62), the process of screening the target lamps whose lamp health coefficient is greater than the preset health coefficient among the artificial light lamps corresponding to the indoor lighting system is: from all lamp devices, screen out lamps with a health coefficient (β) greater than 0.9, and consider these lamps to be healthy and can work normally. For example, assuming that there are several lamp devices, the healthy lamps obtained after screening are: ["LED_01", "LED_03"], that is, the lamps numbered LED_01 and LED_03 meet the health condition. Furthermore, the process of querying the final lamp closest to the arranged indoor area among the target lamps is: according to the preset area-device matching rule, assign lamps to each area, traverse the sorted area list, for each area, find the healthy lamp closest to the area, assign the found lamp to the current area, and set the projection weight of the lamp to that of its corresponding area. Value, further, the calculation of the final power duty cycle of the lamp is: calculate the product of the light field capture efficiency and the lamp health coefficient and multiply it by 100% to obtain the power duty cycle.

[0052] Among them, Table 1 of the corresponding relationship between the intensity of artificial light source and the dimming percentage of lamps and color temperature CCT is as follows:

[0053] Table 2 of dimming control parameters when the illuminance in the light intensity distribution is not greater than the preset illuminance is as follows:

[0054] Furthermore, an embodiment of the present invention utilizes a formula to dynamically calculate the required refractive index of the embedded liquid crystal waveguide layer based on the measured bending displacement of the window frame. The greater the curvature, the higher the refractive index, and the stronger the light deflection. The mechanical deformation of the window frame regulates the refractive index of the internal optical material in real time, thereby achieving light deflection control without mechanically moving parts. The deformation sensitivity coefficient can be designed to adjust the sensitivity, thereby providing a low-cost, fast-response, and mechanically wear-free method for dynamically controlling the incident direction and distribution of light. In conjunction with the dimming system, this method achieves in-depth coordinated management of natural and artificial light.

[0055] In one embodiment of the present invention, generating the light deflection parameter corresponding to the house window frame based on the micron-level bending displacement includes: calculating the waveguide refractive index of the waveguide refraction device corresponding to the house window frame based on the micron-level bending displacement using the following formula:

[0056] in, represents the waveguide refractive index, represents the waveguide base refractive index, represents the micron-level bending displacement, represents the curvature change of the house window frame in the x and y directions, represents the deformation sensitivity coefficient; The refractive index deviation between the waveguide refractive index and the waveguide base refractive index is calculated; and the waveguide base refractive index and the refractive index deviation are used as light deflection parameters.

[0057] Among them, the deformation sensitivity coefficient refers to the control intensity of the refractive index of the window frame deformation (in μm²). For example, a=0.28 of the high-sensitivity material 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 in the absence of light. The waveguide refractive index refers to the dynamic light refractive index controlled by the bending of the window frame. The waveguide refractive device refers to the liquid crystal layer embedded in the window frame, which realizes 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.

[0058] in, The physical meaning of the formula is: the greater the curvature, the higher the refractive index, and the stronger the light deflection. This design dynamically adjusts the refractive index of the window frame to change the propagation path of incident natural light (non-artificial light) (for example, directing sunlight deeper into the room), thereby reducing dependence on artificial lighting and achieving energy-saving goals.

[0059] S5. Generate a light path navigation instruction for the artificial light fixture according to the light deflection parameter and the micron-level bending displacement, and perform energy consumption optimization processing of the indoor lighting system through the dimming control parameter, the light deflection parameter, and the light path navigation instruction.

[0060] In an embodiment of the present invention, a formula is used to calculate the angle at which artificial light needs to be deflected. The product of the waveguide's base refractive index and the refractive index deviation reflects the effect of the refractive index change on the direction of the light. The calculated light deflection angle is bound to the window frame area where the deformation occurs, and specific optical path navigation instructions are generated. This tells the artificial light system how large the deflection angle needs to be. The purpose is to compensate for or coordinate with the natural light changes in which window frame area. In this way, complex light control requirements can be converted into specific, executable instructions, allowing 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.

[0061] In one embodiment of the present invention, generating the optical path navigation instruction for the artificial light fixture based on the light deflection parameters and the micron-level bending displacement includes: calculating the light deflection angle corresponding to the artificial light fixture using the following formula based on the waveguide base refractive index and refractive index deviation in the light deflection parameters:

[0062] in, represents the light deflection angle, represents the refractive index deviation, represents the waveguide base refractive index, represents the deflection sensitivity coefficient; The light path navigation instruction is determined by the window frame area corresponding to the light deflection angle and the micron-level bending displacement.

[0063] The light deflection angle refers to the deflection angle of artificial light emitted by the artificial light fixture. The deflection sensitivity coefficient is obtained by measuring the actual light deflection angle corresponding to the window frame deformation under controllable lighting conditions. 、 、 The parameters are reverse fitted to obtain .

[0064] Among them, Table 3 of the optical path navigation instructions is as follows:

[0065] In one embodiment of the present invention, 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 instructions, including: adjusting the lighting parameters of the lamps of the indoor lighting system through 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 through the light deflection parameters to obtain a refraction adjustment result; adjusting the lighting direction of the lamps of the indoor lighting system through the optical path navigation instructions to obtain a direction adjustment result; and completing the energy consumption optimization processing of the indoor lighting system through the lighting adjustment result, the refraction adjustment result and the direction adjustment result.

[0066] Compared with the problems described in the background technology, the embodiment of the present 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 caused by the natural light and heat effect. This saves a lot of additional light sensors, reduces costs, simplifies installation, and avoids occlusion problems. Furthermore, based on the principle that the degree of bending of the window frame is directly caused by the photothermal effect of the sunlight irradiating it, and has a strong physical correlation, the embodiment of the present invention accurately converts mechanical deformation into illuminance through a formula. The window frame covers the entire lighting surface, and its deformation measurement can reflect the details of the light distribution on the window frame plane, not just a few points. The light intensity distribution map of the entire indoor space can be efficiently reconstructed through interpolation, and the accuracy far exceeds that of a simple model. The embodiment of the present invention uses an interferometer to capture The interference pattern produced by the superposition of artificial light and natural light is captured, and the vortex phase field is extracted by phase-shift interferometry, which directly detects the volatility and topological structure of the light field. Furthermore, the embodiment of the present invention calculates the integral of the phase gradient along a closed path through a formula. This light field capture efficiency accurately quantifies the ability of the light field in the area to "bind" or "converge" light energy, which is depth information that traditional illuminance measurement cannot provide. The embodiment of the present invention dynamically calculates the required intensity of the artificial light source by using a formula in an area with sufficient light. The innovation lies in the introduction of the inverse of the phase gradient. In areas with drastic phase changes, which are usually light field boundaries or areas with drastic interference, more cautious or less artificial light supplementation is required to avoid interfering with the natural light field or causing glare. The lamp health coefficient ensures that the output of aging lamps is accurate. In areas with insufficient lighting, areas with high light field capture efficiency are prioritized for key fill lighting to avoid wasting energy in low-efficiency areas. The optimal lamps that are close to the target area and in good condition are selected based on the health of the lamps, and the accurate power duty cycle is calculated. Not only the dimming percentage and color temperature CCT are output, but also the action performed by a specific lamp at a specific power in a specific area is output, thereby realizing refined intelligent dimming with spatial differentiation, physical perception, and device status perception. Compared with simple threshold control, it is more energy-saving, more comfortable, more accurate, and has better equipment life management. Furthermore, the embodiment of the present invention uses a formula to dynamically calculate the refractive index required for the embedded liquid crystal waveguide layer based on the measured bending displacement of the window frame. The greater the curvature, the higher the refractive index, the stronger the light deflection, and the mechanical deformation of the window frame. The refractive index of the internal optical material is adjusted in real time, thereby realizing light deflection control without mechanical moving parts. The deformation sensitivity coefficient can be designed to adjust the sensitivity, thereby providing a low-cost, fast-response, and mechanical wear-free method for dynamically controlling the incident direction and distribution of light. In conjunction with the dimming system, deep collaborative management of natural light and artificial light is achieved. The embodiment of the present invention uses a formula to calculate the angle at which artificial light needs to be deflected. The product between the basic refractive index of the waveguide and the refractive index deviation reflects the influence of the refractive index change on the direction of the light. The calculated light deflection angle is bound to the window frame area that produces the deformation to generate a specific optical path navigation instruction, which tells the artificial light system how large the deflection angle needs to be. Its purpose is to compensate for or coordinate with the natural light changes in which window frame area.This allows complex light control requirements to be converted into specific, executable instructions, enabling artificial light to actively respond to the dynamic changes in natural light in different areas of the window frame, achieving dynamic light tracking or light avoidance. Therefore, the method and system for optimizing lighting system energy consumption based on big data provided by the embodiments of the present invention can address technical bottlenecks such as limited perception methods, lack of utilization of light field physical characteristics, extensive control strategies, single execution methods, and insufficient collaborative optimization.

[0067] Example 2: like Figure 3 As shown in FIG, a functional module diagram of a lighting system energy consumption optimization system based on big data is shown in the present invention.

[0068] The big data-based lighting system energy consumption optimization system 300 described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the big data-based lighting system energy consumption optimization system can 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. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function. These modules are stored in the electronic device's memory.

[0069] In the embodiment of the present invention, the functions of each module / unit are as follows: The system determination module 301 is used to determine an indoor lighting system, wherein the indoor lighting system includes window frames of a house, artificial light fixtures and an interior space of the house.

[0070] The illumination establishment module 302 is configured to record the micron-level bending displacement of the window frame of the house under natural light, and establish the illumination intensity distribution of the interior space of the house based on the micron-level bending displacement; The efficiency analysis module 303 is configured to capture the interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light, and analyze the light field capture efficiency of the interior space of the house using the interference vortex phase; The parameter generation module 304 is configured to generate dimming control parameters of 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 305 is used to generate a light path navigation instruction for the artificial light fixture based on the light deflection parameters and the micron-level bending displacement, and perform energy consumption optimization processing of the indoor lighting system through the dimming control parameters, the light deflection parameters and the light path navigation instruction.

[0071] In detail, the modules in the lighting system energy consumption optimization system 300 based on big data in the embodiment of the present invention are used in the same manner as above. Figure 1 The same technical means as the method for optimizing lighting system energy consumption based on big data described in the previous section can produce the same technical effects and will not be repeated here.

[0072] 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.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing lighting system energy consumption based on big data, characterized in that: The method comprises: Determining an indoor lighting system, wherein the indoor lighting system includes window frames, artificial light fixtures, and interior space of the house; Recording the micrometer-level bending displacement of the window frame of the house under natural light, and establishing the light intensity distribution of the interior space of the house based on the micrometer-level bending displacement; capturing an interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light, and analyzing the light field capture efficiency of the interior space of the house using the interference vortex phase; generating a dimming control parameter of the indoor lighting system according to the light intensity distribution and the light field capture efficiency, and generating a light deflection parameter corresponding to the window frame of the house based on the micron-level bending displacement; According to the light deflection parameters and the micron-level bending displacement, a light path navigation instruction for the artificial light fixture is generated, and energy consumption optimization processing of the indoor lighting system is performed through the dimming control parameters, the light deflection parameters and the light path navigation instruction.

2. The method for optimizing lighting system energy consumption based on big data according to claim 1, characterized in that: The step of establishing the light intensity distribution in the interior space of the house according to the micron-level bending displacement includes: Calculating the illuminance on the window frame of the house according to the micron-level bending displacement; The interpolation method is used to extend the illuminance on the window frame of the house to the interior space of the house to obtain the light intensity distribution.

3. The method for optimizing lighting system energy consumption based on big data according to claim 1, characterized in that: The capturing of the interference vortex phase between the artificial light emitted by the artificial light fixture and the natural light comprises: inputting the artificial light and the natural light into an interferometer; using a light detector to capture an interference pattern generated by the interferometer under the action 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 by phase shift interferometry; The vortex phase field is taken as the interference vortex phase.

4. The method for optimizing lighting system energy consumption based on big data according to claim 1, wherein: The analyzing the light field capture efficiency of the interior space of the house by using the interference vortex phase includes: calculating a phase gradient in the interfering vortex phase; According to the phase gradient, the light field capture efficiency of the interior space of the house is calculated using the following formula: in, represents the light field capture efficiency, represents the phase gradient, Represents a differential line element along a closed path.

5. The method for optimizing lighting system energy consumption based on big data according to claim 1, wherein: Generating the dimming control parameters of the indoor lighting system according to the light intensity distribution and the light field capture efficiency includes: When the illuminance in the light intensity distribution is greater than a preset illuminance, determining a lamp health coefficient of the indoor lighting system; According to the lamp health coefficient, the light intensity of the artificial light source of the indoor lighting system is calculated using the following formula: in, Indicates the intensity of artificial light source, Indicates the health coefficient of the lamp, represents the illuminance in the light intensity distribution, represents the target illuminance, represents the phase gradient; Query the lamp dimming percentage and color temperature CCT corresponding to the light intensity of the artificial light source; Using the dimming percentage of the lamp and the color temperature CCT as dimming control parameters of the indoor lighting system; When the illuminance in the light intensity distribution is not greater than the preset illuminance, arranging the indoor projection areas corresponding to the light field capture efficiencies in descending order of the light field capture efficiencies to obtain arranged indoor areas; Selecting target lamps whose lamp health coefficient is greater than a preset health coefficient among the artificial light lamps corresponding to the indoor lighting system; Querying the final lamp among the target lamps that is closest to the arranged indoor area; Calculating a power duty cycle of the final lamp according to the light field capture efficiency and the lamp health coefficient; A dimming control parameter is generated among the arranged indoor area, the final lamp, the light field capture efficiency and the power duty cycle.

6. The method for optimizing lighting system energy consumption based on big data according to claim 5, characterized in that: Determining the health coefficient of the lamps of the indoor lighting system includes: Collect information about lamp operating hours, color temperature deviation, and driving current fluctuations in indoor lighting systems; Inputting the lamp working time, the color temperature offset and the driving current fluctuation into a preset LSTM model; Obtain a lamp health coefficient output by the LSTM model regarding the lamp operating time, the color temperature offset, and the driving current fluctuation.

7. The method for optimizing lighting system energy consumption based on big data according to claim 1, wherein: Generating light deflection parameters corresponding to the house window frame based on the micron-level bending displacement includes: Calculating a waveguide refractive index of a waveguide refraction device corresponding to the house window frame based on the micron-level bending displacement; calculating a refractive index deviation between the waveguide refractive index and the waveguide base refractive index; The waveguide base refractive index and the refractive index deviation are used as light deflection parameters.

8. The method for optimizing lighting system energy consumption based on big data according to claim 1, wherein: Generating the light path navigation instruction of the artificial light fixture according to the light deflection parameter and the micron-level bending displacement includes: Based on the waveguide base 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, represents the light deflection angle, represents the refractive index deviation, represents the waveguide base refractive index, represents the deflection sensitivity coefficient; The light path navigation instruction is determined by the window frame area corresponding to the light deflection angle and the micron-level bending displacement.

9. The method for optimizing lighting system energy consumption based on big data according to claim 1, wherein: The performing of the energy consumption optimization process of the indoor lighting system by using the dimming control parameter, the light deflection parameter and the light path navigation instruction includes: Adjusting the lighting parameters of the lamps of the indoor lighting system by using the dimming control parameters to obtain a lighting adjustment result; Adjusting the refraction parameters of the waveguide refraction device corresponding to the house window frame using the light deflection parameters to obtain a refraction adjustment result; Adjusting the lighting direction of the lamps of the indoor lighting system according to the light path navigation instruction to obtain a direction adjustment result; The energy consumption optimization processing of the indoor lighting system is completed through the lighting adjustment result, the refraction adjustment result and the direction adjustment result.

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

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