A building energy consumption control method and system using changes in illumination images

Abstract

This invention discloses a building energy consumption control method and system that utilizes changes in illumination patterns. By acquiring indoor and outdoor illumination images of a building and generating brightness distribution images, the method extracts feature points from areas of varying illumination and corrects them based on the distribution information of stable illumination areas. Based on the corrected feature points, the illumination areas are partitioned and compared, brightness difference indices are calculated, and the uniformity of illumination distribution is determined. Energy-saving control or dynamic adjustment instructions are then generated, enabling refined energy consumption management of lighting equipment and shading devices, improving the overall energy efficiency of the building.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy consumption management, and in particular to a method and system for optimizing energy consumption control by utilizing changes in illumination images. Background Art

[0002] With the development of building technology, dynamic management and energy-saving control of building energy consumption have become research focuses. Existing energy consumption control systems are mostly based on sensor data such as temperature and humidity for adjustment, failing to fully utilize the light distribution information inside the building, resulting in limited control accuracy of lighting, shading and other equipment. Some solutions have attempted to introduce image acquisition technology to perceive lighting conditions, but common problems include simple image processing algorithms, inaccurate positioning of areas with changing lighting conditions, and crude control logic, making it difficult to achieve refined optimization of energy consumption in complex lighting environments. Therefore, there is an urgent need for an intelligent control method that integrates image brightness analysis and regional difference judgment to improve the responsiveness and accuracy of building energy consumption regulation. Summary of the Invention

[0003] The purpose of this invention is to provide a building energy consumption control method and system that utilizes changes in illumination patterns to address the existing issues of low illumination perception accuracy, inaccurate regional identification, and insensitive energy consumption control. By introducing a feature point correction mechanism and calculating a brightness difference index, this invention enables refined analysis of illumination distribution within a building and automatically generates energy-saving or adjustment instructions based on illumination uniformity, thereby improving the operating efficiency of lighting and shading equipment and achieving dynamic optimization and intelligent management of building energy consumption.

[0004] Specifically, the present invention provides a building energy consumption control method using changes in illumination patterns, the building energy consumption control method comprising the following steps:

[0005] Acquire indoor and outdoor lighting images of the building, and generate a brightness distribution image corresponding to the lighting image;

[0006] Based on a preset illumination feature positioning method, a feature point set of the illumination change area in the brightness distribution image is obtained;

[0007] Acquiring distribution information of the feature point set to a stable illumination area in the brightness distribution image, and correcting the feature point set according to the distribution information to obtain a corrected feature point set;

[0008] Based on the corrected feature point set, the illumination area in the illumination image is partitioned and compared to obtain energy consumption optimization information of the illumination area.

[0009] Furthermore, the step of obtaining distribution information of the feature point set to the stable illumination area in the brightness distribution image includes:

[0010] Setting a first reference line and a second reference line having the center of the feature point set as the intersection point, passing through the stable illumination area and being perpendicular to each other, so that the first reference line intersects the illumination variation area at the first and second intersection points, and the second reference line intersects the illumination variation area at the third and fourth intersection points;

[0011] The distances and brightness differences between the center of the feature point set and the first intersection point, the second intersection point, the third intersection point, and the fourth intersection point are used as distribution information.

[0012] Furthermore, the step of correcting the feature point set according to the distribution information to obtain a corrected feature point set includes:

[0013] translating the center of the feature point set along the direction of the first reference line until a first distance and a brightness difference between the center of the feature point set and the first intersection point and a second distance and a brightness difference between the center of the feature point set and the second intersection point are equal, to determine an intermediate feature point set;

[0014] The intermediate feature point set is translated along the direction of the second reference line until a third distance and a brightness difference between the intermediate feature point set and the third intersection point are equal to a fourth distance and a brightness difference between the intermediate feature point set and the fourth intersection point, so as to determine a revised feature point set.

[0015] Furthermore, the step of acquiring indoor and outdoor lighting images of a building and generating a brightness distribution image corresponding to the lighting image includes:

[0016] Collecting illumination images of the indoor and outdoor environments of a building, and performing brightness normalization processing on the illumination images;

[0017] The illumination image after brightness normalization is subjected to edge enhancement processing to obtain the brightness distribution image.

[0018] Furthermore, the step of obtaining a feature point set of the illumination change area in the brightness distribution image based on a preset illumination feature positioning method includes:

[0019] Analyzing the brightness distribution image based on a preset illumination feature positioning method;

[0020] When a feature point set of an illumination change region in the brightness distribution image is identified, the feature point set is used as an initial feature point set, and the steps of: obtaining distribution information of the feature point set to a stable illumination region in the brightness distribution image;

[0021] When the feature point set of the illumination change area in the brightness distribution image cannot be identified, corresponding prompt information is output.

[0022] Furthermore, the step of performing partition comparison on the illumination area in the illumination image based on the modified feature point set to obtain energy consumption optimization information of the illumination area includes:

[0023] Dividing the illuminated area in the illuminated image into a preset number of equal partitions with the corrected feature point set as the center;

[0024] Brightness distribution information of the equal subareas is obtained, and energy consumption optimization information of the illumination area is determined according to differences in the brightness distribution information between the equal subareas.

[0025] Furthermore, the brightness distribution information includes a brightness mean and a gradient value. The step of obtaining the brightness distribution information of the equal partitions and determining the energy consumption optimization information of the illuminated area based on the difference in the brightness distribution information between the equal partitions includes: counting the brightness mean and gradient value of each equal partition, and calculating the brightness difference index between the equal partitions, wherein the brightness difference index is determined by the following formula:

[0026]

[0027] Among them, ΔL is the brightness difference index, M i is the mean brightness of the ith equal partition, M avg is the average brightness of all equal partitions, G i is the brightness gradient value of the i-th equal partition, G avg is the average brightness gradient value of all equal partitions, D i is the distance between the center of the ith equal partition and the building reference point, D max is the maximum reference distance of the building, and N is the number of equal partitions;

[0028] If the brightness difference index is within a preset range, the energy consumption optimization information indicates that the illumination area is uniform, and an energy-saving control instruction is generated;

[0029] If the brightness difference index is not within the preset range, the energy consumption optimization information indicates that the illumination area is uneven, and a dynamic adjustment instruction is generated.

[0030] Furthermore, the step of generating an energy-saving control instruction or a dynamic adjustment instruction includes:

[0031] When the energy consumption optimization information indicates that the illumination area is uniform, an energy-saving control instruction is generated to reduce the power of the lighting equipment or adjust the opening and closing angle of the sunshade device;

[0032] When the energy consumption optimization information indicates that the illumination area is uneven, a dynamic adjustment instruction is generated for increasing the brightness of the lighting equipment or adjusting the angle of the sunshade device for a specific partition.

[0033] The present invention also provides a building energy consumption control system using changes in illumination patterns, the building energy consumption control system comprising:

[0034] An image processing module is used to obtain indoor and outdoor lighting images of a building and generate a brightness distribution image corresponding to the lighting image;

[0035] A feature positioning module, configured to obtain a feature point set of the illumination change area in the brightness distribution image based on a preset illumination feature positioning method;

[0036] a feature correction module, configured to obtain distribution information of the feature point set to the stable illumination area in the brightness distribution image, and correct the feature point set according to the distribution information to obtain a corrected feature point set;

[0037] A partition comparison module is used to perform partition comparison on the illumination area in the illumination image based on the corrected feature point set to obtain energy consumption optimization information of the illumination area.

[0038] Furthermore, the partition comparison module further includes:

[0039] a partitioning unit, configured to divide the illumination area in the illumination image into a preset number of equal partitions with the modified feature point set as the center;

[0040] an analyzing unit, configured to obtain brightness distribution information of the equal subareas, and determine energy consumption optimization information of the illumination area according to differences in the brightness distribution information between the equal subareas;

[0041] An instruction generation unit is used to generate energy-saving control instructions or dynamic adjustment instructions according to the energy consumption optimization information, and send them to the lighting equipment and shading devices in the building.

[0042] The present invention obtains indoor and outdoor lighting images of buildings and generates brightness distribution images, extracts feature point sets of lighting change areas in combination with a preset lighting feature positioning method, and then corrects the feature point sets based on the distribution information of stable lighting areas. This can effectively improve the accuracy of lighting area recognition and avoid recognition errors caused by local lighting anomalies or environmental interference. Furthermore, by performing partition comparison of lighting areas through the correction of feature point sets, energy consumption optimization information is obtained, providing a more accurate judgment basis for subsequent energy consumption control, thereby solving the problems of low image perception accuracy, unstable area recognition, and delayed control response in the prior art, realizing efficient and intelligent management of building lighting and shading equipment, and improving the overall energy efficiency control level of the building. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flow chart of the building energy consumption control method using illumination image changes provided by the present invention;

[0044] Figure 2 A flow chart of the method for obtaining energy consumption optimization information of an illuminated area provided by the present invention;

[0045] Figure 3 This is a framework diagram of the building energy consumption control system using lighting image changes provided by the present invention.

[0046] Reference numerals:

[0047] A building energy consumption control system 100 using illumination image changes includes an image processing module 101 , a feature positioning module 102 , a feature correction module 103 , and a partition comparison module 104 . DETAILED DESCRIPTION

[0048] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0049] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the technical features indicated. Thus, features specified as "first" or "second" may explicitly or implicitly include one or more of such features; and in the description of this application, unless otherwise specified, "plurality" means two or more.

[0050] In order to more clearly illustrate the technical solution of the present invention, the present invention is described in detail below in conjunction with specific embodiments, but this should not be construed as limiting the scope of protection of the present invention.

[0051] In this embodiment, if Figure 1 As shown, a building energy consumption control method using changes in lighting images is provided. This method can achieve accurate energy consumption control in complex lighting environments and provide reliable technical support for building energy-saving management.

[0052] Specifically, step S01: Acquire indoor and outdoor lighting images of a building and generate a brightness distribution image corresponding to the lighting image. First, cameras or light sensors installed inside and outside the building capture lighting images. These cameras can be placed in key indoor areas, such as the ceiling of an office or lobby, to capture both natural and artificial lighting conditions. Outdoors, cameras can be mounted on building walls or roofs to record lighting information from direct sunlight or diffuse light on cloudy days. The captured lighting image can be a color image or a grayscale image, containing information about the spatial distribution of indoor and outdoor light intensity. Furthermore, to generate the brightness distribution image, the captured lighting image is processed to extract its brightness information. For example, the color image is converted to a grayscale image, with the grayscale value of each pixel representing the light intensity, or the brightness component is extracted from the image to generate an image reflecting the light intensity distribution. This brightness distribution image displays the spatial variations in light intensity using the brightness values ​​of each pixel, with areas with high brightness values ​​indicating strong light intensity and areas with low brightness values ​​indicating weak light intensity. This processing ensures that subsequent analysis is based on clear light intensity information.

[0053] Furthermore, step S02: Based on a preset illumination feature location method, a feature point set of illumination variation regions in the brightness distribution image is obtained. The preset illumination feature location method is used to analyze the brightness distribution image, identify illumination variation regions, and extract their feature point sets. Illumination variation regions are areas in the brightness distribution image where illumination intensity changes significantly, such as the boundary where natural light entering a room through a window intersects with artificial lighting. Specifically, the preset illumination feature location method can be implemented by detecting rapid changes in brightness values ​​in the brightness distribution image. For example, the spatial rate of change of brightness values ​​can be analyzed to identify regions where brightness values ​​significantly increase or decrease over a short distance. These regions typically correspond to illumination variation regions. Next, feature point sets are extracted within these regions. The feature point set consists of a set of key points representing typical locations of illumination variation regions, such as light-shadow boundaries or points with the most significant brightness changes. When extracting the feature point set, a set of coordinate points can be generated by identifying key locations within the illumination variation regions, with each coordinate point corresponding to a pixel position in the brightness distribution image. These coordinate points can reflect the spatial distribution characteristics of the illumination variation regions, providing a basis for subsequent analysis.

[0054] Furthermore, step S03 obtains distribution information from the feature point set to the stable illumination region in the brightness distribution image, and modifies the feature point set based on the distribution information to obtain a modified feature point set. The relationship between the feature point set and the stable illumination region is further analyzed to improve the accuracy of the feature point set. A stable illumination region refers to an area in the brightness distribution image where illumination intensity varies minimally, such as an indoor area away from a window or an outdoor area with uniform illumination. First, the stable illumination region is identified in the brightness distribution image. This can be achieved by examining the spatial consistency of the brightness values, for example, by selecting an area with minimal brightness fluctuation as a stable illumination region. Next, distribution information from the feature point set to the stable illumination region is obtained, specifically including the spatial positional relationship and brightness difference between the feature point set and the stable illumination region. For example, the center position of the feature point set is determined, and the distance or brightness difference from the center position to the representative position of the stable illumination region is analyzed. This distribution information reflects the relative characteristics of the illumination variation region and the stable illumination region. Based on this distribution information, the feature point set is modified to more accurately represent the illumination variation region. For example, by adjusting the coordinate positions of a feature point set to more closely match the brightness characteristics of areas with stable lighting, we can reduce deviations caused by noise or outliers. The corrected feature point set is still a set of coordinate points, but its positions better reflect the true characteristics of areas with varying lighting.

[0055] Furthermore, step S04 involves performing a partitioned comparison of the illuminated area in the illumination image based on the modified feature point set to obtain energy optimization information for the illuminated area. Specifically, the illuminated area in the illumination image is divided into multiple sub-areas, centered around the modified feature point set. These sub-areas can be generated based on the spatial distribution of the feature point set, either uniformly or based on feature point density. For example, the illuminated area can be divided into several rectangular regions, each containing a portion of the feature points and covering a portion of the illumination variation region. Next, the brightness distribution of each sub-area is compared to analyze the brightness differences between the sub-areas. A specific method includes examining the brightness distribution characteristics of each sub-area, such as the average brightness level or degree of variation. If the brightness values ​​of some sub-areas are significantly higher or lower than those of other areas, this indicates uneven illumination distribution, and adjustments to lighting equipment or sunshades may be necessary to optimize energy consumption. Based on these comparison results, energy optimization information is generated. This energy optimization information can include a description of the uniformity of the illuminated area, such as "uniform illumination area" or "uneven illumination area," or specific control recommendations, such as "reduce illumination intensity in a certain area" or "increase natural light intake in a certain area." This information is used to guide energy management within the building, for example by reducing lighting power in overly lit areas or adjusting shading devices to optimize light distribution, thereby saving energy.

[0056] It is understood that this method can be implemented in real time or periodically, for example, by collecting lighting images and performing the above analysis every hour to adapt to changing indoor and outdoor lighting conditions. For example, on a clear day, when natural light is abundant, the generated energy optimization information may recommend reducing artificial lighting. On a cloudy day or at night, when areas with less varying lighting conditions may experience less variation, the energy optimization information may recommend increasing lighting intensity in specific areas. This dynamic adjustment effectively balances lighting comfort with energy conservation.

[0057] This embodiment obtains indoor and outdoor lighting images of the building and generates a brightness distribution image, combines a preset lighting feature positioning method to extract a feature point set of the lighting change area, and then corrects the feature point set based on the distribution information of the stable lighting area. This can effectively improve the accuracy of lighting area recognition and avoid recognition errors caused by local lighting anomalies or environmental interference. Furthermore, by performing partition comparison of the lighting area through the correction of the feature point set, energy consumption optimization information is obtained, providing a more accurate judgment basis for subsequent energy consumption control, thereby solving the problems of low image perception accuracy, unstable area recognition, and delayed control response in the existing technology, realizing efficient and intelligent management of building lighting and shading equipment, and improving the overall energy efficiency control level of the building.

[0058] In some embodiments, the step of obtaining distribution information of the feature point set to the stable illumination area in the brightness distribution image includes: setting a first reference line and a second reference line with the center of the feature point set as the intersection, passing through the stable illumination area and perpendicular to each other, so that the first reference line intersects with the illumination change area at the first intersection and the second intersection, and the second reference line intersects with the illumination change area at the third intersection and the fourth intersection; and taking the distance and brightness difference between the center of the feature point set and the first intersection, the second intersection, the third intersection and the fourth intersection as distribution information.

[0059] Specifically, in the step of setting a first reference line and a second reference line having the center of the feature point set as the intersection point, passing through the stable illumination area and being perpendicular to each other, so that the first reference line intersects the illumination change area at the first and second intersection points, and the second reference line intersects the illumination change area at the third and fourth intersection points, the center position of the feature point set is first determined. The feature point set consists of a set of coordinate points representing key positions of the illumination change area, for example, {(x1, y1), (x2, y2), ..., (x n , y n )}. By calculating the average value of these coordinate points, the center position C(x c , y c). This center position is used as a reference for the subsequent setting of reference lines. Furthermore, a stable lighting area is identified in the brightness distribution image. The stable lighting area is an area with little change in lighting intensity, such as a uniformly lit area indoors away from a window. Based on the center of the feature point set, two mutually perpendicular reference lines are set: a first reference line and a second reference line. These two reference lines intersect with the center of the feature point set and ensure that they pass through the stable lighting area. For example, the first reference line can be a horizontal line that extends along the horizontal axis of the brightness distribution image, passes through the center of the feature point set and enters the stable lighting area; the second reference line is a vertical line that extends along the vertical axis, also passes through the center of the feature point set and enters the stable lighting area. Next, the intersection of the first reference line and the lighting change area is determined. The lighting change area is an area where the brightness value changes significantly, such as the area where natural light and artificial lighting intersect. When the first reference line passes through the lighting change area, it will intersect with the boundary of the area, generating two intersection points, respectively referred to as the first intersection point P1(x1, y1) and the second intersection point P2(x2, y2). Similarly, when the second reference line passes through the illumination variation region, it will intersect with the region's boundary, generating a third intersection point P3(x3, y3) and a fourth intersection point P4(x4, y4). These intersection points can be determined by analyzing the brightness changes along the reference line. For example, when the brightness value suddenly changes from a uniform value in a stable illumination region to a non-uniform value in an illumination variation region, the intersection position is marked.

[0060] Furthermore, in the step of using the distances and brightness differences between the center of the feature point set and the first, second, third, and fourth intersections as distribution information, the spatial distances and brightness differences between the center of the feature point set and each intersection are calculated to form distribution information. Specifically, for the first intersection P1, the distance from the center of the feature point set C to P1 is calculated. For example, this distance is determined by the straight-line distance between the two points. Simultaneously, the brightness value of the pixel where the center of the feature point set C is located and the brightness value of the pixel where the first intersection P1 is located are obtained, and the brightness difference between the two is calculated, i.e., the absolute difference in brightness. Similarly, for the second intersection P2, the distance from the center of the feature point set C to P2 and the brightness difference between C and P2 are calculated. The same calculation process is repeated for the third intersection P3 and the fourth intersection P4, respectively, to obtain the distance and brightness difference from C to P3, and the distance and brightness difference from C to P4. These distances and brightness differences together constitute the distribution information, reflecting the positional relationship of the center of the feature point set relative to the boundary of the illumination variation region and the difference in illumination intensity. For example, distribution information can be represented as four sets of data, each containing the distance and brightness difference between an intersection point and the center of the feature point set. This information describes the relative position of the center of the feature point set between areas with varying lighting and areas with stable lighting, providing an accurate reference for subsequent processing.

[0061] It is understandable that the implementation of this step can be performed dynamically during the analysis of the brightness distribution image. For example, when processing an indoor lighting image, the center of the feature point set may be located in the lighting change area near the window, and the stable lighting area may be located deep in the room. The settings of the first reference line and the second reference line can be adjusted according to the actual lighting distribution of the image to ensure that they can effectively pass through the boundary of the stable lighting area and the lighting change area. The determination of the intersection point can also be combined with the brightness value change threshold. For example, when the brightness value changes by more than a preset value, the reference line is considered to have entered the lighting change area, thereby marking the intersection position. The calculation of distance and brightness difference is completed by direct comparison of pixel coordinates and brightness values ​​to ensure the accuracy of the results.

[0062] In some embodiments, the step of correcting the feature point set according to the distribution information to obtain a corrected feature point set includes: translating the center of the feature point set along the direction of the first reference line until the first distance and brightness difference between the center of the feature point set and the first intersection and the second distance and brightness difference between the center of the feature point set and the second intersection are equal to determine the intermediate feature point set; translating the intermediate feature point set along the direction of the second reference line until the third distance and brightness difference between the intermediate feature point set and the third intersection and the fourth distance and brightness difference between the intermediate feature point set and the fourth intersection are equal to determine the corrected feature point set.

[0063] Specifically, in the step of determining the intermediate feature point set by translating the center of the feature point set along the direction of the first reference line until the first distance and brightness difference between the center of the feature point set and the first intersection point and the second distance and brightness difference between the center of the feature point set and the second intersection point are equal, processing is first performed based on the initial position of the center of the feature point set. The center of the feature point set refers to the average position of a set of coordinate points representing key positions in the illumination change area, such as C(x c , y c). The first reference line is a straight line that intersects with the center of the feature point set and passes through a stable illumination area, such as a horizontal line. The first intersection point P1 (x1, y1) and the second intersection point P2 (x2, y2) are the two intersection points of the first reference line and the boundary of the illumination change area. The illumination change area is an area where the brightness value changes significantly, such as the area where natural light and artificial lighting intersect. Further, the first distance from the center C of the feature point set to the first intersection point P1 is calculated, that is, the straight-line distance between C and P1, and the brightness difference between C and P1, that is, the difference between the brightness value of the pixel where C is located and the brightness value of the pixel where P1 is located. Similarly, the second distance and brightness difference from C to the second intersection point P2 are calculated. Then, the position of the center C of the feature point set is translated along the direction of the first reference line, for example, moved left or right in the horizontal direction. The goal of the translation is to make the first distance and brightness difference between the adjusted center C' of the feature point set and the first intersection point P1 equal to the second distance and brightness difference between C' and the second intersection point P2. Specifically, the position of C is adjusted so that the distance from C' to P1 and P2 is equal (for example, C' is located in the middle of P1 and P2), and the brightness value of the pixel where C' is located is balanced with the brightness difference between P1 and P2 (for example, the absolute value of the brightness difference is equal or close). After completing this translation, the new feature point set center C' is obtained, which is called the center of the intermediate feature point set, and the corresponding feature point set is the intermediate feature point set. The intermediate feature point set is the new coordinate point set obtained by moving each coordinate point of the original feature point set accordingly with the translation of the center C.

[0064] Furthermore, in the step of determining a revised feature point set by translating the intermediate feature point set along the second reference line until the third distance and brightness difference between the intermediate feature point set and the third intersection and the fourth distance and brightness difference between the intermediate feature point set and the fourth intersection are equal, processing continues based on the center C' of the intermediate feature point set. The second reference line is a straight line, such as a vertical line, perpendicular to the first reference line, intersecting with the center of the feature point set and passing through an area of ​​stable illumination. The third intersection point P3 (x3, y3) and the fourth intersection point P4 (x4, y4) are the two intersection points of the second reference line with the boundary of the illumination variation area. Similarly, a third distance from the center C' of the intermediate feature point set to the third intersection point P3, i.e., the straight-line distance between C' and P3, and a brightness difference between C' and P3, i.e., the difference between the brightness value of the pixel at C' and the brightness value of the pixel at P3, are calculated. Similarly, a fourth distance and brightness difference from C' to the fourth intersection point P4 are calculated. Next, the position of the center C' of the intermediate feature point set is translated along the direction of the second reference line, for example, by moving it up or down in the vertical direction. The goal of the translation is to make the third distance and brightness difference between the adjusted feature point set center C'' and the third intersection P3 equal to the fourth distance and brightness difference between C'' and the fourth intersection P4. Specifically, the position of C' is adjusted so that the distance from C'' to P3 and P4 is equal (for example, C'' is located in the middle of P3 and P4), and the brightness value of the pixel where C'' is located is also balanced with the brightness difference between P3 and P4 (for example, the absolute value of the brightness difference is equal or close). After completing this translation, the final adjusted feature point set center C'' is obtained, and the corresponding feature point set is the revised feature point set. The revised feature point set is a new set of coordinate points obtained by moving each coordinate point of the intermediate feature point set accordingly with the translation of the center C'.

[0065] It can be understood that during the implementation of this step, the translation process can be carried out in steps to ensure that the adjustment of the center of the feature point set can balance the distance and brightness difference at the same time. For example, when translating along the first reference line, a position that satisfies the distance and brightness difference can be found by gradually moving the center of the feature point set and comparing the distance and brightness difference with the first intersection and the second intersection in real time. Similarly, when translating along the second reference line, a similar process is repeated to ensure that the center of the intermediate feature point set is adjusted to a position where the distance and brightness difference with the third intersection and the fourth intersection are equal. This gradual adjustment method can improve the accuracy of the corrected feature point set and make it better reflect the characteristics of the illumination change area. In actual operation, the step size of the translation can be set according to the resolution of the brightness distribution image or the size of the illumination change area, for example, fine-tuning in pixels.

[0066] In some embodiments, the steps of obtaining indoor and outdoor lighting images of a building and generating a brightness distribution image corresponding to the lighting image include: collecting lighting images of the indoor and outdoor environments of the building and performing brightness normalization processing on the lighting images; performing edge enhancement processing on the lighting images after brightness normalization processing to obtain the brightness distribution image.

[0067] Specifically, in the steps of capturing illumination images of a building's indoor and outdoor environments and performing brightness normalization on the illumination images, illumination images are first acquired using illumination capture devices deployed inside and outside the building. These devices can be high-resolution digital cameras or light sensors installed at key locations within the building to capture indoor and outdoor lighting conditions. For example, indoors, cameras can be placed on the ceilings of key activity areas, such as offices, corridors, or conference rooms, to record the combined effects of natural and artificial lighting. Outdoors, cameras can be mounted on building exterior walls, rooftops, or facing primary light sources to capture the characteristics of direct sunlight, diffuse light, or overcast lighting. The captured illumination images can be color or grayscale images, containing information on the spatial distribution of light intensity. Furthermore, brightness normalization is performed on the captured illumination images to eliminate inconsistent brightness ranges caused by varying lighting conditions or equipment. Specifically, brightness normalization maps the brightness values ​​of the illumination images to a standardized range, for example, adjusting pixel brightness values ​​to between 0 and 1, or between 0 and 255. This can be achieved by dividing the brightness value of each pixel by the maximum brightness value in the image, or by scaling the brightness values ​​to a preset range through a linear transformation. This process ensures that images collected at different times or with different devices have comparable brightness values, facilitating subsequent analysis. For example, an image collected during bright daylight hours may have higher brightness values, while an image collected on a cloudy day or at night may have lower brightness values. Normalization allows the brightness values ​​of these images to be compared on a uniform scale.

[0068] Furthermore, in the step of performing edge enhancement on the brightness-normalized illumination image to obtain the brightness distribution image, the normalized illumination image is further processed to highlight the spatial variation of light intensity, thereby generating a brightness distribution image. Specifically, edge enhancement aims to enhance areas of the image with significant brightness variations, such as the interface between natural and artificial lighting or the transition between light and shadow. These areas typically correspond to rapid changes in light intensity and are important for subsequent analysis of the light distribution. Edge enhancement can be achieved by applying image processing techniques, such as using edge detection algorithms or filtering methods to highlight the boundaries of brightness variations. One implementation method is to apply a high-pass filter to the normalized illumination image to amplify the rapidly varying portions of the brightness values ​​in space, thereby making the areas of varying light intensity more prominent in the image. Another method is to use a gradient calculation method to identify the rates of change of brightness values ​​in the horizontal and vertical directions, thereby highlighting the edges of the brightness variations. After edge enhancement, the resulting image is a brightness distribution image, which clearly displays the spatial distribution characteristics of light intensity through the brightness values ​​of the pixels. In the brightness distribution image, areas with higher brightness values ​​represent areas with stronger lighting, and areas with lower brightness values ​​represent areas with weaker lighting. The enhanced edge area highlights the boundaries of lighting changes, providing clearer lighting distribution information for subsequent analysis.

[0069] It is understandable that during the implementation of this step, the frequency of collecting lighting images can be set according to actual needs, such as collecting once an hour to adapt to daylight changes, or collecting when triggered by specific events (such as significant changes in light intensity). Brightness normalization processing can be adjusted according to the resolution of the acquisition device or the dynamic range of the lighting environment. For example, in a high dynamic range environment, a more complex normalization method may be required to retain details. The intensity of edge enhancement processing can also be optimized according to the characteristics of the lighting image. For example, in an indoor environment with relatively smooth lighting changes, weaker edge enhancement can be used to avoid excessive amplification of noise; while in an outdoor environment with strong lighting contrast, stronger edge enhancement can be used to highlight the boundaries between light and shadow. These adjustments ensure that the brightness distribution image can accurately reflect the lighting distribution characteristics inside and outside the building.

[0070] In some embodiments, the step of obtaining the feature point set of the illumination change area in the brightness distribution image based on the preset illumination feature positioning method includes: analyzing the brightness distribution image based on the preset illumination feature positioning method; when the feature point set of the illumination change area in the brightness distribution image is identified, the feature point set is used as the initial feature point set, and the step of obtaining the distribution information of the feature point set to the stable illumination area in the brightness distribution image is executed; when the feature point set of the illumination change area in the brightness distribution image cannot be identified, the corresponding prompt information is output.

[0071] Specifically, in the step of analyzing the brightness distribution image based on a preset illumination feature location method, the preset illumination feature location method is used to process the brightness distribution image to identify illumination variation regions and extract their feature point sets. A brightness distribution image is an image that reflects the spatial distribution of light intensity, where the brightness value of a pixel represents the light intensity. Illumination variation regions are areas in the brightness distribution image where light intensity changes significantly, such as the boundary where natural light entering a room through a window meets artificial lighting. The preset illumination feature location method is an image analysis-based technique designed to detect significant changes in brightness values ​​in a brightness distribution image. Specifically, this method analyzes the variation characteristics of brightness values ​​in image space, for example, by examining the differences in brightness values ​​between adjacent pixels to identify regions of rapid brightness changes. These regions typically correspond to transitional areas of light intensity, such as light-shadow boundaries or areas where light intensity gradually changes from high to low. During the analysis process, each pixel in the brightness distribution image can be scanned and its brightness value compared with the brightness values ​​of surrounding pixels. If the difference exceeds a preset threshold, the region is marked as an illumination variation region. This analysis method can effectively distinguish areas with changing lighting from areas with relatively uniform lighting intensity, providing a basis for the subsequent extraction of feature point sets.

[0072] Furthermore, when a feature point set of an illumination change region in the brightness distribution image is identified, the feature point set is used as an initial feature point set, and the step of obtaining distribution information of the feature point set to a stable illumination region in the brightness distribution image is performed. If the analysis result indicates that an illumination change region exists in the brightness distribution image, feature point sets within these regions are further extracted. A feature point set is a set of coordinate points representing key positions of an illumination change region, such as {(x1, y1), (x2, y2), ..., (x n , y n )}, these points are usually located at locations where brightness changes significantly, such as the boundary of the illumination change area or the point where the brightness value changes most dramatically. When extracting the feature point set, key points can be found in the identified illumination change area, such as selecting the pixel point with the highest rate of change in brightness value as the feature point, or selecting a representative point on the boundary of the illumination change area. After the feature point set is extracted, it is used as the initial feature point set for subsequent processing steps. Specifically, the initial feature point set will be used to obtain its distribution information with the stable illumination area, which refers to the area with small changes in illumination intensity in the brightness distribution image, such as the uniformly illuminated area indoors away from the window. This subsequent step involves analyzing the spatial and brightness relationship between the initial feature point set and the stable illumination area, but this embodiment only focuses on the extraction of the feature point set and its confirmation as the initial feature point set.

[0073] Furthermore, in the step of outputting a corresponding prompt message when a feature point set representing an illumination variation region in the brightness distribution image cannot be identified, if the analysis results indicate that no significant illumination variation region exists in the brightness distribution image or that a valid feature point set cannot be extracted, a prompt message is generated and output to notify subsequent processing steps or the user. For example, in certain situations, such as at night when indoor lighting is uniform or outdoors on a cloudy day when illumination is completely uniform, the brightness distribution image may exhibit relatively consistent brightness values ​​and lack significant illumination variation regions. In such cases, the preset illumination feature location method may fail to detect regions where the brightness variation exceeds a threshold, or the detected variation regions may be too subtle to form a valid feature point set. In such cases, a prompt message is output. The prompt message may be a textual description, such as "No illumination variation region detected" or "Unable to extract feature point set." This prompt message can be used to trigger an alternative processing flow, such as pausing subsequent analysis steps or suggesting reacquiring the illumination image to obtain more appropriate data. The prompt message may be recorded in a log file or displayed through a user interface, depending on the implementation environment.

[0074] It can be understood that during the implementation of this step, the threshold of the preset illumination feature positioning method can be adjusted according to the characteristics of the brightness distribution image. For example, in an environment with high illumination contrast, a higher brightness change threshold can be set to avoid false detection of noise; in an environment with smoother illumination changes, the threshold can be lowered to capture subtle changes. The extraction of feature point sets can also be optimized according to the size and complexity of the illumination change area, such as extracting more feature points in larger illumination change areas to improve representativeness, and extracting fewer feature points in smaller areas to reduce the amount of calculation. The generation and output of prompt information can be customized according to the actual application scenario. For example, in a real-time monitoring system, prompt information may require immediate feedback, while in periodic analysis, prompt information can be accumulated and processed uniformly.

[0075] In some embodiments, as Figure 2 As shown, the step of performing partition comparison on the illumination area in the illumination image based on the corrected feature point set to obtain energy consumption optimization information of the illumination area includes: S61: dividing the illumination area in the illumination image into a preset number of equal partitions with the corrected feature point set as the center; S62: obtaining brightness distribution information of the equal partitions, and determining the energy consumption optimization information of the illumination area based on the difference in the brightness distribution information between the equal partitions.

[0076] Specifically, in the step of dividing the illumination area in the illumination image into a preset number of equal partitions with the modified feature point set as the center, the center of the modified feature point set is first used as the reference point for the division. The modified feature point set is a set of adjusted coordinate points, such as {(x1', y1'), (x2', y2'), ..., (x n ', y n ')}, represents the optimized position of the illumination change area, and its center C''(x c'' , y c'' ) is calculated by averaging all coordinate points. An illumination image reflects the indoor and outdoor lighting intensity of a building. An illumination region is an area within the image that exhibits illumination variations, such as the intersection of natural and artificial lighting. The illumination region is divided into a preset number of equal partitions, based on the center C'' of the corrected feature point set. Equal partitioning refers to dividing the illumination region into sub-regions of similar size or shape. The preset number can be set based on actual needs, such as 4, 8, or 16 equal partitions. A specific division method can be to divide the illumination region into uniform angles or distances with the center C'' as the origin. For example, the illumination region can be divided into four quadrants, each of which is an equal partition located in the upper left, upper right, lower left, and lower right regions of the center C''; or the illumination region can be divided into multiple sector-shaped regions with the center C'' as the center, each covering the same angular range. When dividing, ensure that each equal partition contains a portion of the illumination region and covers the distribution range of the corrected feature point set as much as possible, so that subsequent analysis can fully reflect the characteristics of the illumination region.

[0077] Furthermore, in the step of obtaining the brightness distribution information of the equal subareas and determining the energy consumption optimization information for the illuminated area based on the differences in the brightness distribution information between the equal subareas, a brightness distribution analysis is performed on each equal subarea, and the differences between the equal subareas are compared to generate the energy consumption optimization information. Specifically, the brightness distribution information reflects the spatial characteristics of the illumination intensity within each equal subarea, such as the average brightness level or degree of variation. To obtain the brightness distribution information, the brightness values ​​of the pixels within each equal subarea are analyzed, and their statistical characteristics, such as the average brightness value or the brightness range of all pixels, are calculated. The brightness values ​​can be directly extracted from the illumination image, such as the pixel values ​​in a grayscale image or the brightness components after conversion from a color image. Next, the brightness distribution information between the equal subareas is compared to identify brightness differences. For example, if the average brightness value of a given equal subarea is significantly higher than that of other equal subareas, this may indicate that the area is exposed to strong natural light or excessive artificial lighting. Conversely, if the average brightness value of a given equal subarea is significantly lower than that of other equal subareas, this may indicate that the area is insufficiently illuminated. Comparing the differences can be achieved by examining the relative deviation of the brightness average values ​​or the differences in the brightness range of each equal subarea. Based on these differences, energy optimization information for the illuminated area is determined. This information describes or recommends control measures for the illumination distribution within the illuminated area, such as "The illuminated area is uniform; it is recommended to reduce overall illumination intensity" or "The illuminated area is uneven; it is recommended to increase lighting in a specific area or adjust shading devices." This information reflects the potential for energy optimization within the illuminated area. For example, energy savings can be achieved by reducing lighting power in overlit areas or increasing lighting intensity in underlit areas, or by adjusting shading devices to introduce more natural light and reduce the need for artificial lighting.

[0078] It is understandable that during the implementation of this step, the number and division method of equal partitions can be adjusted according to the size and complexity of the lighting area. For example, in areas where the lighting changes are more complex, the number of equal partitions can be increased (such as divided into 16 partitions) to improve the analysis accuracy; in areas where the lighting changes are relatively simple, the number of partitions can be reduced (such as divided into 4 partitions) to reduce the calculation complexity. The acquisition and comparison of brightness distribution information can be optimized according to specific needs. For example, only the average brightness value can be compared, or a more detailed analysis can be performed in combination with the spatial distribution characteristics of the brightness value. The generation of energy consumption optimization information can be customized according to the actual application scenario. For example, in an office building, the optimization information may tend to adjust the power of lighting equipment, while in a residential building, more attention may be paid to the adjustment of sunshade devices. These adjustments ensure the practicality and pertinence of energy consumption optimization information.

[0079] In some embodiments, the brightness distribution information includes a brightness mean and a gradient value. The step of obtaining the brightness distribution information of the equal partitions and determining the energy consumption optimization information of the illuminated area based on the difference in the brightness distribution information between the equal partitions includes: counting the brightness mean and gradient value of each equal partition, and calculating the brightness difference index between the equal partitions, wherein the brightness difference index is determined by the following formula:

[0080]

[0081] Among them, ΔL is the brightness difference index, M i is the mean brightness of the ith equal partition, M avg is the average brightness of all equal partitions, G i is the brightness gradient value of the i-th equal partition, G avg is the average brightness gradient value of all equal partitions, D i is the distance between the center of the ith equal partition and the building reference point, D max is the maximum reference distance of the building, and N is the number of equal partitions; if the brightness difference index is within the preset range, the energy consumption optimization information is that the lighting area is uniform, and an energy-saving control instruction is generated; if the brightness difference index is not within the preset range, the energy consumption optimization information is that the lighting area is uneven, and a dynamic adjustment instruction is generated.

[0082] Specifically, in the step of counting the brightness mean and gradient values ​​of each equal partition and calculating the brightness difference index between each equal partition, the brightness distribution information of each equal partition is first analyzed. The equal partition is centered on the corrected feature point set and divides the illumination area into a preset number of sub-regions, such as 4 or 8 regions of similar size. The brightness distribution information includes the brightness mean and gradient value, where the brightness mean reflects the average level of illumination intensity within the partition, and the gradient value reflects the severity of brightness changes within the partition. For each equal partition, its brightness mean M is calculated. i , which is the average brightness value of all pixels in the partition. The brightness value can be extracted from the grayscale value or brightness component of the illumination image, for example, the grayscale value of the pixel is directly used in the grayscale image. Then, the brightness gradient value G of each equal partition is calculated. i The gradient value represents the rate of change of brightness in space, which is usually obtained by analyzing the differences in the brightness values ​​of pixels in the partition in the horizontal and vertical directions. For example, the brightness difference between each pixel and its neighboring pixels can be calculated, and the average or maximum value of these differences can be taken as the gradient value of the partition. After completing the statistics of the brightness mean and gradient value of each equal partition, the average brightness mean M of all equal partitions is calculated. avg (i.e. all M i The average value of the brightness gradient G avg (i.e. all G iFurthermore, the distance D between the center of each equal partition and the building reference point is determined. i ,The building reference point can be a fixed location in the building, such as the center of the entrance or the geometric center of the lighting image;D max is the maximum distance from the center of all possible partitions in the building to the reference point, which is used to normalize the distance effect. Based on this data, the brightness difference index ΔL is calculated, which takes into account the differences in brightness mean, gradient value and distance. Specifically, the brightness difference index is determined as follows: for each equal partition, the square of the difference between its brightness mean and the average brightness mean, and the square of the difference between its gradient value and the average gradient value are calculated, the sum of the sums is taken, and the average is multiplied by a distance-based weight ( ), and finally take the square root. This indicator reflects the overall difference in the brightness distribution of each equal area, and larger values ​​indicate more significant differences.

[0083] Furthermore, if the brightness difference index is within a preset range, the energy consumption optimization information is that the illumination area is uniform, and in the step of generating an energy-saving control instruction, it is checked whether the calculated brightness difference index ΔL is within the preset range. The preset range can be set according to the actual application scenario, for example, 0 to 0.1 indicates that the illumination distribution is uniform. If ΔL is within this range, it means that the brightness mean and gradient values ​​of each equal partition are relatively close, and the illumination intensity distribution of the illumination area is uniform. At this time, energy consumption optimization information is generated, indicating that the illumination area is uniform and it is suitable to take energy-saving measures. Based on this information, energy-saving control instructions are generated. Energy-saving control instructions are specific operational suggestions for reducing energy consumption, such as reducing the power of the overall lighting equipment or adjusting the shading device to reduce the introduction of unnecessary natural light. These instructions are intended to minimize energy consumption while maintaining lighting comfort.

[0084] Furthermore, if the brightness difference index is not within the preset range, the energy consumption optimization information is that the illumination area is uneven, and in the step of generating dynamic adjustment instructions, if the brightness difference index ΔL exceeds the preset range, for example, is greater than 0.1, it means that there are significant differences in the brightness mean or gradient value of each equal partition, and the illumination intensity distribution of the illumination area is uneven. For example, the equal partition close to the window may have a higher brightness mean, while the partition far from the window may have a lower brightness mean. At this time, energy consumption optimization information is generated, indicating that the illumination area is uneven and illumination adjustment is required for specific areas. Based on this information, dynamic adjustment instructions are generated. Dynamic adjustment instructions are local adjustment suggestions for uneven illumination distribution, such as increasing the lighting intensity of insufficiently illuminated partitions, or adjusting shading devices to balance the distribution of natural light. These instructions are intended to improve the uniformity of illumination distribution while optimizing energy consumption.

[0085] It can be understood that during the implementation of this step, the calculation of the brightness mean and gradient value can be optimized according to the resolution and partition size of the illumination image. For example, in a high-resolution image, more pixels within each partition can be sampled to improve the accuracy of the mean and gradient values; while in a low-resolution image, the sampling points can be reduced to reduce the computational complexity. The preset range of the brightness difference index can be adjusted according to the building type and lighting requirements. For example, a stricter uniformity range may be required in an office environment, while it can be appropriately relaxed in a warehouse environment. The generation of energy-saving control instructions and dynamic adjustment instructions can be combined with the actual equipment status, such as giving priority to adjusting lower-power lighting equipment or easier-to-operate shading devices to improve execution efficiency.

[0086] In some embodiments, the step of generating energy-saving control instructions or dynamic adjustment instructions includes: when the energy consumption optimization information indicates that the lighting area is uniform, generating energy-saving control instructions to reduce the power of the lighting equipment or adjust the opening and closing angle of the sunshade device; when the energy consumption optimization information indicates that the lighting area is uneven, generating dynamic adjustment instructions to increase the brightness of the lighting equipment or adjust the angle of the sunshade device for a specific partition.

[0087] Specifically, when the energy consumption optimization information indicates that the illumination area is uniform, in the step of generating an energy-saving control instruction for reducing the power of the lighting device or adjusting the opening and closing angle of the sunshade device, processing is first performed according to the judgment result of the energy consumption optimization information. The energy consumption optimization information indicates that the illumination area is uniform, that is, the illumination intensity distribution of each equal partition is relatively consistent, for example, the brightness mean and gradient value of each partition are relatively small. In this case, the overall illumination intensity of the illumination area may be high or sufficient to meet the usage requirements, so energy use can be optimized by reducing energy consumption. Based on this, an energy-saving control instruction is generated, which specifically includes two operational suggestions: one is to reduce the power of the lighting device, and the other is to adjust the opening and closing angle of the sunshade device. For reducing the power of the lighting device, the instruction can be for the lighting system in the building, such as LED lamps or fluorescent lamps, and recommends reducing its power to a certain level, for example, adjusting the current power from 100% to 70%, so as to reduce energy consumption while maintaining sufficient lighting brightness. Regarding adjusting the opening and closing angles of sunshade devices, instructions can be directed to curtains, blinds, or other sunshade devices, suggesting reducing the opening and closing angles to reduce the entry of natural light. For example, the blinds can be adjusted from fully open to half-open to avoid excessive light. This adjustment is particularly suitable for scenes with abundant natural light during the day. By reducing artificial lighting or limiting the entry of excessive natural light, energy-saving goals can be achieved. The specific content of the energy-saving control instructions can be customized according to the actual lighting environment and equipment status of the building. For example, priority should be given to adjusting lighting equipment with high power consumption or sunshade devices that are easy to operate.

[0088] Furthermore, when the energy optimization information indicates uneven illumination, the step of generating dynamic adjustment instructions for increasing lighting brightness or adjusting the angle of sunshades for specific zones is handled based on the fact that the energy optimization information indicates uneven illumination. Uneven illumination means that the illumination intensity in some equal zones is significantly lower or higher than in other zones, for example, zones near windows have stronger illumination, while zones farther from windows have weaker illumination. To address this situation, dynamic adjustment instructions are generated to improve the uniformity of illumination distribution through local adjustments while optimizing energy consumption. These dynamic adjustment instructions include two recommended actions: increasing lighting brightness for specific zones, and adjusting the angle of sunshades. For increasing lighting brightness, the instructions can specify a specific zone with insufficient illumination, such as a corner of a room away from a window, and recommend increasing the brightness of the lighting in that zone, for example, by increasing the brightness of the LED lights from 50% to 80%, to ensure a comfortable illumination level for that zone. For adjusting the angle of sunshades, the instructions can recommend adjusting the angle of curtains or blinds for zones with insufficient or excessive illumination. For example, in areas with insufficient sunlight, the opening and closing angles of sunshades can be increased to bring in more natural light, such as by adjusting blinds from halfway open to fully open. In areas with excessive sunlight, the opening and closing angles can be reduced to reduce glare, such as by adjusting curtains from fully open to partially blocked. These dynamic adjustment commands are tailored to the lighting characteristics of specific areas, ensuring precise and effective adjustments while avoiding unnecessary increases in energy consumption.

[0089] It can be understood that during the implementation of this step, the generation of energy-saving control instructions and dynamic adjustment instructions can be dynamically adjusted according to the actual equipment configuration and lighting environment of the building. For example, in an office building, the energy-saving control instructions may give priority to reducing the lighting power of the conference room or hall, while the dynamic adjustment instructions may adjust the lighting brightness for specific workstation areas. In a residential environment, the instructions may focus more on adjusting the sunshade to balance the use of natural light. The execution of the instructions can be combined with the building's intelligent control system, such as automatically achieving adjustments through controllers connected to lighting equipment and sunshade devices, or prompting operators to manually execute through a user interface. In addition, the generation of instructions can take time factors into account, such as prioritizing the adjustment of sunshade devices to utilize natural light during the day, and mainly adjusting the brightness of lighting equipment at night. These customized measures ensure the practicality and efficiency of the instructions.

[0090] The present invention also provides another embodiment, such as Figure 3As shown, a building energy consumption control system 100 using changes in illumination images is provided. Specifically, the system 100 includes: an image processing module 101 for acquiring indoor and outdoor illumination images of a building and generating a brightness distribution image corresponding to the illumination image; a feature positioning module 102 for acquiring a feature point set of an illumination change area in the brightness distribution image based on a preset illumination feature positioning method; a feature correction module 103 for acquiring distribution information of the feature point set to a stable illumination area in the brightness distribution image, and correcting the feature point set according to the distribution information to obtain a corrected feature point set; and a partition comparison module 104 for performing partition comparison of the illumination area in the illumination image based on the corrected feature point set to obtain energy consumption optimization information of the illumination area.

[0091] Furthermore, the image processing module 101 is configured to acquire indoor and outdoor lighting images of a building and generate a brightness distribution image corresponding to the lighting image. First, lighting images of the indoor and outdoor environments are acquired by deploying lighting acquisition devices, such as high-resolution digital cameras or light sensors, inside and outside the building. Indoor cameras can be installed on the ceilings of major activity areas, such as offices, conference rooms, or halls, to capture the combined lighting effects of natural and artificial light. Outdoor cameras can be installed on building exterior walls, roofs, or locations facing major light sources to record the characteristics of direct sunlight, diffuse light, or cloudy lighting. The acquired lighting image can be a color image or a grayscale image, containing spatial distribution information of light intensity. Furthermore, the image processing module 101 processes the acquired lighting image to generate a brightness distribution image. Specifically, the image processing module 101 extracts the brightness information of the image, for example, by converting the color image into a grayscale image, using the grayscale value of each pixel to represent the light intensity, or extracting the brightness component from the color image. The result is a brightness distribution image, where the brightness values ​​of the pixels reflect the spatial distribution of light intensity. Areas with high brightness values ​​indicate strong light, while areas with low brightness values ​​indicate weak light. This image processing module 101 ensures that the generated brightness distribution image clearly displays the lighting characteristics inside and outside the building, providing an accurate data foundation for subsequent modules.

[0092] Furthermore, the function of the feature positioning module 102 is to obtain a feature point set of the illumination change area in the brightness distribution image based on a preset illumination feature positioning method. The illumination change area refers to the area where the illumination intensity in the brightness distribution image changes significantly, such as the area where natural light enters the room through the window and intersects with artificial lighting. The feature positioning module 102 analyzes the brightness distribution image through the preset illumination feature positioning method to identify these illumination change areas. Specifically, the module 102 scans the pixels of the brightness distribution image and detects the spatial changes in the brightness values, for example, by comparing the differences in the brightness values ​​of adjacent pixels, to find areas where the brightness changes rapidly. These areas usually correspond to light and shadow boundaries or illumination intensity transition areas. Next, the feature positioning module 102 extracts a feature point set within the illumination change area. The feature point set is a set of coordinate points representing the key positions of the illumination change area, such as {(x1, y1), (x2, y2), ..., (x n , y n These feature points are typically located at locations where brightness changes significantly, such as the boundaries of areas experiencing illumination changes or points where brightness changes most dramatically. When extracting feature points, the feature location module 102 can select pixels with the highest brightness change rates or representative points on area boundaries as feature points. The processing result of the feature location module 102 is a set of coordinate points that accurately reflects the spatial characteristics of the illumination change area, providing a basis for analysis in subsequent modules.

[0093] Furthermore, the function of the feature correction module 103 is to obtain the distribution information of the feature point set to the stable illumination area in the brightness distribution image, and to correct the feature point set according to the distribution information to obtain the corrected feature point set. The stable illumination area refers to the area in the brightness distribution image where the illumination intensity changes less, such as the uniformly illuminated area away from the window indoors or the uniformly illuminated area outdoors. The feature correction module 103 first identifies the stable illumination area in the brightness distribution image, for example, by analyzing the spatial consistency of the brightness value and selecting the area with smaller brightness value fluctuations. Then, the feature correction module 103 calculates the distribution information between the feature point set and the stable illumination area, including the spatial distance and brightness difference from the center of the feature point set to the stable illumination area. For example, the feature correction module 103 determines the center position C(x c , y c ), and calculates the distance to the representative position of the stable illumination area, as well as the brightness difference between the central pixel of the feature point set and the pixel in the stable area. Based on this distribution information, the feature correction module 103 corrects the feature point set and adjusts the feature point coordinates to reduce the influence of noise or abnormal points. For example, by moving the coordinates of the feature point set closer to the brightness characteristics of the stable illumination area, a corrected feature point set {(x1', y1'), (x2', y2'), ..., (x n ', yn The corrected feature point set more accurately reflects the characteristics of the illumination change area and provides optimized data for subsequent partition comparison.

[0094] Furthermore, the partition comparison module 104 performs partition comparison of the illuminated regions in the illumination image based on the modified feature point set to obtain energy optimization information for the illuminated regions. An illuminated region is a region in the illumination image that exhibits illumination variations, such as the intersection of natural and artificial light. Using the center of the modified feature point set as a reference, the partition comparison module 104 divides the illuminated region into multiple subregions, for example, by uniformly partitioning the region to create a number of rectangular or sector-shaped regions. Next, the partition comparison module 104 analyzes the brightness distribution characteristics of each subregion, for example, by calculating the average level or degree of variation of pixel brightness within each subregion and comparing the brightness differences between these subregions. If the brightness values ​​of some subregions are significantly higher or lower than those of other subregions, this indicates uneven illumination distribution, and adjustments to lighting or shading equipment may be necessary. Based on the comparison results, the partition comparison module 104 generates energy optimization information, which describes the uniformity of the illuminated region or provides specific energy optimization recommendations, such as "Illuminated region uniform, recommend reducing lighting power" or "Illuminated region uneven, recommend increasing illumination in a certain area." This information is used to guide energy management within the building, such as optimizing lighting power or adjusting shading devices to balance light distribution.

[0095] It will be appreciated that during implementation of system 100, the various modules can work together to achieve real-time or periodic energy consumption control. For example, image processing module 101 can capture a lighting image every hour, feature location module 102 and feature correction module 103 can then process the data, and partition comparison module 104 can generate energy consumption optimization information. The processing parameters of each module can be adjusted based on the building environment, for example, increasing the acquisition frequency in commercial buildings with frequently fluctuating lighting conditions, or reducing processing complexity in residential environments with stable lighting conditions. System 100 can be implemented using hardware devices (e.g., cameras, processors) and software algorithms (e.g., image processing programs) to ensure efficient operation.

[0096] Optionally, the partition comparison module may further include: a partitioning unit, used to divide the illuminated area in the illumination image into a preset number of equal partitions with the corrected feature point set as the center; an analysis unit, used to obtain the brightness distribution information of the equal partitions, and determine the energy consumption optimization information of the illuminated area based on the difference in the brightness distribution information between the equal partitions; an instruction generation unit, used to generate energy-saving control instructions or dynamic adjustment instructions based on the energy consumption optimization information, and send them to the lighting equipment and shading devices in the building.

[0097] Specifically, the partition unit is used to divide the illumination area in the illumination image into a preset number of equal partitions with the modified feature point set as the center. The modified feature point set is a set of optimized coordinate points, such as {(x1', y1'), (x2', y2'), ..., (x n ', y n ')}, represents the key position of the illumination change area, and its center C''(x c'' , y c'' ) is obtained by calculating the average value of all coordinate points. An illumination image is an image reflecting the indoor and outdoor lighting intensity of a building. An illumination region is an area containing variations in illumination, such as the intersection of natural light and artificial lighting. The partitioning unit divides the illumination region into a preset number of equal partitions based on the center C'' of the corrected feature point set. Equal partitions are sub-regions of similar size or shape. The preset number can be set according to actual needs, for example, 4, 8, or 16 equal partitions. A specific partitioning method can be to divide the illumination region with the center C'' as the origin and at uniform angles or distances. For example, the illumination region can be divided into four quadrants, each serving as an equal partition located in the upper left, upper right, lower left, and lower right regions of the center C''; or, with C'' as the center, the illumination region can be divided into multiple sector-shaped regions, each covering the same angular range. When partitioning, ensure that each equal partition contains a portion of the illumination region and covers the distribution range of the corrected feature point set as much as possible, so that subsequent analysis can fully reflect the characteristics of the illumination region. The processing result of the partition unit is a set of divided sub-areas, which provides the data basis for the analysis unit.

[0098] Furthermore, the analysis unit is responsible for obtaining brightness distribution information for each equal area and, based on the differences in brightness distribution information between equal areas, determining energy optimization information for the illuminated area. Brightness distribution information reflects the spatial characteristics of illumination intensity within each equal area, such as the average brightness level or variability. The analysis unit processes each equal area to extract its brightness distribution information. Specifically, for each equal area, the analysis unit scans the brightness values ​​of the pixels within the area and calculates statistical characteristics of the brightness values, such as the average brightness value or the brightness range of all pixels. Brightness values ​​can be directly extracted from the illumination image, for example, using pixel values ​​from a grayscale image or the brightness components converted from a color image. The analysis unit then compares the brightness distribution information between equal areas to identify differences. For example, if the average brightness value of a given equal area is significantly higher than that of other equal areas, this may indicate that the area is exposed to strong natural light or excessive artificial lighting. If the average brightness value of a given equal area is significantly lower than that of other equal areas, this may indicate insufficient illumination. Comparison of differences can be achieved by examining the relative deviation of the average brightness values ​​or the differences in the brightness range of each equal area. Based on these differences, the analysis unit generates energy optimization information, which describes the uniformity of the illumination area or provides specific energy optimization suggestions. For example, if the brightness distribution of each equal area is similar, the generated information may be "The illumination area is uniform, suitable for reducing overall energy consumption." If the brightness of some areas is significantly higher or lower, the generated information may be "The illumination area is uneven, and lighting adjustments are required for specific areas." Energy optimization information provides decision-making basis for the instruction generation unit.

[0099] Furthermore, the function of the instruction generation unit is to generate energy-saving control instructions or dynamic adjustment instructions based on the energy consumption optimization information, and send them to the lighting equipment and shading devices in the building. If the energy consumption optimization information indicates that the lighting area is uniform, the instruction generation unit generates an energy-saving control instruction and recommends operations to reduce energy consumption. For example, the instruction may recommend reducing the power of the lighting equipment, such as adjusting the power of the LED lamp from 100% to 70% to reduce energy consumption; or recommend adjusting the opening and closing angle of the shading device, such as adjusting the blinds from fully open to half-open to reduce excessive natural light entering. These energy-saving control instructions are suitable for scenes with sufficient and uniform lighting, and are designed to maintain lighting comfort while reducing energy consumption. If the energy consumption optimization information indicates that the lighting area is uneven, the instruction generation unit generates dynamic adjustment instructions to adjust the lighting problems of specific partitions. For example, instructions can suggest increasing the brightness of lighting in areas with insufficient light, such as increasing the brightness of lamps in a certain area from 50% to 80%. Or, they can suggest adjusting the angle of sunshades, such as adjusting the curtains from half-open to fully open in areas with insufficient light to bring in more natural light, or partially closing the curtains in areas with excessive light to reduce glare. The instruction generation unit sends these instructions to the lighting and sunshade devices in the building through a communication interface. For example, the intelligent control system connects to the lamp controller or sunshade motor to achieve automatic adjustment, or prompts the operator to execute the instructions manually through the user interface.

[0100] Understandably, during the implementation of this partition comparison module, the various units can work collaboratively to achieve efficient energy optimization. For example, the partitioning unit can dynamically adjust the number of equal partitions based on the complexity of the lighting area, such as increasing the number of partitions in areas with large lighting variations to improve analysis accuracy. The analysis unit can optimize the method of extracting brightness distribution information, such as by sampling more pixels to improve accuracy, or reducing the sampling points in low-resolution images to reduce the amount of calculation. The instructions generated by the instruction generation unit can be customized based on the building equipment configuration, such as prioritizing dimmable LED lights for power adjustment or prioritizing electric sunshades to simplify operation. Instructions can be sent in real time or in batches at specific time intervals (e.g., every hour) to adapt to changes in the lighting environment.

[0101] The above description is merely an exemplary embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformation made by utilizing the contents of the present invention's description and drawings under the technical concept of the present invention, or directly / indirectly applied in other related technical fields, is included in the patent protection scope of the present invention.

Claims

1. A building energy consumption control method using changes in illumination patterns, characterized in that: The building energy consumption control method comprises the following steps: Acquire indoor and outdoor lighting images of the building, and generate a brightness distribution image corresponding to the lighting image; Based on a preset illumination feature positioning method, a feature point set of the illumination change area in the brightness distribution image is obtained; Setting a first reference line and a second reference line having the center of the feature point set as an intersection point, passing through a stable illumination area, and being perpendicular to each other, so that the first reference line intersects the illumination variation area at the first and second intersection points, and the second reference line intersects the illumination variation area at third and fourth intersection points; using the distances and brightness differences between the center of the feature point set and the first, second, third, and fourth intersection points, respectively, as distribution information, and modifying the feature point set based on the distribution information to obtain a modified feature point set; Based on the corrected feature point set, the illumination area in the illumination image is partitioned and compared to obtain energy consumption optimization information of the illumination area.

2. The building energy consumption control method using illumination pattern changes according to claim 1, characterized in that: The step of correcting the feature point set according to the distribution information to obtain a corrected feature point set includes: translating the center of the feature point set along the direction of the first reference line until a first distance and a brightness difference between the center of the feature point set and the first intersection point and a second distance and a brightness difference between the center of the feature point set and the second intersection point are equal, to determine an intermediate feature point set; The intermediate feature point set is translated along the direction of the second reference line until a third distance and a brightness difference between the intermediate feature point set and the third intersection point are equal to a fourth distance and a brightness difference between the intermediate feature point set and the fourth intersection point, so as to determine a revised feature point set.

3. The building energy consumption control method using illumination pattern changes according to claim 1, characterized in that: The step of acquiring indoor and outdoor lighting images of a building and generating a brightness distribution image corresponding to the lighting image comprises: Collecting illumination images of the indoor and outdoor environments of a building, and performing brightness normalization processing on the illumination images; The illumination image after brightness normalization is subjected to edge enhancement processing to obtain the brightness distribution image.

4. The building energy consumption control method using illumination pattern changes according to claim 1, characterized in that: The step of obtaining a feature point set of the illumination change area in the brightness distribution image based on a preset illumination feature positioning method includes: Analyzing the brightness distribution image based on a preset illumination feature positioning method; When a feature point set of an illumination change region in the brightness distribution image is identified, the feature point set is used as an initial feature point set, and the steps of: obtaining distribution information of the feature point set to a stable illumination region in the brightness distribution image; When the feature point set of the illumination change area in the brightness distribution image cannot be identified, corresponding prompt information is output.

5. The building energy consumption control method using illumination pattern changes according to any one of claims 1 to 4, characterized in that: The step of performing partition comparison on the illumination area in the illumination image based on the modified feature point set to obtain energy consumption optimization information of the illumination area includes: Dividing the illuminated area in the illuminated image into a preset number of equal partitions with the corrected feature point set as the center; Brightness distribution information of the equal subareas is obtained, and energy consumption optimization information of the illumination area is determined according to differences in the brightness distribution information between the equal subareas.

6. The building energy consumption control method using illumination pattern changes according to claim 5, characterized in that: The brightness distribution information includes a brightness mean and a gradient value. The step of obtaining the brightness distribution information of the equal partitions and determining the energy consumption optimization information of the illuminated area based on the difference in the brightness distribution information between the equal partitions includes: counting the brightness mean and gradient value of each equal partition, and calculating a brightness difference index between the equal partitions, wherein the brightness difference index is determined by the following formula: Among them, ΔL is the brightness difference index, is the mean brightness of the ith equal partition, is the average brightness of all equal partitions, is the brightness gradient value of the i-th equal partition, is the average brightness gradient value of all equal partitions, is the distance between the center of the ith equal partition and the building reference point, is the maximum reference distance of the building, and N is the number of equal partitions; If the brightness difference index is within a preset range, the energy consumption optimization information indicates that the illumination area is uniform, and an energy-saving control instruction is generated; If the brightness difference index is not within the preset range, the energy consumption optimization information indicates that the illumination area is uneven, and a dynamic adjustment instruction is generated.

7. The building energy consumption control method using illumination pattern changes according to claim 6, characterized in that: The step of generating an energy-saving control instruction or a dynamic adjustment instruction includes: When the energy consumption optimization information indicates that the illumination area is uniform, an energy-saving control instruction is generated to reduce the power of the lighting equipment or adjust the opening and closing angle of the sunshade device; When the energy consumption optimization information indicates that the illumination area is uneven, a dynamic adjustment instruction is generated for increasing the brightness of the lighting equipment or adjusting the angle of the sunshade device for a specific partition.

8. A building energy consumption control system using changes in illumination patterns, characterized in that: The building energy consumption control system includes: An image processing module is used to obtain indoor and outdoor lighting images of a building and generate a brightness distribution image corresponding to the lighting image; A feature positioning module, configured to obtain a feature point set of the illumination change area in the brightness distribution image based on a preset illumination feature positioning method; a feature correction module, configured to set a first reference line and a second reference line having the center of the feature point set as an intersection point, passing through a stable illumination area, and being perpendicular to each other, such that the first reference line intersects the illumination variation area at the first and second intersection points, and the second reference line intersects the illumination variation area at third and fourth intersection points; using the distances and brightness differences between the center of the feature point set and the first, second, third, and fourth intersection points, respectively, as distribution information, and correcting the feature point set based on the distribution information to obtain a corrected feature point set; A partition comparison module is used to perform partition comparison on the illumination area in the illumination image based on the corrected feature point set to obtain energy consumption optimization information of the illumination area.

9. A building energy consumption control system using light pattern changes according to claim 8, characterized in that: The partition comparison module further includes: a partitioning unit, configured to divide the illumination area in the illumination image into a preset number of equal partitions with the modified feature point set as the center; an analyzing unit, configured to obtain brightness distribution information of the equal subareas, and determine energy consumption optimization information of the illumination area according to differences in the brightness distribution information between the equal subareas; An instruction generation unit is used to generate energy-saving control instructions or dynamic adjustment instructions according to the energy consumption optimization information, and send them to the lighting equipment and shading devices in the building.

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