Intelligent dimming down lamp control system

Abstract

The application discloses a kind of intelligent dimming down lamp control systems, belongs to dimming down lamp control technical field, including dimming analysis module, control module and server;The dimming analysis module is used to carry out dimming data analysis, obtains light model, identifies the position of dimming down lamp, obtains the environmental parameter of the light area corresponding to dimming down lamp, the environmental parameter obtained is input into light model, obtains the dimming mode of this dimming down lamp, the dimming mode obtained is sent to control module;The control module is used to carry out the dimming control of dimming down lamp, receives the dimming mode sent by dimming analysis module, is marked as to be applied dimming mode, identifies the dimming information that dimming down lamp is being applied, the obtained dimming information is compared with to be applied dimming mode, when dimming information is different from to be applied dimming mode, to be applied dimming mode is marked as target dimming mode, adjusts dimming down lamp according to target dimming mode.

Description

Technical Field

[0001] This invention belongs to the field of dimming downlight control technology, specifically an intelligent dimming downlight control system. Background Technology

[0002] Downlights are recessed lighting fixtures that emit light downwards into the ceiling. They are widely used in hotels, homes, and cafes. Different occasions or places require different levels of brightness. Most of the market uses bulbs of different wattages to achieve the setting of light brightness, but this method is not smart enough, replacement is troublesome, and it cannot meet people's current pursuit of a better life. Therefore, this invention provides an intelligent dimming downlight control system. Summary of the Invention

[0003] To address the problems of the above solutions, this invention provides an intelligent dimming downlight control system.

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] A smart dimming downlight control system includes a dimming analysis module, a control module, and a server;

[0006] The dimming analysis module is used to perform dimming data analysis, obtain a light data model, identify the position of the dimming downlight, obtain the environmental parameters of the corresponding illumination area of ​​the dimming downlight, input the obtained environmental parameters into the light data model, obtain the dimming mode of the dimming downlight, and send the obtained dimming mode to the control module.

[0007] The control module is used to control the dimming of the downlights. It receives the dimming mode sent by the dimming analysis module, marks it as the dimming mode to be applied, identifies the dimming information currently being applied by the downlight, compares the obtained dimming information with the dimming mode to be applied, and when the dimming information is different from the dimming mode to be applied, marks the dimming mode to be applied as the target dimming mode and adjusts the downlight according to the target dimming mode; when the dimming information is the same as the dimming mode to be applied, no corresponding operation is performed.

[0008] Furthermore, methods for obtaining optical data models include:

[0009] Obtain the illumination area data corresponding to the dimming downlights, set the corresponding area model based on the obtained illumination area data, simulate and train the area model based on the area where the building is located, obtain the light data model, and upload the light data model to the dimming analysis module.

[0010] Furthermore, the types of regional models include two-dimensional data models and three-dimensional data models.

[0011] Furthermore, methods for determining the types of regional models include:

[0012] Acquire illumination area data, analyze the acquired illumination area data to obtain the corresponding spatial complexity value, obtain user requirements and dimmable downlight models, set the corresponding adjustment coefficient based on the obtained user requirements, match the corresponding model value based on the obtained dimmable downlight models, calculate the corresponding category value based on the obtained spatial complexity value, adjustment coefficient, and model value, and determine the category of the area model type based on the calculated category value.

[0013] Furthermore, the method for calculating the corresponding category value based on the obtained spatial complexity value, adjustment coefficient, and model value includes:

[0014] The spatial complexity value, adjustment coefficient, and model value are labeled KZ, α, and XZ, respectively. The corresponding category value is calculated using the formula QT = (b1 × KZ + b2 × XZ) × α, where b1 and b2 are proportionality coefficients with a value range of 0. <b1≤1,0<b2≤1。

[0015] Furthermore, the method for determining the type of regional model based on the calculated type value is as follows:

[0016] When the category value is greater than the threshold X1, a three-dimensional data model is used; when the category value is not greater than the threshold X1, a two-dimensional data model is used.

[0017] Furthermore, it also includes a drive module, which is used to change the position of the dimming downlight, specifically by means of:

[0018] The system acquires the movement area of ​​the dimmable downlight and marks the corresponding fixed reshaping point within the movement area. It also acquires user behavior information in real time, analyzes the acquired user behavior information to obtain corresponding movement data, and controls the dimmable downlight based on the acquired movement data. When the corresponding end command is detected, the dimmable downlight is moved to the corresponding fixed reshaping point.

[0019] Furthermore, methods for analyzing the obtained user behavior information include:

[0020] Match the user's behavior information with the corresponding movement adjustment method, obtain the user's image data, analyze the obtained image data, and obtain the corresponding movement data.

[0021] Compared with the prior art, the beneficial effects of the present invention are: through the cooperation between the dimming analysis module and the control module, intelligent control of the dimming downlight is realized. By intelligently determining the appropriate dimming parameters based on the position of the dimming downlight and the current indoor environmental parameters, the dimming downlight is automatically controlled, eliminating the need for manual adjustment by the user. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a block diagram illustrating the principle of the present invention. Detailed Implementation

[0024] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0025] like Figure 1 As shown, an intelligent dimming downlight control system includes a dimming analysis module, a control module, and a server;

[0026] The dimming analysis module is used to perform dimming data analysis, and the specific methods include:

[0027] The system acquires a light data model, identifies the location of the dimmer downlight, and obtains environmental parameters of the corresponding illumination area, such as illumination data and temperature data. These can be collected manually by setting the corresponding data acquisition items. The acquired environmental parameters are then input into the light data model to obtain the dimming mode of the dimmer downlight, and the obtained dimming mode is sent to the control module.

[0028] Methods for obtaining optical data models include:

[0029] When designing the installation of dimmable downlights, data on the affected illumination areas is obtained, such as data for the living room area and part of the hallway, including space dimensions and the size and placement of decorative items. Based on this data, a corresponding area model is created. This model is a simplified representation of the illumination area using existing modeling techniques, mostly in two dimensions, with some using three dimensions. The type of area model includes both two-dimensional and three-dimensional models, determined by the complexity of the illumination area. The area model is then simulated and trained based on the location of the building to obtain a data model, which is then uploaded to the dimming analysis module. In other words, the data model is not built within the dimmable downlight control system but is analyzed and created externally. This reduces the computational load on the control system and minimizes the size, cost, and selling price of the downlights.

[0030] The simulation training of the regional model based on the location of the building involves analyzing the parameters that may affect the adjustment of lighting in the area where the building is located, manually setting the parameters and the corresponding dimming adjustment methods for the dimmer downlights, and integrating them to form a training set. The obtained training set and the regional model are then combined to build a corresponding lighting data model. The lighting data model generates the corresponding dimming method based on the environmental parameters of the lighting area and the position of the dimmer downlights. In other words, the lighting area is determined by the position of the dimmer downlights, and the corresponding dimming method is generated based on the environmental parameters of the lighting area. For the undisclosed parts, they can be directly implemented using existing technologies.

[0031] Methods for determining the types of regional models include:

[0032] The process involves acquiring and analyzing illumination area data to obtain corresponding spatial complexity values, identifying user requirements and dimmable downlight models, and setting corresponding adjustment coefficients based on these requirements. These requirements are determined by relevant staff when the user specifies the downlight model. The matching of adjustment coefficients is then performed using a matching table discussed and set by an expert group, which includes the required range for each adjustment coefficient, or by using existing data matching algorithms. Next, the process involves matching the corresponding model values ​​based on the acquired downlight models. The expert group sets a matching table for these model values, primarily considering whether the corresponding model can be set using a 3D data model. Finally, the process involves calculating the corresponding category value based on the acquired spatial complexity value, adjustment coefficients, and model value, and then determining the type of area model based on the calculated category value.

[0033] Methods for analyzing the obtained illumination area data include:

[0034] A corresponding illumination region data analysis model is established based on a CNN or DNN network. The model is trained by manually setting up a training set. The trained illumination region data is then analyzed using the model to obtain the corresponding spatial complexity value. Since neural networks are a common technology in this field, the specific establishment and training process will not be described in detail.

[0035] Methods for calculating the corresponding category value based on the obtained spatial complexity value, adjustment factor, and model value include:

[0036] The spatial complexity value, adjustment coefficient, and model value are labeled KZ, α, and XZ, respectively. The corresponding category value is calculated using the formula QT = (b1 × KZ + b2 × XZ) × α, where b1 and b2 are proportionality coefficients with a value range of 0. <b1≤1,0<b2≤1。

[0037] The method for determining the type of regional model based on the calculated type value is as follows:

[0038] When the category value is greater than the threshold X1, a three-dimensional data model is used; when the category value is not greater than the threshold X1, a two-dimensional data model is used.

[0039] The control module is used to control the dimming of the downlights. It receives the dimming mode sent by the dimming analysis module, marks it as the dimming mode to be applied, identifies the dimming information currently being applied by the downlight, compares the obtained dimming information with the dimming mode to be applied, and when the dimming information is different from the dimming mode to be applied, it means that the result to be adjusted by the dimming mode to be applied is different from the dimming information; the dimming mode to be applied is marked as the target dimming mode, and the downlight is adjusted according to the target dimming mode; when the dimming information is the same as the dimming mode to be applied, no corresponding operation is performed.

[0040] In one embodiment, since current dimmable downlights are generally recessed and fixed, they cannot be moved, which does not meet the needs of some users who seek personalization; therefore, in this embodiment, the system also includes a driving module, which is used to change the position of the dimmable downlight, specifically by means of:

[0041] The system acquires the movement area of ​​the dimmable downlight, confirming it according to actual settings. For example, it may move within a defined area on the ceiling. The specific movement method is set according to needs and the method used, such as magnetic movement, rail movement, or drone movement. Corresponding fixed reshaping points are marked within the movement area; these fixed reshaping points are the original design positions of the dimmable downlight. User behavior information is acquired in real time, analyzed, and used to obtain corresponding movement data. The dimmable downlight is then controlled based on this movement data. When a corresponding end command is detected, meaning the driver module is no longer needed, the dimmable downlight is moved to the corresponding fixed reshaping point.

[0042] There are several ways to obtain user behavior information. One method, which is relatively easy to analyze, involves acquiring existing behavior patterns and setting corresponding control commands for each pattern, such as remote control commands or voice recognition commands. The user behavior information is then obtained by recognizing these commands. This method is simple to operate and requires minimal data processing. Another approach is to connect to the user's home image acquisition system or use a system with built-in image acquisition capabilities to obtain corresponding image data. This data is then analyzed to obtain the corresponding behavior data. Specifically, this involves manually setting specific actions, each corresponding to a control command. Using existing image action recognition technology, the user's actions are identified, and once a match is found, the corresponding behavior information is obtained.

[0043] Methods for analyzing the obtained user behavior information include:

[0044] Match the user's behavior information with the corresponding movement adjustment method, obtain the user's image data, analyze the obtained image data, and obtain the corresponding movement data.

[0045] If there is no image data, generate motion data directly according to the corresponding motion adjustment method.

[0046] Methods for matching user behavior information with corresponding mobile adjustment methods include:

[0047] The system acquires user behavior information and manually sets corresponding movement adjustment methods for each behavior. Each movement adjustment method has a corresponding initial adjustment distance. If there is no corresponding movement distance adjustment, the system will directly generate corresponding movement data according to the movement adjustment method. This is similar to user behavior information acquired using a simple method. The system then identifies user behavior information and matches corresponding movement adjustment methods based on the identified user behavior information.

[0048] The method for analyzing the acquired image data is as follows: A corresponding image data analysis model is established based on a CNN or DNN network. The model is trained using a manually configured training set. The successfully trained model is then applied to the driver module. The model analyzes the acquired image data to obtain the corresponding motion adjustment distance. Motion data is generated based on the obtained motion adjustment distance and the motion adjustment method. The user's optimization data is recorded in real time; this includes data adjusted when the user is dissatisfied with the motion data generated by the image data analysis model. This forms a corresponding optimization training set, which is used to optimize the image data analysis model.

[0049] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0050] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A smart dimming downlight control system, characterized in that, Includes a dimming analysis module, a control module, and a server; The dimming analysis module is used to perform dimming data analysis, obtain a light data model, identify the position of the dimming downlight, obtain the environmental parameters of the corresponding illumination area of ​​the dimming downlight, input the obtained environmental parameters into the light data model, obtain the dimming mode of the dimming downlight, and send the obtained dimming mode to the control module. The method for obtaining the light data model includes: acquiring the illumination area data corresponding to the dimming downlights, setting the corresponding area model based on the acquired illumination area data, simulating and training the area model based on the area where the building is located to obtain the light data model, and uploading the light data model to the dimming analysis module; the types of the area model include two-dimensional data models and three-dimensional data models. The methods for determining the type of the region model include: Acquire illumination area data, analyze the acquired illumination area data to obtain the corresponding spatial complexity value, obtain user requirements and dimmable downlight models, set the corresponding adjustment coefficient according to the obtained user requirements, match the corresponding model value according to the obtained dimmable downlight models, calculate the corresponding category value according to the obtained spatial complexity value, adjustment coefficient and model value, and determine the category of the area model based on the calculated category value. The method for determining the type of region model based on the calculated type value is as follows: When the category value is greater than the threshold X1, a three-dimensional data model is used; when the category value is not greater than the threshold X1, a two-dimensional data model is used. The control module is used to control the dimming of the downlights. It receives the dimming mode sent by the dimming analysis module, marks it as the dimming mode to be applied, identifies the dimming information currently being applied by the downlight, compares the obtained dimming information with the dimming mode to be applied, and when the dimming information is different from the dimming mode to be applied, marks the dimming mode to be applied as the target dimming mode and adjusts the downlight according to the target dimming mode; when the dimming information is the same as the dimming mode to be applied, no corresponding operation is performed.

2. The intelligent dimming downlight control system according to claim 1, characterized in that, Methods for calculating the corresponding category value based on the obtained spatial complexity value, adjustment factor, and model value include: The spatial complexity value, adjustment coefficient, and model value are labeled KZ, α, and XZ, respectively. The corresponding category value is calculated using the formula QT=(b1×KZ+b2×XZ)×α, where b1 and b2 are proportionality coefficients with a value range of 0. <b1≤1,0<b2≤1。 3. The intelligent dimming downlight control system according to claim 1, characterized in that, It also includes a drive module, which is used to change the position of the dimming downlight, specifically including: The system acquires the movement area of ​​the dimmable downlight and marks the corresponding fixed reshaping point within the movement area. It also acquires user behavior information in real time, analyzes the acquired user behavior information to obtain corresponding movement data, and controls the dimmable downlight based on the acquired movement data. When the corresponding end command is detected, the dimmable downlight is moved to the corresponding fixed reshaping point.

4. The intelligent dimming downlight control system according to claim 3, characterized in that, Methods for analyzing the obtained user behavior information include: Match the user's behavior information with the corresponding movement adjustment method, obtain the user's image data, analyze the obtained image data, and obtain the corresponding movement data.

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