A control method, a terminal, an intelligent device and a system for an intelligent lighting device

Abstract

The present application discloses a control method for an intelligent lighting device, which is applied to the terminal side. The method includes: in response to a first operation for acquiring a current image, acquiring the current image from an image resource, identifying the current image to obtain an identification result, and acquiring at least one or more target color data for light source color control from the identification result, so that the intelligent lighting device controls the light source color based on the target color data. The present application not only simplifies the dimming control, but also obtains accurate control, increases the interest and flexibility of the control, and improves the dimming effect of the lighting.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control, and particularly to a control method, a terminal, an intelligent device and a system for an intelligent lighting device. Background Art

[0002] An intelligent lighting device is an Internet of Things terminal that realizes control based on an embedded interconnection and interoperability module, wherein the control at least includes switch control and dimming control.

[0003] After several years of development, intelligent lighting devices have been widely used. In the dimming control of intelligent devices, usually a preset color is selected and adjusted manually through a palette. If it is desired to adjust the illumination light to the color of a specific object, such as the color of the living room background wall, not only is the operation troublesome, but also an accurate effect cannot be achieved. Summary of the Invention

[0004] The present invention provides a control method for an intelligent lighting device to achieve precise dimming control.

[0005] In a first aspect of the present invention, there is provided a control method for an intelligent lighting device, which is applied to the terminal side and includes:

[0006] Responding to a first operation for acquiring a current image, acquiring the current image from an image resource,

[0007] Identifying the current image to obtain an identification result,

[0008] Obtaining at least one or more target color data for light source color control from the identification result, so that the intelligent lighting device controls the light source color based on the target color data.

[0009] Preferably, the responding to a first operation for acquiring a current image and acquiring the current image from an image resource includes:

[0010] Responding to an image acquisition operation of the terminal on a target object, acquiring the acquired image to obtain the image resource, and / or, responding to a selection operation of selecting an image from the image files of the terminal, acquiring the selected image to obtain the image resource,

[0011] Displaying the current image;

[0012] Wherein,

[0013] The image file comes from an image file received by the terminal from the network and / or an image file stored locally in the terminal, and includes at least one of a picture file and a video file.

[0014] Preferably, the identifying the current image to obtain an identification result includes:

[0015] Use a first machine learning model to perform object detection on the current image and display the recognition result. Among them, the first machine learning model is an object detection model for detecting at least one of the target color, the target object, and the target object with the target color.

[0016] Obtaining at least one or more target color data for light source color control from the recognition result includes:

[0017] In response to a second operation for selecting a target image and / or a target area from the recognition result, obtain the target color data from the selected target image and / or target area.

[0018] The target color data includes at least one of static color and dynamic color.

[0019] Preferably, obtaining at least one or more target color data for light source color control from the recognition result, so that the intelligent lighting device controls the light source color based on the target color data, further includes:

[0020] In response to a third operation for configuring the target color data, configure the obtained target color data according to the configuration strategy.

[0021] Send the configuration result to the intelligent lighting device, so that the intelligent lighting device controls the light source color according to the configuration result to obtain a light source color change that conforms to the configuration strategy.

[0022] Among them,

[0023] The configuration strategy includes at least one of a pre-set configuration strategy and a custom configuration strategy.

[0024] Preferably, obtaining at least one or more target color data for light source color control from the recognition result, so that the intelligent lighting device controls the light source color based on the target color data, further includes:

[0025] In response to a fourth operation for collecting the main color data of the current environment, input the target color data, the main color data, and / or the lighting parameter data of the intelligent lighting device into a second machine learning model, and use the second machine learning model to correct the target color data to obtain the corrected target color data. Among them, the second machine learning model is a matching model based on the color data of the environment main color data and / or the lighting parameter data and the corrected color data.

[0026] Send the corrected target color data to the intelligent lighting device, so that the intelligent lighting device controls the light source color according to the corrected target color data.

[0027] Preferably, obtaining at least one or more target color data for light source color control from the recognition result, so that the intelligent lighting device controls the light source color based on the target color data, further includes:

[0028] In response to a fourth operation for collecting the main color data of the current environment, input the target color data or the corrected target color data, and the main color data of the environment and / or the lighting parameter data of the intelligent lighting device into a third machine learning model. Using the third machine learning model, convert the target color data or the corrected target color data into light wave parameter data, where the light wave parameters include at least one of length, strength, and proportion. Among them, the third machine learning model is a matching model of color data and light wave parameter data based on the main color data of the environment and / or lighting parameter data.

[0029] Or,

[0030] Input the target color data or the corrected target color data into a third machine learning model. Using the third machine learning model, convert the target color data or the corrected target color data into light wave parameter data, where the third machine learning model is a matching model of color data and light wave parameter data.

[0031] Send the light wave parameter data to the intelligent lighting device, so that the intelligent lighting device controls the light source color according to the light wave parameter data.

[0032] Preferably, obtaining at least one or more target color data for light source color control from the recognition result, so that the intelligent lighting device controls the light source color based on the target color data, includes:

[0033] In response to a fifth operation for dimming mode selection, where the dimming mode includes single dimming and real-time dimming.

[0034] When the selected dimming mode is the single dimming mode, send the target color data once, so that the intelligent lighting device performs single light source color control according to the target color data.

[0035] When the selected dimming mode is the real-time dimming mode, send the target color data in real time, so that the intelligent lighting device performs real-time control according to the target color data to obtain a light source color that follows the change of the target color data, where the target color data changes in real time following the current image.

[0036] A second aspect of the present invention provides a control method for an intelligent lighting device, which is applied to the intelligent lighting device side. The method includes:

[0037] Based on at least one or more target color data for light source color control obtained by the terminal, controlling the light source color,

[0038] wherein,

[0039] The target color data is obtained by the terminal in response to a first operation for acquiring a current image, acquiring the current image from an image resource, and based on the recognition result of the current image.

[0040] Preferably, the controlling the light source color based on at least one or more target color data for light source color control obtained by the terminal further includes:

[0041] Receiving a configuration result from the terminal, and controlling the light source color according to the configuration result to obtain a light source color change that conforms to the configuration strategy,

[0042] wherein,

[0043] The configuration result is obtained by the terminal in response to a second input operation for configuring the target color data, and configuring the acquired target color data according to the configuration strategy.

[0044] Preferably, the controlling the light source color based on at least one or more target color data for light source color control obtained by the terminal includes:

[0045] Receiving the target color data sent by the terminal once, and performing a single light source color control according to the target color data;

[0046] Or,

[0047] Receiving the target color data sent by the terminal in real time, and performing real-time control according to the target color data to obtain a light source color that follows the change of the target color data, wherein the target color data changes in real time following the change of the current image;

[0048] wherein,

[0049] The sent target color data is sent by the terminal in response to a fifth operation for dimming mode selection.

[0050] Preferably, the controlling the light source color based on at least one or more target color data for light source color control obtained by the terminal includes:

[0051] Input the target color data or the corrected target color data into a fourth machine learning model. Using the fourth machine learning model, convert the target color data or the corrected target color data into light wave parameter data. The fourth machine learning model is a matching model between color data and light wave parameter data.

[0052] Or,

[0053] Input the target color data or the corrected target color data, the collected ambient main color data, and / or the lighting parameter data of the intelligent lighting device into a fourth machine learning model. Using the fourth machine learning model, convert the target color data or the corrected target color data into light wave parameter data. The fourth machine learning model is a matching model between color data and light wave parameter data based on lighting parameter data and / or ambient main color data.

[0054] Control the color of the light source according to the light wave parameter data.

[0055] Wherein,

[0056] The corrected target color data is obtained by the terminal inputting the target color data, the collected ambient main color data, and / or the lighting parameter data of the intelligent lighting device into a second machine learning model in response to a fourth operation for collecting the current ambient main color data, and using the second machine learning model to correct the target color data; or,

[0057] The corrected target color data is obtained by the intelligent lighting device receiving the ambient main color data collected by the terminal in response to the fourth operation, inputting the target color data, the collected ambient main color data, and / or the lighting parameter data of the intelligent lighting device into a second machine learning model, and using the second machine learning model to correct the target color data.

[0058] The second machine learning model is a matching model between color data and corrected color data based on ambient main color data and / or lighting parameter data.

[0059] In a third aspect of the present invention, there is provided a terminal, which includes a memory and a processor. The memory stores a computer program, and the processor is configured to execute the computer program to implement any one of the intelligent lighting device control methods on the terminal side.

[0060] In a fourth aspect of the present invention, there is provided an intelligent lighting device, which includes a memory and a processor. The memory stores a computer program, and the processor is configured to execute the computer program to implement any one of the intelligent lighting device control methods on the intelligent lighting device side.

[0061] In a fifth aspect of the present invention, there is provided a control system for intelligent lighting devices, including the terminal described above and the intelligent lighting devices described above.

[0062] In a sixth aspect of the present invention, there is provided a computer-readable storage medium storing a computer program, which when executed by a processor, implements any of the intelligent lighting device control methods on the terminal side and / or any of the intelligent lighting device control methods on the intelligent device side.

[0063] The control method for intelligent lighting devices provided in this application obtains target color data through a terminal and adjusts the light source color of the intelligent lighting devices based on the target color data, which not only simplifies the dimming control but also achieves accurate control, increases the interest and flexibility of control, and improves the dimming effect of lighting. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 FIG. is a schematic flowchart of a method for controlling an intelligent lighting device according to an embodiment of this application.

[0065] Figure 2a FIG. is a schematic flowchart of a method for controlling an intelligent lighting device according to an embodiment of this application.

[0066] Figure 2b FIG. is another schematic flowchart of a method for controlling an intelligent lighting device according to an embodiment of this application.

[0067] Figure 3 FIG. is a schematic diagram for obtaining image resources.

[0068] Figure 4 FIG. is a schematic diagram for obtaining image resources.

[0069] Figure 5 FIG. is a schematic diagram for selecting a target area based on a target image.

[0070] Figure 6a FIG. is a networking schematic diagram for an intelligent lighting device to support the access point mode to act as a hotspot.

[0071] Figure 6b FIG. is a networking schematic diagram for both the terminal and the intelligent lighting device to access a wireless router.

[0072] Figure 6c FIG. shows that the cloud platform forwards control information to the intelligent lighting device through a long connection established with the intelligent lighting device.

[0073] Figure 7 FIG. is a schematic diagram of a control system for intelligent lighting devices according to an embodiment of this application.

[0074] Figure 8A schematic diagram of a terminal according to an embodiment of the present application.

[0075] Figure 9 A schematic diagram of an intelligent lighting device according to an embodiment of the present application.

[0076] Figure 10 Another schematic diagram of a terminal or an intelligent lighting device according to an embodiment of the present application. Detailed implementation manners

[0077] In order to make the purpose, technical means and advantages of the present application clearer and more understandable, the following further describes the present application in detail with reference to the accompanying drawings.

[0078] The applicant has noticed that the light emitted by various light sources forms different colored lights due to different wavelengths, intensities, and proportional properties of the light waves, thus forming the color of the light source. For example, the light of an ordinary light bulb contains more yellow and orange wavelength light and presents a yellowish taste, while the ordinary fluorescent lamp contains more blue wavelength light and presents a bluish taste. Then, the light emitted from the light source shows various colors due to the strength or lack of proportion of the light of the wavelengths contained therein. In the control of intelligent lighting devices, it is difficult to accurately control the color of the light source, which is reflected in that the dimming performed according to visual senses always has a gap with the desired target color, and the effect of the light source color is not good.

[0079] The applicant has found that this is because the color seen by visual senses depends on the emitted light. For example, red is the result of the absorption of green and blue wavelength light by an object and the reflection of red light to the visual senses. Similarly, green and red wavelength light is absorbed by the object and reflected as blue, and blue and red wavelength light is absorbed and reflected as green. Different objects absorb and emit different lights, and the colors perceived by visual senses are different. As a result, the manual dimming based on visual senses is always unsatisfactory and inaccurate.

[0080] In view of this, the present application proposes a control method for an intelligent lighting device to improve the accurate control of the light source color of the lighting device.

[0081] See Figure 1 as shown Figure 1 A flowchart of a control method for an intelligent lighting device according to an embodiment of the present application. The method includes:

[0082] Step 101, in response to a first operation for obtaining a current image, obtain the current image from the image resource,

[0083] Step 102, identify the current image to obtain an identification result,

[0084] Step 103: Obtain at least one piece of target color data for light source color control from the recognition result, so that the intelligent lighting device controls the light source color based on the target color data.

[0085] In the embodiment of the present application, the target color data obtained through the image recognition result is used to adjust the light color of the light emitted by the intelligent lighting device, avoiding the cumbersome operation of manual dimming and improving the accuracy of dimming.

[0086] For ease of understanding the embodiments of the present application, the following gives a specific description.

[0087] See Figure 2a as shown Figure 2a which is a schematic flowchart of a control method for an intelligent lighting device according to an embodiment of the present application. The control method includes:

[0088] Step 201: The terminal responds to a first input operation for obtaining a current image, obtains an image resource, and displays the obtained current image.

[0089] In this step,

[0090] One implementation is to align the terminal with the target object and perform the current image acquisition as the first operation to obtain a target image. For example, through the APP application on the terminal, open the camera, move the terminal or adjust the camera focus so that the target to be photographed is clearly within the shooting frame. The terminal responds to the first operation and displays the currently acquired image in a preview manner. In this way, the effect of obtaining the target color data by scanning can be achieved, which is convenient for user operation and improves the user experience.

[0091] See Figure 3 as shown Figure 3 which is a schematic diagram for obtaining an image resource. In the figure, the terminal acquires an image of the target object and previews the current image. In this way, the color of the target object can be used as the light source color.

[0092] Another implementation is to select a target image from the image files of the terminal as the first operation. Among them, the image file can be an image file received by the terminal from the network or an image file stored locally in the terminal. The image file can be a static picture file or a video file. The terminal responds to the first operation and displays the currently selected image. In this way, the existing rich image materials can be fully utilized to obtain the target color data, thereby enriching the light source color.

[0093] See Figure 4 as shown Figure 4 which is a schematic diagram for obtaining an image resource. In the figure, a target image is selected from the image library of the terminal and the selected image is displayed.

[0094] Step 202: Use the first machine learning model to perform target detection on the current image and display the recognition result.

[0095] Because the captured image and the selected image typically contain other images such as a background, a machine learning model is used to perform target recognition and classification on the current image, including at least one of a target color, a target object, and a target object having a target color, so as to obtain a target image of the target object. The first machine learning model is a target detection model that detects at least one of the target color, the target object, and the target object having a target color, and the recognition result includes at least one of the target color, the target object, and the target object having a target color.

[0096] For example, from an image containing apples of multiple colors, apples with Qixia red color can be identified; target colors such as banana yellow and coffee brown can be identified from the image; or specific targets can be identified from the image, such as a puppy, etc., so as to obtain a target image of the target object.

[0097] Step 203 : In response to a second operation for selecting a target image and / or a target area of a target object from the recognition result, acquiring at least one target color data from the selected target area.

[0098] In order to improve the convenience and interest of use, operations such as cropping can be performed based on the target image to select a target area, so as to obtain target color data from the target area.

[0099] See also Figure 5 As shown, Figure 5 This is a schematic diagram of target region selection based on a target image. In the figure, the target region can be obtained by drawing the region.

[0100] After the target area is determined, pixel information included in the target area is extracted as target color data. Preferably, the average value of the extracted pixel information can be calculated and the average value can be used as the target color data.

[0101] Multiple target regions can be selected. As an example, they can be different regions in the same frame of an image, or different regions or the same region in different frames. They can also be a certain segment of a video image, such as a video of the changing colors of the afterglow. For the case of multiple target regions, the pixel information of each target region can be extracted separately, and thus multiple target color data can be obtained. Each target color data can be different. In this way, the target color data can include static colors, such as the colors obtained from a certain static image frame, and can also include dynamic colors, such as a set of colors obtained from a video image frame, in which different colors correspond to different times. In addition, from the perspective of data format, the target color data can be RGB data or YUV data.

[0102] Through steps 201 to 203, rich target color data can be obtained from the image resources.

[0103] Step 204, in response to a third operation for configuring the target color data, configure the obtained target color data according to the configuration strategy.

[0104] In this step, dimming design can be carried out through the configuration strategy. The configuration strategy can be a pre-configured system configuration strategy or a custom configuration strategy. The configuration strategy is composed and combined of different configuration information, including but not limited to:

[0105] In the case of a single target color data, configure at least one of the following: the duration maintained by the target color, the gradient color based on the target color, and the gradient method.

[0106] In the case of multiple target color data, configure at least one of the following: the duration maintained by each target color data, the gradient color of each target color, the gradient method, the switching frequency between each target color data, the switching method, the number of combined target color data, the combination order, the switching frequency between multiple combinations, the switching method, the number of mixed target colors, and the proportion of each target color used for mixing.

[0107] Among them, the switching method and the gradient method can include multiple modes, such as wave type, tide type, the rhythm of a certain piece of music, etc. The configured cooperation result can be previewed and displayed.

[0108] For example, the target color data including the setting sun can obtain the simulated light effect of the setting sun by configuring the duration, realizing the reproduction of the natural light effect in nature.

[0109] Step 205, in order to obtain a better light effect, use the second machine learning model to correct the target color data.

[0110] As an example, given that the final light effect of dimming control is related to the current environment. For instance, the same target color data may result in different final light effects in a current environment with a red dominant color tone and a white dominant color tone. In particular, when the target color data is not collected from the current environment, such as when the target color is from an image file. Therefore, the dominant color tone data of the current environment and the target color data are input into the second machine learning model, and the second machine learning model is used to correct the target color data in order to obtain the target color that matches the current environment.

[0111] Among them, the second machine learning model can train the color sample data in combination with the environmental dominant color tone sample data to obtain a matching model between the color data based on the environmental dominant color tone data and the corrected color data.

[0112] As another example, given that the lighting parameters of intelligent lighting devices are different due to different lighting principles, the second machine learning model can also train the color sample data in combination with the lighting parameter sample data of the intelligent lighting device to obtain a matching model between the color data based on the lighting parameters of the intelligent lighting device and the corrected color data.

[0113] As yet another example, the second machine learning model can also train the color sample data in combination with the lighting parameter sample data and the environmental dominant color tone sample data of the intelligent lighting device to obtain a matching model between the color data based on the lighting parameters and the environmental dominant color tone data of the intelligent lighting device and the corrected color data.

[0114] The dominant color tone data of the current environment can be obtained by collecting through a terminal. That is to say, in response to a fourth operation for collecting the dominant color tone data of the current environment, the target color data and the collected environmental dominant color tone data and / or lighting parameters are input into the second machine learning model so that the second machine learning model corrects the target color data.

[0115] It should be understood that this step can also be performed on the side of the intelligent lighting device. In this case, the terminal sends the environmental dominant color tone data collected in response to the fourth operation to the intelligent lighting device.

[0116] Step 206, in response to a fifth operation for dimming mode selection, send the corrected target color data and the configuration result to the intelligent lighting device according to the selected dimming mode.

[0117] As an example, utilize the second machine learning model.

[0118] If the dimming mode is the single dimming mode, the corrected target color data and the configuration result are sent once, that is, the corrected target color data and the configuration result are sent to the intelligent lighting device, and the intelligent lighting device can start dimming in response to the received data.

[0119] If the dimming mode is the real-time dimming mode, the corrected target color data and the configuration result are sent to the intelligent lighting device in real time, so that the intelligent lighting device performs real-time control according to the target color data to obtain a light source color that follows the change of the current target color data, where the target color data changes in real time following the current image. For example, the terminal collects the current image in real time, and the target color data (which can also be the corrected target color data) obtained from the current real-time image is sent to the intelligent lighting device in real time, and the intelligent lighting device dims based on the real-time target color data.

[0120] The communication link for the terminal to communicate with the intelligent lighting device can include at least one of the following connection methods:

[0121] Bluetooth connection;

[0122] Local area network connection;

[0123] Internet cloud platform connection, and the intelligent lighting device accesses the Internet cloud platform in a long connection mode.

[0124] In the case where the intelligent lighting device supports the Bluetooth function, the terminal and the Bluetooth can perform interconnection communication. The terminal searches for surrounding Bluetooth devices, initiates a connection request after finding the intelligent lighting device, and establishes a connection, thereby realizing the control of the intelligent lighting device by the terminal.

[0125] In the case where the intelligent lighting device and the terminal are in the same local area network, the terminal can establish a connection for communication through the communication connection protocol supported by the local area network of the intelligent lighting device (usually the Wi-Fi protocol). It includes two methods. One is as Figure 6a shown. The intelligent lighting device supports the access point (AP) mode to act as a hotspot. The terminal scans the surrounding AP hotspots, finds the AP and connects to the intelligent lighting device AP, and realizes the control of the intelligent lighting device by the terminal after establishing the connection; the other is as Figure 6b shown. The terminal and the intelligent lighting device are both connected to the wireless router, obtain the IP address of the local area network through the wireless router, and the terminal and the intelligent lighting device establish a connection for communication through the local area network IP address.

[0126] The connection of the Internet cloud platform means that both the intelligent lighting device and the terminal are connected to the Internet cloud platform. In this connection method, the intelligent lighting device accesses the Internet cloud platform through Wi-Fi, NB-Iot, LoRa, etc. to establish a long connection and complete device platform registration; the terminal binds the intelligent lighting device to its own account through the App, obtains the information of the intelligent lighting device through the cloud platform, sends the control information of the intelligent lighting device to the cloud platform, and the cloud platform forwards the control information to the intelligent lighting device through the long connection established between the intelligent lighting device and the cloud platform. As shown in Figure 6c as follows.

[0127] Step 207, the intelligent lighting device receives the corrected target color data and the configuration result, and controls the light source color according to the corrected target color data and the configuration result.

[0128] In this step, as an example, the intelligent lighting device converts the corrected target color data into light wave parameter data, and controls the light source color according to the light wave parameter data.

[0129] Preferably, the fourth machine learning model can be used to convert the corrected target color data into light wave parameter data, and the light wave parameters include at least one of length, strength, and proportion.

[0130] Among them, the fourth machine learning model trains the color sample data to obtain a matching model of color and light wave parameter data. Alternatively, the fourth machine learning model can train the color sample data in combination with the lighting parameter data of the intelligent lighting device to obtain a matching model of color data and light wave parameter data based on the lighting parameter data. In this way, it is beneficial to improve the accuracy of the light wave parameters, thereby improving the light efficiency of the light source color.

[0131] See Figure 2b as follows. Figure 2b This is another schematic flowchart of the control method of the intelligent lighting device according to the embodiment of the present application. The above steps 205 to 207 can also be:

[0132] Step 205', the terminal uses the third machine learning model to convert the target color data or the corrected target color data into light wave parameter data, and the light wave parameters include at least one of length, strength, and proportion.

[0133] Among them, the third machine learning model trains the color sample data to obtain a matching model of color and light wave parameter data. Alternatively, the third machine learning model can train the color sample data in combination with the lighting parameter sample data of the intelligent lighting device to obtain a matching model of color data and light wave parameter data based on the lighting parameter data.

[0134] Or,

[0135] In response to a fourth operation for collecting the data of the main color tone of the current environment, the terminal inputs the target color data or the corrected target color data, as well as the collected data of the main color tone of the environment and / or the lighting parameters of the intelligent lighting device, into a third machine learning model. Using the third machine learning model, the target color data or the corrected target color data is converted into light wave parameter data.

[0136] Among them, the third machine learning model combines the sample data of the main color tone of the environment and / or the sample data of the lighting parameters of the intelligent lighting device to train the color sample data, and obtains a matching model between the color data based on the lighting parameters and / or the main color tone of the environment and the light wave parameter data.

[0137] Step 206', in response to a fifth operation for dimming mode selection, sends the light wave parameter data and the configuration result to the intelligent lighting device according to the selected dimming mode.

[0138] Step 207', the intelligent lighting device receives the light wave parameter data and the configuration result, and controls the color of the light source according to the light wave data and the configuration result.

[0139] It should be understood that the correction of the target color data and the conversion of the target color data or the corrected target color data into light wave parameter data can be performed either on the terminal side or on the intelligent lighting device side, and can usually be determined in combination with the system design.

[0140] In the embodiment of the present application, through rich image resources, the target color data for light source color control is obtained, enriching the colors for dimming. Through operations such as scanning or clicking, the color of the light source changes, simplifying the dimming operation and improving the convenience and interest of use. Through the selection of configuration strategies, the diversity of dimming light effect design is realized, improving the initiative of user use. By using a machine learning model, through correcting the target color data by combining the current main color tone of the environment and / or lighting device parameters, or converting the target color data into light wave parameter data by combining the current main color tone of the environment and / or lighting device parameters, not only the light effect is improved, but also the accuracy of dimming control is improved, enhancing the adaptability of the target color data and the light wave parameter data.

[0141] The embodiment of the present application is not limited to the intelligent lighting device in the smart home, and is also applicable to large-stage light effects and live broadcast room light effects.

[0142] See Figure 7 as shown Figure 7This is a schematic diagram of the intelligent lighting device control system according to an embodiment of the present application. The system includes a terminal, an intelligent lighting device, and an Internet of Things platform. Among them, the terminal and the intelligent lighting device are respectively connected to the Internet of Things platform.

[0143] See Figure 8 as shown Figure 8 This is a schematic diagram of a terminal according to an embodiment of the present application. The terminal includes:

[0144] An interaction module, configured to obtain a current image from image resources in response to a first operation for obtaining the current image.

[0145] A target detection module, configured to identify the current image to obtain an identification result.

[0146] An acquisition module, configured to obtain at least one piece of target color data for light source color control from the identification result, so that the intelligent lighting device controls the light source color based on the target color data.

[0147] The acquisition module includes:

[0148] A correction sub-module, configured to input the target color data and the environmental main color data into a second machine learning model, and use the second machine learning model to correct the target color data to obtain corrected target color data. Among them, the second machine learning model is a matching model of color data based on environmental main color data and corrected color data.

[0149] Or

[0150] Input the target color data, the environmental main color data, and the lighting parameter data of the intelligent lighting device into a second machine learning model, and use the second machine learning model to correct the target color data to obtain corrected target color data. Among them, the second machine learning model is a matching model of color data based on environmental main color data and lighting parameter data of the intelligent lighting device.

[0151] Send the corrected target color data to the intelligent lighting device, so that the intelligent lighting device performs light source color control according to the corrected target color data.

[0152] The acquisition module includes:

[0153] A conversion sub-module, configured to input the target color data and the environmental main color data into a third machine learning model, and use the third machine learning model to convert the target color data into light wave parameter data. The light wave parameters include at least one of length, strength, and proportion. Among them, the third machine learning model is a matching model of color data of environmental main color data and light wave parameter data.

[0154] Or,

[0155] In response to a fourth operation for collecting data on the main color tone of the current environment, input the target color data, the main color tone data of the environment, and the lighting parameter data of the intelligent lighting device into a third machine learning model, and use the third machine learning model to convert the target color data into light wave parameter data, where the third machine learning model is a matching model of color data and light wave parameter data based on the main color tone data of the environment and the lighting parameter data.

[0156] Or,

[0157] Input the target color data or the corrected target color data into a third machine learning model, and use the third machine learning model to convert the target color data or the corrected target color data into light wave parameter data, where the third machine learning model is a matching model of color data and light wave parameter data.

[0158] Or,

[0159] Input the target color data and the lighting parameter data of the intelligent lighting device into a third machine learning model, and use the third machine learning model to convert the target color data into light wave parameter data, where the third machine learning model is a matching model of color data and light wave parameter data based on the lighting parameter data;

[0160] Send the light wave parameter data to the intelligent lighting device, so that the intelligent lighting device performs light source color control according to the light wave parameter data.

[0161] The acquisition module includes:

[0162] A sending sub-module, configured to send at least one of the target color data, the corrected target color data, the light wave parameter data, and the configuration result to the intelligent lighting device.

[0163] Wherein,

[0164] The target detection module is configured to use a first machine learning model to perform target detection on the current image and display the recognition result, where the first machine learning model is a target detection model for detecting at least one of a target color, a target object, and a target object with a target color.

[0165] The interaction module includes,

[0166] The first interaction sub-module is configured to obtain the captured image in response to an image capture operation of the terminal on a target object to obtain the image resource, and / or obtain the selected image in response to a selection operation of selecting an image from the image files of the terminal to obtain the image resource.

[0167] Display the current image.

[0168] The second interaction sub-module is configured to obtain the target color data from the selected target image and / or target area in response to a second operation for selecting a target image and / or target area from the recognition result.

[0169] The third interaction sub-module is configured to configure the obtained target color data according to a configuration policy in response to a third operation for configuring the target color data, and send the configuration result to the sending sub-module, so that the intelligent lighting device controls the light source color according to the configuration result to obtain a light source color change that conforms to the configuration policy.

[0170] The fourth interaction sub-module is configured to send the collected current ambient main color data to the calibration sub-module or the conversion sub-module or the sending sub-module in response to a fourth operation for collecting the current ambient main color data.

[0171] The fifth interaction sub-module is configured to, in response to a fifth operation for selecting a dimming method, when the selected dimming method is a single dimming method, send the target color data once, so that the intelligent lighting device performs a single light source color control according to the target color data.

[0172] When the selected dimming method is a real-time dimming method, send the target color data in real time, so that the intelligent lighting device performs real-time control according to the target color data to obtain a light source color that follows the change of the target color data, where the target color data changes in real time following the current image.

[0173] See Figure 9 as shown in Figure 9 is a schematic diagram of an intelligent lighting device according to an embodiment of the present application. The intelligent lighting device includes:

[0174] A control module for controlling the light source color based on at least one or more target color data obtained by the terminal for light source color control.

[0175] Wherein,

[0176] The target color data is obtained by the terminal from the image resource to obtain the current image in response to a first operation for obtaining the current image, and is obtained based on the recognition result of the current image.

[0177] The control module is further configured to receive a configuration result from the terminal, and control the light source color according to the configuration result, so as to obtain a light source color change that conforms to the configuration strategy.

[0178] Wherein,

[0179] The configuration result is obtained by the terminal in response to a second input operation for configuring the target color data, and the acquired target color data is configured according to the configuration strategy.

[0180] The control module is further configured to receive the target color data sent by the terminal once, and perform single-time light source color control according to the target color data;

[0181] Or,

[0182] Receive the target color data sent by the terminal in real time, and perform real-time control according to the target color data, so as to obtain a light source color that follows the change of the target color data, wherein the target color data changes in real time following the current image;

[0183] Wherein,

[0184] The sent target color data is sent by the terminal in response to a fifth operation for selecting a dimming method.

[0185] The intelligent lighting device further includes:

[0186] A conversion module, configured to receive the target color data or the corrected target color data from the terminal, input the target color data or the corrected target color data into a fourth machine learning model, and use the fourth machine learning model to convert the target color data or the corrected target color data into light wave parameter data, wherein the fourth machine learning model is a matching model between color data and light wave parameter data.

[0187] Or,

[0188] Input the target color data or the corrected target color data, as well as the lighting parameter data of the intelligent lighting device and / or the received ambient main color data into the fourth machine learning model, and use the fourth machine learning model to convert the target color data or the corrected target color data into light wave parameter data, wherein the fourth machine learning model is a matching model between color data and light wave parameter data based on the lighting parameter data and / or the received ambient main color data.

[0189] The control module is further configured to control the light source color according to the light wave parameter data.

[0190] The intelligent lighting device further includes:

[0191] A calibration module, configured to receive the target color data from the terminal and the ambient main color data, input the target color data and the ambient main color data into a second machine learning model, and use the second machine learning model to calibrate the target color data to obtain calibrated target color data, where the second machine learning model is a matching model based on the ambient main color data and the calibrated color data.

[0192] Or,

[0193] Input the target color data, the ambient main color data, and the lighting parameter data of the intelligent lighting device into a second machine learning model, and use the second machine learning model to calibrate the target color data to obtain calibrated target color data, where the second machine learning model is a matching model based on the ambient main color data and the lighting parameter data of the intelligent lighting device and the color data and the calibrated color data.

[0194] Send the calibrated target color data to the control module, so that the control module performs light source color control according to the calibrated target color data, or send the calibrated target color data to the conversion module.

[0195] See Figure 10 as shown Figure 10 is another schematic diagram of the terminal or the intelligent lighting device according to an embodiment of the present application. It includes a memory or a processor, the memory stores a computer program, and the processor is configured to execute the computer program to implement any of the intelligent lighting device control methods.

[0196] The memory may include a random access memory (RAM), or may also include a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0197] The above-mentioned processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0198] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the steps of any of the intelligent lighting device control methods are implemented.

[0199] For the device / network-side device / storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, refer to the partial description of the method embodiments.

[0200] In this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0201] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A control method for an intelligent lighting device, characterized in that, Applied to the terminal side, the method includes: In response to a first operation for obtaining a current image, obtain the current image from the image resource, Perform recognition on the current image to obtain a recognition result, Obtain at least one or more target color data for light source color control from the recognition result, In response to a fourth operation for collecting the main color data of the current environment, input the target color data, as well as the main color data and / or the lighting parameter data of the intelligent lighting device, into a second machine learning model, and use the second machine learning model to correct the target color data to obtain the corrected target color data, where the second machine learning model is a matching model based on the matching between the color data and / or lighting parameter data of the environment main color data and the corrected color data, Send the corrected target color data to the intelligent lighting device, so that the intelligent lighting device performs light source color control according to the corrected target color data.

2. The control method according to claim 1, wherein, The step of "In response to a first operation for obtaining a current image, obtain the current image from the image resource" includes: In response to the image acquisition operation of the terminal for the target object, obtain the acquired image to obtain the image resource, and / or, in response to the selection operation for selecting an image from the image files of the terminal, obtain the selected image to obtain the image resource, Display the current image; Wherein, The image file comes from the image file received by the terminal from the network and / or the image file stored locally in the terminal, and includes at least one of a picture file and a video file.

3. The control method according to claim 2, characterized in that The step of "Perform recognition on the current image to obtain a recognition result" includes: Use a first machine learning model to perform target detection on the current image and display the recognition result, where the first machine learning model is a target detection model for detecting at least one of a target color, a target object, and a target object with a target color, The step of "Obtain at least one or more target color data for light source color control from the recognition result" includes: In response to a second operation for selecting a target image and / or a target area from the recognition result, obtain the target color data from the selected target image and / or target area, The target color data includes at least one of static color and dynamic color.

4. The control method according to any one of claims 1 to 3, characterized in that, The method further includes: In response to a third operation for configuring the target color data, configure the obtained target color data according to a configuration strategy, Send the configuration result to the intelligent lighting device, so that the intelligent lighting device controls the light source color according to the configuration result to obtain a light source color change that conforms to the configuration strategy, Wherein, The configuration strategy includes at least one of a pre-set configuration strategy and a custom configuration strategy.

5. The control method according to claim 1, wherein The method further includes: In response to a fourth operation for collecting the main color data of the current environment, input the target color data or the corrected target color data, as well as the main color data of the environment and / or the lighting parameter data of the intelligent lighting device, into a third machine learning model, and use the third machine learning model to convert the target color data or the corrected target color data into light wave parameter data, Among them, the optical wave parameters include at least one of length, strength, and ratio, the third machine learning model is a matching model of color data and optical wave parameter data based on ambient dominant color data and / or lighting parameter data.

6. The control method according to claim 1, wherein The method further includes: In response to a fourth operation for collecting current ambient dominant color data, input the target color data or the corrected target color data into the third machine learning model, and use the third machine learning model to convert the target color data or the corrected target color data into optical wave parameter data, where the third machine learning model is a matching model of color data and optical wave parameter data; Send the optical wave parameter data to the intelligent lighting device, so that the intelligent lighting device controls the light source color according to the optical wave parameter data.

7. The control method according to claim 1, characterized in that The method further includes: In response to a fifth operation for dimming mode selection, where the dimming mode includes single dimming and real-time dimming, In the case where the selected dimming mode is the single dimming mode, send the target color data once, so that the intelligent lighting device controls the light source color once according to the target color data; In the case where the selected dimming mode is the real-time dimming mode, send the target color data in real time, so that the intelligent lighting device performs real-time control according to the target color data to obtain a light source color that follows the change of the target color data, where the target color data changes in real time following the current image.

8. A control method for an intelligent lighting device, characterized in that, Applied to the intelligent lighting device side, the method includes: Controlling the light source color based on the corrected target color data, Among them, the corrected target color data is obtained in the following manner: The terminal responds to a first operation for obtaining the current image, obtains the current image from the image resource, and based on the recognition result of the current image, obtains at least one or more target color data for light source color control, The terminal responds to a fourth operation for collecting current ambient dominant color data, inputs the target color data, as well as the dominant color data and / or the lighting parameter data of the intelligent lighting device, into the second machine learning model, and uses the second machine learning model to correct the target color data to obtain the corrected target color data; or, the intelligent lighting device receives the ambient dominant color data collected by the terminal in response to the fourth operation, inputs the target color data and the collected ambient dominant color data and / or the lighting parameter data of the intelligent lighting device into the second machine learning model, and uses the second machine learning model to correct the target color data to obtain the corrected target color data; where the second machine learning model is a matching model of color data and / or lighting parameter data based on ambient dominant color data and the corrected color data.

9. The control method according to claim 8, characterized in that, The method further includes: Receiving a configuration result from the terminal, and controlling the light source color according to the configuration result to obtain a light source color change that conforms to the configuration strategy, Among them, the configuration result is obtained by the terminal responding to a second input operation for configuring the target color data, and configuring the obtained target color data according to the configuration strategy.

10. The control method according to claim 8, characterized in that The method further includes: Receiving the target color data sent by the terminal once, and performing single-time light source color control according to the target color data; Or, Receiving the target color data sent by the terminal in real time, and performing real-time control according to the target color data to obtain a light source color that follows the change of the target color data, where the target color data changes in real time following the current image; Wherein, The sent target color data is sent by the terminal in response to a fifth operation for dimming mode selection.

11. The control method according to claim 8, characterized in that, The method further includes: Inputting the target color data or the corrected target color data into a fourth machine learning model, and using the fourth machine learning model to convert the target color data or the corrected target color data into light wave parameter data, where the fourth machine learning model is a matching model between color data and light wave parameter data; Or, Inputting the target color data or the corrected target color data, the collected ambient main color data, and / or the lighting parameter data of the intelligent lighting device into a fourth machine learning model, and using the fourth machine learning model to convert the target color data or the corrected target color data into light wave parameter data, where the fourth machine learning model is a matching model between color data and light wave parameter data based on lighting parameter data and / or ambient main color data; Controlling the light source color according to the light wave parameter data.

12. A terminal, characterized in that, The terminal includes a memory and a processor, the memory stores a computer program, and the processor is configured to execute the computer program to implement the intelligent lighting device control method according to any one of claims 1 to 7.

13. An intelligent lighting device, characterized in that, The intelligent lighting device includes a memory and a processor, the memory stores a computer program, and the processor is configured to execute the computer program to implement the intelligent lighting device control method according to any one of claims 8 to 11.

14. An intelligent lighting device control system, characterized in that, Including the terminal according to claim 12 and the intelligent lighting device according to claim 13, and a connection is established between the terminal and the intelligent lighting device through a communication link.

15. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the intelligent lighting device control method according to any one of claims 1 to 7, and / or the intelligent lighting device control method according to any one of claims 8 to 11.

Images