Lighting device adjustment method and apparatus, and lighting device and storage medium

Abstract

A lighting device adjustment method and apparatus, and a lighting device and a storage medium. The lighting device adjustment method comprises: acquiring user expression information and / or user authorization information; inputting the user expression information and / or the user authorization information into a lighting instruction recommendation model, so as to obtain a target lighting instruction set corresponding to the user expression information and / or the user authorization information; receiving a selection instruction of a user, and on the basis of the selection instruction, selecting a target lighting instruction from the target lighting instruction set; and adjusting a lighting device on the basis of the target lighting instruction. In the method, the user expression information and / or the user authorization information are input into the lighting instruction recommendation model, such that a target lighting instruction set to be recommended is obtained; and a target lighting device is then obtained in response to the selection instruction of the user for the target lighting instruction set, such that the lighting device is adjusted, thereby recommending a lighting effect to the user in a personalized manner.

Description

Lighting equipment adjustment method, device, lighting equipment and storage medium Technical Field

[0001] The present application belongs to the field of lighting technology, and in particular relates to a lighting equipment adjustment method, device, lighting equipment and storage medium. Background Art

[0002] With the continuous development of lighting technology, healthy and energy-saving smart lighting has become a trend. For lighting equipment, in order to adapt to different scene requirements, lighting equipment is usually equipped with multiple adjustable lighting modes, allowing users to adjust the lighting mode of the lighting equipment according to their needs to suit the corresponding scene requirements.

[0003] However, there are potentially tens of millions of lighting effects, and each user has their own preferred lighting effects. Related technologies often control lighting devices by analyzing factors such as sound and light in the lighting environment. This fails to meet the differentiated needs of individual users and, therefore, fails to provide personalized lighting effect recommendations.

[0004] Summary of the Invention

[0005] The embodiments of the present application provide a lighting device adjustment method, apparatus, lighting device, and storage medium to solve the problem of being unable to provide personalized lighting effect recommendations to users.

[0006] A first aspect of an embodiment of the present application provides a lighting device adjustment method, which is applied to a lighting device, and the method includes: obtaining user expression information and / or user authorization information; inputting the user expression information and / or user authorization information into a lighting instruction recommendation model to obtain a target lighting instruction set corresponding to the user expression information and / or the user authorization information; receiving and selecting a target lighting instruction from the target lighting instruction set according to a user selection instruction; and adjusting the lighting device according to the target lighting instruction.

[0007] Furthermore, in the above method provided in the embodiment of the present application, the user expression information includes at least one of voice expression, text expression, facial expression and body expression; the user authorization information includes at least one of user attribute information and scene information.

[0008] Furthermore, in the above method provided in the embodiment of the present application, if the lighting instruction recommendation model includes a lighting instruction recall model and a lighting instruction refinement model, then the step of inputting the user expression information and / or the user authorization information into the lighting instruction recommendation model to obtain a target lighting instruction set corresponding to the user expression information and / or the user authorization information includes: inputting the user expression information and / or the user authorization information into the lighting instruction recall model to obtain a first probability value of the lighting instruction corresponding to the user expression information and / or the user authorization information, and selecting multiple lighting instructions whose first probability value exceeds a preset probability threshold as the initial lighting instruction set; inputting the user expression information and / or the user authorization information, and the initial lighting instruction set into the lighting instruction refinement model to obtain a second probability value of each initial lighting instruction in the initial lighting instruction set corresponding to the user expression information and / or the user authorization information, and sorting the initial lighting instruction set according to the second probability value to obtain a target lighting instruction set.

[0009] Furthermore, in the above method provided in the embodiment of the present application, if the lighting instruction recommendation model includes a lighting instruction recall model, a lighting instruction coarse sorting model and a lighting instruction fine sorting model, then the step of inputting the user expression information and / or the user authorization information into the lighting instruction recommendation model to obtain a target lighting instruction set corresponding to the user expression information and / or the user authorization information also includes: inputting the user expression information and / or the user authorization information into the lighting instruction recall model to obtain a first probability value of the lighting instruction corresponding to the user expression information and / or the user authorization information, and selecting a plurality of lighting instructions whose first probability value exceeds a preset probability threshold as the initial lighting instruction set; ... The user expression information and / or the user authorization information, and the initial lighting instruction set are input into the coarse-sorting model of the lighting instruction to obtain an intermediate probability value of each initial lighting instruction in the initial lighting instruction set corresponding to the user expression information and / or the user authorization information, and the initial lighting instruction set is filtered and sorted according to the intermediate probability value to obtain an intermediate lighting instruction set; the user expression information and / or the user authorization information, and the intermediate lighting instruction set are input into the fine-sorting model of the lighting instruction to obtain a second probability value of each intermediate lighting instruction in the intermediate lighting instruction set corresponding to the user expression information and / or the user authorization information, and the intermediate lighting instruction set is sorted according to the second probability value to obtain a target lighting instruction set.

[0010] Furthermore, in the above method provided in the embodiment of the present application, if the lighting instruction recommendation model includes a lighting instruction ranking model, then the inputting of the user expression information and / or the user authorization information into the lighting instruction recommendation model to obtain the target lighting instruction set corresponding to the user expression information and / or the user authorization information also includes: determining multiple lighting instructions corresponding to the user expression information and / or the user authorization information as a preset lighting instruction set; inputting the user expression information and / or the user authorization information, and the preset lighting instruction set into the lighting instruction ranking model to obtain the target probability value of each preset lighting instruction in the preset lighting instruction set corresponding to the user expression information and / or the user authorization information, and sorting the preset lighting instruction set according to the target probability value to obtain the target lighting instruction set; or inputting the user expression information and / or the user authorization information into the lighting instruction ranking model to obtain the lighting instruction set corresponding to the user expression information and / or the user authorization information and the probability value of each lighting instruction in the lighting instruction set, and filtering and sorting the lighting instruction set according to the probability value to obtain the target lighting instruction set.

[0011] Furthermore, in the above method provided in the embodiment of the present application, the determination of the multiple lighting instructions corresponding to the user expression information and / or the user authorization as a preset lighting instruction set includes: traversing the mapping relationship between the preset user expression information and / or user authorization information and the lighting instructions, and obtaining a preset lighting instruction set composed of multiple lighting instructions.

[0012] Furthermore, in the above method provided in the embodiment of the present application, after obtaining the user expression information and / or user authorization information, the method also includes: determining the user attribute information corresponding to the user authorization information; if the user is determined to be a target user based on the user attribute information, calling the lighting instruction determination model to process the user attribute information, and obtaining the target user group corresponding to the user attribute information and the target lighting instruction corresponding to the target user group; and adjusting the lighting equipment according to the target lighting instruction.

[0013] The second aspect of an embodiment of the present application also provides a lighting equipment adjustment device, which includes: a user information acquisition module for acquiring user expression information and / or user authorization information; a lighting instruction recommendation module for inputting the user expression information and / or user authorization information into a lighting instruction recommendation model to obtain a target lighting instruction set corresponding to the user expression information and / or the user authorization information; a lighting instruction selection module for receiving and selecting a target lighting instruction from the target lighting instruction set according to a user's selection instruction; and a lighting equipment adjustment module for adjusting the lighting equipment according to the target lighting instruction.

[0014] A third aspect of an embodiment of the present application further provides a lighting device, which includes a controller and a memory, and the controller is configured to implement any one of the above-mentioned lighting device adjustment methods when executing a computer program stored in the memory.

[0015] A fourth aspect of the embodiments of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a controller, any one of the above-mentioned lighting device adjustment methods is implemented.

[0016] The lighting device adjustment method provided in the embodiments of the present application obtains user expression information and / or user authorization information; inputs the user expression information and / or user authorization information into a lighting instruction recommendation model to obtain a target lighting instruction set corresponding to the user expression information and / or user authorization information; then receives and selects a target lighting instruction from the target lighting instruction set based on a user selection instruction, and adjusts the lighting device based on the target lighting instruction. The embodiments of the present application input the user expression information and / or user authorization information into the lighting instruction recommendation model to obtain a target lighting instruction set to be recommended, then responds to the user's selection instruction for the target lighting instruction set to obtain a target lighting device, thereby adjusting the lighting device and providing personalized lighting effects recommendations for the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] FIG1 is a diagram showing an application scenario of a lighting equipment adjustment method provided in an embodiment of the present application.

[0018] FIG2 is a flow chart of a lighting equipment adjustment method provided in an embodiment of the present application.

[0019] FIG3 is a flowchart of determining a target lighting instruction set provided by an embodiment of the present application.

[0020] FIG4 is a flowchart of determining a target lighting instruction set provided by another embodiment of the present application.

[0021] FIG5 is a flowchart of determining a target lighting instruction set provided by yet another embodiment of the present application.

[0022] FIG6 is a flow chart of switching target lighting instructions provided in an embodiment of the present application.

[0023] FIG7 is a schematic structural diagram of a lighting equipment adjustment device provided in an embodiment of the present application.

[0024] FIG8 is a schematic structural diagram of a lighting device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] It should be noted that the terms "first" and "second" in the description, claims and drawings of this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0026] It should also be noted that the method disclosed in the embodiments of the present application or the method shown in the flowchart includes one or more steps for implementing the method. Without departing from the scope of the claims, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0027] The following will describe some embodiments with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0028] Combined with Figure 1, the application scenario of the embodiment of the present application is illustrated. The user expression information can come from the display selection interface of the lighting device (for example, the display selection interface contains multiple expression information, and the user expression information is determined by monitoring the user's selection of expression information), or a client installed on a mobile device, or a mobile terminal on an electronic device, etc., which does not restrict user input operations, and is not limited here. The embodiment of the present application only takes the user expression information coming from the client as an example, and the client is communicatively connected with the lighting device. The client can be a mobile device, a camera device, a video doorbell or door lock, or other devices with text collection, voice collection and image recording functions to ensure that the user expression information can be obtained. A lighting device refers to a lighting device that can provide a lighting function.

[0029] In some embodiments, the client is used to collect user expression information and / or user authorization information, where the expression methods may include but are not limited to voice expression, text expression, facial expression, and body expression. The user expression information may be an expression of a demand for lighting instructions, where the lighting instructions include light color parameters, color rhythm parameters, and light brightness parameters. In this way, the user expression information may be an expression of a demand for at least one of the light color parameters, color rhythm parameters, and light brightness parameters. For example, the user expression information may be an expression of a demand for light color parameters such as "yellow light, red light," or an expression of a demand for color rhythm parameters such as "the rhythm mode changes every 0.5 seconds." In one embodiment, the above expression methods may be text or voice expression; in other embodiments, facial expression and body expression may also be set, and lighting effects corresponding to facial expression and body expression may be pre-set. The user's demand for the lighting instruction is determined by obtaining the lighting effects corresponding to the facial expression or body expression. The lighting effects corresponding to the facial expression and body expression can be set according to actual needs and are not limited here. For example, the lighting effect corresponding to the expression of "smiling" is the required expression of light color parameters such as "yellow light, red light", and the lighting effect corresponding to the expression of "laughing" is the required expression of color rhythm parameters such as "rhythmic mode is 0.5s interval change". For another example, the lighting effect corresponding to a specific gesture action is the required expression of light color parameters such as "yellow light, red light", and the lighting effect corresponding to a specific body posture is the required expression of color rhythm parameters such as "rhythmic mode is 0.5s interval change". In other embodiments, the user expression information can also be the expression information of lighting equipment usage scenarios such as "I want to read a book", "I want to sleep" and "I want to cook", which is not limited here. In some embodiments, the user's authorization information can be collected by selecting the corresponding equipment according to actual needs, which is not limited here. Exemplarily, user authorization information may include but is not limited to information on the type of music played at the current moment or closest to the current moment (for example, rock, sad, light music, etc.), music volume, music rhythm and other forms of music expression collected by authorized voice recording equipment, or scenes of the area where the lighting equipment is located captured by authorized camera equipment (for example, gray walls, white walls, types of lighting equipment, images of a gray rainy day outside the window, etc.), or authorization to obtain information collected by other smart devices, for example, using a mobile phone to collect user attribute information (for example, age, gender, education level, region, marital status, current mood (for example, the user's mood can be determined by collecting the user's expression) and the user's previous usage habits of the lighting equipment (for example, the user's historical expression information corresponds to lighting instructions, etc.)).

[0030] A lighting instruction recommendation model is stored in the lighting device. The lighting instruction recommendation model is used to analyze user expression information and / or user authorization information to obtain multiple lighting instructions. The lighting device then adjusts and controls the lighting effect at the current moment according to the target lighting instruction selected by the user from the multiple lighting instructions.

[0031] FIG2 is a flow chart of a lighting device adjustment method provided in an embodiment of the present application, which is applied to lighting devices. As shown in FIG2 , the order of the steps in the flow chart can be changed and some steps can be omitted according to different requirements.

[0032] S11, obtaining user expression information and / or user authorization information.

[0033] In some embodiments, the number of users corresponding to the user expression information may be one or more, that is, the user expression information may be output by a single user or by multiple users, and this is not limited here. For example, when the user expression information is the user expression information of a single user, the user expression information is output by user a; or, when the user expression information includes the user expression information of multiple users, user a and user b output the same user expression information, and "the same" here may mean that user a and user b output expression information with the same semantics, for example, user a and user b both output user expression information of "I am having a party"; or, user a and user b output different user expression information, and "different" here may mean that user a and user b output expression information with different semantics, for example, user a outputs user expression information of "I am having a party" and user b outputs user expression information of "I want to read a book".

[0034] In some embodiments, the number of user expression information may be one or more. It may be one user expression information sent by a single user, multiple user expression information sent by a single user, or multiple user expression information sent by multiple users, without limitation. When a client sends a single user expression information, a target lighting instruction set may be recommended to the user based on the user expression information, and then the lighting device may be adjusted based on the target lighting instruction selected by the user from the target lighting instruction set. When a client sends multiple user expression information sent by a single user, if the multiple user expression information are of different information types, the multiple user expression information may be combined to form a user expression information group, and then a target lighting instruction set may be recommended to the user based on the user expression information group, and then the lighting device may be adjusted based on the target lighting instruction selected by the user from the target lighting instruction set. When a client sends multiple user expression information sent by a single user, if the multiple user expression information are of the same information type, indicating that the information the user wants to express has been updated, the multiple user expression information may need to be filtered, and then a target lighting instruction set may be recommended to the user based on the filtered user expression information, and then the lighting device may be adjusted based on the target lighting instruction selected by the user from the target lighting instruction set. When a client sends multiple user expression messages from multiple users, if multiple users send user expression messages for different areas on their respective clients at the same time, a target lighting instruction set is recommended to the user based on the user expression information, and the lighting equipment in the corresponding area is adjusted accordingly based on the target lighting instruction selected by the user from the target lighting instruction set. If multiple users send user expression messages for the same area on their respective clients at the same time, a master device is selected from the multiple clients, and a target lighting instruction set is recommended to the user based on the user expression information corresponding to the master device, and the lighting equipment is adjusted according to the target lighting instruction selected by the user from the target lighting instruction set. Alternatively, a prompt is output for the multiple users to select user expression information, and a target lighting instruction set is recommended to the user based on the selected user expression information, and the lighting equipment is adjusted according to the target lighting instruction selected by the user from the target lighting instruction set. Information types include but are not limited to voice type, text type, expression type, and body type.

[0035] S12: Input the user expression information and / or user authorization information into a lighting instruction recommendation model to obtain a target lighting instruction set corresponding to the user expression information and / or the user authorization information.

[0036] In some embodiments, the target lighting instruction set is a set consisting of several target lighting instructions. The number of target lighting instructions is not limited and can be 1 or 2 or more. The target lighting instruction refers to an instruction composed of lighting parameters for adjusting the lighting effect of the lighting equipment. The lighting parameters may include but are not limited to one or more of light color parameters, color rhythm parameters and light brightness parameters. In one embodiment, the lighting parameters can be expressed in the form of coding, and unique coding is set for the above-mentioned light color parameters, color rhythm parameters and light brightness parameters respectively to obtain light color coding, color rhythm coding and light brightness coding. The coding can be in the form of digital coding, letter coding or color coding. The lighting instruction can be a set of light color coding, color rhythm coding and light brightness coding with the highest probability value.

[0037] In some embodiments, user expression information and / or user authorization information is input into a lighting instruction recommendation model, which processes the information to obtain a target lighting instruction set. The lighting instruction recommendation model may be a neural network model. The neural network model is trained using the user expression information and / or user authorization information as input vectors and the corresponding lighting instructions as output vectors until the model converges, resulting in a trained lighting instruction recommendation model. In one embodiment, the training data of the lighting instruction recommendation model is the historical interaction information between the user and the lighting device. The historical interaction information may include user expression information and / or user authorization information and corresponding lighting instructions. For example, the user expression information is "I am having a party, red and orange lights", the user authorization information is "singing happily, with a loud volume", "female, 23 years old, undergraduate, Guangdong" and "the indoor scene is white tones", and the corresponding lighting instructions are "red, purple and orange lights, and the rhythmic mode is 0.5s per interval change"; the user expression information is "I want to read a book", the user authorization information is "light music, low volume", "female, 23 years old, undergraduate, Guangdong" and "the indoor scene is gray tones", and the corresponding lighting instructions are "bright white light, and the rhythmic mode remains unchanged".

[0038] In another embodiment, the training data may also be the user expression information and / or user authorization information of other users who are similar to the user portrait of the user corresponding to the current lighting device, as well as the corresponding lighting instructions finally displayed. The lighting devices of different users are used as blockchain nodes to construct a blockchain network. With the authorization of the user, each lighting device can provide the correspondence between the user expression information and / or user authorization information and the lighting instructions. For example, it is assumed that there are blockchain nodes A, blockchain node B and blockchain node C in the blockchain network, wherein blockchain node A corresponds to the lighting device of user A, blockchain node B corresponds to the lighting device of user B, and blockchain node C corresponds to the lighting device of user C. In one embodiment, historical user expression information whose similarity with the user expression information and / or user authorization information is higher than a preset similarity threshold is determined from the blockchain network. The preset similarity threshold is a pre-set value for identifying the similarity between user expression information and / or user authorization information. For example, the user expression information and / or user authorization information corresponding to user D is {I'm having a party; Hi singing}, the user expression information and / or user authorization information corresponding to user A stored in blockchain node A is {I'm having a party, Hi singing}, the user expression information and / or user authorization information corresponding to user B stored in blockchain node B is {I'm reading; Light music}, and the user expression information and / or user authorization information corresponding to user C stored in blockchain node C is {I'm cooking; Light music}. Therefore, it can be seen that user A's user expression information and / or user authorization information is closest to user D's. User A's user expression information and / or user authorization information is used as historical user expression information and / or historical user authorization information whose similarity to user D's user expression information and / or user authorization information exceeds a preset similarity threshold. In one embodiment, the user expression information and / or user authorization information can be vectorized and then the Euclidean distance between each vector is calculated to determine the similarity.

[0039] In one embodiment, the number of blockchain nodes in the blockchain network may be large. Therefore, a first user profile corresponding to user D (for example, user age, user gender, user occupation, model of lighting equipment selected by the user, user address, etc.) and a second user profile of each user in the blockchain network can also be constructed. Users who are similar or identical to the first user profile are selected from multiple second user portraits as users with behavioral preferences similar to user D. Historical user expression information and / or historical user authorization information whose similarity with user expression information and / or user authorization information is higher than a preset similarity threshold is determined from such users to improve the accuracy of determining the historical user expression information, thereby improving the accuracy of training the lighting instruction recommendation model. According to the lighting instruction recommendation model, a target lighting instruction set is recommended to the user, so that the recommended lighting instruction set is closer to the lighting effect liked by the user, so that the lighting effect is adapted to the actual needs of the user.

[0040] S13: Receive and select a target lighting instruction from the target lighting instruction set according to a user's selection instruction.

[0041] In some embodiments, the target lighting instruction set is output to a client or displayed on a lighting device. A user selects the target lighting instruction set on the client or lighting device. The selection method may include touch selection, voice selection, etc., which are not limited here. In response to the user's selection instruction, the lighting device selects the target lighting instruction from the target lighting instruction set according to the selection instruction.

[0042] In some embodiments, after recommending a target lighting instruction set to a user, the lighting device monitors whether a user selection instruction is received within a preset time period. If a user selection instruction is received, it indicates that the target lighting instruction set contains a lighting effect that meets the user's personalized needs. If a user selection instruction is not received, it indicates that the target lighting instruction set does not contain a lighting effect that meets the user's personalized needs. The preset time period may be pre-set, for example, one minute. In one embodiment, if a user selection instruction is not received, the lighting device may prompt the user to enter a lighting instruction and record the lighting instruction for subsequent updating of the lighting instruction recommendation model. In other embodiments, the lighting device may output multiple lighting parameter adjustment strategies, receive and determine a target lighting parameter adjustment strategy selected by the user, and adjust the target lighting instruction based on the target lighting parameter condition strategy to obtain an updated target lighting instruction. The lighting parameter adjustment strategy refers to a strategy for adjusting the lighting parameters included in the target lighting instruction. The lighting parameters may include, but are not limited to, light color parameters, color rhythm parameters, and light brightness parameters. The lighting parameters within a lighting parameter adjustment strategy can be randomly generated or determined based on user feedback (for example, if user feedback is "too bright," the light brightness parameter is lowered; if user feedback is "too dim," the light brightness parameter is increased). The number of lighting parameter adjustment strategies can be one or more, and there is no limit here.

[0043] S14: Adjust the lighting device according to the target lighting instruction.

[0044] In one embodiment, the lighting parameters within the target lighting instruction are obtained, and the lighting effect of the lighting device is directly switched according to the lighting parameters to achieve the lighting effect adjustment. This application directly switches the lighting effect of the lighting device according to the lighting parameters corresponding to the target lighting instruction, without having to analyze the current lighting parameters of the lighting device, thereby achieving rapid adjustment of the lighting effect.

[0045] The lighting device adjustment method provided in the embodiments of the present application obtains user expression information and / or user authorization information; inputs the user expression information and / or user authorization information into a lighting instruction recommendation model to obtain a target lighting instruction set corresponding to the user expression information and / or user authorization information; then receives and selects a target lighting instruction from the target lighting instruction set based on a user selection instruction, and adjusts the lighting device based on the target lighting instruction. The embodiments of the present application input the user expression information and / or user authorization information into the lighting instruction recommendation model to obtain a target lighting instruction set to be recommended, then responds to the user's selection instruction for the target lighting instruction set to obtain a target lighting device, thereby adjusting the lighting device and providing personalized lighting effects recommendations for the user.

[0046] In some embodiments, the user may be an existing user who has sent user expression information multiple times, or a new user who has sent user expression information for the first time or rarely sends user expression information. New users who have sent user expression information for the first time or rarely send user expression information are considered target users. For these users, due to their lack of interaction with the lighting device, the lighting device lacks a basis for recommending lighting instructions to them. To address this issue, embodiments of the present application may also process the user expression information of these target users through meta-learning to obtain corresponding target lighting instructions, thereby continuously improving the effectiveness of lighting instruction recommendations.

[0047] Accordingly, after determining the user expression information and / or user authorization information, the method further includes: determining user attribute information corresponding to the user authorization information; if the user is determined to be a target user based on the user attribute information, calling the lighting instruction determination model to process the user attribute information, and obtaining a target user group corresponding to the user attribute information and a target lighting instruction corresponding to the target user group; and adjusting the lighting equipment according to the target lighting instruction.

[0048] In one embodiment, user attribute information may include, but is not limited to, age, gender, education level, region, marital status, current mood (for example, the user's mood can be determined by collecting the user's facial expressions), and the user's previous usage habits of lighting equipment (for example, the user's historical expression information corresponds to lighting instructions, etc.). The above-mentioned user attribute information can be obtained through user registration information when the user uses the lighting equipment, or through semantic analysis of the chat content in the chat software authorized by the user. The user's region can also be determined through the GPS positioning system, and the user's age, gender, and mood can be determined through facial recognition, etc., without limitation. In one embodiment, by determining the user's previous usage habits of the lighting equipment in the user attribute information, it can be determined whether the user is a target user. For example, if the user attribute information does not contain or contains a small amount of the user's previous usage habits of the lighting equipment, the user can be determined to be a target user; if the user attribute information contains a large amount of the user's previous usage habits of the lighting equipment, the user can be determined to be a non-target user.

[0049] In one embodiment, the lighting command determination model may be a meta-learning model whose input data is user attribute information and whose output data is a target user group and the target lighting commands corresponding to the target user group. A target user group refers to a collection of multiple users with the same user attribute information. For example, there may be user group A and user group B. User group A consists of females aged 18-25 from location A, while user group B consists of males aged 25-35 from location B. The lighting command determination model processes the input user attribute information to determine the degree of match between the user attribute information and the user attribute information within a pre-set user group. It then selects the user group with the highest match as the target user group. In one embodiment, a correlation exists between user groups and lighting commands. By querying this correlation, the target lighting commands corresponding to the target user group can be obtained. The lighting equipment is then adjusted according to the target lighting commands. In one embodiment, the number of target lighting commands corresponding to the target user group may be one or more. When the number of target lighting instructions corresponding to the target user group is multiple, the multiple target lighting instructions can be output to the user, and the user selects the required target lighting instruction from the multiple target lighting instructions to adjust the lighting equipment.

[0050] In some embodiments, after invoking a lighting instruction determination model to process the user attribute information and obtain a target user group corresponding to the user attribute information and a target lighting instruction corresponding to the target user group, the method further includes: if the user provides negative feedback on the target lighting instruction corresponding to the target user group, receiving user expression information and / or user authorization information of the target user, and inputting the user expression information and / or user authorization information into a lighting instruction recommendation model to obtain a target lighting instruction set corresponding to the user expression information and / or user authorization information; receiving and selecting a target lighting instruction from the target lighting instruction set based on the user's selection instruction; and adjusting the lighting device based on the target lighting instruction. Negative feedback refers to feedback information indicating that the user is dissatisfied with the target lighting instruction corresponding to the target user group. Negative feedback may include, but is not limited to, negative facial expression feedback, negative body language feedback, negative voice feedback, negative text feedback, and failure to respond within a preset time interval. Negative facial expression feedback may include expressions such as "frown" and "crying face", negative body feedback may include body movements such as "hands on hips", negative voice feedback may include outputting voice information such as "unsatisfied" and "dislike", and negative text feedback may be outputting text information such as "unsatisfied" and "dislike" to the lighting device. The preset time interval is the preset user response time to the target lighting instruction corresponding to the target user group. For example, the preset time interval may be 30 seconds. The embodiment of the present application can receive user expression information and / or user authorization information of the target user when the meta-learning model does not recommend a target lighting instruction that satisfies the target user, and then use the lighting instruction recommendation model to process the user expression information and / or user authorization information, recommend a target lighting instruction set to the user, and the user selects a satisfactory target lighting instruction from the target lighting instruction set to adjust the lighting device, so that the lighting effect is adapted to the actual needs of the user.

[0051] In some embodiments, the lighting instruction recommendation model may include one or more sub-models of a lighting instruction recall model, a lighting instruction coarse sorting model, a lighting instruction fine sorting model, and a lighting instruction rearrangement model. Exemplarily, the lighting instruction recommendation model may include a lighting instruction recall model and a lighting instruction fine sorting model, wherein the lighting instruction recall model is used to recall an initial lighting instruction set associated with user expression information and / or the user authorization information from a large number of lighting instructions, and the lighting instruction fine sorting model is used to prioritize the initial lighting instruction set so that the user can select a target lighting instruction from the target lighting instruction set. Figure 3 is a flow chart for determining a target lighting instruction set provided in an embodiment of the present application, and the method for determining a target lighting instruction set is applied to a lighting device. As shown in Figure 3, the following steps are included:

[0052] S21, input the user expression information and / or the user authorization information into the lighting instruction recall model, obtain a first probability value of the lighting instruction corresponding to the user expression information and / or the user authorization information, and select multiple lighting instructions whose first probability values ​​exceed a preset probability threshold as an initial lighting instruction set.

[0053] In some embodiments, the user expression information and / or the user authorization information is input as input data into the lighting instruction recall model, and a first probability value of each lighting instruction being selected by the user can be obtained. The lighting instructions whose first probability values ​​exceed a preset probability threshold are selected to form an initial lighting instruction set, and the preset probability threshold is a pre-set value.

[0054] In one embodiment, the recall algorithm corresponding to the lighting instruction recall model may include at least one of an embedded recall algorithm, a collaborative filtering recall algorithm, and a graph theory recall algorithm, which are not limited herein. For example, if the recall algorithm is an embedded recall algorithm, the user expression information and / or user authorization information and the lighting instruction are respectively mapped into low-dimensional dense vectors, and then the lighting instructions corresponding to the user expression information and / or user authorization information are recalled through an efficient retrieval method; if the recall algorithm is a collaborative filtering recall algorithm, a user group with the same or similar user expression information and / or user authorization information is determined through collaborative filtering, and an initial lighting instruction set is determined based on the lighting instructions corresponding to the user group; if the recall algorithm is a graph theory recall algorithm, the user expression information and / or user authorization information is used as the first node, and the lighting instruction is used as the second node. Based on the interaction relationship between the first node and the second node, an adjacency matrix of the lighting instructions corresponding to the user expression information and / or user authorization information is constructed, and then the initial lighting instruction set of interest corresponding to the user expression information and / or the user authorization information is determined based on the adjacency matrix.

[0055] S22, input the user expression information and / or the user authorization information, and the initial lighting instruction set into the lighting instruction sorting model, obtain a second probability value of each initial lighting instruction in the initial lighting instruction set corresponding to the user expression information and / or the user authorization information, and sort the initial lighting instruction set according to the second probability value to obtain a target lighting instruction set.

[0056] In some embodiments, the user expression information and / or the user authorization information, and the initial lighting instruction set are used as inputs of a lighting instruction refinement model, and the lighting instruction refinement model calculates a second probability value of each initial lighting instruction in the initial lighting instruction set being selected by the user. Then, the initial lighting instruction set is sorted in descending order according to the second probability values ​​to obtain a target lighting instruction set.

[0057] The embodiment of the present application combines the lighting instruction recall model with the user expression information and / or user authorization information of the lighting instruction precision ranking model. The lighting instruction recall model is used to recall the initial lighting instruction set related to the user expression information and / or the user authorization information from a large number of lighting instructions. The lighting instruction precision ranking model is used to prioritize the initial lighting instruction set so that the user can select the target lighting instruction from the target lighting instruction set, thereby being able to provide personalized lighting effect recommendations for the user.

[0058] In some embodiments, in addition to the lighting instruction recall model and the lighting instruction fine ranking model, the lighting instruction recommendation model may also include a lighting instruction coarse ranking model, which is used to screen and roughly sort the initial lighting instruction set. Figure 4 is a flow chart for determining a target lighting instruction set provided by another embodiment of the present application. The target lighting instruction set determination method is applied to a lighting device. As shown in Figure 4, the following steps are included:

[0059] S31, input the user expression information and / or the user authorization information into the lighting instruction recall model, obtain a first probability value of the lighting instruction corresponding to the user expression information and / or the user authorization information, and select multiple lighting instructions whose first probability values ​​exceed a preset probability threshold as an initial lighting instruction set.

[0060] S32, input the user expression information and / or the user authorization information, and the initial lighting instruction set into the lighting instruction coarse sorting model, obtain the intermediate probability value of each initial lighting instruction in the initial lighting instruction set corresponding to the user expression information and / or the user authorization information, and filter and sort the initial lighting instruction set according to the intermediate probability value to obtain an intermediate lighting instruction set.

[0061] In some embodiments, user expression information and / or user authorization information, as well as an initial lighting instruction set, are input as input data into a lighting instruction coarse sorting model of the input device. The lighting instruction coarse sorting model calculates the intermediate probability value of each initial lighting instruction in the initial lighting instruction set being selected by the user. Afterwards, initial lighting instructions whose intermediate probability values ​​exceed a predetermined probability threshold are selected from the initial lighting instruction set, and the initial lighting instructions are sorted in descending order according to the intermediate probability values ​​to obtain an intermediate lighting instruction set, wherein the predetermined probability threshold is a pre-set value.

[0062] S33: Input the user expression information and / or the user authorization information, and the intermediate lighting instruction set into the lighting instruction refinement model to obtain a second probability value of each intermediate lighting instruction in the intermediate lighting instruction set corresponding to the user expression information and / or the user authorization information, and sort the intermediate lighting instruction set according to the second probability value to obtain a target lighting instruction set.

[0063] The embodiment of the present application uses a lighting instruction coarse sorting model to screen and roughly sort the initial lighting instruction set, and further reduces the number of selected lighting instructions while ensuring a certain degree of accuracy, so that users can quickly select target lighting instructions from the target lighting instruction set, thereby improving the efficiency of personalized lighting effect recommendation.

[0064] In some embodiments, the multiple lighting instructions input into the lighting instruction refinement model may also be pre-set. FIG5 is a flowchart of determining a target lighting instruction set according to another embodiment of the present application. The method for determining the target lighting instruction set is applied to a lighting device. As shown in FIG5 , the method includes the following steps:

[0065] S41, determining a plurality of lighting instructions corresponding to the user expression information and / or the user authorization information as a preset lighting instruction set.

[0066] In some embodiments, a preset database is provided that stores a mapping relationship between preset user expression information and / or user authorization information and a preset lighting command set. In one embodiment, if only user expression information exists and no authorization is granted to obtain information regarding the currently playing music, user attributes, or indoor scene, a first mapping relationship between the user expression information and the preset lighting command set is stored in the preset database. If no user expression information exists and only authorized information regarding the currently playing music, user attributes, or indoor scene is obtained, a second mapping relationship between the user authorization information and the preset lighting command set is stored in the preset database. If user expression information exists and authorized information regarding the currently playing music, user attributes, or indoor scene is obtained, a third mapping relationship between the user expression information and the user authorization information and the preset lighting command set is stored in the preset database. If user expression information exists and authorized information regarding the currently playing music, user attributes, or indoor scene is obtained, the user expression information and the user authorization information are combined according to a preset data format, and then the third mapping relationship is traversed based on the combined information to obtain the preset lighting command set. The preset data format is preset, for example, the preset data format may be {user expression information; user authorization information}, which is not limited here.

[0067] S42, input the user expression information and / or the user authorization information, and the preset lighting instruction set into the lighting instruction sorting model, obtain the target probability value of each preset lighting instruction in the preset lighting instruction set corresponding to the user expression information and / or the user authorization information, and sort the preset lighting instruction set according to the target probability value to obtain the target lighting instruction set.

[0068] The embodiment of the present application determines the preset lighting instruction set through the mapping relationship between user expression information and / or user authorization information and the preset lighting instruction set. Compared with obtaining the lighting instruction set through model calculation, the lighting instruction set can be obtained quickly, thereby improving the efficiency of personalized lighting effect recommendation.

[0069] In some embodiments, if the data volume of the lighting instructions corresponding to the user expression information and / or the user authorization information is small (for example, the number of lighting instructions is 10), the lighting instruction recommendation model may not distinguish between the lighting instruction recall model and the lighting instruction refined ranking model. Exemplarily, the method further includes: inputting the user expression information and / or the user authorization information into the lighting instruction refined ranking model, obtaining the lighting instruction set corresponding to the user expression information and / or the user authorization information and the probability value of each lighting instruction in the lighting instruction set, and filtering and sorting the lighting instruction set according to the probability value to obtain the target lighting instruction set. The embodiment of the present application can directly use the lighting instruction refined ranking model to process the user expression information and / or the user authorization information when the data volume of the lighting effect is small, obtain the target lighting instruction set to be recommended, simplify the process of determining the target lighting instruction set, and thus more quickly recommend lighting effects to users in a personalized manner.

[0070] In some embodiments, in addition to the lighting instruction recall model, the lighting instruction coarse sorting model, and the lighting instruction fine sorting model, the lighting instruction recommendation model may also include a lighting instruction re-sorting model. The processing process of the lighting instruction re-sorting model may be after the lighting instruction fine sorting model, and is used to screen and process the multiple lighting instructions sorted by the lighting instruction fine sorting model. For example, the lighting instruction re-sorting model is used to remove the lighting instructions that the user has already used from the multiple lighting instructions, so as to ensure the diversity among the multiple recommended lighting instructions. Wherein, a preset tag can be added to each lighting instruction, and the preset tag is used to identify the number of times the lighting instruction is used. By querying the preset tag, the lighting instructions that the user has already used can be determined from the multiple lighting instructions. The embodiment of the present application combines the lighting instruction re-sorting model to screen multiple lighting instructions, so as to ensure the diversity among the multiple recommended lighting instructions, thereby more accurately recommending personalized lighting effects to users.

[0071] FIG6 is a flow chart of switching target lighting instructions provided by an embodiment of the present application. The switching process of target lighting instructions is applied to lighting equipment. As shown in FIG6 , the process includes the following steps:

[0072] S51 , if it is detected that a preset event is triggered, the correspondence between the preset events and the instruction switching rules is traversed to obtain the target instruction switching rule corresponding to the preset event.

[0073] In some embodiments, a preset event refers to a pre-set event that will cause a change in the lighting effect of a lighting device. For each preset event, there is a corresponding instruction switching rule, which refers to a rule for switching target lighting instructions in at least one group.

[0074] S52: Switch the target lighting instruction in the target lighting instruction set according to the target instruction switching rule.

[0075] In one embodiment, a preset event may include a user selecting a lighting effect. For example, if a user switches lighting effects by single-clicking or double-clicking in the client, if a single-click or double-click operation is detected in the client, the current target lighting instruction in a set of target lighting instructions will be switched to the next target lighting instruction. For another example, if a user selects to execute a set of target lighting instructions with a 5-minute interval, then if the interval is detected to have reached 5 minutes, the lighting device will switch to the next target lighting instruction every 5 minutes according to priority until all instructions are executed. In other embodiments, preset events may also include changes in ambient sound, ambient brightness, music, temperature, and time. For example, if the preset event is a time change, for example, between 7:00 PM and 10:00 PM, the user selects target lighting instruction A at 7:00 PM; at 9:00 PM, the next target lighting instruction will be switched to B, where the brightness of target lighting instruction B is dimmer than that of target lighting instruction A, or the light-off interval of target lighting instruction B is longer than that of target lighting instruction A.

[0076] When a preset event is detected to be triggered, the embodiment of the present application switches the target lighting instruction set according to the instruction switching rules corresponding to the preset event, so that the lighting instruction can be dynamically adjusted according to user needs, thereby providing personalized lighting effects for users.

[0077] In some embodiments, if the user expression information includes user expression information of multiple users, the method further includes: outputting a prompt message; and upon receiving a user confirmation instruction in response to the prompt message, executing the user expression information corresponding to the confirmation instruction. The prompt message serves to notify the user that the lighting device has received user expression information from multiple users and needs to determine the user expression information to be executed from the multiple user expression information. The confirmation instruction may be information indicating a user's selection of the multiple user expression information and may be a touch instruction, voice instruction, or text instruction, etc., without limitation herein.

[0078] In some embodiments, if the user expression information includes user expression information of multiple users, the method further includes: determining the client corresponding to the user expression information; and if the client is a target client, executing the user expression information corresponding to the target client. Multiple users send user expression information through their respective clients. In practical application scenarios, a master client (also referred to as a target client in this application) corresponding to a lighting device can be bound. When the master client outputs user expression information, the user expression information is preferentially executed. In other embodiments, if multiple user expression information is output by the same client, voiceprint recognition or other methods can be used to determine whether multiple users are outputting the information. A master user (e.g., the head of household) is then selected from the multiple users, and the master user's user expression information is preferentially executed. For example, taking the user expression information as a voice expression, when there are multiple voice expressions, a pre-trained voiceprint recognition model can be used to process the multiple voice expressions to determine the voiceprint features corresponding to each voice expression. When the voiceprint features are the same, the number of users corresponding to the user expression is determined to be one, i.e., one user outputs multiple voice expressions. When the voiceprint features differ, the number of users corresponding to the user expression is determined to be multiple.

[0079] Please refer to Figure 7, which is a schematic diagram of the structure of a lighting device adjustment device provided in an embodiment of the present application. In some embodiments, the lighting device adjustment device 20 may include multiple functional modules composed of computer program segments. The computer programs for each program segment in the lighting device adjustment device 20 may be stored in the memory of a computer device 30 and executed by at least one controller to perform the lighting device adjustment function (see Figure 2 for details).

[0080] In this embodiment, the lighting device adjustment device 20 can be divided into multiple functional modules based on their functions. These modules may include a user information acquisition module 201, a lighting instruction recommendation module 202, a lighting instruction selection module 203, and a lighting device adjustment module 204. A module, as used herein, refers to a series of computer program segments that can be executed by at least one controller and perform fixed functions, and is stored in a memory. The functions of each module in this embodiment will be described in detail in subsequent embodiments.

[0081] The user information acquisition module 201 may be used to acquire user expression information and / or user authorization information.

[0082] The lighting instruction recommendation module 202 may be configured to input the user expression information and / or user authorization information into a lighting instruction recommendation model to obtain a target lighting instruction set corresponding to the user expression information and / or user authorization information.

[0083] The lighting instruction selection module 203 may be configured to receive and select a target lighting instruction from the target lighting instruction set according to a user's selection instruction.

[0084] The lighting device adjustment module 204 may be configured to adjust the lighting device according to the target lighting instruction.

[0085] In some embodiments, the lighting instruction recommendation module 202 is further used to input the user expression information and / or the user authorization information into the lighting instruction recall model to obtain a first probability value of the lighting instruction corresponding to the user expression information and / or the user authorization information, and select multiple lighting instructions whose first probability values ​​exceed a preset probability threshold as the initial lighting instruction set; input the user expression information and / or the user authorization information, and the initial lighting instruction set into the lighting instruction sorting model to obtain a second probability value of each initial lighting instruction in the initial lighting instruction set corresponding to the user expression information and / or the user authorization information, and sort the initial lighting instruction set according to the second probability value to obtain a target lighting instruction set.

[0086] In some embodiments, the lighting instruction recommendation module 202 is further configured to input the user expression information and / or the user authorization information into the lighting instruction recall model to obtain a first probability value of the lighting instruction corresponding to the user expression information and / or the user authorization information, and select multiple lighting instructions whose first probability values ​​exceed a preset probability threshold as an initial lighting instruction set; input the user expression information and / or the user authorization information, and the initial lighting instruction set into the lighting instruction coarse ranking model to obtain an intermediate probability value of each initial lighting instruction in the initial lighting instruction set corresponding to the user expression information and / or the user authorization information, and filter and sort the initial lighting instruction set according to the intermediate probability value to obtain an intermediate lighting instruction set; input the user expression information and / or the user authorization information, and the intermediate lighting instruction set into the lighting instruction fine ranking model to obtain a second probability value of each intermediate lighting instruction in the intermediate lighting instruction set corresponding to the user expression information and / or the user authorization information, and sort the intermediate lighting instruction set according to the second probability value to obtain a target lighting instruction set.

[0087] In some embodiments, the lighting instruction recommendation module 202 is further used to determine multiple lighting instructions corresponding to the user expression information and / or the user authorization information as a preset lighting instruction set; input the user expression information and / or the user authorization information into the lighting instruction ranking model to obtain the target probability value of each preset lighting instruction in the preset lighting instruction set corresponding to the user expression information and / or the user authorization information, and sort the preset lighting instruction set according to the target probability value to obtain a target lighting instruction set; or input the user expression information and / or the user authorization information into the lighting instruction ranking model to obtain the lighting instruction set corresponding to the user expression information and / or the user authorization information and the probability value of each lighting instruction in the lighting instruction set, and filter and sort the lighting instruction set according to the probability value to obtain the target lighting instruction set.

[0088] In some embodiments, the lighting instruction recommendation module 202 is further configured to traverse a preset mapping relationship between user expression information and / or user authorization information and lighting instructions to obtain a preset lighting instruction set consisting of multiple lighting instructions.

[0089] In some embodiments, the user information acquisition module 201 is also used to determine the user attribute information corresponding to the user authorization information; if the user is determined to be a target user based on the user attribute information, the lighting instruction determination model is called to process the user attribute information to obtain the target user group corresponding to the user attribute information and the target lighting instruction corresponding to the target user group; and the lighting equipment is adjusted according to the target lighting instruction.

[0090] It can be understood that the lighting equipment adjustment device 20 and the lighting equipment adjustment method of the above embodiment belong to the same inventive concept. The specific implementation method of each module of the lighting equipment adjustment device 20 corresponds to the various steps of the lighting equipment adjustment method in the above embodiment, and this application will not go into details here.

[0091] The module division described above is a logical functional division, and other division methods may be used in actual implementation. In addition, the functional modules in the various embodiments of the present application can be integrated into the same processing unit, or each module can exist physically separately, or two or more modules can be integrated into the same unit. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0092] Figure 8 is a schematic diagram of the structure of a lighting device provided in an embodiment of the present application. As shown in Figure 8, the lighting device 30 includes a memory 31, at least one controller 32, and at least one communication bus 33. The controller 32 is configured to implement the lighting device adjustment method when executing a computer program stored in the memory 31. The at least one communication bus 33 is configured to enable communication between the memory 31 and the at least one controller 32.

[0093] The structure of the lighting device shown in FIG8 does not constitute a limitation on the embodiments of the present application. The lighting device 30 may also include more or less other hardware or software than shown in the figure, or a different arrangement of components.

[0094] In some embodiments of the present application, the lighting device 30 can also be connected to a client device, which includes but is not limited to any electronic product that can interact with a user through a keyboard, mouse, remote control, touchpad or voice control device, such as a personal computer, tablet computer, smart phone, digital camera, etc.

[0095] It should be noted that the lighting device 30 is only an example. Other existing or future electronic products that are suitable for this application should also be included in the scope of protection of this application and included here by reference.

[0096] In some embodiments, the lighting device 30 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0097] The memory 31 stores a computer program that, when executed by at least one controller 32, implements all or part of the steps in the lighting device adjustment method. The memory 31 includes a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0098] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the lighting device 30, etc.

[0099] In some embodiments, at least one controller 32 is the control core (Control Unit) of the lighting device 30, which uses various interfaces and lines to connect the various components of the entire lighting device 30, and executes various functions and processes data of the lighting device 30 by running or executing programs or modules stored in the memory 31, and calling data stored in the memory 31. For example, when the at least one controller 32 executes the computer program stored in the memory, it implements all or part of the steps of the lighting device adjustment method in the embodiment of the present application; or implements all or part of the functions of the lighting device adjustment device. The at least one controller 32 can be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of integrated circuits with the same or different functions, including one or more central processing units (CPUs), microcontrollers, digital processing chips, graphics controllers, and a combination of various control chips.

[0100] The above-mentioned integrated unit implemented in the form of a software functional module can be stored in a computer-readable storage medium. The above-mentioned software functional module stored in a storage medium includes a number of instructions for causing a lighting device (which can be a personal computer, lighting device, or network device, etc.) or a controller (processor) to execute portions of the methods of various embodiments of the present application.

[0101] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is only a logical function division, and other division methods may be used in actual implementation.

[0102] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, and may be located in one place or distributed across multiple network elements. Some or all of these modules may be selected to achieve the objectives of this embodiment based on actual needs.

[0103] In addition, the functional modules in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0104] It is obvious to those skilled in the art that the present application is not limited to the details of the above-mentioned exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the present application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "including" does not exclude other units or, and the singular does not exclude the plural. Multiple units or devices stated in the specification may also be implemented by one unit or device through software or hardware. Words such as first, second, etc. are used to indicate names and do not indicate any particular order.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A lighting equipment adjustment method, applied to lighting equipment, characterized in that: The method comprises: Obtain user expression information and / or user authorization information; Inputting the user expression information and / or the user authorization information into a lighting instruction recommendation model to obtain a target lighting instruction set corresponding to the user expression information and / or the user authorization information; receiving and selecting a target lighting instruction from the target lighting instruction set according to a user's selection instruction; The lighting device is adjusted according to the target lighting instruction.

2. The method according to claim 1, characterized in that The user expression information includes at least one of voice expression, text expression, facial expression and body expression; the user authorization information includes at least one of user attribute information, music information and scene information.

3. The method according to claim 1, characterized in that If the lighting instruction recommendation model includes a lighting instruction recall model and a lighting instruction refinement model, then the step of inputting the user expression information and / or the user authorization information into the lighting instruction recommendation model to obtain a target lighting instruction set corresponding to the user expression information and / or the user authorization information includes: Inputting the user expression information and / or the user authorization information into the lighting instruction recall model, obtaining a first probability value of the lighting instruction corresponding to the user expression information and / or the user authorization information, and selecting a plurality of lighting instructions whose first probability values ​​exceed a preset probability threshold as an initial lighting instruction set; The user expression information and / or the user authorization information, and the initial lighting instruction set are input into the lighting instruction refinement model to obtain a second probability value of each initial lighting instruction in the initial lighting instruction set corresponding to the user expression information and / or the user authorization information, and the initial lighting instruction set is sorted according to the second probability value to obtain a target lighting instruction set.

4. The method according to claim 1, characterized in that If the lighting instruction recommendation model includes a lighting instruction recall model, a lighting instruction rough sorting model, and a lighting instruction fine sorting model, then the step of inputting the user expression information and / or the user authorization information into the lighting instruction recommendation model to obtain a target lighting instruction set corresponding to the user expression information and / or the user authorization information further includes: Inputting the user expression information and / or the user authorization information into the lighting instruction recall model, obtaining a first probability value of the lighting instruction corresponding to the user expression information and / or the user authorization information, and selecting a plurality of lighting instructions whose first probability values ​​exceed a preset probability threshold as an initial lighting instruction set; The user expression information and / or the user authorization information, and the initial lighting instruction set are input into the lighting instruction rough arrangement model to obtain the initial lighting instruction set corresponding to the user expression information and / or the user authorization information. The intermediate probability value of each initial lighting instruction in the initial lighting instruction set is obtained, and the initial lighting instruction set is screened and sorted according to the intermediate probability value to obtain an intermediate lighting instruction set; The user expression information and / or the user authorization information, and the intermediate lighting instruction set are input into the lighting instruction refinement model to obtain a second probability value of each intermediate lighting instruction in the intermediate lighting instruction set corresponding to the user expression information and / or the user authorization information, and the intermediate lighting instruction set is sorted according to the second probability value to obtain a target lighting instruction set.

5. The method according to claim 1, characterized in that If the lighting instruction recommendation model includes a lighting instruction refinement model, the step of inputting the user expression information and / or the user authorization information into the lighting instruction recommendation model to obtain a target lighting instruction set corresponding to the user expression information and / or the user authorization information further includes: Determine a plurality of lighting instructions corresponding to the user expression information and / or the user authorization information as a preset lighting instruction set; input the user expression information and / or the user authorization information, and the preset lighting instruction set into the lighting instruction sorting model to obtain a target probability value of each preset lighting instruction in the preset lighting instruction set corresponding to the user expression information and / or the user authorization information, and sort the preset lighting instruction set according to the target probability value to obtain the target lighting instruction set; or The user expression information and / or the user authorization information is input into the lighting instruction sorting model to obtain a lighting instruction set corresponding to the user expression information and / or the user authorization information and a probability value of each lighting instruction in the lighting instruction set, and the lighting instruction set is filtered and sorted according to the probability value to obtain the target lighting instruction set.

6. The method according to claim 5, characterized in that The determining the plurality of lighting instructions corresponding to the user expression information and / or the user authorization as a preset lighting instruction set includes: The mapping relationship between the preset user expression information and / or user authorization information and the lighting instructions is traversed to obtain a preset lighting instruction set consisting of a plurality of lighting instructions.

7. The method according to claim 2, characterized in that After obtaining the user expression information and / or the user authorization information, the method further includes: Determine user attribute information corresponding to the user authorization information; If the user is determined to be a target user according to the user attribute information, calling a lighting instruction determination model to process the user attribute information to obtain a target user group corresponding to the user attribute information and a target lighting instruction corresponding to the target user group; The lighting device is adjusted according to the target lighting instruction.

8. A lighting equipment adjustment device, applied to lighting equipment, characterized in that: The device comprises: A user information acquisition module, used to acquire user expression information and / or user authorization information; a lighting instruction recommendation module, configured to input the user expression information and / or the user authorization information into a lighting instruction recommendation model, and obtain a target lighting instruction set corresponding to the user expression information and / or the user authorization information; A lighting instruction selection module, used for receiving and selecting a target lighting instruction from the target lighting instruction set according to a user's selection instruction; A lighting device adjustment module is used to adjust the lighting device according to the target lighting instruction.

9. A lighting device, characterized in that: The lighting device comprises a controller and a memory, and the controller is used to implement the lighting device adjustment method according to any one of claims 1 to 7 when executing a computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the controller, the lighting device adjustment method according to any one of claims 1 to 7 is implemented.

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