Light strip control method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application relate to a lamp strip control method and device, electronic equipment and storage medium. The method comprises: obtaining control information and user attribute information of a target lamp strip, wherein the target lamp strip comprises a plurality of lamp beads; determining lamp effect parameters of the lamp beads comprised by the target lamp strip based on the control information and the user attribute information; and controlling the lamp beads comprised by the target lamp strip according to the lamp effect parameters. Thus, the lamp effect parameters of each lamp bead comprised by the lamp strip can be automatically determined for the user based on the control information and the user attribute information of the lamp strip, and each lamp bead is controlled accordingly. Thus, the difficulty for the user to set the lamp effect in line with his / her expectation is reduced, thereby improving the efficiency of setting the lamp effect in line with his / her expectation.

Description

Technical Field

[0001] This application relates to the field of smart homes, and more particularly to a method, device, electronic device, and storage medium for controlling light strips. Background Technology

[0002] In recent years, with the popularization of smart homes and the increasing demand for personalized decoration, string lights (or light strips) have become an important part of home and building exterior decoration, and are increasingly popular among users. To meet users' needs for personalized lighting effects, various lighting effect software apps have emerged on the market.

[0003] However, while these software programs offer a large number of pre-installed lighting effects, the sheer variety of these effects means users need to try them out one by one and apply them to their actual products to find a lighting solution that meets their needs. Furthermore, the custom editing functions are quite complex and difficult to use, making them unfriendly to the average user. Users may want aesthetically pleasing lighting effects to decorate the atmosphere, but limited by their personal aesthetic sense and skills, they cannot customize the ideal lighting effects and need to repeatedly click and experiment.

[0004] It is evident that improving the efficiency of users setting lighting effects that meet their expectations is a technical issue worthy of attention. Summary of the Invention

[0005] In view of this, in order to solve some or all of the above-mentioned technical problems, embodiments of this application provide a light strip control method, device, electronic device and storage medium.

[0006] In a first aspect, embodiments of this application provide a light strip control method, the method comprising:

[0007] Acquire control information and user attribute information of the target light strip, wherein the target light strip includes multiple LED beads;

[0008] Based on the control information and the user attribute information, the lighting effect parameters of the LED beads included in the target light strip are determined;

[0009] The LED beads included in the target light strip are controlled according to the aforementioned lighting effect parameters.

[0010] In one possible implementation, before determining the luminous efficacy parameters of the LEDs included in the target light strip based on the control information and the user attribute information, the method further includes:

[0011] The information of the LED beads included in the target light strip is determined; wherein, the information of the LED beads includes at least one of the following: the height of the LED bead, the angle corresponding to the LED bead, and the distance corresponding to the LED bead; the angle corresponding to the LED bead is: the angle between the illumination direction of the LED bead and the ground; the distance corresponding to the LED bead is: the distance between the LED bead and the target LED bead; the target LED bead is the adjacent LED bead in the target direction of the LED bead.

[0012] Based on the LED information, the target LED strip is divided into multiple segments, wherein each segment includes at least one LED; and

[0013] The step of determining the lighting effect parameters of the LED beads included in the target light strip based on the control information and the user attribute information includes:

[0014] Based on the control information and the user attribute information, the lighting effect parameters of the LED beads included in the multiple segments are determined.

[0015] In one possible implementation,

[0016] The height of the LED bead is determined as follows:

[0017] Determine the angle between the illumination direction of the LED and the ground to obtain the target angle;

[0018] Based on the target angle, the distance between the LED and the ground is determined, thus obtaining the height of the LED; and

[0019] The distance corresponding to this LED bead is determined in the following way:

[0020] Based on the target angle, the distance between the LED and the target LED is determined, thus obtaining the distance corresponding to the LED.

[0021] In one possible implementation, the segmentation satisfies at least one of the following conditions:

[0022] The first height difference is less than or equal to the first height threshold, wherein the first height difference is the height difference between the LED in the segment and the target LED of the LED; and the first angle difference is less than or equal to the first angle threshold, wherein the first angle difference is the difference between the angle corresponding to the LED in the segment and the angle corresponding to the target LED of the LED.

[0023] The first height difference is greater than the first height threshold; the second height difference is less than the second height threshold, wherein the second height difference is the difference between the maximum height difference between the LEDs in the segment and the minimum height difference between the LEDs in the segment; and the first angle difference is less than or equal to the first angle threshold.

[0024] The segmented boundary LEDs satisfy the following condition: the distance between the boundary LED and the target LED of the boundary LED is greater than a first distance or less than a second distance, wherein the second distance is less than the first distance;

[0025] The number of boundary LEDs in a single sub-segment of the segment is less than or equal to 2.

[0026] In one possible implementation, after dividing the target light strip into multiple segments based on the LED information, the method further includes:

[0027] Determine the category information of each of the at least two said segments; and

[0028] The step of determining the lighting effect parameters of the LEDs included in the multiple segments based on the control information and the user attribute information includes:

[0029] Based on the control information, the user attribute information, and the category information, the lighting effect parameters of the LED beads included in the multiple segments are determined.

[0030] In one possible implementation, determining the luminous efficacy parameters of the LEDs included in the target light strip based on the control information and the user attribute information includes:

[0031] By employing at least one of a pre-trained machine learning model and a pre-established lighting effect knowledge base, lighting effect parameters corresponding to the control information and the user attribute information are determined, thereby obtaining the lighting effect parameters of the LED beads included in the target light strip;

[0032] The machine learning model is used to characterize the correspondence between control information, user attribute information, and lighting effect parameters.

[0033] The lighting effect knowledge base is used to represent the correspondence between control information, user attribute information, and target quantity group lighting effect parameters.

[0034] In one possible implementation, determining the lighting effect parameters corresponding to the control information and the user attribute information by employing at least one of a pre-trained machine learning model and a pre-established lighting effect knowledge base includes:

[0035] The control information and the user attribute information are input into a pre-trained machine learning model to obtain the output data of the machine learning model;

[0036] Determine whether the output data represents lighting effect parameters corresponding to the control information and the user attribute information to obtain discrimination information;

[0037] If the discrimination information indicates that the output data is not a lighting effect parameter corresponding to the control information and the user attribute information, a pre-established lighting effect knowledge base is used to determine the target number of lighting effect parameters corresponding to the control information and the user attribute information.

[0038] In one possible implementation, the control information is input by the user; and

[0039] The step of determining the lighting effect parameters of the LED beads included in the target light strip based on the control information and the user attribute information includes:

[0040] Based on the control information and the user attribute information, multiple sets of lighting effect parameters of the lamp beads included in the target light strip are determined;

[0041] From the multiple sets of lighting effect parameters, a target number of lighting effect parameters are determined, wherein the target number of lighting effect parameters are used to return to the control terminal of the light strip; and

[0042] The step of controlling the LED beads included in the target light strip according to the lighting effect parameters includes:

[0043] From the lighting effect parameters in the target quantity group, determine the selected lighting effect parameters;

[0044] The LED beads included in the target LED strip are controlled according to the selected lighting effect parameters.

[0045] In one possible implementation, determining the target number of sets of lighting effect parameters from the plurality of sets of lighting effect parameters includes:

[0046] Based on the return priorities corresponding to the multiple sets of lighting effect parameters, a target number of sets of lighting effect parameters are determined from the multiple sets of lighting effect parameters; and

[0047] After determining the target number of sets of lighting effect parameters from multiple sets of the lighting effect parameters, the method further includes:

[0048] Reduce the return priority of the lighting effect parameters corresponding to the target number group.

[0049] Secondly, embodiments of this application provide a light strip control device, the device comprising:

[0050] The acquisition unit is used to acquire control information and user attribute information of the target light strip, wherein the target light strip includes multiple LED beads;

[0051] The first determining unit is used to determine the lighting effect parameters of the LED beads included in the target light strip based on the control information and the user attribute information.

[0052] The control unit is used to control the LED beads included in the target light strip according to the lighting effect parameters.

[0053] In one possible implementation, before determining the luminous efficacy parameters of the LEDs included in the target light strip based on the control information and the user attribute information, the device further includes:

[0054] The second determining unit is used to determine the lamp information of the lamps included in the target light strip; wherein the lamp information includes at least one of the following: the height of the lamp, the angle corresponding to the lamp, and the distance corresponding to the lamp; the angle corresponding to the lamp is: the angle between the illumination direction of the lamp and the ground; the distance corresponding to the lamp is: the distance between the lamp and the target lamp; the target lamp is the adjacent lamp in the target direction of the lamp.

[0055] A segmentation unit is configured to segment the target light strip based on the LED information to obtain multiple segments, wherein each segment includes at least one LED; and

[0056] The step of determining the lighting effect parameters of the LED beads included in the target light strip based on the control information and the user attribute information includes:

[0057] Based on the control information and the user attribute information, the lighting effect parameters of the LED beads included in the multiple segments are determined.

[0058] In one possible implementation, the height of the LED is determined as follows:

[0059] Determine the angle between the illumination direction of the LED and the ground to obtain the target angle;

[0060] Based on the target angle, the distance between the LED and the ground is determined, thus obtaining the height of the LED; and

[0061] The distance corresponding to this LED bead is determined in the following way:

[0062] Based on the target angle, the distance between the LED and the target LED is determined, thus obtaining the distance corresponding to the LED.

[0063] In one possible implementation, the segmentation satisfies any of the following conditions:

[0064] The first height difference is less than or equal to the first height threshold, wherein the first height difference is the height difference between the LED in the segment and the target LED of the LED; and the first angle difference is less than or equal to the first angle threshold, wherein the first angle difference is the difference between the angle corresponding to the LED in the segment and the angle corresponding to the target LED of the LED.

[0065] The first height difference is greater than the first height threshold; the second height difference is less than the second height threshold, wherein the second height difference is the difference between the maximum height difference between the LEDs in the segment and the minimum height difference between the LEDs in the segment; and the first angle difference is less than or equal to the first angle threshold.

[0066] The segmented boundary LEDs satisfy the following condition: the distance between the boundary LED and the target LED of the boundary LED is greater than a first distance or less than a second distance, wherein the second distance is less than the first distance;

[0067] The number of boundary LEDs in a single sub-segment of the segment is less than or equal to 2.

[0068] In one possible implementation, after dividing the target light strip into multiple segments based on the LED information, the device further includes:

[0069] The third determining unit is used to determine the category information of each of the at least two segments; and

[0070] The step of determining the lighting effect parameters of the LEDs included in the multiple segments based on the control information and the user attribute information includes:

[0071] Based on the control information, the user attribute information, and the category information, the lighting effect parameters of the LED beads included in the multiple segments are determined.

[0072] In one possible implementation, determining the luminous efficacy parameters of the LEDs included in the target light strip based on the control information and the user attribute information includes:

[0073] By employing at least one of a pre-trained machine learning model and a pre-established lighting effect knowledge base, lighting effect parameters corresponding to the control information and the user attribute information are determined, thereby obtaining the lighting effect parameters of the LED beads included in the target light strip;

[0074] The machine learning model is used to characterize the correspondence between control information, user attribute information, and lighting effect parameters.

[0075] The lighting effect knowledge base is used to represent the correspondence between control information, user attribute information, and target quantity group lighting effect parameters.

[0076] In one possible implementation, determining the lighting effect parameters corresponding to the control information and the user attribute information by employing at least one of a pre-trained machine learning model and a pre-established lighting effect knowledge base includes:

[0077] The control information and the user attribute information are input into a pre-trained machine learning model to obtain the output data of the machine learning model;

[0078] Determine whether the output data represents lighting effect parameters corresponding to the control information and the user attribute information to obtain discrimination information;

[0079] If the discrimination information indicates that the output data is not a lighting effect parameter corresponding to the control information and the user attribute information, a pre-established lighting effect knowledge base is used to determine the target number of lighting effect parameters corresponding to the control information and the user attribute information.

[0080] In one possible implementation, the control information is input by the user; and

[0081] The step of determining the lighting effect parameters of the LED beads included in the target light strip based on the control information and the user attribute information includes:

[0082] Based on the control information and the user attribute information, multiple sets of lighting effect parameters of the lamp beads included in the target light strip are determined;

[0083] From the multiple sets of lighting effect parameters, a target number of lighting effect parameters are determined, wherein the target number of lighting effect parameters are used to return to the control terminal of the light strip; and

[0084] The step of controlling the LED beads included in the target light strip according to the lighting effect parameters includes:

[0085] From the lighting effect parameters in the target quantity group, determine the selected lighting effect parameters;

[0086] The LED beads included in the target LED strip are controlled according to the selected lighting effect parameters.

[0087] In one possible implementation, determining the target number of sets of lighting effect parameters from the plurality of sets of lighting effect parameters includes:

[0088] Based on the return priorities corresponding to the multiple sets of lighting effect parameters, a target number of sets of lighting effect parameters are determined from the multiple sets of lighting effect parameters; and

[0089] After determining the target number of sets of lighting effect parameters from multiple sets of the lighting effect parameters, the device further includes:

[0090] Reduce the return priority of the lighting effect parameters corresponding to the target number group.

[0091] Thirdly, embodiments of this application provide an electronic device, including:

[0092] Memory, used to store computer programs;

[0093] A processor is configured to execute a computer program stored in the memory, and when the computer program is executed, to implement the method of any embodiment of the light strip control method of the first aspect of this application.

[0094] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the method of any embodiment of the light strip control method of the first aspect described above.

[0095] Fifthly, embodiments of this application provide a computer program that includes computer-readable code. When the computer-readable code is executed on a device, it causes a processor in the device to implement the method of any embodiment of the LED strip control method of the first aspect described above.

[0096] The LED strip control method provided in this application can acquire control information and user attribute information of a target LED strip, wherein the target LED strip includes multiple LED beads. Then, based on the control information and the user attribute information, the lighting effect parameters of the LED beads included in the target LED strip are determined. Finally, the LED beads included in the target LED strip are controlled according to the lighting effect parameters. Therefore, based on the control information and user attribute information of the LED strip, the lighting effect parameters of each LED bead in the LED strip can be automatically determined for the user, and each LED bead can be controlled accordingly. This reduces the difficulty for users to set lighting effects that meet their expectations, thereby improving the efficiency of setting lighting effects that meet their expectations. Attached Figure Description

[0097] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0098] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0099] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0100] Figure 1 A schematic flowchart illustrating a light strip control method provided in an embodiment of this application;

[0101] Figure 2 A flowchart illustrating another LED strip control method provided in this application embodiment;

[0102] Figure 3A A flowchart illustrating another LED strip control method provided in this application embodiment;

[0103] Figure 3B This is a schematic diagram of a segmentation method;

[0104] Figure 3C A flowchart illustrating another LED strip control method provided in this application embodiment;

[0105] Figure 4 This is a schematic diagram of the structure of a light strip control device provided in an embodiment of this application;

[0106] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0107] Various exemplary embodiments of this application will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this application.

[0108] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of this application are only used to distinguish different steps, devices or modules, and do not represent any specific technical meaning, nor do they indicate the logical order between them.

[0109] It should also be understood that in this embodiment, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0110] It should also be understood that any component, data or structure mentioned in the embodiments of this application can generally be understood as one or more unless explicitly defined or given contrary guidance in the context.

[0111] Furthermore, the term "and / or" in this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this application generally indicates that the preceding and following related objects have an "or" relationship.

[0112] It should also be understood that the description of the various embodiments in this application emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0113] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the scope of this application and its application or use.

[0114] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0115] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0116] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. To facilitate understanding of the embodiments of this application, the application will be described in detail below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0117] Furthermore, it should be noted that the users described in this application (e.g., users corresponding to user attribute information) can be distinguished by user identifiers. For example, a user identifier can be a login account. In this scenario, if different people log in using the same account, they can be considered the same user; if the same person logs in using different accounts, they can be considered different users. As another example, when a device is not logged in, a user identifier can be assigned based on the device's identifier. In this scenario, if different people operate using devices with the same device identifier, they can be considered the same user; if the same person operates using devices with different device identifiers, they can be considered different users.

[0118] To address the technical problem of how to improve the efficiency of users setting lighting effects that meet their expectations in the prior art, this application provides a light strip control method that can improve the efficiency of users setting lighting effects that meet their expectations.

[0119] Figure 1This is a flowchart illustrating a light strip control method provided in an embodiment of this application. This method can be applied to one or more electronic devices such as smartphones, laptops, desktop computers, portable computers, and servers. Furthermore, the execution entity of this method can be hardware or software. When the execution entity is hardware, it can be one or more of the aforementioned electronic devices. For example, a single electronic device can execute this method, or multiple electronic devices can cooperate with each other to execute this method. When the execution entity is software, this method can be implemented as multiple software programs or software modules, or as a single software program or software module. No specific limitations are imposed here.

[0120] like Figure 1 As shown, the method specifically includes:

[0121] Step 101: Obtain control information and user attribute information of the target light strip, wherein the target light strip includes multiple LED beads.

[0122] In this embodiment, the target light strip can be any type of light strip. A light strip, also known as a light bar, can include multiple LED beads. The target light strip can be a flexible light strip, which can be bent and fixed to objects with decorative needs, such as houses and trees. The individual LED beads on the target light strip can be controlled according to different lighting effect parameters. These lighting effect parameters may include, but are not limited to: color, brightness, light change speed, and light change frequency.

[0123] In practice, by setting the lighting effect parameters of the LED beads in the light strip (such as the target light strip mentioned above), different atmospheres can be created, making the target light strip suitable for different scenes, themes, atmospheres, music, etc.

[0124] The control information described above can be used to control the target light strip. As an example, the control information can be user-inputted voice or text, or a control command generated by the user pressing a button.

[0125] User attribute information can be the information of the user associated with the target light strip. For example, the target light strip can be controlled via a control terminal (such as an app installed on a user terminal), and the information of the user logged into that control terminal can serve as the aforementioned user attribute information. For instance, the aforementioned user attribute information could refer to personal user information set by the user when logging into the control terminal, such as country, region, number of times the light effect is used, and duration.

[0126] Step 102: Based on the control information and the user attribute information, determine the lighting effect parameters of the LED beads included in the target light strip.

[0127] In this embodiment, step 102 can be performed in a variety of ways.

[0128] As an example, a pre-trained machine learning model can be used to determine the lighting effect parameters of the LEDs included in the target light strip based on the control information and the user attribute information. The machine learning model described above can represent the correspondence between the control information, user attribute information, and lighting effect parameters.

[0129] As another example, a pre-established knowledge base can also be used to determine the lighting effect parameters of the LEDs included in the target light strip based on the control information and the user attribute information. The knowledge base can represent the correspondence between the control information, user attribute information, and lighting effect parameters.

[0130] Specifically, each LED in the target LED strip can correspond to a unique code. Therefore, the lighting effect parameters of the LED can be determined by determining the lighting effect parameters corresponding to each code.

[0131] Furthermore, the lighting effect parameters of each LED in the target light strip can be determined individually based on the control information and the user attribute information. Alternatively, the lighting effect parameters of all LEDs in the target light strip as a whole can be determined based on the control information and the user attribute information. Or, the lighting effect parameters of the LEDs in each segment of the target light strip can be determined based on the control information and the user attribute information.

[0132] Step 103: Control the LED beads included in the target LED strip according to the lighting effect parameters.

[0133] In this embodiment, after determining the lighting effect parameters of each LED, the corresponding LED can be controlled according to the determined lighting effect parameters.

[0134] In some optional implementations of this embodiment, the lighting effect parameters of the LEDs included in the target light strip can be determined based on the control information and the user attribute information in the following manner: using at least one of a pre-trained machine learning model and a pre-established lighting effect knowledge base, the lighting effect parameters corresponding to the control information and the user attribute information are determined, thereby obtaining the lighting effect parameters of the LEDs included in the target light strip.

[0135] The machine learning model is used to characterize the correspondence between control information, user attribute information, and lighting effect parameters.

[0136] As examples, the aforementioned machine learning models may include: large language models, Long Short-Term Memory (LSTM) networks, etc.

[0137] A large language model refers to a language model containing hundreds of billions (or more) parameters trained on massive amounts of text data. Here, a large language model can be used to perform text understanding and semantic analysis on the target light strip as a whole or in different segments, based on the position segmentation information and the content of user input, to generate lighting effect parameters for each segment of the light beads, ultimately achieving the effect of different lighting effects for light beads in different positions.

[0138] Long Short-Term Memory (LSTM) networks are powerful recurrent neural network structures that, through the introduction of gates, can overcome the gradient problem of traditional RNNs, making them excellent at handling long sequences and natural language processing tasks. Here, an LSTM model can be used beforehand to model and train the pre-processed lighting effect encoding sequence and various lighting effect parameters. Then, based on the trained model, the various lighting effect parameters for each segment of the generated lighting effect, such as color, speed, brightness, and animation, are input to generate the corresponding lighting effect encoding sequence.

[0139] The lighting effect knowledge base is used to represent the correspondence between control information, user attribute information, and target quantity group lighting effect parameters. Here, the lighting effect knowledge base may include user input (i.e., the aforementioned control information), the lighting effects the user prefers under that input (i.e., the aforementioned target quantity group lighting effect parameters), and the user's personal dimension information (i.e., the aforementioned user attribute information), such as country, region, number of times the lighting effect is used, and duration.

[0140] It is understandable that, among the above optional implementation methods, at least one of a pre-trained machine learning model and a pre-established lighting effect knowledge base can be used to determine the lighting effect parameters of the LEDs included in the target light strip. In this way, the determined lighting effect parameters can better meet the user's expectations.

[0141] In some application scenarios of the above-mentioned optional implementation methods, the lighting effect parameters corresponding to the control information and the user attribute information can be determined by using at least one of a pre-trained machine learning model and a pre-established lighting effect knowledge base:

[0142] Step 1: Input the control information and the user attribute information into a pre-trained machine learning model to obtain the output data of the machine learning model.

[0143] Step two: Determine whether the output data represents the lighting effect parameters corresponding to the control information and the user attribute information to obtain discrimination information.

[0144] Step 3: If the discrimination information indicates that the output data is not a lighting effect parameter corresponding to the control information and the user attribute information, a target number of lighting effect parameters corresponding to the control information and the user attribute information are determined using a pre-established lighting effect knowledge base.

[0145] The discrimination information indicates that the output data is not a lighting effect parameter corresponding to the control information and the user attribute information, which means that the large language model cannot recognize the user's intention and cannot return a valid lighting effect scheme.

[0146] Understandably, in the above application scenarios, if the large language model cannot recognize the user's intent and cannot return a valid lighting effect solution, a lighting effect knowledge base can be used to return similar or popular lighting effects as a supplement. For example, popular lighting effects can be returned, i.e., the top three lighting effects in terms of usage frequency over a period of time. Therefore, by combining the machine learning model and the lighting effect knowledge base, the lighting effect parameters can be determined, making the determined lighting effect parameters more in line with the user's expectations.

[0147] In some optional implementations of this embodiment, the control information is input by the user. For example, the control information may be text and / or voice input by the user.

[0148] Based on this, the lighting effect parameters of the LEDs included in the target light strip can be determined using the control information and the user attribute information in the following manner:

[0149] The first step is to determine multiple sets of lighting effect parameters for the LED beads included in the target light strip based on the control information and the user attribute information.

[0150] As an example, at least one of a pre-trained machine learning model and a pre-established lighting effect knowledge base can be used to determine multiple sets of lighting effect parameters of the LED beads included in the target light strip based on the control information and the user attribute information.

[0151] The second step is to determine the target number of sets of lighting effect parameters from the multiple sets of lighting effect parameters.

[0152] The target quantity can be a predetermined and fixed value, or it can be a preset proportion of the determined number of lighting effect parameters.

[0153] The target quantity group of lighting effect parameters is used to return to the control terminal (e.g., a mobile app) of the light strip. That is, after the second step above is executed, the target quantity group of lighting effect parameters can be returned to the control terminal for display.

[0154] Based on this, the LED beads included in the target light strip can be controlled according to the aforementioned lighting effect parameters in the following manner:

[0155] Step 1: Determine the selected lighting effect parameters from the lighting effect parameters in the target quantity group.

[0156] Here, after the target number of lighting effect parameters are displayed on the control terminal, the user can select a set of lighting effect parameters to obtain the selected lighting effect parameters.

[0157] Step two: Control the LED beads included in the target light strip according to the selected lighting effect parameters.

[0158] Here, after determining the selected lighting effect parameters, the corresponding LED beads can be controlled according to the selected lighting effect parameters.

[0159] It is understandable that among the above optional implementation methods, multiple sets of lighting effect parameters can be recommended to the user so that the user can choose from them, thereby making the determined lighting effect parameters more in line with the user's expectations.

[0160] In some application scenarios of the above optional implementation methods, the following method can be used to determine the target number of groups of lighting effect parameters from multiple groups of lighting effect parameters: based on the return priority corresponding to each of the multiple groups of lighting effect parameters, the target number of groups of lighting effect parameters can be determined from the multiple groups of lighting effect parameters.

[0161] For example, the target number of groups of lighting effect parameters can be determined from multiple groups of lighting effect parameters in descending order of return priority.

[0162] Based on this, after determining the target number of lighting effect parameters from multiple sets of lighting effect parameters, the return priority corresponding to the target number of lighting effect parameters can be reduced.

[0163] Understandably, in the above application scenarios, after determining the lighting effect parameters that need to be returned to the control terminal, the frequency of subsequent returns of the corresponding lighting effect parameters can be reduced by lowering their return priority. For example, when the user inputs the same content multiple times (i.e., the control information mentioned above), and the results returned by the model or knowledge base are similar, certain rules can be applied during sorting. When sorting and returning, the priority of previously returned lighting effect schemes can be lowered, and lighting effect schemes that have not been returned before can be returned first. This can ensure that different lighting effect schemes are returned when the user inputs the same content multiple times, maintaining the diversity of lighting effects.

[0164] The LED strip control method provided in this application can acquire control information and user attribute information of a target LED strip, wherein the target LED strip includes multiple LED beads. Then, based on the control information and the user attribute information, the lighting effect parameters of the LED beads included in the target LED strip are determined. Finally, the LED beads included in the target LED strip are controlled according to the lighting effect parameters. Therefore, based on the control information and user attribute information of the LED strip, the lighting effect parameters of each LED bead in the LED strip can be automatically determined for the user, and each LED bead can be controlled accordingly. This reduces the difficulty for users to set lighting effects that meet their expectations, thereby improving the efficiency of setting lighting effects that meet their expectations.

[0165] Figure 2 This is a flowchart illustrating another LED strip control method provided in an embodiment of this application. Figure 2 As shown, the method specifically includes:

[0166] Step 201: Obtain control information and user attribute information of the target light strip, wherein the target light strip includes multiple LED beads.

[0167] In this embodiment, step 201 and Figure 1 Step 101 in the corresponding embodiment is basically the same, and will not be repeated here.

[0168] Step 202: Determine the LED information of the LED beads included in the target LED strip; wherein the LED information includes at least one of the following: the height of the LED bead, the angle corresponding to the LED bead, and the distance corresponding to the LED bead.

[0169] In this embodiment, the height of the LED bead can represent the distance between the LED bead and the ground. The angle corresponding to the LED bead is the angle between the illumination direction of the LED bead (e.g., the center beam of the LED bead) and the ground. The distance corresponding to the LED bead is the distance between the LED bead and the target LED bead. The target LED bead is the adjacent LED bead in the target direction of the LED bead. The target direction can represent the straight-line connection direction between the LED beads.

[0170] The target LED can include the LED adjacent to the left and / or the LED adjacent to the right in the target direction.

[0171] Here, the ground is assumed to be horizontal. The distance detection module of each LED base uses a level to obtain the initial angle α between the distance sensor's illumination surface and the horizontal plane (i.e., the angle corresponding to that LED). Then, the distance sensor is adjusted to be perpendicular to the ground by an angle of (90°-α), and the distance from the LED base to the obstruction in the vertical direction is measured to obtain the height of the LED. Simultaneously, the distance detection module detects the distance between the left and right LEDs along the connecting straight line (i.e., the target direction mentioned above) to obtain the distance corresponding to that LED.

[0172] In some optional implementations of this embodiment, the height of the LED bead is determined in the following manner:

[0173] First, determine the angle between the illumination direction of the LED and the ground to obtain the target angle.

[0174] The target angle can be defined as the angle between the illumination direction of the LED and the ground.

[0175] Then, based on the target angle, the distance between the LED bead and the ground is determined, and the height of the LED bead is obtained.

[0176] Here, the distance between the LED and the ground can be obtained via a distance sensor. For example, this distance sensor can be mounted on the base of the LED. Thus, the distance between the LED and the ground can be obtained from the distance between the LED and an obstacle (e.g., the ground) obtained by the distance sensor. If the angle corresponding to the LED is a right angle, the distance sensor can directly obtain the distance between the LED and the ground. If the angle corresponding to the LED is not a right angle, the distance between the LED and the ground can be calculated using the Pythagorean theorem based on the distance obtained by the distance sensor.

[0177] Based on this, the distance corresponding to the LED bead is determined as follows: based on the target angle, the distance between the LED bead and the target LED bead is determined, thus obtaining the distance corresponding to the LED bead.

[0178] In addition, it should be noted that in determining the height of the LED beads and the corresponding distance between them, the target angle may not actually be adjusted to a right angle.

[0179] It is understandable that, in the above optional implementation methods, by determining the height of the LED beads and the corresponding distance of the LED beads based on the target angle, more accurate LED bead information such as the height of the LED beads and the corresponding distance of the LED beads can be obtained. In this way, the LED strip can be segmented more accurately through subsequent steps.

[0180] Step 203: Based on the LED information, divide the target LED strip into multiple segments, wherein each segment includes at least one LED.

[0181] In this embodiment, the individual LEDs in each segment can be approximately located on a straight line.

[0182] In some optional implementations of this embodiment, the segmentation satisfies at least one of the following conditions:

[0183] Condition 1: The first height difference is less than or equal to the first height threshold, and the first angle difference is less than or equal to the first angle threshold.

[0184] Wherein, the first height difference is: the height difference between the LED in the segment and its target LED (e.g., the left and right adjacent LEDs in the target direction). The first angle difference is: the difference between the angle corresponding to the LED in the segment and the angle corresponding to its target LED.

[0185] Here, when the vertical height distance between multiple adjacent LED beads and the obstruction (e.g., the ground) is within 100 mm (i.e., the first height threshold), and the difference in horizontal angle between them and the adjacent LED beads (i.e., the first angle threshold) is within 10°, these LED beads can be considered to be on the same horizontal line. By analogy, adjacent LED beads with the same information can be divided into a segment.

[0186] Condition 2: The first height difference is greater than the first height threshold, the second height difference is less than the second height threshold, and the first angle difference is less than or equal to the first angle threshold.

[0187] Wherein, the second height difference is: the difference between the maximum height difference between the LED beads in the segment and the minimum height difference between the LED beads in the segment.

[0188] Here, when the vertical height difference between multiple adjacent LED beads and the obstruction (e.g., the ground) and the height difference between adjacent LED beads (i.e., the first height difference mentioned above) is greater than 100 mm (i.e., the first height threshold mentioned above), but the difference between the maximum and minimum values ​​of the difference (i.e., the second height difference mentioned above) is less than 100 mm (i.e., the second height threshold mentioned above), and the difference between the horizontal angle and the adjacent LED beads (i.e., the first angle difference mentioned above) is within 10° (i.e., the first angle threshold mentioned above), these LED beads can be considered to be in the same segment.

[0189] Condition 3: The boundary LEDs of the segment meet the following conditions: the distance between the boundary LED and the target LED of the boundary LED is greater than the first distance or less than the second distance.

[0190] Wherein, the second distance is less than the first distance.

[0191] Here, the distance between the LED bead and its adjacent LED bead in the left-right straight direction (i.e., the target direction mentioned above) is used to determine whether the LED bead is a segment boundary point. When the straight-line connection distance between the LED bead and the LED bead on the left or right is greater than 550 mm (i.e., the first distance mentioned above) or less than 250 mm (i.e., the second distance mentioned above), the LED bead can be considered as a segment boundary point (i.e., a boundary LED bead).

[0192] Condition 4: The number of boundary LEDs in a single sub-segment of the segment is less than or equal to 2.

[0193] It is understandable that, among the above optional implementation methods, adopting the above conditions can more accurately divide the target light strip into segments, thereby realizing segmented control of the target light strip.

[0194] Step 204: Based on the control information and the user attribute information, determine the lighting effect parameters of the LED beads included in the multiple segments.

[0195] In this embodiment, step 204 can be performed in a variety of ways.

[0196] As an example, a pre-trained machine learning model can be used to determine the lighting effect parameters of the LEDs included in the multiple segments based on the control information and the user attribute information. The machine learning model can represent the correspondence between the control information, the user attribute information, and the lighting effect parameters of the LEDs included in the multiple segments.

[0197] As another example, a pre-established knowledge base can also be used to determine the lighting effect parameters of the LEDs included in the multiple segments based on the control information and the user attribute information. The knowledge base can represent the correspondence between the control information, the user attribute information, and the lighting effect parameters of the LEDs included in the multiple segments.

[0198] Specifically, each LED in the target LED strip can correspond to a unique code. Therefore, the lighting effect parameters of the LED can be determined by determining the lighting effect parameters corresponding to each code.

[0199] Step 205: Control the LED beads included in the target LED strip according to the lighting effect parameters.

[0200] In this embodiment, step 205 and Figure 1 Step 103 in the corresponding embodiment is basically the same, and will not be repeated here.

[0201] In some optional implementations of this embodiment, after dividing the target light strip into multiple segments based on the LED information, the category information of each segment in at least two of the segments can also be determined.

[0202] The category information can include one of the following: the front of the eaves, the side of the eaves, the slope of the building, etc. For example, if the height of the LED beads in a segment and the corresponding angle of the LED beads are within the error range, then the corresponding category information can represent the front and side of the eaves. If the height of the LED beads in a segment increases or decreases, then the corresponding category information can represent the slope of the building.

[0203] Based on this, the following method can be used to determine the lighting effect parameters of the LEDs included in the multiple segments based on the control information and the user attribute information:

[0204] Based on the control information, the user attribute information, and the category information, the lighting effect parameters of the LED beads included in the multiple segments are determined.

[0205] As an example, a pre-trained machine learning model can be used to determine the lighting effect parameters of the LEDs included in the multiple segments based on the control information, the user attribute information, and the category information. The machine learning model can represent the correspondence between the control information, user attribute information, category information, and the lighting effect parameters of the LEDs included in the multiple segments.

[0206] As another example, a pre-established knowledge base can also be used to determine the lighting effect parameters of the LEDs included in the multiple segments based on the control information, the user attribute information, and the category information. The knowledge base can represent the correspondence between the control information, user attribute information, category information, and the lighting effect parameters of the LEDs included in the multiple segments.

[0207] It should be noted that, in addition to the contents described above, this embodiment may also include... Figure 1 The corresponding technical features described in the corresponding embodiments, thereby achieving Figure 1 For details on the technical effects of the LED strip control method shown, please refer to [link / reference]. Figure 1 The relevant descriptions are presented concisely and will not be elaborated upon here.

[0208] The light strip control method provided in this application embodiment can achieve more refined control of the light strip by segmenting it, thereby making the target light strip more suitable for different scenes, themes, atmospheres, music, etc.

[0209] The embodiments of this application are described below by way of example. However, it should be noted that the embodiments of this application may have the features described below, but the following description does not constitute a limitation on the protection scope of the embodiments of this application.

[0210] In recent years, with the popularization of smart homes and the increasing demand for personalized decoration, string lights (i.e., the aforementioned target light strips) have become increasingly popular as an important component of home and building exterior decoration. To meet users' needs for personalized lighting effects, various lighting effect software apps have emerged on the market. However, although these apps offer a large number of pre-set lighting effects, the sheer variety requires users to try them out individually and apply them to actual products to find a lighting solution that meets their needs. Furthermore, the custom editing functions are relatively complex and difficult to use, making them unfriendly to ordinary users.

[0211] Current industry solutions primarily involve adding categorization to preset lighting effect schemes. However, in practice, users still need to try and apply each scheme under that category individually. Custom editing requires users to configure settings for each item.

[0212] Therefore, users may want aesthetically pleasing lighting effects to decorate the atmosphere, but due to limitations in their personal aesthetic sense and abilities, they find it difficult to customize the ideal lighting effect and need to click and try repeatedly.

[0213] This method can utilize large-scale language models and light string position information to generate segmented lighting effect parameters and full-segment lighting effects. It is applicable to lighting control technology in various smart home scenarios and can be applied to various lighting devices such as smart light strings and light strips on the eaves of houses.

[0214] Large-scale language models refer to language models containing hundreds of billions (or more) parameters, which are trained on large amounts of text data.

[0215] LSTM: Long Short-Term Memory (LSTM) is a powerful recurrent neural network structure that overcomes the gradient problem of traditional RNNs by introducing a gate mechanism, making it excellent in processing long sequences and natural language processing tasks.

[0216] 3D model: a three-dimensional model, mainly referring to a three-dimensional house model.

[0217] This method consists of the following two parts:

[0218] 1. Segmented lighting effect parameter generation.

[0219] The distance from the base of each LED bead in the light string (i.e., the target light strip) to an obstruction (such as the ground) in the vertical direction (i.e., the height of the LED bead), the angle with the horizontal plane (i.e., the angle corresponding to the LED bead), and the spacing distance in the straight-line connection direction between the LED beads (i.e., the distance corresponding to the LED beads) are obtained through the ranging module and sent to the backend server. The backend server calculates and analyzes the segmentation information of the LED beads according to their different positions based on the vertical distance and the spacing distance in the straight-line connection direction. Using a large language model, text understanding and semantic analysis are performed on the LED beads in different segments based on the position segmentation information and the user input (i.e., the control information mentioned above) to generate the lighting effect parameters for each segment of LED beads, ultimately achieving the effect of different lighting effects for LED beads in different positions.

[0220] 2. Generation of lighting effect parameters for the entire section.

[0221] This section supplements the content of the first section. When the position segmentation information of the LED beads on the light string is not available, the large language model can be used to perform text understanding and semantic analysis on the input content based on the user's text or voice input (i.e., the control information mentioned above) to generate the lighting effect parameters corresponding to the entire light string and thus make recommendations to the user. In this scenario, the segmentation situation is no longer considered.

[0222] Specifically, this method includes:

[0223] I. Generation of segmented lighting effect parameters.

[0224] As an example, please refer to Figure 3A , Figure 3A This is a flowchart illustrating another LED strip control method provided in an embodiment of this application.

[0225] Step 1: Obtain the angle between the LED and the horizontal plane (i.e., the angle corresponding to the LED), the distance from the obstruction in the vertical direction (i.e., the height of the LED), and the interval distance in the straight connection direction of the LED (i.e., the distance corresponding to the LED).

[0226] Here, the prerequisite for obtaining the above information is that the ground is assumed to be horizontal. Each LED base distance detection module uses a level to obtain the initial angle α between the distance sensor's illumination surface direction and the horizontal plane (i.e., the angle corresponding to the LED), and reports this angle α data to the backend server. Then, the distance sensor is adjusted to be perpendicular to the ground by an angle of (90°-α), and the distance from the LED base to the obstruction in the vertical direction (i.e., the height of the LED) is measured. The current height and angle of the LED are reported. Simultaneously, the distance detection module checks whether the distance between the left and right LEDs in the connecting straight line (i.e., the distance corresponding to the LED) conforms to a specified value, and reports the distance values ​​of adjacent LEDs in the same connecting straight line direction. For example, please refer to... Figure 3B , Figure 3B This is a schematic diagram of a segmentation method. Figure 3B The installation wiring direction of the LED chips is shown.

[0227] Step 2: The backend server analyzes and calculates the height, angle, and distance information of each LED reported by the LED string (i.e., the LED information mentioned above), and outputs LED segmentation information. That is, based on the LED information, the target LED strip is divided into multiple segments.

[0228] After obtaining the vertical height, horizontal angle, and distance between adjacent LEDs in the straight line connection direction for each LED in step 1, the LEDs are segmented according to their coding order. The length of each segment and the first and last LED numbers are output. The original spacing between LEDs on the device is 500 mm.

[0229] When the vertical height distance between multiple adjacent LED beads and the obstruction (e.g., the ground) is within 100 mm (i.e., the first height threshold), and the difference in horizontal angle between them and the adjacent LED beads (i.e., the first angle threshold) is within 10° (i.e., the first angle threshold), then these LED beads can be considered to be on the same horizontal line. Similarly, adjacent LED beads with the same information can be divided into a segment, as shown in segment 1 of the segmentation results in the table below.

[0230] When the vertical height difference between multiple adjacent LED beads and the obstruction (e.g., the ground) and the height difference between adjacent LED beads (i.e., the first height difference mentioned above) is greater than 100 mm (i.e., the first height threshold mentioned above), but the difference between the maximum and minimum values ​​of the difference (i.e., the second height difference mentioned above) is less than 100 mm (i.e., the second height threshold mentioned above), and the difference between the horizontal angle and the adjacent LED beads (i.e., the first angle difference mentioned above) is within 10° (i.e., the first angle threshold mentioned above), these LED beads can be considered to be in the same segment, as shown in segment 2 of the segmentation results in the table below.

[0231] The distance between the LED and its adjacent LED in the left-right straight line direction (i.e., the target direction mentioned above) is used to determine whether the LED is a segment boundary point. When the straight line connection distance between the LED and the LED on the left or right is greater than 550 mm (i.e., the first distance mentioned above) or less than 250 mm (i.e., the second distance mentioned above), the LED can be considered as a segment boundary point (i.e., a boundary LED). Based on this logic, LEDs 1 to 3, 9, 10, 14, and 18 are divided into segment boundary points.

[0232] Following the principle of subdivision, with each subdivision having no more than 2 dividing points, segment 1 is subdivided into segment 1-1 and segment 1-2, where segment 1-1 contains 2 boundary points and segment 1-2 contains 2 boundary points and 5 intermediate points; segment 2 is subdivided into 2-1 and 2-2, where 2-1 contains 2 boundary points and 3 intermediate points and 2-2 contains 1 boundary point and 3 intermediate points.

[0233] Additional explanation: The above division is a relatively detailed one. After detailed division, the information from the first segment can also be used to generate lighting effects later.

[0234]

[0235] Step 3: For the information of each segment of the LED beads output in Step 2, use the pre-trained classification model to label the position (i.e. the category information mentioned above) according to the distance and angle information. For example, segments 1-1 and 1-2 can be labeled as the front and side of the eaves because the vertical distance and angle are within the error range. Segments 2-1 and 2-2 can be labeled as the slope of the house because the vertical distance of the LED beads increases and decreases.

[0236] Step 4: Based on the segment length and corresponding position label information of the segments in Steps 2 and 3, as well as the content information input by the user (i.e., the control information mentioned above) and the user's own dimension information (i.e., the user attribute information mentioned above), use the large language model to generate different lighting effect parameters and lighting effect encoding sequences for each segment, concatenate the segmented lighting effect encoding sequences in order, sort the various schemes and return them.

[0237] Step 4.1: The overall module's input text information includes three parts: user input, segment length and location labels, and user-specific dimensions. User input supports both voice and text input. When a user selects voice input, the voice acquisition module collects the user's voice input, and the voice recognition module preprocesses the user's voice input and converts it into text information based on acoustic and language models. The segment length and location label data come from the outputs of Steps 2 and 3. The user-specific dimension information refers to the user's personal information, such as country, set when logging into the client software. Subsequent steps will use this information to recommend lighting effects that better match the user's personalization.

[0238] Step 4.2: For the input text information content in Step 4., use the pre-trained large language model to identify the user's intent and return the corresponding lighting effect scheme to the user. See the subsequent steps of Step 4.2 for details. In this step, if the large language model cannot identify the user's intent and cannot return a valid lighting effect scheme, the schemes in the supplementary instructions can be used to return similar or popular lighting effects to the user as a supplement. See the supplementary instructions for details.

[0239] Step 4.2.1: For the text information obtained in Step 4.1, perform semantic recognition and text classification using a pre-trained large-scale natural language processing model. First, determine the user's goal: whether they need new lighting effect parameters or adjustments based on existing lighting effects. Analyze and extract key information from the text, such as theme, sentiment, scene, and country. For example, if the user inputs "Spring Festival," the semantic analysis will indicate that the user needs lighting effect parameters, and the key information extracted will be: theme is Spring Festival, sentiment is likely festive, scene is a traditional Chinese festival, and country is China.

[0240] Step 4.2.2: If the user needs to generate new lighting effect parameters, based on the extracted key information and the pre-set database of color psychology and emotional types corresponding to lighting effects, multiple lighting effect schemes are generated using similarity matching algorithms such as cosine similarity. For example, in the example mentioned in Step 4.2.1, the generated parameters for each segment are as follows: Segment 1 has colors of gold, yellow, and red, a medium speed, a brightness of 80%, and a lantern effect; Segment 2 has colors of gold, yellow, and red, a medium speed, a brightness of 80%, and a flowing light effect. The LSTM model is used beforehand to model and train the pre-processed lighting effect encoding sequence and various lighting effect parameter data. Then, based on the trained model, the generated lighting effect parameters for each segment (color, speed, brightness, and animation) are input to generate the corresponding lighting effect encoding sequence.

[0241] Step 4.2.3: If the user requires adjustments to existing lighting effects, extract the lighting effect parameters and segment names that need to be adjusted from the text, and confirm the lighting effect parameters that need to be adjusted, such as color, animation type, speed, and brightness; use the lighting effect coding rule engine to replace the color, speed, and brightness of the existing lighting animation coding sequences to obtain the replaced lighting effect coding sequences.

[0242] Color similarity can be calculated using a weighted Euclidean distance method. Lighting effect similarity can be preset. For brightness control, a preset percentage (e.g., 0%) can be adjusted each time. For speed control, the light switching duration can be increased or decreased by a preset duration (e.g., 5 seconds) each time.

[0243] Step 4.2 Supplementary Explanation: After generating the lighting effect coding sequence through the above steps and accumulating a large amount of input and output content through pre-set lighting effects, a lighting effect knowledge base can be established. The lighting effect knowledge base includes user input, the lighting effects the user likes under that input, and the user's personal dimension information such as country, region, number of times the lighting effect is used, and duration. Subsequently, for the user input information, text similarity matching can be performed in the lighting effect knowledge base to recall the top (i.e., the target number) K similar lighting effect coding sequences in the corresponding lighting effect knowledge base that are similar to the input information. If the model cannot return a valid lighting effect solution, the top three (i.e., the target number) most popular lighting effects within a certain period of time can be directly returned from the lighting effect knowledge base.

[0244] Step 4.3: Concatenate the segmented lighting effect codes returned by the model and rule schemes in sequence. Then, score the concatenated lighting effect schemes and the lighting effect schemes returned by the knowledge base according to multiple dimensions such as the user survey, the user's recent preferences, and the similarity with the input content, and sort them comprehensively. Return the top three lighting effect schemes and their corresponding lighting effect code sequences. When the user inputs the same content multiple times and the model returns similar results, certain rules can be applied during sorting. When returning the results, the priority of previously returned lighting effect schemes should be reduced, and lighting effect schemes that have not been returned before should be returned first. This ensures that different lighting effect schemes are returned when the user inputs the same content multiple times, maintaining the diversity of lighting effects.

[0245] II. Generation of lighting effect parameters for the entire section.

[0246] The generation of full-segment lighting effect parameters is a supplementary solution to the segmented lighting effect generation in the first part. When the position segment information of the LED beads on the light string is not available, the lighting effect parameters corresponding to the full-segment light string can be generated directly based on the user's text or voice input and user dimension information, using a large language model to perform text understanding and semantic analysis on the input content, thereby making recommendations to the user. In this scenario, the segment position is no longer considered.

[0247] The process of generating lighting effects using a large language model based on user input and user dimension information is largely the same as in Part 1. The difference lies in that the model input no longer includes segmented position information, and the model output is changed to a complete lighting effect encoding sequence, while the rest remains unchanged. For an example, please refer to... Figure 3C , Figure 3C This is a flowchart illustrating another LED strip control method provided in an embodiment of this application.

[0248] It should be noted that, in addition to the contents described above, this embodiment may also include the technical features described in the above embodiments, thereby achieving the technical effects of the above-described light strip control method. Please refer to the above description for details. For the sake of brevity, it will not be elaborated here.

[0249] The light strip control method provided in this application offers an intelligent and user-friendly lighting generation solution. It can quickly and efficiently generate various ambient lighting effects tailored to user needs and LED positions, eliminating the need for repeated trials among numerous preset effects or complex custom page settings. The method intelligently segments LEDs at different positions and determines key lighting effect parameters using a large language model based on user input and user-dimensional information, generating segmented and full-segment lighting effect solutions.

[0250] Figure 4 This is a schematic diagram of a light strip control device provided in an embodiment of this application. Specifically, it includes:

[0251] The acquisition unit 401 is used to acquire control information and user attribute information of the target light strip, wherein the target light strip includes multiple LED beads;

[0252] The first determining unit 402 is used to determine the lighting effect parameters of the lamp beads included in the target light strip based on the control information and the user attribute information;

[0253] Control unit 403 is used to control the LED beads included in the target light strip according to the lighting effect parameters.

[0254] In one possible implementation, before determining the luminous efficacy parameters of the LEDs included in the target light strip based on the control information and the user attribute information, the device further includes:

[0255] The second determining unit (not shown in the figure) is used to determine the lamp information of the lamps included in the target light strip; wherein, the lamp information includes at least one of the following: the height of the lamp, the angle corresponding to the lamp, and the distance corresponding to the lamp; the angle corresponding to the lamp is: the angle between the illumination direction of the lamp and the ground; the distance corresponding to the lamp is: the distance between the lamp and the target lamp; the target lamp is the adjacent lamp in the target direction of the lamp.

[0256] A segmentation unit (not shown in the figure) is used to segment the target light strip based on the LED information to obtain multiple segments, wherein each segment includes at least one LED; and

[0257] The step of determining the lighting effect parameters of the LED beads included in the target light strip based on the control information and the user attribute information includes:

[0258] Based on the control information and the user attribute information, the lighting effect parameters of the LED beads included in the multiple segments are determined.

[0259] In one possible implementation, the height of the LED is determined as follows:

[0260] Determine the angle between the illumination direction of the LED and the ground to obtain the target angle;

[0261] Based on the target angle, the distance between the LED and the ground is determined, thus obtaining the height of the LED; and

[0262] The distance corresponding to this LED bead is determined in the following way:

[0263] Based on the target angle, the distance between the LED and the target LED is determined, thus obtaining the distance corresponding to the LED.

[0264] In one possible implementation, the segmentation satisfies any of the following conditions:

[0265] The first height difference is less than or equal to the first height threshold, wherein the first height difference is the height difference between the LED in the segment and the target LED of the LED; and the first angle difference is less than or equal to the first angle threshold, wherein the first angle difference is the difference between the angle corresponding to the LED in the segment and the angle corresponding to the target LED of the LED.

[0266] The first height difference is greater than the first height threshold; the second height difference is less than the second height threshold, wherein the second height difference is the difference between the maximum height difference between the LEDs in the segment and the minimum height difference between the LEDs in the segment; and the first angle difference is less than or equal to the first angle threshold.

[0267] The segmented boundary LEDs satisfy the following condition: the distance between the boundary LED and the target LED of the boundary LED is greater than a first distance or less than a second distance, wherein the second distance is less than the first distance;

[0268] The number of boundary LEDs in a single sub-segment of the segment is less than or equal to 2.

[0269] In one possible implementation, after dividing the target light strip into multiple segments based on the LED information, the device further includes:

[0270] A third determining unit (not shown in the figure) is used to determine the category information of each of the at least two segments; and

[0271] The step of determining the lighting effect parameters of the LEDs included in the multiple segments based on the control information and the user attribute information includes:

[0272] Based on the control information, the user attribute information, and the category information, the lighting effect parameters of the LED beads included in the multiple segments are determined.

[0273] In one possible implementation, determining the luminous efficacy parameters of the LEDs included in the target light strip based on the control information and the user attribute information includes:

[0274] By employing at least one of a pre-trained machine learning model and a pre-established lighting effect knowledge base, lighting effect parameters corresponding to the control information and the user attribute information are determined, thereby obtaining the lighting effect parameters of the LED beads included in the target light strip;

[0275] The machine learning model is used to characterize the correspondence between control information, user attribute information, and lighting effect parameters.

[0276] The lighting effect knowledge base is used to represent the correspondence between control information, user attribute information, and target quantity group lighting effect parameters.

[0277] In one possible implementation, determining the lighting effect parameters corresponding to the control information and the user attribute information by employing at least one of a pre-trained machine learning model and a pre-established lighting effect knowledge base includes:

[0278] The control information and the user attribute information are input into a pre-trained machine learning model to obtain the output data of the machine learning model;

[0279] Determine whether the output data represents lighting effect parameters corresponding to the control information and the user attribute information to obtain discrimination information;

[0280] If the discrimination information indicates that the output data is not a lighting effect parameter corresponding to the control information and the user attribute information, a pre-established lighting effect knowledge base is used to determine the target number of lighting effect parameters corresponding to the control information and the user attribute information.

[0281] In one possible implementation, the control information is input by the user; and

[0282] The step of determining the lighting effect parameters of the LED beads included in the target light strip based on the control information and the user attribute information includes:

[0283] Based on the control information and the user attribute information, multiple sets of lighting effect parameters of the lamp beads included in the target light strip are determined;

[0284] From the multiple sets of lighting effect parameters, a target number of lighting effect parameters are determined, wherein the target number of lighting effect parameters are used to return to the control terminal of the light strip; and

[0285] The step of controlling the LED beads included in the target light strip according to the lighting effect parameters includes:

[0286] From the lighting effect parameters in the target quantity group, determine the selected lighting effect parameters;

[0287] The LED beads included in the target LED strip are controlled according to the selected lighting effect parameters.

[0288] In one possible implementation, determining the target number of sets of lighting effect parameters from the plurality of sets of lighting effect parameters includes:

[0289] Based on the return priorities corresponding to the multiple sets of lighting effect parameters, a target number of sets of lighting effect parameters are determined from the multiple sets of lighting effect parameters; and

[0290] After determining the target number of sets of lighting effect parameters from multiple sets of the lighting effect parameters, the device further includes:

[0291] Reduce the return priority of the lighting effect parameters corresponding to the target number group.

[0292] The light strip control device provided in this embodiment can be as follows: Figure 4 The light strip control device shown can execute all the steps of the light strip control methods described above, thereby achieving the technical effects of the light strip control methods described above. For details, please refer to the relevant descriptions above. For the sake of brevity, it will not be elaborated here.

[0293] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 The illustrated electronic device 500 includes at least one processor 501, a memory 502, at least one network interface 504, and other user interfaces 503. The various components in the electronic device 500 are coupled together via a bus system 505. It is understood that the bus system 505 is used to implement communication between these components. In addition to a data bus, the bus system 505 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 5 The general designated all buses as Bus System 505.

[0294] The user interface 503 may include a display, keyboard, or clicking device (e.g., mouse, trackball, touchpad, or touchscreen).

[0295] It is understood that the memory 502 in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 502 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0296] In some implementations, memory 502 stores elements, executable units or data structures, or subsets thereof, or extended sets thereof: operating system 5021 and application program 5022.

[0297] The operating system 5021 includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application program 5022 includes various applications, such as a media player and a browser, used to implement various application functions. Programs implementing the methods of this application embodiment can be included in application program 5022.

[0298] In this embodiment, by calling the program or instructions stored in memory 502, specifically the program or instructions stored in application program 5022, processor 501 executes the method steps provided in each method embodiment, including, for example:

[0299] Acquire control information and user attribute information of the target light strip, wherein the target light strip includes multiple LED beads;

[0300] Based on the control information and the user attribute information, the lighting effect parameters of the LED beads included in the target light strip are determined;

[0301] The LED beads included in the target light strip are controlled according to the aforementioned lighting effect parameters.

[0302] The methods disclosed in the embodiments of this application can be applied to or implemented by processor 501. Processor 501 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 501 or by instructions in the form of software. The processor 501 may be a general-purpose processor, 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, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software units in the decoding processor. The software units may be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 502. Processor 501 reads the information in memory 502 and, in conjunction with its hardware, completes the steps of the above method.

[0303] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described above in this application, or combinations thereof.

[0304] For software implementation, the techniques described herein can be implemented by units that perform the functions described above. The software code can be stored in memory and executed by a processor. The memory can be implemented within the processor or external to the processor.

[0305] The electronic device provided in this embodiment may be as follows: Figure 5 The electronic device shown can execute all the steps of the above-described LED strip control methods, thereby achieving the technical effects of the above-described LED strip control methods. For details, please refer to the above descriptions. For the sake of brevity, further details are omitted here.

[0306] This application also provides a storage medium (computer-readable storage medium). This storage medium stores one or more programs. The storage medium may include volatile memory, such as random access memory; it may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive; and it may also include combinations of the above types of memory.

[0307] One or more programs in the storage medium can be executed by one or more processors to implement the above-mentioned LED strip control method executed on the electronic device side.

[0308] The processor described above is used to execute the LED strip control program stored in the memory to implement the following steps of the LED strip control method executed on the electronic device side:

[0309] Acquire control information and user attribute information of the target light strip, wherein the target light strip includes multiple LED beads;

[0310] Based on the control information and the user attribute information, the lighting effect parameters of the LED beads included in the target light strip are determined;

[0311] The LED beads included in the target light strip are controlled according to the aforementioned lighting effect parameters.

[0312] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0313] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0314] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0315] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method of controlling a light strip, characterized by, The method comprises: obtaining control information and user attribute information of a target light strip, wherein the target light strip comprises a plurality of lamp beads; determining, based on the control information and the user attribute information, light effect parameters of the lamp beads comprised by the target light strip; controlling the lamp beads comprised by the target light strip according to the light effect parameters; before the determining, based on the control information and the user attribute information, of the light effect parameters of the lamp beads comprised by the target light strip, the method further comprises: determining lamp bead information of the lamp beads comprised by the target light strip; wherein the lamp bead information comprises at least one of the following: a height of the lamp bead, an angle corresponding to the lamp bead, and a distance corresponding to the lamp bead; the angle corresponding to the lamp bead is an angle between an irradiation direction of the lamp bead and a ground; the distance corresponding to the lamp bead is a distance between the lamp bead and a target lamp bead; the target lamp bead is a neighboring lamp bead in a target direction of the lamp bead; based on the lamp bead information, dividing the target light strip to obtain a plurality of segments, wherein each segment comprises at least one lamp bead; and the determining, based on the control information and the user attribute information, of the light effect parameters of the lamp beads comprised by the target light strip comprises: determining, based on the control information and the user attribute information, the light effect parameters of the lamp beads comprised by the plurality of segments.

2. The method of claim 1, wherein the height of the lamp bead is determined in the following manner: determining an included angle between the irradiation direction of the lamp bead and the ground to obtain a target included angle; based on the target included angle, determining a distance between the lamp bead and the ground to obtain the height of the lamp bead; and the distance corresponding to the lamp bead is determined in the following manner: based on the target included angle, determining a distance between the lamp bead and the target lamp bead to obtain the distance corresponding to the lamp bead.

3. The method of claim 1, wherein, the segment satisfies at least one of the following conditions: a first height difference is less than or equal to a first height threshold, wherein the first height difference is a height difference between a lamp bead in the segment and a target lamp bead of the lamp bead; and a first angle difference is less than or equal to a first angle threshold, wherein the first angle difference is a difference between an angle corresponding to the lamp bead in the segment and an angle corresponding to the target lamp bead of the lamp bead; the first height difference is greater than the first height threshold; a second height difference is less than a second height threshold, wherein the second height difference is a difference between a maximum height difference between the lamp beads in the segment and a minimum height difference between the lamp beads in the segment; and the first angle difference is less than or equal to the first angle threshold; a boundary lamp bead of the segment satisfies the following condition: a distance between the boundary lamp bead and a target lamp bead of the boundary lamp bead is greater than a first distance or less than a second distance, wherein the second distance is less than the first distance; a number of boundary lamp beads in a single sub-segment comprised by the segment is less than or equal to 2.

4. The method of claim 1, wherein, after the dividing, based on the lamp bead information, of the target light strip to obtain the plurality of segments, the method further comprises: determining class information of each of the at least two segments; and the determining, based on the control information and the user attribute information, of the light effect parameters of the lamp beads comprised by the plurality of segments comprises: Determine the light effect parameters of the lamp beads included in the target light strip based on the control information and the user attribute information.

5. The method of claim 1, wherein, The determination of the light effect parameters of the lamp beads included in the target light strip based on the control information and the user attribute information comprises: Determine the light effect parameters corresponding to the control information and the user attribute information by using at least one of a pre-trained machine learning model and a pre-established light effect knowledge base, to obtain the light effect parameters of the lamp beads included in the target light strip. The machine learning model is used to represent the corresponding relationship between the control information, the user attribute information and the light effect parameters. The light effect knowledge base is used to represent the corresponding relationship between the control information, the user attribute information and the target number of groups of light effect parameters.

6. The method of claim 5, wherein, The determination of the light effect parameters corresponding to the control information and the user attribute information by using at least one of a pre-trained machine learning model and a pre-established light effect knowledge base comprises: Input the control information and the user attribute information into the pre-trained machine learning model to obtain the output data of the machine learning model; Determine whether the output data represents the light effect parameters corresponding to the control information and the user attribute information to obtain discrimination information; In the case that the discrimination information represents that the output data is not the light effect parameters corresponding to the control information and the user attribute information, determine the target number of groups of light effect parameters corresponding to the control information and the user attribute information by using the pre-established light effect knowledge base.

7. The method of claim 1, wherein, The control information is input by a user; And The determination of the light effect parameters of the lamp beads included in the target light strip based on the control information and the user attribute information comprises: Determine a plurality of groups of light effect parameters of the lamp beads included in the target light strip based on the control information and the user attribute information; From the plurality of groups of light effect parameters, determine a target number of groups of light effect parameters, wherein the target number of groups of light effect parameters are used to return to the control end of the light strip; and The control of the lamp beads included in the target light strip according to the light effect parameters comprises: From the target number of groups of light effect parameters, determine selected light effect parameters; Control the lamp beads included in the target light strip according to the selected light effect parameters.

8. The method of claim 7, wherein, The determination of the target number of groups of light effect parameters from the plurality of groups of light effect parameters comprises: Based on the return priorities corresponding to the plurality of groups of light effect parameters, determine the target number of groups of light effect parameters from the plurality of groups of light effect parameters; and After determining the target number of groups of light effect parameters from the plurality of groups of light effect parameters, the method further comprises: Lower the return priorities corresponding to the target number of groups of light effect parameters.

9. A light strip control device, characterized in that The device comprises: An acquisition unit configured to acquire control information and user attribute information of a target light strip, wherein the target light strip comprises a plurality of lamp beads; A first determination unit configured to determine light effect parameters of the lamp beads included in the target light strip based on the control information and the user attribute information; A control unit configured to control the lamp beads included in the target light strip according to the light effect parameters. Before the determining the light effect parameter of the lamp beads included in the target lamp strip based on the control information and the user attribute information, the apparatus further comprises: a second determining unit, configured to determine lamp bead information of the lamp beads included in the target lamp strip, wherein the lamp bead information comprises at least one of the following: height of the lamp bead, angle corresponding to the lamp bead, and distance corresponding to the lamp bead; the angle corresponding to the lamp bead is an angle between an irradiation direction of the lamp bead and the ground; the distance corresponding to the lamp bead is a distance between the lamp bead and a target lamp bead; the target lamp bead is a neighboring lamp bead in a target direction of the lamp bead; a dividing unit, configured to divide the target lamp strip based on the lamp bead information to obtain a plurality of segments, wherein each segment comprises at least one lamp bead; and the first determining unit is further configured to: determine the light effect parameter of the lamp beads included in the plurality of segments based on the control information and the user attribute information.

10. An electronic device, comprising: comprise: a memory, configured to store a computer program; a processor, configured to execute the computer program stored in the memory, and when the computer program is executed, implement the method in any one of claims 1-8.

11. A computer readable storage medium having stored thereon a computer program, characterized in that, when the computer program is executed by the processor, implement the method in any one of claims 1-8.

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