Ambience lamp and lamp effect control method, device and medium thereof

Abstract

This application relates to an ambient lighting fixture and its lighting effect control method, device, and medium. The method includes: acquiring the music description information of a target piece of music; determining a target color set for generating lighting effect control information based on the image information in the music description information; matching a target lighting effect template for generating the lighting effect control information from a lighting effect template library based on the text information in the music description information; applying the target color set to the target lighting effect template to obtain the lighting effect control information; and controlling the ambient lighting fixture to play corresponding lighting effects based on the lighting effect control information. This application efficiently integrates the image and text information of the music, accurately matches the overall feeling of the lighting effects and music, adapts to the limited computing power of embedded chips, ensures smooth playback of lighting effects, and provides users with a cost-effective and high-quality ambient lighting experience.

Description

Technical Field

[0001] This application relates to the field of lighting effect control technology, and in particular to an ambient lighting fixture and its lighting effect control method, device and medium. Background Technology

[0002] In applications where voice-controlled lighting effects are generated, traditional technologies typically extract rhythmic information from music to control the lighting effects. For example, stage lighting systems and automotive lighting systems, with their ample hardware, are capable of handling complex audio data processing. These systems can analyze drum beats in audio data in real time to generate lighting effects that match the music's rhythm, creating a strong visual impact and immersive experience for the audience. However, these technologies are not entirely suitable for ambient lighting in everyday home settings.

[0003] On the one hand, traditional technologies, when associating music with lighting effects, habitually rely on melodic information. While this method can represent the feelings conveyed by music in real time, it has certain limitations. Melodic information can reflect the emotions and atmosphere of music at a particular moment, but it may not accurately reflect the inherent overall feeling of the music itself, which is the effect the songwriter truly intended to convey. Therefore, traditional technologies often fail to fully reflect the overall style and emotion of the music when generating lighting effects, resulting in an inaccurate match between lighting effects and music, and failing to provide users with the best visual and auditory experience.

[0004] On the other hand, the ambient lighting fixtures of this application are typically used in daily life and often appear in home settings. In such scenarios, product economy is a key constraint. Due to cost considerations, these ambient lighting fixtures usually use embedded chips to control lighting effects. The computing power of embedded chips is relatively limited, making it impossible to directly apply complex traditional technical solutions. Forcibly embedding technical solutions that require real-time analysis of audio data into these fixtures would result in significant computational overhead, causing stuttering during lighting effect playback and impacting the user experience. Furthermore, ambient lighting fixtures are typically composed of a large number of LEDs, and their embedded chips need to adapt to high frame rate requirements, generating massive amounts of LED brightness and color control data per second, which further limits their computing power. Therefore, the application of traditional technical solutions in home settings is significantly limited, failing to fully realize their potential to enhance ambiance and improve user experience.

[0005] In summary, existing technologies face two main technical challenges when applied to ambient lighting in home settings: first, traditional technologies rely solely on melody information, failing to accurately reflect the overall feel of the music and resulting in inaccurate matching between lighting effects and music; second, the limited computing power of embedded chips hinders the processing of complex audio data, leading to choppy playback of lighting effects. These issues restrict the effectiveness of ambient lighting in home settings, preventing it from fully realizing its potential to enhance ambiance and improve user experience. Summary of the Invention

[0006] The primary objective of this application is to solve at least one of the above-mentioned problems by providing an ambient lighting fixture and a method, device, and medium for controlling its lighting effects.

[0007] To achieve the various objectives of this application, the following technical solution is adopted:

[0008] A method for controlling the lighting effects of an ambient lighting fixture, provided for one of the purposes of this application, includes the following steps:

[0009] Obtain the description information of the target piece of music;

[0010] Based on the image information in the music description, determine the target color set used to generate the lighting effect control information;

[0011] Based on the text information in the music description, a target lighting effect template for generating the lighting effect control information is matched from the lighting effect template library;

[0012] The target color set is fitted into the target lighting effect template to obtain the lighting effect control information, and the ambient lighting fixtures are controlled to play the corresponding lighting effects according to the lighting effect control information.

[0013] An ambient lighting effect control device, proposed to meet one of the purposes of this application, is a method for controlling the lighting effect of ambient lighting fixtures, comprising:

[0014] The information acquisition module is configured to acquire the description information of the target piece of music;

[0015] The color matching determination module is configured to determine the target color set for generating lighting effect control information based on the image information in the music introduction information;

[0016] The template matching module is configured to match a target lighting effect template for generating the lighting effect control information from the lighting effect template library based on the text information in the music description information.

[0017] The lighting effect control module is configured to apply the target color set to the target lighting effect template to obtain the lighting effect control information, and control the ambient lighting fixtures to play corresponding lighting effects based on the lighting effect control information.

[0018] On another note, an ambient lighting fixture provided for one of the purposes of this application includes a controller and at least one lighting unit, the controller being communicatively connected to the lighting unit, the controller being configured to execute the steps of the ambient lighting fixture lighting effect control method to control the lighting unit to display lighting effects.

[0019] In another aspect, a computer-readable storage medium is provided to suit another purpose of this application, which stores, in the form of computer-readable instructions, a computer program implemented according to the ambient lighting effect control method described above, which, when called by a computer, executes the steps included in the corresponding method.

[0020] This application, through an innovative technical solution, effectively addresses the problems faced by ambient lighting fixtures in home settings, demonstrating significant beneficial effects and technological advantages. Firstly, this application acquires the introductory information of the target music, encompassing image and text information such as album art, song titles, lyrics, and genre descriptions, comprehensively capturing the characteristics of the music. This information collaboratively defines the target color set and target lighting effect template, generating lighting effect control information to accurately match and convey the overall feeling of the music, breaking through the limitations of traditional technologies and bringing users a personalized visual experience. Secondly, considering the limited computing power of embedded chips used in home ambient lighting fixtures, this application designs efficient algorithms to avoid competing with display refresh mechanisms for computing power, ensuring smooth lighting effect playback, avoiding stuttering, and improving user experience. Furthermore, this application achieves a balance in terms of purpose, cost, and efficiency: providing a lighting effect control method that accurately reflects the musical feeling, meeting home needs; avoiding complex solutions, reducing costs, and improving economic efficiency; optimizing algorithms to ensure high efficiency and real-time performance, enabling smooth operation even with limited computing power, creating high-quality lighting products, enhancing market competitiveness, and providing users with a superior experience. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0022] Figure 1 This is a schematic diagram of the structure of an exemplary ambient lighting fixture of this application;

[0023] Figure 2 This is a flowchart illustrating a typical embodiment of the ambient lighting effect control method of this application;

[0024] Figure 3 This is a schematic block diagram of the ambient lighting effect control device of this application;

[0025] Figure 4 This is a schematic diagram of the structure of a computer device used in this application. Detailed Implementation

[0026] The ambient lighting fixtures in this application, such as Figure 1 As shown, it includes a controller 80 and lighting units 82 and 84. The controller 80 and the lighting units 82 and 84 can be directly connected by wire or wirelessly, as long as they can communicate with each other.

[0027] The number of lighting units 82 and 84 is unlimited, depending only on the support capabilities of the controller 80. Lighting units 82 and 84 are responsible for controlling the orderly illumination of a large number of LEDs within them according to the lighting effect control information sent by the controller 80, thus displaying the corresponding lighting effects. Lighting units 82 and 84 can be a simplified lighting component that can respond to the controller 80 as a whole.

[0028] In some embodiments, an ambient lighting fixture consisting of multiple such lighting units 82, 84 connected to the same controller 80 can be considered as a lighting unit within a larger framework, and then communicated with the upper-level controller 80 within that larger framework to form the ambient lighting fixture of this application. In other words, based on the architecture of the ambient lighting fixture of this application, which includes a controller 80 and lighting units, the lighting units can also be nested to form the ambient lighting fixture of this application, as long as the upper and lower level controllers 80 that form the nested relationship can agree on this beforehand.

[0029] In some embodiments, the ambient lighting fixture may include, in addition to the control chip that functions as controller 80, components such as control panels, communication components, displays, and audio pickup units that can be configured as needed.

[0030] The control chip can be implemented using various embedded chips, such as Bluetooth SoC (System on Chip), WiFi SoC, MCU (Micro Controller Unit), DSP (Digital Signal Processing), and other types of chips. The control chip typically includes a central processing unit (CPU) and a memory. The memory and CPU are used to store and execute program instructions to achieve the corresponding functions, respectively. The control panel usually provides one or more buttons for controlling the controller 80, selecting various preset lighting effects, etc. A communication component is used to establish wireless communication connections with the various lighting units 82 and 84. The display screen can be used to display various control information to cooperate with the buttons on the control panel, supporting human-machine interaction. The control panel and the display screen can also be integrated into the same touch screen.

[0031] In embodiments including an audio pickup unit, the audio pickup unit is used to collect ambient audio data and process it with the controller 80. The controller 80 can perform audio preprocessing on the ambient audio data, detect human voice audio data from it, find the target music to which the human voice audio data belongs, and then generate lighting effect control information using the ambient lighting effect control method of this application based on the music description information of the target music. Based on the lighting effect control information, the controller controls each lighting unit 82, 84 to play the specified lighting effect collaboratively or synchronously.

[0032] In some embodiments, the controller 80 of the ambient lighting fixture of this application can be implemented in a separate computer device, as long as the computer device is equipped with a control chip that functions as the controller 80. When the controller 80 is implemented in a computer device, the various resources inherent in the computer device can be shared to save overall implementation costs. The computer device referred to herein can be any terminal device for user use, such as a smartphone, personal computer, laptop computer, tablet computer, etc.

[0033] Based on the above product architecture and working principle of ambient lighting fixtures, the ambient lighting effect control method of this application can be implemented as a computer program, stored in the storage medium of the controller 80 of the ambient lighting fixture of this application, and executed by the controller 80 after being called from the storage medium, so as to control the various lighting units 82 and 84 connected to it to play the corresponding lighting effects.

[0034] Please see Figure 2 In some embodiments, the ambient lighting effect control method of this application includes:

[0035] Step S5100: Obtain the music description information of the target music;

[0036] Music description information is a multi-dimensional collection of information used to describe the target piece of music, including but not limited to image and text information. Image information can be visual elements related to the target music, such as album cover images, which typically contain colors and patterns that reflect the style, theme, or emotion of the music. Text information can include any one or more of the following: song title, lyrics, description of the musical style, background information, etc. This textual content can provide a deeper understanding and appreciation of the music.

[0037] To obtain the description information of a target musical piece, this application can employ various specific implementation methods. For example, when a user is using ambient lighting, an audio pickup unit can be used to collect audio data from the environment. The audio pickup unit can be a microphone, capable of capturing the music signal being played. Then, a controller analyzes the collected audio data, extracts its audio features, and identifies the target musical piece to which the audio data belongs from a pre-established musical piece database on a local or cloud server based on semantic features. In a further embodiment, human voice detection can be performed on the audio data first. Only when human voice audio data is confirmed to exist can the target musical piece be determined by matching its audio features from the musical piece database. The musical piece database is a collection storing a large number of musical pieces and their related information. It can identify the target musical piece based on audio features, lyrics, or other identifiers. Once the target musical piece is identified, the description information corresponding to that target musical piece can be obtained from the musical piece database.

[0038] In another embodiment, the controller of the ambient lighting fixture is connected to music playback software that runs locally or is played on an external device connected to the controller. When the music playback software plays a target song, it can send a music notification message to the controller. In response to the music notification message, the controller obtains the corresponding song description information from the music playback software through an interface call.

[0039] In another embodiment, the controller of the ambient lighting fixture can use its equipped camera unit to capture images corresponding to the graphic and text information on the external display interface, perform text and image recognition on these images, and determine the corresponding image and text information as music introduction information.

[0040] In another implementation, if the user already knows the target song to play, they can directly select or input relevant information about the target song, such as the song title or artist name, through a user interface. The controller then retrieves the corresponding song description information from the music database based on this input information.

[0041] In other embodiments, when the target piece of music is identified, the music description information of the target piece of music can also be obtained from an external database or the public network, for example, by obtaining the music description information of the target piece of music through the API interface of a music streaming platform, thereby obtaining the corresponding image information and text information.

[0042] Regardless of the method used to obtain music information, the key is to ensure that the information is accurate and complete, providing a solid basis for subsequent lighting effect generation. For example, for image information, it's necessary to accurately read and analyze the color and pattern features of the album cover image; for text information, it's necessary to extract key content such as the emotional tone of the lyrics and the stylistic features of the musical description. This information will be directly used in subsequent steps to determine the target color set and match the target lighting effect template, thereby ensuring that the generated lighting effects accurately reflect the overall feel of the target music.

[0043] The image and text information in the music description can be carried by independent information carriers or by the same carrier. For example, in one embodiment, the image information is carried by the album cover image, while the text information is carried by the song title. In another embodiment, only the album cover image of the target song is retrieved from a music database or the public internet. However, this album cover image carries both image and text information. By applying mature image processing technology, the image and text information in the album cover image can be separated for subsequent processing.

[0044] Step S5200: Determine the target color set for generating lighting effect control information based on the image information in the music introduction information;

[0045] Image information is an important component of musical descriptions, typically presented in the form of album cover images. These images contain rich colors and patterns that reflect the style, theme, or emotion of the music. By analyzing this image information, color features that match the style of the music can be extracted, thereby generating a target color set, which provides a basis for subsequent lighting effect control.

[0046] To generate a target color set, various methods can be used to utilize image information to obtain its characteristic colors and construct the target color set.

[0047] In one embodiment, the number of colors in the original image carrying image information can be counted to determine the area ratio of each different color in the original image. Then, the colors with the largest area ratio can be selected as target colors to construct a target color set.

[0048] In another embodiment, inference can be performed using a pre-trained, convergent image feature color extraction model. This model is trained to perform semantic understanding based on a given image and then constructs a target color set from a predetermined number of feature colors. Accordingly, by inputting the original image carrying image information into the image feature color extraction model, the corresponding target color set can be obtained.

[0049] In some embodiments, the target color set determined above can be further processed in more detail, either before or after the process, such as removing black or gray tones or increasing the color difference between the target colors.

[0050] The determination of the target color set can also be adjusted according to different music styles and user needs. For example, in one embodiment, a preset classification model is first used to classify the music based on the image information in the music description to determine the music type of the target music. Then, the composition of the target colors in the target color set is adjusted according to the music type. For example, for lyrical music of the gentle type, the target color set preferably contains more soft hues, such as light blue or pink; while for electronic music of the dynamic type, the target color set can contain more vibrant hues, such as bright yellow or orange.

[0051] It is evident that by flexibly applying the various implementation methods described above, personalized lighting effects can be generated for different types of target music, enhancing the user's auditory and visual experience.

[0052] Step S5300: Based on the text information in the music description information, match the target lighting effect template used to generate the lighting effect control information from the lighting effect template library;

[0053] To achieve precise lighting effect control, this application provides a lighting effect template library. This library contains various types of lighting effect templates, each corresponding to a specific lighting effect expression, providing the lighting unit with the template content needed to generate lighting effect control information. These lighting effect templates cover a wide range of lighting effect modes, from simple to complex. For example, some templates can define the flashing frequency and intensity of the light to match rhythmic music; others can define the color gradient speed and direction of the light to suit lyrical or gentle music styles.

[0054] Each lighting effect template is described in detail using its descriptive information set. This set provides information about the template from multiple preset dimensions, which may include, but are not limited to, the type of effect (e.g., flashing, fading, flowing), the applicable music style (e.g., rock, pop, classical), the speed of the effect (fast, medium, slow), the color scheme (warm, cool, multicolored), and other characteristics related to the effect's presentation. For example, a lighting effect template's descriptive information set might indicate that it is suitable for fast-paced electronic music, with a fast flashing effect, multicolored color scheme, and fast speed. In this way, each lighting effect template can be precisely defined and distinguished.

[0055] Based on the text information in the music description, a target lighting effect template for generating lighting effect control information is matched from the lighting effect template library. This can be achieved by comparing the text information in the music description with the description information sets of each template in the lighting effect template library. For example, if the text information in the music description contains semantics indicating that the music is a fast-paced pop song, then a lighting effect template with a description information set indicating suitability for pop music and a fast rhythm can be matched from the lighting effect template library based on this semantics. In other embodiments, the matching relationship between text information and description information sets can also be achieved through fuzzy matching or precise matching of keywords. Such matching methods can determine the target lighting effect template, but by performing semantic analysis on the text information and matching the target lighting effect template based on semantics, the style and emotion of the music can be more accurately understood, thereby selecting the most suitable lighting effect template.

[0056] To further improve matching accuracy, in other embodiments, this application may employ more complex matching algorithms. For example, keyword extraction and sentiment analysis can be performed on the text information in the music description, and then these analysis results can be comprehensively compared with the descriptive information set of the lighting effect template. Furthermore, user preference settings can be introduced to adjust the matching results according to the user's personal preferences. For example, if a user prefers a softer lighting effect, even if the music itself has a brisk rhythm, a relatively softer lighting effect template that still reflects the rhythm of the music can be selected.

[0057] Step S5400: Fit the target color set into the target lighting effect template to obtain the lighting effect control information, and control the ambient lighting fixtures to play the corresponding lighting effects according to the lighting effect control information.

[0058] As revealed earlier, the target lighting effect template describes the lighting effect's appearance through its template content. This template content can define a target color set as input parameters and use instructional formatting text to define how these target color sets control the lighting units to produce the corresponding lighting effects. Based on the determined target color set, the lighting effect control information can be obtained by fitting the target color set into the target lighting effect template.

[0059] Specifically, a target lighting effect template is a preset lighting effect representation that defines in detail how to control the lighting effect of a lighting unit using a target color set. For example, a target lighting effect template might define specific representations such as the flashing frequency, color gradation speed, and light flow direction, and the implementation of these representations depends on the input of the target color set. The target color set is embedded into the target lighting effect template as an input parameter, thereby generating specific lighting effect control information.

[0060] For example, suppose the target lighting effect template defines a "rhythmic flashing" pattern, suitable for music with a strong rhythm. The template content might specify that when the input target color set is red and blue, the lighting unit will flash red and blue alternately, with the flashing frequency synchronized with the music rhythm. In this case, after the target color set (red and blue) is applied to the target lighting effect template, the generated lighting effect control information will instruct the lighting unit to play the lighting effect according to the preset flashing pattern and frequency.

[0061] In another example, the target lighting effect template might define a "gradient flow" pattern suitable for lyrical or soft music. The template content might specify that colors from the target color set will gradually flow across the lighting units at a certain speed. For example, if the target color set is light blue and pink, the generated lighting effect control information would indicate that the lighting units display these two colors with a soft gradient effect, thereby creating a soothing atmosphere.

[0062] To ensure the accuracy and effectiveness of lighting effect control information, the template content of the target lighting effect typically includes detailed instructional formatting specifications. This text defines, in clear instruction form, how the target color set should be applied to the lighting effect performance of the luminaire unit. For example, the template content may specify specific parameters such as color switching time, brightness changes, and color mixing methods. These parameters can be adjusted according to different music styles and user needs to achieve optimal lighting effect performance.

[0063] In practical applications, based on the matched target lighting effect template, the controller embeds the target color set into the template to generate specific lighting effect control information. The controller then sends this lighting effect control information to the lighting unit, which plays the corresponding lighting effect based on the received information. The resulting lighting effect accurately reflects the overall feel of the target music while adapting to different music styles and user preferences.

[0064] Through the above embodiments, this application effectively solves the problems encountered in ambient lighting fixtures, and has significant beneficial effects and technical advantages, including but not limited to the following aspects:

[0065] First, this application captures the characteristics of a target song more comprehensively by obtaining its introductory information, including both image and text information. Image information can include visual elements such as album covers, while text information can include song titles, lyrics, and descriptions of the musical style. This information works together to define the target color set and target lighting effect template, ultimately generating corresponding lighting effect control information for playback. This allows the application to more accurately define lighting effects that match the target song, thus more precisely conveying the overall feeling the song aims to express. This comprehensive information fusion approach breaks through the limitations of traditional technologies that rely solely on melody information, enabling lighting effects to reflect the overall style and emotion of the music, providing users with a richer and more personalized visual experience.

[0066] Secondly, this application specifically considers the hardware conditions of ambient lighting fixtures in home settings. Since these fixtures typically use embedded chips for lighting effect control, and the computing power of embedded chips is relatively limited, the technical solution of this application is well-suited to these hardware conditions. Through efficient algorithm design, this application avoids competing with the display refresh mechanism of the ambient lighting fixtures for the already limited computing power of the embedded chips, achieving the function of playing lighting effects based on target music without increasing excessive computational overhead. This not only ensures the smoothness of the lighting effect playback but also avoids stuttering issues caused by insufficient computing power, thereby improving the user experience.

[0067] Furthermore, this application achieves a good balance in terms of purpose, cost, and efficiency. In terms of purpose, this application provides a lighting effect control method that accurately reflects the overall feeling of music, meeting users' needs for ambient lighting in home settings. Regarding cost, this application avoids complex technical solutions, thereby reducing hardware requirements and costs, making the product more economical. In terms of efficiency, this application ensures high efficiency and real-time performance of lighting effect control through optimized algorithms, enabling smooth operation even on embedded chips with limited computing power. This balanced effect allows this application to produce higher-quality lighting products, effectively solving specific technical problems in a specific field, not only enhancing the product's market competitiveness but also providing users with a better product experience.

[0068] Based on any embodiment of the method in this application, obtaining the description information of the target musical piece includes:

[0069] Step S5110: Collect ambient audio data through the audio pickup unit, and determine whether the ambient audio data contains human voice audio data;

[0070] The acquisition of the music description information of the target music can be achieved through automatic identification by the controller. The controller collects environmental audio data through the audio pickup unit and determines whether the data contains human voice audio data. If human voice audio data is included, the controller further identifies and determines the target music and obtains its corresponding music description information.

[0071] The audio pickup unit can be a microphone or other device capable of capturing audio signals. These devices typically have a certain sensitivity and frequency response range, enabling them to effectively capture audio signals from the environment. The acquired environmental audio data may contain various sounds, such as background noise, human voices, and sounds from other electronic devices. Therefore, it is possible to further determine whether the environmental audio data contains human voice audio data to clarify whether it belongs to a target piece of music that includes vocal performances.

[0072] Considering that users frequently play songs with vocals, for this specific application, we can first perform human activity detection (VAD) on the ambient audio data. When human activity is detected in the ambient audio data, it can be preliminarily confirmed that the ambient audio data contains human audio data.

[0073] Step S5120: When the human voice audio data exists, identify the target music that matches the environmental audio data from the music database;

[0074] When the ambient audio data collected by the audio pickup unit is determined to contain human voice audio data, the controller can identify the target music to which the human voice audio data belongs from the music database. Specifically, this can be achieved by comparing and matching the collected ambient audio data with the audio features of the music in the music database.

[0075] Specifically, a music database can be a collection storing a large number of musical pieces and their related information. Each piece of music has unique audio characteristics, which may include, but are not limited to, audio fingerprints, melody patterns, and rhythmic structures. When the controller detects that the ambient audio data contains human voice audio data, it can use these audio characteristics to identify the target music piece.

[0076] For example, audio fingerprints can be used as audio features. Audio fingerprint recognition technology generates a unique identifier (fingerprint) for environmental audio data and matches it with preset fingerprints for each piece of music in a database, thereby quickly and accurately identifying the target music. Audio fingerprint recognition technology has high recognition accuracy and anti-interference capabilities, enabling it to accurately identify target music in complex audio environments.

[0077] Besides audio fingerprinting technology, various other techniques can be used to identify target music. For example, machine learning algorithms, such as convolutional neural networks (CNNs) in deep learning, can be used to train a model on a large amount of music data, enabling the model to recognize the audio features of different pieces of music. When the collected ambient audio data is input into the trained model, the model will output the music information, such as the music ID, that best matches the ambient audio data, thereby achieving the identification of the target music.

[0078] Step S5130: Obtain the music description information of the target music from the music database. The music description information includes preset image information and text information corresponding to the target music. The image information and text information are represented separately on different information carriers, or they are integrated and represented on the same information carrier.

[0079] The music database not only stores the IDs of each piece of music and their corresponding audio characteristics, but also stores the corresponding music description information for each piece. This information includes preset image and text information corresponding to the target music. The image and text information can be represented separately on different information carriers, or they can be integrated and represented on the same information carrier.

[0080] In practical applications, image and text information can be stored and represented in various ways. For example, image information can be stored as digital image files, such as JPEG or PNG formats, while text information can be stored as text files or database records. In some embodiments, image and text information can also be integrated into a single information carrier, such as including both image and text information in a multimedia file, or embedding images and text in HTML format within a webpage.

[0081] The process of obtaining music description information is automated and can be completed by the controller from the music database. Once the target music is identified, the controller can retrieve the corresponding music description information from the database.

[0082] This embodiment collects environmental audio data through an audio pickup unit and determines whether it contains human voice audio data. It then identifies the target music from a music database and obtains its description information. This process achieves automatic identification and information retrieval of the target music, eliminating the need for manual input of music information by the user and improving operational convenience and efficiency. Simultaneously, by utilizing audio fingerprint recognition technology or machine learning algorithms, the target music can be accurately identified in complex audio environments, ensuring the accuracy and reliability of the identification. Furthermore, by acquiring music description information that includes both image and text information, a more comprehensive and richer basis is provided for subsequent lighting effect generation, enabling the generated lighting effects to more accurately reflect the overall feel of the target music and enhancing the user's auditory and visual experience.

[0083] Based on any embodiment of the method in this application, determining a target color set for generating lighting effect control information according to the image information in the music description information includes:

[0084] Step S5210: Compress the original image carrying the image information in the music introduction information to obtain a target size image carrying the image information;

[0085] In this embodiment, the original image carrying image information in the music introduction information is compressed to obtain a target-size image carrying the image information. This is the first step in determining the target color set. The aim is to reduce the resolution and file size of the image through image compression technology, thereby reducing the computational complexity of subsequent processing, while retaining the key color information in the image.

[0086] Image compression is a common image processing technique aimed at preserving the main visual features of an image while minimizing its data size. In this application, the original image may have a high resolution and a large file size, and direct color analysis on it could result in high computational costs. Therefore, image compression can convert the original image into a lower-resolution target-size image, which not only speeds up processing but also removes noise and details from the image to some extent, allowing color analysis to focus more on the image's main color components.

[0087] In practice, various image compression algorithms can be employed, such as JPEG compression and PNG compression. These algorithms can significantly reduce the amount of image data while maintaining image quality. For example, JPEG compression, through Discrete Cosine Transform (DCT) and quantization processing, can effectively remove redundant information from the image while preserving its main visual features. PNG compression, on the other hand, uses lossless compression technology to reduce the size of image files without sacrificing image quality.

[0088] In this application, the specific implementation method of image compression can be selected according to actual needs. For example, if high image quality is required, a lower compression ratio can be selected; if high processing speed is required, a higher compression ratio can be selected. In addition, appropriate compression algorithms and parameters can be selected according to the intended use of the target image.

[0089] Step S5220: Calculate the frequency of occurrence of each color in the pixel set of the target specification image, and determine the multiple colors with the highest frequency to form a candidate color set;

[0090] Candidate color sets are determined by performing color frequency statistics on the target-size image. This involves analyzing the pixel colors in the image based on the first color space, counting the frequency of each color, and selecting the most frequent colors to form a candidate color set. These colors typically represent the main color features of the image, providing a foundation for further color selection and lighting effect generation.

[0091] Specifically, the first color space refers to the mathematical model used to represent colors. In this embodiment, the RGB color space is used as the first color space. In the RGB color space, each color consists of three components: red, green, and blue, with each component typically ranging from 0 to 255. By analyzing each pixel in the target image, the frequency of each color can be statistically determined.

[0092] In practice, various methods can be used to count color frequencies. The recommended method is to use a color histogram. A color histogram is a statistical tool that divides the color space into multiple intervals (or "buckets") and counts the number of pixels within each interval. For example, in the RGB color space, the value range of each color component can be divided into several smaller intervals, and then the number of pixels within each interval can be counted. In this way, the frequency of each color can be quickly obtained.

[0093] For example, suppose the target image is an album cover with dark blue and white as its primary colors. By performing color frequency statistics on this image, it can be found that the number of dark blue and white pixels is much higher than that of other colors, so these two colors will be selected as part of the candidate color set.

[0094] Furthermore, to improve the accuracy of statistics, color quantization can be performed. Color quantization is a technique that reduces the number of colors in a color space. By merging similar colors into a representative color, the color statistics process can be simplified and efficiency improved. For example, colors in the RGB color space can be quantized into a finite set of colors, such as 256 colors, and then the frequency of these colors can be counted.

[0095] The number of colors required for the candidate color set can be determined as needed. For example, 8, 16, or 32 colors can be selected to form the candidate color set. In a preferred implementation, the number of colors is between 8 and 16 to achieve a suitable overall effect of the lighting effect. This avoids the lighting effect from appearing cluttered due to too many colors, and also avoids it from appearing monotonous due to too few colors.

[0096] Step S5230: Detect whether each color in the candidate color set meets the preset color difference condition, and delete colors that do not meet the color difference condition to obtain the target color set.

[0097] In the second color space, the candidate color set is checked to see if each color meets the preset color difference conditions. This allows for the selection of the final target color set. The purpose is to ensure that the selected colors not only represent the main color features of the image, but also have sufficient visual distinguishability, thereby providing high-quality color input for subsequent lighting effect control.

[0098] Specifically, a second color space is a color representation model different from the first color space (such as RGB), and is typically used to more accurately describe the visual attributes of color. For example, in this embodiment, the HSV (hue, saturation, lightness) color space can be used as the second color space, which decomposes the visual attributes of color into three independent components, making color processing more consistent with human visual perception.

[0099] To facilitate calculations, in this embodiment, the colors in the candidate color set are first converted to a second color space for more accurate color analysis and filtering.

[0100] Preset color difference conditions can be thresholds set based on the visual attributes of colors to determine the distinguishability between colors. For example, in the HSV color space, thresholds can be set for saturation and lightness to ensure that the selected colors have sufficient vibrancy and brightness. For instance, a saturation threshold of 50% and a lightness threshold of 45% can be set. Only when both saturation and lightness meet these conditions is the color considered to have sufficient visual impact and thus retained.

[0101] In practice, each color in the candidate color set is normalized, converting it from a first color space to a second color space. Then, the saturation and brightness of each color are checked against preset thresholds. If a color's saturation or brightness falls below a set threshold, it is considered visually unappealing and is removed from the candidate color set. Ultimately, the remaining colors constitute the target color set, which not only represents the main color features of the image but also possesses good visual distinguishability.

[0102] To further optimize the target color set, additional processing can be applied to the filtered colors. For example, if the target color set contains too many colors, it can be filtered based on color frequency or visual importance, retaining only the most important colors. Conversely, if the number of colors is insufficient, generative models (such as large AI models) can be used to generate additional colors based on existing color and musical description information to enrich the target color set.

[0103] This embodiment efficiently and accurately extracts the target color set from image information, demonstrating unique advantages compared to other embodiments. First, image compression technology converts the original image into a target-size image, effectively reducing image resolution and file size, decreasing the computational complexity of subsequent processing, while preserving key color information, providing a more efficient data foundation for subsequent analysis. Second, color frequency statistics are performed using a first color space to select the most frequently occurring colors to form a candidate color set. This process, based on color frequency, ensures that the selected colors represent the main visual features of the image, providing a representative color basis for lighting effect generation. Finally, color difference condition detection is performed in a second color space to further filter colors that meet specific visual effect requirements, ensuring that the target color set is not only representative but also possesses good visual differentiation and expressiveness. This series of steps, combined to achieve a specific optimization scheme, maintains low system overhead while enabling the final determined target color set to more accurately reflect the core color features of the image information, providing high-quality color input for generating lighting effects that match the target music, thereby improving the overall expressiveness of the lighting effects and the user experience.

[0104] Based on any embodiment of the method in this application, after determining that the multiple most frequently occurring colors constitute a candidate color set, the method includes:

[0105] Step S6100: Based on each color component corresponding to each color defined in the first color space, identify the first target color in the candidate color set whose each color component is lower than a preset first component threshold, and delete the first target color from the candidate color set.

[0106] As a way to optimize the candidate color set, by analyzing the color components based on the first color space (such as RGB), colors whose color components are all below a preset threshold can be identified and deleted. This can remove colors that are too dark or close to black from the candidate color set, thereby improving the quality and visual effect of the final target color set.

[0107] Specifically, each color in the first color space is composed of multiple components. For example, in the RGB color space, each color consists of three components: red (R), green (G), and blue (B). The value of each component typically ranges from 0 to 255. The preset first component threshold is a standard value used to judge the brightness of a color. If the value of each component of a color is lower than this preset first component threshold, then the color is considered too dark or close to black, and is generally unsuitable for lighting effect control because it may not provide sufficient brightness or color saturation visually.

[0108] During implementation, a specific threshold can be set, for example, setting the first component threshold to 30. This means that if the R, G, and B component values ​​of a color are all below 30, then this color will be identified as the first target color and removed from the candidate color set. This processing method can effectively remove colors that are not visually obvious or may affect the overall lighting effect.

[0109] The threshold for the first component can be adjusted according to actual needs. For example, the value of the threshold can be adjusted based on different application scenarios or user preferences. In scenarios with high brightness requirements, the threshold can be set higher to ensure that the filtered colors have sufficient brightness; while in scenarios with high requirements for color detail, the threshold can be appropriately lowered to retain more color details.

[0110] Step S6200: Based on each color component, identify the second target color in the candidate color set whose absolute difference between any two color components is lower than a preset second component threshold, and delete the second target color from the candidate color set.

[0111] As another way to optimize the candidate color set, grayscale colors can be identified and removed by detecting the differences between different color components of the same candidate color, thereby improving the color richness and visual effect of the target color set.

[0112] Specifically, based on the color components of a first color space (such as RGB), the differences between the R, G, and B component values ​​of each color in the candidate color set are analyzed. If the absolute difference between any pair of color components is lower than a preset second component threshold, the color is identified as gray, i.e., the second target color, and is removed from the candidate color set.

[0113] In the RGB color space, when the R, G, and B component values ​​of a color are similar, the color tends towards gray. For example, when the R, G, and B component values ​​are 128, 128, and 128 respectively, the color is neutral gray. To quantify the degree of this "similarity," this application sets a second component threshold. If the absolute differences between R and G, R and B, and G and B are all below this threshold, for example, set to 10, the color is considered gray. This processing method can effectively remove colors that lack visual color saturation, preventing them from negatively impacting lighting effects.

[0114] The threshold setting for the second component can be adjusted according to actual needs. In some application scenarios, if a more vibrant lighting effect is required, the threshold can be set lower to strictly filter out non-gray colors; while in scenarios with high requirements for color transition, the threshold can be appropriately increased to retain more mid-tones. Through this flexible threshold setting, this application can adapt to different lighting effect needs and user preferences.

[0115] In practice, each color in the candidate color set is analyzed, and the absolute differences between its R, G, and B components are calculated. If the component differences of a certain color are all below the second component threshold, it is identified as the second target color and removed from the candidate color set. This process not only improves the color purity of the target color set but also ensures that the final generated lighting effect better reflects the overall feeling of the target music, while avoiding visual monotony caused by too much gray.

[0116] Through the above embodiments, this application optimizes the candidate color set, enabling more accurate selection of colors suitable for lighting effect control compared to other embodiments, thereby significantly improving the quality and visual effect of the target color set. Firstly, by analyzing color components based on the first color space, colors that are too dark or close to black are identified and removed, effectively avoiding the potential for poor visual effects in lighting effects. Simultaneously, by flexibly adjusting the first component threshold, the brightness and saturation of colors can be precisely controlled according to different application scenarios and user preferences. Then, by detecting differences between color components, gray-toned colors are identified and removed, further enhancing the color richness and visual effect of the target color set. This processing method not only improves the color purity of the target color set but also ensures that the final generated lighting effect better reflects the overall feeling of the target music, while avoiding visual monotony caused by excessive gray tones. Through these two optimization steps, this embodiment can provide higher-quality color input for subsequent lighting effect control, thereby improving the overall performance of the lighting effect and the user experience.

[0117] Based on any embodiment of the method in this application, detecting whether each color in the candidate color set satisfies a preset color difference condition, and deleting colors that do not satisfy the color difference condition to obtain the target color set includes:

[0118] Step S5231: Normalize each candidate color in the candidate color set to convert each candidate color from the representation in the first color space to the representation in the second color space;

[0119] By normalizing each candidate color in the candidate color set, these colors can be efficiently and quickly converted from a representation in a first color space (such as RGB) to a representation in a second color space (such as HSV). Since different color spaces have their own advantages in describing the visual attributes of colors, and the second color space is generally more suitable for visual color effect analysis, such a conversion is necessary.

[0120] During normalization, the values ​​of each color component can be standardized to a uniform range, typically from 0 to 1. In this embodiment, through normalization, the color component values ​​in the RGB color space are converted from the range of 0 to 255 to the range of 0 to 1, and then converted into percentage values, thereby converting these colors to the HSV color space.

[0121] Through normalization, each color in the candidate color set is converted into a representation in the HSV color space. This conversion makes the subsequent color selection process more intuitive and efficient because the HSV color space is closer to human visual perception of color.

[0122] Step S5232: Based on the representation of the second color space, detect and determine the third target color in the candidate color set whose saturation and / or brightness meet the corresponding preset color difference conditions, retain the third target color in the candidate color set, and delete all other candidate colors.

[0123] Once the representation of the second color space (such as HSV) is obtained, the colors in the candidate color set can be further filtered to determine the third target color that meets the preset color difference conditions. Specifically, this can be achieved by analyzing the color saturation and / or brightness to ensure that the selected color has sufficient visual impact and distinguishability, thereby providing high-quality color input for subsequent lighting effect control.

[0124] Specifically, the HSV color space decomposes the visual attributes of color into three independent components: hue, saturation, and value. Hue represents the type of color, saturation represents the purity of the color, and value represents the lightness or darkness of the color. In this step, the preset color difference conditions typically include thresholds for saturation and value. For example, in this embodiment, the saturation threshold can be set to 50%, and the value threshold to 45%. These thresholds are set to filter out colors that have sufficient visual vibrancy and brightness, while removing colors that are too dull or lack color saturation.

[0125] In practice, each color in the candidate color set is examined, and its saturation and brightness are assessed to determine if they meet preset threshold conditions. If the saturation or brightness of a color is below the set threshold, it is considered visually unappealing and is removed from the candidate color set. Conversely, if the saturation and / or brightness of a color both meet or exceed the preset threshold, it is retained and becomes the third target color. Ultimately, the retained third target colors constitute the target color set, which not only represents the main color features of the image but also possesses good visual distinction and expressiveness.

[0126] Step S5233: Take the candidate color set containing only the third target color as the target color set, and convert the target color in the target color set back to the representation of the first color space.

[0127] The third target color in the filtered candidate color set is taken as the final target color set, and these colors are converted from the second color space (such as HSV) back to the representation of the first color space (such as RGB). This ensures that the target color set not only meets the requirements of visual effect, but can also be used to construct lighting effect control information.

[0128] Specifically, the candidate color set has already selected third target colors that meet the preset color difference conditions. These colors have sufficient saturation and brightness in the HSV color space, providing good visual impact and differentiation. However, for compatibility with subsequent steps, these colors need to be converted back to the first color space, namely the RGB color space. The RGB color space is the standard color space used by most display devices and lighting control systems, therefore, converting the target color set to RGB representation is necessary.

[0129] In practice, the conversion of color from the HSV color space to the RGB color space is a standard mathematical conversion process. Specifically, it involves first converting the values ​​of the three components in the HSV color space—Hue, Saturation, and Value—to their corresponding RGB component values. This conversion process can be achieved using known mathematical formulas or algorithms, ensuring that the color retains its original visual attributes during the conversion.

[0130] For example, suppose a color in the HSV color space has a hue of 60 degrees, a saturation of 70%, and a lightness of 80%. Using the standard HSV-to-RGB conversion formula, the corresponding value of that color in the RGB color space can be calculated. This conversion ensures accurate color representation across different color spaces, allowing the target color set to be correctly applied by the lighting control system.

[0131] Furthermore, to further optimize the target color set, additional processing can be applied to the filtered colors. For example, if the target color set contains too many colors, it can be filtered based on color frequency or visual importance, retaining only the most important colors. Conversely, if the number of colors is insufficient, generative models (such as large AI models) can be used to generate additional colors based on existing color and musical description information to enrich the target color set.

[0132] This embodiment achieves efficient selection of target color sets that meet visual effect requirements from a candidate color set through refined color processing, and converts them into a standard color space representation suitable for constructing lighting effect control information. First, by normalizing the candidate color set and converting it to a second color space (such as HSV), the visual attributes of colors, such as saturation and brightness, can be analyzed more intuitively and effectively. This conversion makes the color selection process more precise, effectively removing colors that lack visual vibrancy or brightness, thus ensuring that the selected colors have sufficient visual impact and distinguishability. Second, by setting saturation and brightness thresholds in the HSV color space, the target color set can be further optimized, making it not only represent the main color features of the image but also adaptable to different lighting effect needs and user preferences. Finally, the selected target color set is converted back to a first color space (such as RGB), ensuring that these colors can be recognized and applied in the subsequent lighting effect control process. This embodiment not only improves the quality of the target color set but also ensures its compatibility with existing lighting effect control systems, thus providing high-quality color input for generating lighting effects that match the target music, enhancing the overall expressiveness of the lighting effects and the user experience.

[0133] Based on any embodiment of the method in this application, after detecting whether each color in the candidate color set satisfies a preset color difference condition, deleting colors that do not satisfy the color difference condition to obtain the target color set, the method includes:

[0134] Step S5240: Determine whether the number of colors in the target color set is zero. If it is zero, call the preset color matching generation model to generate a target color set that meets the preset color matching conditions based on the image information and / or text information in the music introduction information.

[0135] To ensure the effectiveness and practicality of the target color set, the number of colors in the target color set is judged. When the number of colors in the target color set is zero, it indicates that no colors were obtained after the screening steps disclosed above in this application. In this case, a preset color matching generation model needs to be called to generate a target color set that meets the preset color matching conditions based on the image information and / or text information in the music introduction information.

[0136] Specifically, a color scheme generation model is an algorithm- or rule-based model that generates a set of colors based on input information. In this embodiment, the model uses image information (such as the color features of an album cover) and text information (such as lyrics, genre descriptions, etc.) from the music description as input, or one of the two, to generate a target color set that matches the style of the target music. For example, if the text information indicates that the target music is a gentle ballad, the color scheme generation model might generate a target color set dominated by light blue or pink; if the image information shows that the album cover is dominated by red and yellow, the model might generate a target color set that includes these colors.

[0137] Color matching generation models can be implemented in several ways. One approach is a rule-based system, which predefines a series of color matching rules and selects the appropriate rule to generate a color set based on the input image and text information. Another approach utilizes machine learning algorithms, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) in deep learning, to learn and generate target color sets that match the style of a musical piece by training on a large number of musical pieces and their corresponding color sets.

[0138] In practice, when an empty target color set is detected, the color matching generation model is triggered. The model first analyzes the image and text content in the music introduction information, extracting key features such as the main colors in the images, the emotional tone in the text, and the description of the musical style. Then, based on these features, it generates a set of colors that meet the preset color matching conditions. These colors not only match the style of the music but also provide a good visual effect.

[0139] Step S5250: When the number of colors in the target color set is not zero, continue to determine whether the number of colors in the target color set is lower than a preset lower threshold. If it is lower, call the preset color matching generation model with the target color in the target color set as the benchmark, and refer to the image information and / or text information in the music introduction information to generate a target color set that meets the preset color matching conditions.

[0140] When the color data in the target color set is not zero, the system continues to check whether the number of colors in the target color set is lower than a preset lower threshold. This is to further optimize the number of colors in the target color set and ensure that it meets the preset lower threshold requirement. Specifically, when the number of colors in the target color set is lower than the preset lower threshold but greater than 0, it indicates that the number of colors obtained is insufficient to meet the basic requirements of lighting effect control. At this time, a preset color matching generation model needs to be called. Based on the existing colors in the target color set, and referring to the image information and / or text information in the music introduction information, a target color set that meets the preset color matching conditions is generated.

[0141] The role of the color generation model in this step is to supplement the target color set, bringing its color count to a preset lower threshold. This model uses existing target colors as a reference, combined with key features from the music description information, such as the main colors in images, emotional inclinations in text, and stylistic descriptions, to generate a new set of colors that are consistent with the existing color style and match the overall feel of the music. These newly generated colors are added to the target color set to enrich its content and ensure it provides a sufficiently diverse range of color choices for subsequent lighting effect control.

[0142] In practice, when the number of colors in the target color set falls below a preset threshold, the color generation model first analyzes the existing target color set, extracting its color features and style. Then, combining the images and text content in the music description, it generates new colors using preset color matching rules or machine learning algorithms. For example, if the existing target color set is predominantly warm-toned, and the music description indicates that the target music is a vibrant pop song, the color generation model might generate some bright warm-toned colors, such as orange or yellow, to complement the target color set.

[0143] Furthermore, when generating new colors, the color generation model ensures that these colors visually harmonize with existing colors and conform to the overall style of the music. For example, if the text description in the music introduction indicates that the target music has a romantic mood, the model may generate some soft pink or purple tones to enhance the emotional expression of the target color set.

[0144] Step S5260: Determine whether the number of colors in the target color set exceeds a preset upper limit threshold. If it does, retain the target colors with the highest frequency in the target color set according to the preset upper limit threshold, and delete the other redundant target colors.

[0145] The number of colors in the target color set is continuously evaluated to ensure it does not exceed a preset upper limit threshold. When the number of colors in the target color set exceeds this preset upper limit threshold, only the most frequently occurring target color is retained according to preset rules, and other redundant target colors are deleted. This process continues to optimize the number of colors in the target color set, ensuring that it meets the lighting effect control requirements without causing the visual effect to become too complex or chaotic due to an excessive number of colors.

[0146] Specifically, the preset upper limit threshold is set based on the actual needs of lighting effect control, and is used to limit the maximum number of colors in the target color set. For example, processing too many colors may lead to performance degradation or poor visual effects; a reasonable upper limit threshold can be set to avoid these problems. When the number of colors in the target color set exceeds this threshold, the color set needs to be simplified.

[0147] In practice, based on the frequency of each target color obtained through statistical analysis as disclosed above, the target colors are sorted from highest to lowest frequency, and the color with the highest frequency is retained until a preset upper limit threshold is reached. Other colors are deleted to ensure that the number of colors in the target color set does not exceed the upper limit threshold.

[0148] For example, suppose the preset upper limit threshold is 8, and the target color set after the previous steps contains 10 colors. Through statistics and sorting, it is found that 8 colors occur most frequently, and these 8 colors will be retained, while the other 2 colors with lower frequency of occurrence will be deleted. This processing method ensures that the target color set meets the processing capacity and visual effect requirements of the ambient lighting fixtures in terms of quantity.

[0149] Furthermore, to further optimize the target color set, the visual importance of colors and their matching with the style of the music can also be considered. For example, if a certain color does not appear most frequently but has a significant visual impact on the overall lighting effect, or is highly compatible with the style of the music, the retention strategy can be adjusted appropriately to ensure that these colors are retained.

[0150] Through the above embodiments, this application ensures that the target color set meets the requirements of lighting effect control in both quantity and quality, thereby achieving a balance between visual effect and system performance. First, when the target color set is empty, a color matching generation model is used to generate a color set that meets preset conditions based on the music description information, avoiding the problem of an empty set caused by excessive color selection and ensuring the feasibility of lighting effect control. Second, the number of colors in the color set is further optimized. When the number of colors is below a preset lower threshold, new colors consistent with the existing color style are generated to enrich the content of the color set and enhance the expressiveness of the lighting effect. Finally, by limiting the number of colors in the color set to no more than a preset upper limit, visual confusion and system performance degradation caused by too many colors are avoided, ensuring the efficiency of lighting effect control and the harmony of visual effects. These steps work together to ensure that the target color set not only accurately reflects the style of the music but also provides a high-quality visual effect while adapting to the processing capabilities of the lighting effect control system, thereby improving the overall user experience.

[0151] Based on any embodiment of the method in this application, according to the text information in the music description information, a target lighting effect template for generating the lighting effect control information is matched from the lighting effect template library, including:

[0152] Step S5310: Obtain the description information set of each lighting effect template in the lighting effect template library. The description information set of each lighting effect template provides the description information of the lighting effect template from multiple preset description dimensions.

[0153] In this embodiment, for the lighting effect template library mentioned above, the description information set of each lighting effect template provides detailed information about the template from multiple preset description dimensions. These description dimensions may include, but are not limited to, the type of lighting effect (e.g., flashing, fading, flowing), the applicable music style (e.g., rock, pop, classical), the speed of the lighting effect (fast, medium, slow), the color tendency of the lighting effect (warm, cool, multicolored), and other features related to the form of lighting effect expression. For example, the description information set of a lighting effect template may indicate that it is suitable for fast-paced electronic music, the lighting effect type is fast flashing, the color tendency is multicolored, and the speed is fast.

[0154] The purpose of obtaining these descriptive information sets is to provide sufficient basis for subsequent lighting effect template matching. Through this detailed descriptive information, the characteristics and applicable scenarios of each lighting effect template can be understood more accurately, thus supporting the selection of the most suitable lighting effect template. Specifically, these descriptive information sets will be used to compare and match with the text information in the music introduction to determine which lighting effect templates best match the style and mood of the target music.

[0155] In practice, a lighting effect template library can be a database storing a large number of preset lighting effect templates, each containing a corresponding set of descriptive information. These information sets can be generated through manual annotation or automatic extraction and stored in a structured format within the database. For example, each lighting effect template can be assigned one or more tags, corresponding to different descriptive dimensions, such as "suitable for pop music" or "lighting effect type is gradient." In this way, the characteristics of each lighting effect template can be clearly described and distinguished.

[0156] Furthermore, to improve the accuracy and efficiency of matching, the information in the descriptive information set can be further refined and quantified. For example, the speed of lighting effects can be quantified into specific numerical ranges, and color tendency can be quantified into specific color parameters. This quantification process enables the descriptive information set to not only provide qualitative descriptions but also support quantitative analysis and comparison.

[0157] Step S5320: Call a preset matching instruction set, and combine it with the text information in the music introduction information, the description information set of each lighting effect template, and the target color set to provide matching prompt information. The matching instruction set includes a first control instruction and a second control instruction executed sequentially. The first control instruction is used to indicate that the lighting effect matching model first determines a subset of lighting effect templates that semantically match the text information in the music introduction information based on each description information set. The second control instruction is used to instruct the lighting effect matching model to score each lighting effect template in the subset of lighting effect templates from multiple description dimensions, and then merge them to determine the total score of each lighting effect template, and select the lighting effect template with the highest total score as the target lighting effect template.

[0158] In this step, by calling the preset matching instruction set, the text information in the music introduction, the description information set of each light effect template in the light effect template library, and the target color set are combined into matching prompt information. Then, the target light effect template that matches the target music is determined by the light effect matching model. The purpose is to accurately select the most suitable light effect template for the current music through multi-dimensional analysis and scoring.

[0159] Specifically, the matching instruction set contains two sequentially executed control instructions. The first control instruction, based on the description information set of each light effect template, determines a subset of light effect templates that semantically match the textual information in the music introduction. This process primarily relies on semantic analysis of the textual information, comparing key information in the music introduction (such as style, emotion, and lyrics) with the applicable scenarios and style characteristics in the light effect template descriptions to filter out a preliminary matching subset of light effect templates.

[0160] The second control instruction further refines the scoring of the selected subset of lighting effect templates. The lighting effect matching model scores each template across multiple pre-defined dimensions, such as lighting effect type, effect performance, atmosphere, and visual impact. The scores from these dimensions are then combined using the mean or weighted mean to determine the total score for each template. Finally, the template with the highest total score is selected as the target lighting effect template. This multi-dimensional scoring mechanism comprehensively considers various aspects of the lighting effect template, ensuring that the selected template not only matches the music in style but also achieves optimal visual performance. The comprehensive scoring balances these factors, allowing for the selection of the most suitable target lighting effect template.

[0161] In practice, the matching instruction set is predefined, which includes placeholders for inserting text information, description information set, and target color set. By replacing the placeholders, this information is merged into the same data structure to become matching prompt information.

[0162] In this embodiment, a pre-tuned and trained large language model is used as the lighting effect matching model. Matching prompts can be output to the lighting effect matching model so that the model can determine the target lighting effect template based on the matching prompts.

[0163] Step S5330: Input the matching prompt information into the lighting effect matching model to obtain the target lighting effect template determined by the lighting effect matching model.

[0164] The matching prompts generated in the previous step are input into the lighting effect matching model. The model processes the first and second control commands in the matching prompts to determine the target lighting effect template that matches the target music. By comprehensively analyzing the music description information, the lighting effect template description information set, and the target color set, the model accurately selects the most suitable lighting effect template for the current music.

[0165] Specifically, the matching prompts include textual information from the music description, a set of descriptive information for the lighting effect templates, and a target color set. This information is integrated into a unified data structure for the lighting effect matching model to process. The lighting effect matching model is a pre-tuned and trained large language model capable of understanding and processing natural language text, and performing complex matching and scoring operations based on the input information.

[0166] During processing, the lighting effect matching model first determines a subset of lighting effect templates that semantically match the textual information in the music description, based on the first control instruction and the description information set of each lighting effect template. By comparing key information in the music description (such as style, emotion, and lyrics) with the applicable scenarios and style characteristics in the lighting effect template descriptions, a preliminary matching subset of lighting effect templates is selected. For example, if the music description indicates that it is a fast-paced pop song, the matching model will prioritize selecting lighting effect templates suitable for pop music and with a fast-paced rhythm.

[0167] Subsequently, the lighting effect matching model, according to the second control instruction, scores each lighting effect template in the selected subset from multiple descriptive dimensions. The score for each dimension can be determined based on preset rules or through the model's internal learning mechanism. Then, the model uses the mean or weighted mean to fuse the scores from these dimensions to determine the total score for each lighting effect template. Finally, the lighting effect template with the highest total score is selected as the target lighting effect template. This multi-dimensional scoring mechanism comprehensively considers various aspects of the lighting effect template, ensuring that the selected template not only matches the music stylistically but also achieves optimal visual presentation.

[0168] In practice, the format and content of the matching prompts need to match the input requirements of the lighting effect matching model. For example, the matching prompts can be a JSON object containing multiple fields, each corresponding to a different information type (such as text information, a set of descriptive information, a target color set, etc.). After receiving the matching prompts, the lighting effect matching model processes them according to preset instructions and rules, and outputs the final selected target lighting effect template.

[0169] This embodiment significantly improves the matching accuracy and visual expressiveness of lighting effects with target music through a refined lighting effect template matching process. First, by acquiring multi-dimensional descriptive information sets of each template in the lighting effect template library, detailed basis is provided for subsequent matching, allowing the characteristics of lighting effect templates to be clearly described and distinguished. Next, using a preset matching instruction set, the music introduction information, descriptive information set, and target color set are integrated into matching prompts. Through semantic analysis and a multi-dimensional scoring mechanism, a subset of lighting effect templates that best match the style and emotion of the music is accurately selected, and the target lighting effect template with the highest total score is further determined from this subset. This matching method, which comprehensively considers textual semantics, visual effects, and musical style, not only ensures a high degree of consistency between the lighting effects and the music in style but also balances various visual and musical factors through quantitative scoring, thus providing users with a high-quality, personalized lighting effect experience. Furthermore, applying a finely tuned and trained large language model as the lighting effect matching model further improves the intelligence and accuracy of the matching, making the entire lighting effect control process more efficient and precise.

[0170] Please see Figure 3 This application provides an ambient lighting effect control device to meet one of its objectives. It is a functional embodiment of the ambient lighting effect control method of this application. The device includes an information acquisition module 5100, a color matching determination module 5200, a template matching module 5300, and a lighting effect control module 5400. The information acquisition module 5100 is configured to acquire the music description information of a target piece of music. The color matching determination module 5200 is configured to determine a target color set for generating lighting effect control information based on the image information in the music description information. The template matching module 5300 is configured to match a target lighting effect template for generating the lighting effect control information from a lighting effect template library based on the text information in the music description information. The lighting effect control module 5400 is configured to apply the target color set to the target lighting effect template to obtain the lighting effect control information, and control the ambient lighting to play corresponding lighting effects based on the lighting effect control information.

[0171] Based on any embodiment of the device in this application, the information acquisition module 5100 includes: an audio pickup module, configured to acquire environmental audio data through an audio pickup unit and determine whether the environmental audio data contains human voice audio data; a target recognition module, configured to identify a target music matching the environmental audio data from a music database when the human voice audio data exists; and an information retrieval module, configured to retrieve music description information of the target music from the music database, wherein the music description information includes preset image information and text information corresponding to the target music, and the image information and text information are each represented separately on different information carriers, or are integrated and represented on the same information carrier.

[0172] Based on any embodiment of the device in this application, the color matching determination module 5200 includes: an image compression module, configured to compress the original image carrying the image information in the music introduction information to obtain a target size image carrying the image information; a statistical color selection module, configured to statistically analyze the occurrence frequency of each color in the pixel set of the target size image and determine the multiple colors with the highest occurrence frequency to form a candidate color set; and a color optimization module, configured to detect whether each color in the candidate color set meets a preset color difference condition and delete colors that do not meet the color difference condition to obtain the target color set.

[0173] Based on any embodiment of the device in this application, the statistical color selection module further includes: a dark color filtering module, configured to identify, based on each color component corresponding to each color defined in the first color space, a first target color in the candidate color set, where each color component is lower than a preset first component threshold, and delete the first target color from the candidate color set; and a grayscale filtering module, configured to identify, based on each color component, a second target color in the candidate color set, where the absolute difference between any two color components is lower than a preset second component threshold, and delete the second target color from the candidate color set.

[0174] Based on any embodiment of the device in this application, the color selection module includes: a space conversion module, configured to normalize each candidate color in the candidate color set to convert each candidate color from a representation in a first color space to a representation in a second color space; a luminance selection module, configured to detect and determine a third target color in the candidate color set whose saturation and / or luminance meet its corresponding preset color difference conditions based on the representation in the second color space, retain the third target color in the candidate color set, and delete all other candidate colors; and a space restoration module, configured to take the candidate color set containing only the third target color as the target color set, and convert and restore the target color in the target color set to a representation in the first color space.

[0175] Based on any embodiment of the device in this application, the color optimization module further includes: a missing color generation module, configured to determine whether the number of colors in the target color set is zero; when it is zero, calling a preset color matching generation model to generate a target color set that meets preset color matching conditions based on the image information and / or text information in the music introduction information; a missing color supplementation module, configured to, when the number of colors in the target color set is not zero, continue to determine whether the number of colors in the target color set is lower than a preset lower threshold; when it is lower, calling a preset color matching generation model to generate a target color set that meets preset color matching conditions based on the target colors in the target color set and referring to the image information and / or text information in the music introduction information; and a redundant color filtering module, configured to determine whether the number of colors in the target color set exceeds a preset upper threshold; when it exceeds the upper threshold, retaining the multiple target colors with the highest frequency in the target color set according to the preset upper threshold and deleting other redundant target colors.

[0176] Based on any embodiment of the device in this application, the template matching module 5300 includes: a description acquisition module, configured to acquire a set of description information for each light effect template in the light effect template library, wherein the description information set for each light effect template provides description information for the light effect template from multiple preset description dimensions; a prompt construction module, configured to call a preset matching instruction set, and combine it with the text information in the music introduction information, the description information sets of each light effect template, and the target color set to form a matching prompt message, wherein the matching instruction set includes a first control instruction and a second control instruction executed sequentially, the first control instruction being used to indicate that the light effect matching model first determines a subset of light effect templates that semantically match the text information in the music introduction information based on each set of description information; the second control instruction being used to instruct the light effect matching model to score each light effect template in the subset of light effect templates from multiple description dimensions, and then merge to determine the total score of each light effect template, and select the light effect template with the highest total score as the target light effect template; and a target determination module, configured to input the matching prompt message into the light effect matching model to obtain the target light effect template determined by the light effect matching model.

[0177] To address the aforementioned technical problems, embodiments of this application also provide computer equipment. For example... Figure 4 The diagram shows the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. The computer-readable storage medium stores an operating system, a database, and computer-readable instructions. The database may store a sequence of control information. When the computer-readable instructions are executed by the processor, they enable the processor to implement an ambient lighting effect control method. The processor of the computer device provides computing and control capabilities, supporting the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When these computer-readable instructions are executed by the processor, they enable the processor to execute the ambient lighting effect control method of this application. The network interface of the computer device is used for communication with a terminal. Those skilled in the art will understand that… Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0178] In this embodiment, the processor is used to execute... Figure 3The system contains the specific functions of each module and its sub-modules, and the memory stores the program code and various data required to execute these modules or sub-modules. A network interface is used for data transmission between the user terminal and the server. In this embodiment, the memory stores the program code and data required to execute all modules / sub-modules in the ambient lighting effect control device of this application, and the server can call the server's program code and data to execute the functions of all sub-modules.

[0179] This application also provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the ambient lighting effect control method of any embodiment of this application.

[0180] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0181] Those skilled in the art will understand that the steps, measures, and solutions in the various operations, methods, and processes discussed in this application can be alternated, modified, combined, or deleted. Furthermore, other steps, measures, and solutions in the various operations, methods, and processes discussed in this application can also be alternated, modified, rearranged, decomposed, combined, or deleted. Furthermore, steps, measures, and solutions in the prior art that are similar to those in the open-source operations, methods, and processes of this application can also be alternated, modified, rearranged, decomposed, combined, or deleted.

[0182] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for controlling the lighting effects of ambient lighting fixtures, characterized in that, include: Obtaining the description information of a target piece of music includes: collecting environmental audio data through an audio pickup unit, determining whether the environmental audio data contains human voice audio data; when the human voice audio data exists, identifying the target piece of music that matches the environmental audio data from a music database; obtaining the description information of the target piece of music from the music database, wherein the description information includes preset image information and text information corresponding to the target piece of music, wherein the image information and text information are each represented separately on different information carriers, or are integrated and represented on the same information carrier; Based on the image information in the music description, determine the target color set used to generate the lighting effect control information; Based on the text information in the music description, a target lighting effect template for generating the lighting effect control information is matched from the lighting effect template library. This includes: obtaining a set of description information for each lighting effect template in the lighting effect template library, where each lighting effect template's description information set provides description information from multiple preset description dimensions; calling a preset matching instruction set and combining it with the text information in the music description, the description information sets of each lighting effect template, and the target color set to generate matching prompt information, wherein the matching instruction set includes a first control instruction and a second control instruction executed sequentially. The first control instruction indicates that the lighting effect matching model first determines a subset of lighting effect templates that semantically match the text information in the music description based on each description information set; the second control instruction instructs the lighting effect matching model to score each lighting effect template in the subset of lighting effect templates from multiple description dimensions, then merges the scores to determine the total score for each lighting effect template, and selects the lighting effect template with the highest total score as the target lighting effect template; and inputting the matching prompt information into the lighting effect matching model to obtain the target lighting effect template determined by the lighting effect matching model. The target color set is fitted into the target lighting effect template to obtain the lighting effect control information, and the ambient lighting fixtures are controlled to play the corresponding lighting effects according to the lighting effect control information.

2. The ambient lighting effect control method according to claim 1, characterized in that, Based on the image information in the music description, a target color set for generating lighting effect control information is determined, including: The original image carrying the image information in the music description information is compressed to obtain a target-size image carrying the image information. The frequency of each color in the pixel set of the target specification image is counted, and the multiple colors with the highest frequency are determined to form a candidate color set; Detect whether each color in the candidate color set meets the preset color difference condition, and delete colors that do not meet the color difference condition to obtain the target color set.

3. The ambient lighting effect control method according to claim 2, characterized in that, After identifying the most frequently occurring colors to form a candidate color set, it includes: Based on each color component corresponding to each color defined in the first color space, identify the first target color in the candidate color set whose each color component is lower than a preset first component threshold, and delete the first target color from the candidate color set; Based on each color component, a second target color is identified in the candidate color set where the absolute difference between any two color components is lower than a preset second component threshold, and the second target color is removed from the candidate color set.

4. The ambient lighting effect control method according to claim 2, characterized in that, Detecting whether each color in the candidate color set meets a preset color difference condition, and deleting colors that do not meet the color difference condition to obtain the target color set, includes: Normalization is performed on each candidate color in the candidate color set to convert each candidate color from its representation in the first color space to its representation in the second color space. Based on the representation of the second color space, a third target color is detected and determined whose saturation and / or brightness in the candidate color set meet its corresponding preset color difference conditions. The third target color in the candidate color set is retained, and all other candidate colors are deleted. The candidate color set containing only the third target color is taken as the target color set, and the target colors in the target color set are converted back to the representation in the first color space.

5. The ambient lighting effect control method according to claim 2, characterized in that, After detecting whether each color in the candidate color set meets a preset color difference condition, deleting colors that do not meet the color difference condition to obtain the target color set, the process includes: Determine whether the number of colors in the target color set is zero. If it is zero, call the preset color matching generation model to generate a target color set that meets the preset color matching conditions based on the image information and / or text information in the music introduction information. When the number of colors in the target color set is not zero, it is further determined whether the number of colors in the target color set is lower than a preset lower threshold. If it is lower, a preset color matching generation model is called to generate a target color set that meets the preset color matching conditions, based on the target colors in the target color set and referring to the image information and / or text information in the music introduction information. Determine whether the number of colors in the target color set exceeds a preset upper limit threshold. If it does, retain the target colors with the highest frequency in the target color set according to the preset upper limit threshold, and delete the other redundant target colors.

6. An ambient lighting effect control device, characterized in that, include: The information acquisition module is configured to acquire the music description information of a target piece of music, including: collecting environmental audio data through an audio pickup unit, determining whether the environmental audio data contains human voice audio data; when the human voice audio data exists, identifying the target piece of music that matches the environmental audio data from a music database; and acquiring the music description information of the target piece of music from the music database, wherein the music description information includes preset image information and text information corresponding to the target piece of music, wherein the image information and text information are each represented separately on different information carriers, or are integrated and represented on the same information carrier; The color matching determination module is configured to determine the target color set for generating lighting effect control information based on the image information in the music introduction information; The template matching module is configured to match a target lighting effect template for generating the lighting effect control information from a lighting effect template library based on the text information in the music introduction information. This includes: obtaining a set of description information for each lighting effect template in the lighting effect template library, where each lighting effect template's description information set provides description information from multiple preset description dimensions; calling a preset matching instruction set and combining it with the text information in the music introduction information, the description information sets of each lighting effect template, and the target color set to generate a matching prompt message. The matching instruction set includes a first control instruction and a second control instruction executed sequentially. The first control instruction indicates that the lighting effect matching model first determines a subset of lighting effect templates that semantically match the text information in the music introduction information based on each description information set; the second control instruction instructs the lighting effect matching model to score each lighting effect template in the subset of lighting effect templates from multiple description dimensions, then merges the scores to determine the total score for each lighting effect template, selecting the lighting effect template with the highest total score as the target lighting effect template; and inputting the matching prompt message into the lighting effect matching model to obtain the target lighting effect template determined by the lighting effect matching model. The lighting effect control module is configured to apply the target color set to the target lighting effect template to obtain the lighting effect control information, and control the ambient lighting fixtures to play corresponding lighting effects based on the lighting effect control information.

7. The ambient lighting effect control device according to claim 6, characterized in that, The color matching determination module includes: The image compression module is configured to compress the original image carrying the image information in the music introduction information to obtain a target size image carrying the image information. The statistical color selection module is configured to count the frequency of occurrence of each color in the pixel set of the target specification image, and determine the multiple colors with the highest frequency to form a candidate color set; The color selection module is configured to detect whether each color in the candidate color set meets a preset color difference condition, and delete colors that do not meet the color difference condition to obtain the target color set.

8. The ambient lighting effect control device according to claim 7, characterized in that, The color matching determination module also includes: The dark color filtering module is configured to identify a first target color in the candidate color set whose color components are all below a preset first component threshold, based on each color component corresponding to each color defined in the first color space, and delete the first target color from the candidate color set. The grayscale filtering module is configured to identify, based on each color component, a second target color in the candidate color set whose absolute difference between any two color components is less than a preset second component threshold, and delete the second target color from the candidate color set.

9. An ambient lighting fixture, comprising a controller and at least one lighting unit, wherein the controller is communicatively connected to the lighting unit, characterized in that, The controller is used to perform the steps of the ambient lighting effect control method as described in any one of claims 1 to 5, so as to control the lighting unit to display the lighting effect.

10. A computer-readable storage medium, characterized in that, It stores a computer program in the form of computer-readable instructions, which, when invoked by a computer, performs the steps included in the method as described in any one of claims 1 to 5.

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