Emotional intelligent dialogue interaction method and system based on light language
By combining speech recognition and light mode control technology in the light mode control system of the control module and the target light group, the problem of extracting voice emotional features from noisy environments and converting them into light modes is solved, and emotional light language interaction is realized , providing rich emotional feedback and efficient interactive experience.
Patent Information
- Application Number
- CN202411572885.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-06
- Publication Date
- 2025-02-14
AI Technical Summary
The prior art is difficult to accurately extract effective emotional characteristic signals from speech in noisy environments and convert them into appropriate lighting modes to achieve emotional optical language interactions.
By setting up the control module to connect to the target light group, the color information of the lamp beads is obtained and input into the pre-established illumination model, and the lighting mode is determined; at the same time, the speech is obtained and converted into text, the semantic features are extracted, and the semantic features and illumination model is based on the semantic features and illumination model. The lighting mode is determined by the relationship between the voice; finally, the lighting intensity control coefficient of the lighting mode is controlled according to the sound intensity of the voice.
Realize dynamic mapping of speech features (including semantic information and sound intensity) to the lighting mode and brightness of the target light group, providing richer and more intuitive emotional feedback, enhancing the interactive experience between users and devices, and in noisy environments Ensure the smoothness and effectiveness of interaction.
Smart Images
Figure CN119450863A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of light language interaction technology, and in particular to an emotional intelligent dialogue interaction method and system based on light language. Background Art
[0002] In modern intelligent interaction systems, how to make the communication between users and devices more emotionally expressive has become a key research topic. Traditional voice interaction systems mainly focus on speech recognition and text conversion, usually only provide simple sound feedback, and lack effective visual emotional output methods. This makes the interaction process between users and devices lack rich emotional expression, which is not conducive to improving user experience and immersion. Therefore, in order to meet users' needs for more natural and emotional interaction, exploring visual feedback that can convey emotional characteristics has become an important direction in the field of intelligent interaction.
[0003] At present, the light language in emotional expression (conveying emotions and states through the color, brightness and pattern of lights) has gradually attracted attention. Light has intuitive visual expression and can express different emotional characteristics through effects such as color, brightness and flickering. However, in the actual emotional interaction process, there are still multiple challenges in realizing the conversion from speech to light language. Not only is it impossible to determine the emotional characteristics of speech, but it is also impossible to extract appropriate emotional signals from speech and map them to lighting patterns. In addition, in noisy environments, speech signals are often interfered with. Therefore, how to find the real target speech in a noisy environment to complete the interaction is also a big problem.
[0004] Therefore, there is an urgent need to invent an emotional dialogue interaction technology of light language to solve the problem in the existing technology of how to accurately extract effective emotional feature signals from speech in a noisy environment and convert them into appropriate lighting modes to achieve emotional light language interaction. Summary of the invention
[0005] In view of this, the present invention proposes an emotional intelligent dialogue interaction method and system based on light language, aiming to solve the problem in the current technology of how to accurately extract effective emotional feature signals from speech in a noisy environment and convert them into appropriate lighting modes to achieve emotional light language interaction.
[0006] The present invention proposes an emotional intelligent dialogue interaction method based on light language, comprising: Setting a control module, and connecting the control module to a target light group, so that the control module controls the target light group; Acquire the lamp bead color information of the target lamp group, input the lamp bead color information into a pre-established illumination model, and determine the illumination mode of the target lamp group; Acquire the voice in the preset area and convert the voice into text for output; Extracting and outputting semantic features in the text, and determining the illumination mode of the target light group according to the relationship between the semantic features and the semantic features in the illumination model; Acquire the sound intensity of the speech, and control the illumination intensity control coefficient of the illumination mode according to the sound intensity; The illumination mode and illumination intensity control coefficient of the target light group are input into the control module, so that the control module controls the target light group according to the illumination mode and illumination intensity control coefficient of the target light group.
[0007] Furthermore, the lamp bead color information is input into a pre-established illumination model to determine the illumination mode of the target lamp group, including: Presetting a preset associated phrase and a preset light illumination mode, wherein the preset light illumination mode includes: a warm light illumination mode and a cold light illumination mode; Establishing a lighting relationship between the preset associated phrases and the preset lighting illumination modes, and establishing the illumination model according to each of the lighting relationship; According to the lamp bead color information, the color characteristics required in the warm color illumination mode and the cold color illumination mode are matched, wherein: If the lamp bead color information matches the color characteristics of the warm color lighting mode, determining that the lighting mode of the target lamp group is the warm color lighting mode; If the lamp bead color information matches the color characteristics of the cold color lighting mode, determining that the lighting mode of the target lamp group is the cold color lighting mode; If the lamp bead color information matches the color characteristics of both the warm color lighting mode and the cold color lighting mode, it is determined that the lighting mode of the target lamp group is the warm color lighting mode or the cold color lighting mode.
[0008] Furthermore, when determining the illumination mode of the target light group according to the relationship between the semantic feature and the semantic feature in the illumination model, it includes: Obtaining the correlation between the semantic feature and the preset keyword group of each illumination relation formula, and determining the illumination relation formula corresponding to the preset keyword group matching the semantic feature according to the correlation; The preset illumination mode of the target light group is determined according to the illumination relationship, wherein: If the illumination mode of the target light group is consistent with the preset illumination mode, determining the preset illumination mode as the illumination mode of the target light group; If the illumination mode of the target light group is inconsistent with the preset illumination mode, it is determined that the illumination mode of the target light group is none.
[0009] Furthermore, obtaining the correlation between the semantic feature and the keyword group of each illumination relationship formula includes: Acquire a keyword group in the semantic feature, and determine a key noun in the keyword group according to the word order of the keyword group; Matching is performed between the key noun and the key words in the preset key word group of the illumination relationship formula, wherein: If the key nouns in the keyword group are the same as the keywords in the preset keyword group, then determining the correlation between the semantic feature and the illumination relationship equation is 1; If the key noun in the keyword group is the same as the key noun in the preset keyword group, and the keyword group contains additional key nouns, then determining that the correlation between the semantic feature and the illumination relationship is 0.5; If the key nouns in the keyword group are not the same as the keywords in the preset keyword group, it is determined that the correlation degree between the semantic feature and the illumination relationship is 0.
[0010] Furthermore, when determining the correlation between the semantic feature and the illumination relational expression, it also includes: Determining whether to perform a secondary judgment on the correlation between the semantic feature and the illumination relationship equation according to the correlation, wherein: If the correlation degree is 1 or 0, it is determined that no secondary judgment is performed on the correlation degree between the semantic feature and the illumination relationship equation; If the correlation degree is 0.5, it is determined that a second judgment is required on the correlation degree between the semantic feature and the illumination relationship equation.
[0011] Further, when it is determined that a second judgment is needed on the correlation between the semantic feature and the illumination relationship equation, the method includes: Determine the keyword group as a questionable noun if there are additional key nouns; Determine whether the questionable noun and the keywords in the preset keyword group are synonyms, wherein: If it is a synonym, the questionable noun is replaced by the keyword to form a judgment phrase; If it is not a synonym, it is determined whether the questionable noun is a hyponym of the keyword in the preset keyword group, wherein: If it is a hyponym, the questionable noun is replaced by the keyword to form a judgment phrase; If it is not a hyponym, the questionable noun will not be replaced.
[0012] Furthermore, if it is determined that the doubtful noun needs to be replaced, a second judgment is made on the relevance between the replaced judgment phrase and the illumination relationship equation, including: A secondary match is performed between the judgment phrase and the keywords in the preset keyword group, wherein: If the key noun in the judgment phrase is the same as the key word in the preset keyword group, then the correlation between the semantic feature and the illumination relationship equation is determined to be 1; If the key noun in the judgment phrase is different from the key words in the preset key word group, it is determined that the correlation degree between the semantic feature and the illumination relationship equation is 0.
[0013] Furthermore, when the illumination intensity control coefficient of the illumination mode is controlled according to the sound intensity, it includes: Acquire the sound intensity difference between the speech and the preset sound intensity, and the sound intensity ratio between the speech and the noise in the speech; Obtain the main voice amplification value and noise reduction value during voice processing; Substitute the sound intensity difference, the sound intensity ratio, the main voice increase value and the noise reduction value into the formula to determine the control coefficient. The formula is as follows: ; Among them, K is the control coefficient, P is the sound intensity difference, A is the sound intensity ratio, R is the main voice amplification value, Rn is the noise reduction value, * is the multiplication sign, and f is the reference coefficient of the lighting mode.
[0014] Furthermore, obtaining the voice in the preset area and converting the voice into text for output includes: Separating the speech in the preset area into main speech and background noise according to the speech separation technology; Suppressing the background noise according to spectral subtraction; amplifying the sound intensity of the main voice according to a voice compensation algorithm; The amplified main voice is obtained and converted into text.
[0015] Compared with the prior art, the beneficial effect of the present invention is that by introducing light language in emotional intelligent dialogue interaction, the voice features (including semantic information and sound intensity) are dynamically mapped to the lighting mode and brightness of the target light group, so as to realize the intuitive visual expression of emotions. This interactive mode enables users to obtain richer and more intuitive emotional feedback in communication with the device. For example, when the user expresses joy or excitement, the light group can automatically adjust to bright warm tones to enhance the sense of pleasure; when expressing sadness or heavy emotions, the light can be changed to soft cold tones to create a quiet and contemplative atmosphere. This instant change of light not only enhances the effectiveness of emotional transmission, but also makes it easier for users to understand the system's response during the interaction process, forming a natural and pleasant communication experience. In addition, through the intelligent management of the control module, the adaptability to environmental changes is improved. In actual applications, users may be in different environments, and factors such as surrounding light and noise may affect the clarity and recognition effect of speech. By combining the sound intensity of speech with the control coefficient of the lighting mode, the light intensity can be automatically adjusted in a noisy or complex environment to ensure the smoothness and effectiveness of the interaction. For example, when the ambient noise is loud, the system can automatically increase the brightness of the lights to make them more visually appealing, thereby guiding the user's attention and ensuring the accuracy of emotional expression.
[0016] On the other hand, the present application also provides an emotional intelligent dialogue interaction system based on light language, including: A control module connected to the target light group, the control module is used to control the target light group; A model unit, electrically connected to the control module, and configured to obtain lamp bead color information of the target lamp group, input the lamp bead color information into a pre-established illumination model, and determine an illumination mode of the target lamp group; A sound collection and processing module is used to obtain the voice in a preset area and convert the voice into text for output; the sound collection and processing module is also used to obtain the sound intensity of the voice and control the light intensity control coefficient of the light mode according to the sound intensity; A central control module, electrically connected to the sound collection and processing module and the model unit, the central control module is used to extract and output semantic features in the text, and determine the illumination mode of the target light group according to the relationship between the semantic features and the semantic features in the illumination model; the central control module is also used to input the illumination mode of the target light group and the illumination intensity control coefficient into the control module; Wherein, the control module is also used to control the target light group according to the lighting mode and the lighting intensity control coefficient of the target light group.
[0017] It can be understood that the above-mentioned embodiments of the present invention, a method and system for emotional intelligent dialogue interaction based on light language, have the same beneficial effects and will not be described in detail. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A flowchart of an emotional intelligent dialogue interaction method based on light language provided by an embodiment of the present invention; Figure 2 A functional block diagram of an emotional intelligent dialogue interaction system based on light language provided in an embodiment of the present invention. DETAILED DESCRIPTION
[0019] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.
[0020] like Figure 1 In some embodiments of the present application, this embodiment provides an emotional intelligent dialogue interaction method based on light language, including: Step S100: setting a control module, and connecting the control module to a target light group, so that the control module controls the target light group.
[0021] Step S200: Acquire the lamp bead color information of the target lamp group, input the lamp bead color information into a pre-established illumination model, and determine the illumination mode of the target lamp group.
[0022] Specifically, when inputting the color information of the lamp beads into a pre-established illumination model, determining the illumination mode of the target lamp group includes: pre-setting a preset associated phrase and a preset light illumination mode, wherein the preset light illumination mode includes: a warm light illumination mode and a cold light illumination mode. Establishing an illumination relationship between the preset associated phrase and the preset light illumination mode, and establishing an illumination model according to each illumination relationship. Matching the color information of the lamp beads with the required color features in the warm light illumination mode and the cold light illumination mode, wherein: if the color information of the lamp beads matches the color features of the warm light illumination mode, determining the illumination mode of the target lamp group to be a warm light illumination mode. If the color information of the lamp beads matches the color features of the cold light illumination mode, determining the illumination mode of the target lamp group to be a cold light illumination mode. If the color information of the lamp beads matches the color features of both the warm light illumination mode and the cold light illumination mode, determining the illumination mode of the target lamp group to be a warm light illumination mode or a cold light illumination mode.
[0023] It can be understood that by establishing an association between the color information of the lamp beads and the preset lighting mode, the lighting mode of the target lamp group is dynamically determined. Specifically, the warm color lighting mode and the cold color lighting mode are pre-set, and specific color characteristics are defined for each mode. This setting can quickly identify and match the corresponding lighting mode when processing the color information of the lamp beads, thereby achieving flexible emotional expression and adaptive adjustment during the interaction process. In actual operation, after the color information of the lamp beads is obtained, this information is input into the pre-established illumination model. This model not only includes preset lighting modes, but also preset associated phrases related to these modes, thereby forming a multi-dimensional lighting relationship network. This network can quickly determine the relationship between the lamp beads and different lighting modes by analyzing the color characteristics of the lamp beads. When the color characteristics of the lamp beads match the warm light mode, it can immediately confirm that the light mode of the target lamp group is warm, thereby creating a warm and friendly atmosphere, which is suitable for creating a relaxed and pleasant emotional environment; when the color characteristics of the lamp beads match the cool light mode, the cool light mode will be selected to create a calm and rational atmosphere, which is suitable for rigorous or focused scenes. At the same time, in the case of double matching, that is, the color information of the lamp beads meets the characteristics of both warm and cool light modes, it can flexibly select the appropriate light mode according to the preset priority or context information. This design has higher adaptability and can be dynamically adjusted according to different situational needs and user emotional states. For example, in a social occasion, the user's mood may change, and the lighting effect can be responded to and adjusted in time to enhance the warmth of the social atmosphere, or provide a calmer environment selection in some cases, thereby enhancing the naturalness of interaction and user experience.
[0024] Step S300: Acquire the voice in the preset area, and convert the voice into text for output.
[0025] Specifically, when obtaining the voice in the preset area and converting the voice into text for output, it includes: separating the voice in the preset area into main voice and background noise according to the voice separation technology; suppressing the background noise according to the spectral subtraction method; amplifying the sound intensity of the main voice according to the voice compensation algorithm; obtaining the amplified main voice and converting it into text.
[0026] It can be understood that the acquired audio signal is divided into the main voice and background noise by using the speech separation technology. This process is the key to achieve accurate speech recognition, because in many practical scenarios, the user's voice is often affected by environmental noise, such as the noise of the crowd, the noise of traffic, etc. Through advanced speech separation algorithms, the main voice can be identified and extracted, making it stand out from the interference sound, thus laying the foundation for subsequent processing. Next, by analyzing the spectrum of the speech signal, the frequency components of the background noise are subtracted from the overall signal. The advantage of spectral subtraction is that it can effectively reduce the volume of background noise without significantly affecting the main voice, thereby improving the signal-to-noise ratio of the main voice. This method is particularly suitable for dealing with variable noise environments when processing real-time speech signals, and can significantly improve the intelligibility and recognition rate of speech. Subsequently, the voice compensation algorithm is used to amplify the sound intensity of the main voice. This step is to ensure that the extracted main voice signal has sufficient volume and clarity when converted to text. The speech compensation algorithm intelligently analyzes and adjusts the amplitude of the speech signal so that its volume can adapt to the subsequent speech recognition model. This not only improves the clarity of the voice signal, but also reduces recognition errors caused by insufficient volume, thereby ensuring the accuracy of the final output text. Finally, the amplified main voice after the above processing is converted into text to achieve high-quality voice recognition and conversion. By combining multiple signal processing technologies, it ensures that the user's voice information in different environments can be accurately captured and converted into usable text data. This technology not only improves the accuracy and efficiency of voice recognition, but also provides a more reliable foundation for emotional intelligence dialogue, thereby enhancing the interactive experience between users and devices.
[0027] Step S400: extracting semantic features from the output text, and determining the illumination mode of the target light group according to the relationship between the semantic features and the semantic features in the illumination model.
[0028] Specifically, when determining the illumination mode of the target light group according to the relationship between the semantic features and the semantic features in the illumination model, it includes: obtaining the correlation between the semantic features and the preset keyword groups of each illumination relationship formula, and determining the illumination relationship formula corresponding to the preset keyword group matching the semantic features according to the correlation. Determine the preset illumination mode of the target light group according to the illumination relationship formula, wherein: if the illumination mode of the target light group is consistent with the preset illumination mode, the preset illumination mode is determined as the illumination mode of the target light group. If the illumination mode of the target light group is inconsistent with the preset illumination mode, the illumination mode of the target light group is determined to be none.
[0029] It can be understood that by obtaining the input semantic features and performing a correlation analysis with the preset keyword groups in each lighting relationship formula. Through this analysis, the keyword group that best matches the current semantic features can be identified, thereby determining its corresponding lighting relationship formula. This process ensures that the selection of the lighting mode is based on the actual semantic content rather than a static setting, thereby enhancing the flexibility and adaptability of lighting control. When determining the lighting mode of the target light group, the matching lighting relationship formula is applied to the target light group to determine whether its lighting mode is consistent with the preset lighting mode. If consistent, the lighting mode of the target light group is confirmed to be the preset mode, thereby achieving precise control of the lighting effect; if inconsistent, the lighting mode of the target light group will be marked as "none", indicating that the current input semantic features have not been effectively mapped to any preset lighting mode.
[0030] Specifically, when obtaining the correlation between the semantic feature and the keyword group of each illumination relationship formula, it includes: obtaining the keyword group in the semantic feature, and determining the key noun in the keyword group according to the word order of the keyword group. Matching is performed between the key noun and the keywords in the preset keyword group of the illumination relationship formula, wherein: if the key noun in the keyword group is the same as the keyword in the preset keyword group, the correlation between the semantic feature and the illumination relationship formula is determined to be 1. If the key noun in the keyword group is the same as the keyword in the preset keyword group, and there are additional key nouns in the keyword group, the correlation between the semantic feature and the illumination relationship formula is determined to be 0.5. If the key noun in the keyword group is different from the keywords in the preset keyword group, the correlation between the semantic feature and the illumination relationship formula is determined to be 0.
[0031] Specifically, when determining the correlation between the semantic feature and the illumination relationship formula, it also includes: determining whether to perform a secondary judgment on the correlation between the semantic feature and the illumination relationship formula according to the correlation, wherein: if the correlation is 1 or 0, it is determined that the correlation between the semantic feature and the illumination relationship formula is not to be judged secondary. If the correlation is 0.5, it is determined that a secondary judgment is required on the correlation between the semantic feature and the illumination relationship formula.
[0032] Specifically, when it is determined that a secondary judgment is needed on the correlation between the semantic feature and the illumination relationship formula, it includes: determining that the keyword group contains additional key nouns as questionable nouns. Determine whether the questionable noun and the keywords in the preset keyword group are synonyms, wherein: if they are synonyms, the questionable noun is replaced with the keyword, and a judgment phrase is formed. If it is not a synonym, determine whether the questionable noun is a hyponym of the keyword in the preset keyword group, wherein: if it is a hyponym, the questionable noun is replaced with the keyword, and a judgment phrase is formed. If it is not a hyponym, the questionable noun is not replaced.
[0033] Specifically, if it is determined that the questionable noun needs to be replaced, the correlation between the replaced judgment phrase and the illumination relationship formula is judged twice, including: performing a secondary match between the judgment phrase and the keywords in the preset keyword group, wherein: if the key noun in the judgment phrase and the keywords in the preset keyword group are the same, then the correlation between the semantic feature and the illumination relationship formula is determined to be 1. If the key noun in the judgment phrase and the keywords in the preset keyword group are not the same, then the correlation between the semantic feature and the illumination relationship formula is determined to be 0.
[0034] It can be understood that by obtaining the key words in the semantic features and judging the key nouns according to the word order, the basic data is provided for the subsequent matching. This process emphasizes the structural nature of the language, especially in emotional expression, the key nouns often carry the most important information. Next, the key nouns are matched with the preset keywords in the illumination relationship formula. At this stage, the set rules divide the matching results into three categories: complete match, partial match and mismatch. This classification is not only simple and clear, but also provides a clear processing strategy. When it is completely matched, the correlation is assigned a value of 1, indicating a direct connection between the semantic feature and the illumination pattern; when it is partially matched, it is assigned a value of 0.5, which means that there is a certain semantic similarity, but further analysis is still required; and when it is completely mismatched, it is directly assigned a value of 0, indicating a lack of association between the two. This hierarchical strategy can quickly respond to inputs in different situations and flexibly adjust its judgment of the illumination pattern. In addition, when the correlation is 0.5, the accuracy of the match is further enhanced by starting the secondary judgment mechanism. This process is crucial because many emotional expressions may not be presented in a straightforward way, but are conveyed implicitly through the transformation of synonyms or hyponyms. By identifying the noun in question and judging its synonymy or hyponymy with the preset keywords, more semantic details can be captured. This flexibility ensures that even in complex language environments, appropriate responses can be made to the user's emotional expressions. Finally, after completing the second judgment, the correlation between the judgment phrase and the lighting relationship formula is evaluated again. The design of this link reflects the depth and intelligence of the technology, so that it can have stronger adaptability in dynamic interaction. Through multiple rounds of verification and analysis, it can ensure that the output lighting pattern is not only highly consistent with the user's emotional state, but also can provide more personalized responses in different interaction scenarios.
[0035] Step S500: Acquire the sound intensity of the speech, and control the light intensity control coefficient of the light mode according to the sound intensity.
[0036] Specifically, when the light intensity control coefficient of the light mode is controlled according to the sound intensity, it includes: obtaining the sound intensity difference between the voice and the preset sound intensity, and the sound intensity ratio between the voice and the noise in the voice. Obtain the main voice increase value and the noise reduction value during voice processing. Substitute the sound intensity difference, the sound intensity ratio, the main voice increase value and the noise reduction value into the formula to determine the control coefficient, and the formula is as follows: Among them, K is the control coefficient, P is the sound intensity difference, A is the sound intensity ratio, R is the main voice amplification value, Rn is the noise reduction value, * is the multiplication sign, and f is the reference coefficient of the lighting mode.
[0037] It can be understood that by obtaining the sound intensity difference between the speech and the preset sound intensity, and the sound intensity ratio of the main speech and the background noise in the speech, the clarity and intensity of the speech signal can be quantified. The importance of this process is that it provides the necessary benchmark for subsequent regulation to ensure that the target speech can be effectively recognized and enhanced in a dynamic environment. Then, the quality of the sound signal is further optimized by introducing the main speech increase value and the noise reduction value. The main speech increase value reflects the degree of amplification of the effective speech signal, while the noise reduction value indicates the ability to suppress background noise. The combination of the two can enhance the recognition ability of the target sound and make it more stable in a complex audio environment. Finally, by substituting the above parameters into the formula of the control coefficient, the control coefficient K is obtained, which comprehensively considers the sound intensity difference, the sound intensity ratio, the main speech increase value and the noise reduction value. This calculation mechanism not only simplifies the sound processing process, but also improves the flexibility and accuracy of the illumination mode regulation. The determination of the control coefficient K can automatically adjust the intensity of the illumination mode according to the quality of the real-time speech signal, thereby realizing intuitive feedback of emotional expression.
[0038] Step S600: input the illumination mode and illumination intensity control coefficient of the target light group into the control module, so that the control module controls the target light group according to the illumination mode and illumination intensity control coefficient of the target light group.
[0039] In the above embodiment, by introducing light language in emotional intelligent dialogue interaction, the voice features (including semantic information and sound intensity) are dynamically mapped to the lighting mode and brightness of the target light group, so as to achieve intuitive visual expression of emotions. This interactive mode enables users to obtain richer and more intuitive emotional feedback in communication with the device. For example, when the user expresses joy or excitement, the light group can automatically adjust to bright warm tones to enhance the sense of pleasure. When expressing sadness or heavy emotions, the light can be changed to soft cold tones to create a quiet and contemplative atmosphere. This instant change of light not only enhances the effectiveness of emotional transmission, but also makes it easier for users to understand the reaction of light language during the interaction process, forming a natural and pleasant communication experience. In addition, through the intelligent management of the control module, the adaptability to environmental changes is improved. In actual applications, users may be in different environments, and factors such as surrounding light and noise may affect the clarity and recognition effect of speech. By combining the sound intensity of speech with the control coefficient of the lighting mode, the light intensity can be automatically adjusted in a noisy or complex environment to ensure the smoothness and effectiveness of the interaction. For example, when the ambient noise is loud, the brightness of the lights can be automatically enhanced to make them more visually attractive, thereby guiding the user's attention and ensuring the accuracy of emotional expression.
[0040] In another preferred embodiment based on the above embodiment, Figure 2 The embodiment shown provides an emotional intelligent dialogue interaction system based on light language, including: a control module, a model unit, a sound collection and processing module and a central control module.
[0041] Specifically, the control module is connected to the target light group, and the control module is used to control the target light group. The model unit is electrically connected to the control module, and the model unit is used to obtain the color information of the lamp beads of the target light group, input the color information of the lamp beads into the pre-established illumination model, and determine the illumination mode of the target light group. The sound collection and processing module is used to obtain the voice in the preset area, and convert the voice into text for output. The sound collection and processing module is also used to obtain the sound intensity of the voice, and control the illumination intensity control coefficient of the illumination mode according to the sound intensity. The central control module is electrically connected to the sound collection and processing module and the model unit, and the central control module is used to extract semantic features in the output text, and determine the illumination mode of the target light group according to the relationship between the semantic features and the semantic features in the illumination model. The central control module is also used to input the illumination mode and illumination intensity control coefficient of the target light group into the control module. Among them, the control module is also used to control the target light group according to the illumination mode and illumination intensity control coefficient of the target light group.
[0042] It can be understood that the above-mentioned embodiments of the present invention, a method and system for emotional intelligent dialogue interaction based on light language, have the same beneficial effects and will not be described in detail.
[0043] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
[0044] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0045] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. An emotional intelligent dialogue interaction method based on light language, characterized in that: include: Setting a control module, and connecting the control module to the target light group, so that the control module controls the target light group; Acquire the lamp bead color information of the target lamp group, input the lamp bead color information into a pre-established illumination model, and determine the illumination mode of the target lamp group; Acquire the voice in the preset area and convert the voice into text for output; Extracting and outputting semantic features in the text, and determining the illumination mode of the target light group according to the relationship between the semantic features and the semantic features in the illumination model; Acquire the sound intensity of the speech, and control the illumination intensity control coefficient of the illumination mode according to the sound intensity; The illumination mode and illumination intensity control coefficient of the target light group are input into the control module, so that the control module controls the target light group according to the illumination mode and illumination intensity control coefficient of the target light group.
2. The method for emotional intelligent dialogue interaction based on light language according to claim 1, characterized in that: Inputting the lamp bead color information into a pre-established illumination model to determine the illumination mode of the target lamp group includes: Presetting a preset associated phrase and a preset light illumination mode, wherein the preset light illumination mode includes: a warm light illumination mode and a cold light illumination mode; Establishing a lighting relationship between the preset associated phrases and the preset lighting illumination modes, and establishing the illumination model according to each of the lighting relationship; According to the lamp bead color information, the color characteristics required in the warm color illumination mode and the cold color illumination mode are matched, wherein: If the lamp bead color information matches the color characteristics of the warm color lighting mode, determining that the lighting mode of the target lamp group is the warm color lighting mode; If the lamp bead color information matches the color characteristics of the cold color lighting mode, determining that the lighting mode of the target lamp group is the cold color lighting mode; If the lamp bead color information matches the color characteristics of both the warm color lighting mode and the cold color lighting mode, it is determined that the lighting mode of the target lamp group is the warm color lighting mode or the cold color lighting mode.
3. The method for emotional intelligent dialogue interaction based on light language according to claim 2, characterized in that: Determining the illumination mode of the target light group according to the relationship between the semantic feature and the semantic feature in the illumination model includes: Obtaining the correlation between the semantic feature and the preset keyword group of each illumination relation formula, and determining the illumination relation formula corresponding to the preset keyword group matching the semantic feature according to the correlation; The preset illumination mode of the target light group is determined according to the illumination relationship, wherein: If the illumination mode of the target light group is consistent with the preset illumination mode, determining the preset illumination mode as the illumination mode of the target light group; If the illumination mode of the target light group is inconsistent with the preset illumination mode, it is determined that the illumination mode of the target light group is none.
4. The method for emotional intelligent dialogue interaction based on light language according to claim 3, characterized in that: When obtaining the correlation between the semantic feature and the keyword group of each illumination relationship formula, it includes: Acquire a keyword group in the semantic feature, and determine a key noun in the keyword group according to the word order of the keyword group; Matching is performed between the key noun and the key words in the preset key word group of the illumination relationship formula, wherein: If the key nouns in the keyword group are the same as the keywords in the preset keyword group, then determining the correlation between the semantic feature and the illumination relationship equation is 1; If the key noun in the keyword group is the same as the key noun in the preset keyword group, and the keyword group contains additional key nouns, then determining that the correlation between the semantic feature and the illumination relationship is 0.5; If the key nouns in the keyword group are not the same as the keywords in the preset keyword group, it is determined that the correlation degree between the semantic feature and the illumination relationship is 0.
5. The method for emotional intelligent dialogue interaction based on light language according to claim 4, characterized in that: When determining the correlation between the semantic feature and the illumination relational expression, the method further includes: Determining whether to perform a secondary judgment on the correlation between the semantic feature and the illumination relationship equation according to the correlation, wherein: If the correlation degree is 1 or 0, it is determined that no secondary judgment is performed on the correlation degree between the semantic feature and the illumination relationship equation; If the correlation degree is 0.5, it is determined that a second judgment is required on the correlation degree between the semantic feature and the illumination relationship equation.
6. The method for emotional intelligent dialogue interaction based on light language according to claim 5, characterized in that: When it is determined that a secondary judgment is required on the correlation between the semantic feature and the illumination relational expression, the method includes: Determine the keyword group as a questionable noun if there are additional key nouns; Determine whether the questionable noun and the keywords in the preset keyword group are synonyms, wherein: If it is a synonym, the questionable noun is replaced by the keyword to form a judgment phrase; If it is not a synonym, it is determined whether the questionable noun is a hyponym of the keyword in the preset keyword group, wherein: If it is a hyponym, the questionable noun is replaced by the keyword to form a judgment phrase; If it is not a hyponym, the questionable noun will not be replaced.
7. The method for emotional intelligent dialogue interaction based on light language according to claim 6, characterized in that: If it is determined that the doubtful noun needs to be replaced, a second judgment is made on the relevance between the replaced judgment phrase and the illumination relational expression, including: A secondary match is performed between the judgment phrase and the keywords in the preset keyword group, wherein: If the key noun in the judgment phrase is the same as the key word in the preset keyword group, then the correlation between the semantic feature and the illumination relationship equation is determined to be 1; If the key noun in the judgment phrase is different from the key words in the preset key word group, it is determined that the correlation degree between the semantic feature and the illumination relationship equation is 0.
8. The method for emotional intelligent dialogue interaction based on light language according to claim 1, characterized in that: When the illumination intensity control coefficient of the illumination mode is controlled according to the sound intensity, it includes: Acquire the sound intensity difference between the speech and a preset sound intensity, and the sound intensity ratio between the speech and the noise in the speech; Obtain the main voice amplification value and noise reduction value during voice processing; Substitute the sound intensity difference, the sound intensity ratio, the main voice increase value and the noise reduction value into the formula to determine the control coefficient. The formula is as follows: ； Among them, K is the control coefficient, P is the sound intensity difference, A is the sound intensity ratio, R is the main voice amplification value, Rn is the noise reduction value, * is the multiplication sign, and f is the reference coefficient of the lighting mode.
9. The method for emotional intelligent dialogue interaction based on light language according to claim 1, characterized in that: Acquiring speech in a preset area and converting the speech into text for output includes: Separating the speech in the preset area into main speech and background noise according to the speech separation technology; Suppressing the background noise according to spectral subtraction; amplifying the sound intensity of the main voice according to a voice compensation algorithm; The amplified main voice is obtained and converted into text.
10. An emotional intelligent dialogue interaction system based on light language, applicable to an emotional intelligent dialogue interaction method based on light language as claimed in any one of claims 1 to 9, characterized in that: include: A control module connected to the target light group, the control module is used to control the target light group; A model unit, electrically connected to the control module, and used for obtaining the lamp bead color information of the target lamp group, inputting the lamp bead color information into a pre-established illumination model, and determining the illumination mode of the target lamp group; A sound collection and processing module is used to obtain the voice in the preset area and convert the voice into text for output; the sound collection and processing module is also used to obtain the sound intensity of the voice and control the light intensity control coefficient of the light mode according to the sound intensity; A central control module, electrically connected to the sound collection and processing module and the model unit, the central control module is used to extract and output semantic features in the text, and determine the illumination mode of the target light group according to the relationship between the semantic features and the semantic features in the illumination model; the central control module is also used to input the illumination mode of the target light group and the illumination intensity control coefficient into the control module; Wherein, the control module is also used to control the target light group according to the lighting mode and the lighting intensity control coefficient of the target light group.