AI Voice Control System for Lamps Based on Dual Protocols of WiFi and Bluetooth

Abstract

The present invention discloses an AI voice control system for lamps based on Wi-Fi and Bluetooth dual protocols, which relates to the technical field of voice control. It includes a pattern recognition module, a voice reception module, a voice analysis module, a status analysis module, and an intelligent adjustment module. Among them, the voice reception module is used to collect the voice data of users, and the voice data includes emotion data, vocabulary data, and user data. The voice analysis module is used to establish a user-lighting adaptation model based on machine learning according to the voice data, generate a lighting control coefficient, and obtain a first control scheme according to the lighting control coefficient. The status analysis module is used to obtain the current status data, and the status data includes festival data and time data, and optimize the first control scheme in combination with the lighting control coefficient to obtain a second control scheme. Through intelligent interaction and emotional care, the intelligent lamp in this application is not only a simple lighting device, but also enhances the user's sense of identity and dependence on the system.

Description

Technical Field

[0001] The present invention relates to the technical field of voice control, and more specifically, to an AI voice control system for lamps based on dual protocols of WiFi and Bluetooth. Background Art

[0002] The WiFi protocol refers to a wireless network communication protocol, which is usually used to connect electronic devices to a local area network (LAN) and the Internet. It is based on the IEEE802.11 standard and uses the 2.4GHz or 5GHz frequency band for communication. The WiFi technology provides a high-speed data transmission rate and can support various network services, such as Internet access, file sharing, streaming media, etc. Through the WiFi connection, users can wirelessly connect to the network at home, in the office or in public places to achieve convenient communication and information exchange. The popularization of the WiFi technology has made the wireless network an indispensable part of modern life, promoting the development of the mobile Internet and the wide application of intelligent devices;

[0003] The Bluetooth protocol is a short-range wireless communication protocol designed to enable data exchange and connection between various devices. It uses low-power radio frequency technology to communicate in the 2.4GHz frequency band, allowing devices to communicate with each other within a range of about 10 meters. The Bluetooth technology is widely used in various portable devices such as mobile phones, tablets, smart speakers, headphones, etc. to achieve functions such as audio transmission, file sharing, device control, etc. The characteristics of the Bluetooth protocol include a simple and easy connection process, a low-power design, and wide device compatibility, making it an important technology in the field of modern wireless communication;

[0004] The AI voice control system is an intelligent control system based on artificial intelligence technology, which can interact with users through voice commands and perform corresponding tasks according to the instructions of users. The system usually includes modules such as voice recognition, natural language understanding, dialogue management, and execution control. Through the cooperation of these modules, it can recognize the voice commands of users, understand the intentions of users, and take corresponding measures to complete tasks. The AI voice control system is widely used in the fields of smart home, smart phone, car navigation system, etc., enabling users to operate devices through natural voice interaction, greatly improving the convenience and intelligence of the user experience.

[0005] For example, the invention application with publication number: CN105700389A discloses an intelligent home natural language control method, which includes: (1) a voice receiving device receives a voice control command; (2) a control unit analyzes the command; (3) executes the command; in the step (2), the following sub-steps are further included: (21) analyzing the integrity of the voice control command, if the voice control command contains a complete electrical appliance type and an action command for control execution, directly enter step (3); otherwise, enter step (22); (22) if the electrical appliance type is not specified in the voice control command, locate the user's position, find the electrical appliance closest to the user according to the user's position, and determine that electrical appliance as the specified electrical appliance, and execute step (3). The intelligent home natural language control method of the present invention uses the method of indoor positioning of the user, and intelligently analyzes and judges the information implicitly contained in the user's incomplete voice control command according to the user's position, improving the recognition ability of the voice control system for natural language.

[0006] For example, the invention announcement with announcement number: CN106773918B discloses a user ladder diagram language control method for a controller based on a single-chip microcomputer. By dividing the memory of the single-chip microcomputer into three storage areas; ladder diagram compilation: compiling the components and logical operations involved in the ladder diagram language; scanning the ladder diagram according to the ladder diagram language scanning algorithm; forming a ladder diagram compilation file; loading the ladder diagram compilation file into the user program register module; the single-chip microcomputer executes ladder diagram logic scanning: establishing an interface function in the single-chip microcomputer kernel program module; modifying the ladder diagram, compiling the modified ladder diagram by the ladder diagram compilation file, the kernel program of the single-chip microcomputer periodically scans the external device memory mapped address, and calls the logical operation in the user program module, and transmits the result to the device. The present invention realizes the separation of kernel drive, logical operation and control, and realizes operation logic programming with ladder diagram language, reducing the difficulty of on-site debugging.

[0007] In the above disclosed technical solutions, at least the following technical problems exist:

[0008] In the traditional lamp voice control system, the lamp is often adjusted rigidly according to the user's command, without considering the user's mood and the current scene, and providing an intelligent adjustment mode.

[0009] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0010] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a lamp AI voice control system based on WiFi and Bluetooth dual protocols, which controls the lamp through AI voice to solve the problems proposed in the above background technology.

[0011] To achieve the above object, the present invention provides the following technical solutions:

[0012] An AI voice control system for lamps based on dual protocols of WiFi and Bluetooth, including a pattern recognition module, a voice reception module, a voice analysis module, a status analysis module, and an intelligent adjustment module; wherein, the pattern recognition module is used to obtain the voice pattern of the current lamp, and the voice pattern includes an adaptive mode and a normal mode; the voice reception module is used to collect the voice data of the user, and the voice data includes emotion data, vocabulary data, and user data; the voice analysis module is used to establish a user-light adaptation model based on machine learning according to the voice data, generate a light control coefficient, and obtain a first control scheme according to the light control coefficient; the status analysis module is used to obtain the current status data, and the status data includes festival data and time data, and optimize the first control scheme in combination with the light control coefficient to obtain a second control scheme; the intelligent adjustment module: is used to control the lamp according to the second control scheme when receiving the voice pattern of the adaptive mode.

[0013] In a preferred embodiment, the emotion data includes a tone emotion coefficient; the vocabulary data includes a vocabulary sentiment coefficient; the user data includes a user characteristic influence coefficient.

[0014] In a preferred embodiment, the status data includes festival data and time data, and the specific optimization of the first control scheme in combination with the light control coefficient is as follows: Set the date importance reference score Kj 基 ; Obtain the importance of the current date. The importance of the date is divided into ordinary, festival, and important date. When the importance of the date belongs to ordinary, add Kj1 points. When the importance of the date belongs to an important date, subtract Kj2 points. When the importance of the date belongs to a festival, neither add nor subtract points. When the importance of the date belongs to ordinary, Kj2 = 0, Kj3 = 0. When the importance of the date belongs to a festival, Kj1 = 0, Kj3 = 0; When the importance of the date belongs to an important date, Kj1 = 0, Kj1 = 0; Through the formula Kj 基 +Kj1 - Kj2 = Kj 终 , calculate the date score Kj 终 ; Set the time reference score and mark it as Fj 基 , obtain the real-time time. When the real-time time exceeds the preset value A1, subtract Fj1 points. When the real-time time is between A1 and A2, add Fj2 points. When the real-time time is lower than A2, subtract Fj3 points. When the real-time time exceeds the preset value A1, Fj2 and Fj3 are both 0. When the real-time time is between A1 and A2, Fj1 and Fj3 are both 0. When the real-time time is lower than A2, Fj1 and Fj2 are 0; Through the formula Fj 基 -Fj1 + Fj2-Fj3 = Fj 终 Obtain the time score; Assign the date importance reference score Kj基 a correction value θ i is assigned to the time reference score Fj 基 a correction value δ i , θ i > δ i , θ i + δ i = 1; Through the formula Kj 终 * θ i + Fj 终 * δ i = Ju d , the correction coefficient Ju is obtained d ; Multiply the correction coefficient by the lighting control coefficient to obtain a scheme comparison value; Correlate the scheme comparison value with a preset scheme comparison table to obtain a second control scheme.

[0015] In a preferred embodiment, the specific method for obtaining the tone emotion coefficient is as follows: First, extract the time-domain waveform of the fundamental frequency through speech signal processing technology, and then calculate the fundamental frequency change rate as the frequency feature; Extract the volume and intensity through speech signal processing technology, and calculate the energy mean as the energy feature; Extract the speech rate of the speech signal and the duration of the speech segment through speech signal processing technology, and calculate the duration change rate as the duration feature; Calculate the tone emotion coefficient through machine learning by integrating the frequency feature, energy feature, and duration feature.

[0016] In a preferred embodiment, the specific method for obtaining the lexical sentiment coefficient is as follows: Sentiment vocabulary analysis: Use natural language processing technology to construct a sentiment vocabulary dictionary, which includes positive and negative sentiment vocabulary; Then, analyze the user's text input and calculate the quantity and intensity of positive and negative sentiment vocabulary appearing in the text; Sentiment classification model: Use sentiment analysis algorithms or deep learning models to classify the user's text into different sentiment categories; Set sentiment scores and weights for different text sentiment data, and obtain the text sentiment coefficient by calculating the weighted average of all the sentiment vocabulary in the text.

[0017] In a preferred embodiment, the specific method for obtaining the user characteristic influence coefficient is as follows:

[0018] Obtain characteristic factors that record the user's gender, age, and occupation. Represent each characteristic factor with one or more parameters. Represent gender with binary variables, age with continuous variables, and occupation with categorical variables; Use machine learning to obtain a characteristic vector for each user; Based on the characteristic vector, obtain the corresponding emotional state data for the user. Each user's emotional state can be represented by one or more emotional factors, forming an emotional vector; Through regression analysis of the user's characteristic vector and the corresponding emotional vector, obtain the influence coefficients of different user characteristic factors on the emotional state.

[0019] Technical effects and advantages of the lighting AI voice control system based on WiFi and Bluetooth dual protocols of the present invention:

[0020] 1. The present invention realizes intelligent lamp control through the collaboration of functional modules such as pattern recognition, emotion analysis and intelligent adjustment. Users can select the light adjustment method through voice commands and adaptive modes, which enhances the flexibility of the system and user satisfaction. Emotion analysis and personalized services make light adjustment more intelligent and considerate, improving the quality of life and experience of users. At the same time, through intelligent interaction and emotional care, the system makes smart lamps not only simple lighting equipment, but also emotional partners in the lives of users, enhancing the user's sense of identity and dependence on the system.

[0021] 2. The present invention can realize adaptive adjustment of intelligent lamps by combining a speech analysis module and a state analysis module. Specifically, the speech analysis module uses the tone emotion coefficient, the vocabulary emotion coefficient and the user characteristic influence coefficient to establish a lighting adaptation model, generate a lighting control coefficient, and generate a first control scheme accordingly. The state analysis module optimizes the control scheme according to the holiday and time data and generates a second control scheme. The system has significant technical effects and can dynamically adjust the lighting settings according to the user's emotions and the current scene, providing a more personalized and intelligent lighting experience, improving user satisfaction and usage experience, and enhancing the flexibility and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The figure is a schematic diagram of the structure of the AI ​​voice control system for lamps based on WiFi and Bluetooth dual protocols of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Embodiment 1, Figure 1 The present invention provides a lighting AI voice control system based on WiFi and Bluetooth dual protocols, including a pattern recognition module, a voice receiving module, a voice analysis module, a state analysis module, and an intelligent adjustment module;

[0025] Wherein, the mode recognition module is used to obtain the voice mode of the current lamp, and the voice mode includes an adaptive mode and a normal mode;

[0026] A voice receiving module, used to collect the user's voice data, where the voice data includes emotion data, vocabulary data, and user data;

[0027] A voice analysis module, used to establish a user lighting adaptation model based on machine learning according to the voice data, generate a lighting control coefficient, and obtain a first control scheme according to the lighting control coefficient;

[0028] A status analysis module, used to obtain the current status data, where the status data includes festival data and time data, and optimize the first control scheme in combination with the lighting control coefficient to obtain a second control scheme;

[0029] An intelligent adjustment module: used to control the lighting fixture according to the second control scheme when receiving the voice mode of the adaptive mode.

[0030] The WiFi protocol refers to a wireless network communication protocol, which is usually used to connect electronic devices to a local area network (LAN) and the Internet. It is based on the IEEE 802.11 standard and uses the 2.4GHz or 5GHz frequency band for communication. The WiFi technology provides a high-speed data transmission rate and can support a variety of network services, such as Internet access, file sharing, streaming media, etc. Through the WiFi connection, users can wirelessly connect to the network at home, in the office, or in public places to achieve convenient communication and information exchange. The popularization of the WiFi technology has made the wireless network an indispensable part of modern life, promoting the development of the mobile Internet and the wide application of intelligent devices;

[0031] The Bluetooth protocol is a short-range wireless communication protocol designed to achieve data exchange and connection between various devices. It uses low-power radio frequency technology to communicate in the 2.4GHz frequency band, allowing devices to communicate with each other within a range of about 10 meters. The Bluetooth technology is widely used in various portable devices such as mobile phones, tablets, smart speakers, and headphones to achieve functions such as audio transmission, file sharing, and device control. The characteristics of the Bluetooth protocol include a simple and easy-to-use connection process, a low-power design, and wide device compatibility, making it an important technology in the field of modern wireless communication;

[0032] The AI voice control system is an intelligent control system based on artificial intelligence technology, which can interact with users through voice commands and execute corresponding tasks according to the user's instructions. The system usually includes modules such as voice recognition, natural language understanding, dialogue management, and execution control. Through the cooperation of these modules, it can identify the user's voice commands, understand the user's intentions, and take corresponding measures to complete the tasks. The AI voice control system is widely used in fields such as smart homes, smartphones, and car navigation systems, enabling users to operate devices through natural voice interaction, greatly improving the convenience and intelligence of the user experience.

[0033] In traditional lighting voice control systems, the adjustment of lights often rigidly follows the user's commands without considering the user's emotions and the current scene, and does not provide an intelligent adjustment mode. Therefore, this application provides a lighting AI voice control system for intelligently analyzing the user's emotions to achieve the purpose of adaptively adjusting the lights.

[0034] A pattern recognition module is used to obtain the voice pattern of the current lighting fixture, and the voice pattern includes an adaptive mode and a normal mode;

[0035] Specifically, this lighting fixture includes an adaptive mode and a normal mode. The normal mode is a normal voice control system that does not involve emotion analysis; the adaptive mode is to turn on the function of this system;

[0036] The pattern recognition module gives users more choices and autonomy. Users can choose their favorite mode without being forced to use a certain mode, which increases the flexibility of the system, improves user satisfaction and usage experience; through the pattern recognition module, the system can be conveniently upgraded and expanded in functions. For example, new modes can be introduced in the future to enhance the functions and practicality of the system;

[0037] The pattern recognition module can record and analyze the usage patterns and habits of users, helping developers understand user needs, further optimizing system design, and providing a better user experience.

[0038] A voice reception module is used to collect the user's voice data, and the voice data includes emotion data, vocabulary data, and user data;

[0039] The emotion data includes a tone emotion coefficient; the vocabulary data includes a vocabulary sentiment coefficient; the user data includes a user characteristic influence coefficient.

[0040] Emotion data plays a key role in the lighting AI voice control system. By capturing and analyzing the user's emotional state, it helps the system make more intelligent and user-friendly decisions. Specifically, emotion data can reflect the user's current emotions, such as happiness, sadness, anger, etc., through features such as voice tone, volume, and speech rate. Using this emotion data, the system can dynamically adjust the color, brightness, and mode of the lights to better match the user's mood and needs. For example, when the user feels tired, the system can automatically dim the lights to create a comfortable environment; when the user is in a high mood, the brightness and color saturation of the lights can be increased to enhance the vitality of the environment. In this way, emotion data not only improves the user's usage experience but also makes the intelligent lighting more considerate and emotionally caring.

[0041] The tone emotion coefficient has the following advantages for analyzing the user's mood state and further controlling the lighting of the fixture:

[0042] Enhance user experience: Adjusting the lighting according to the user's emotional state can provide a more considerate and user - demand - compliant lighting environment. For example, when the user feels stressed or down, the system can adjust the lighting to create a relaxing and comfortable atmosphere, enhancing the user's overall experience.

[0043] Personalized service: Through the tone - emotion coefficient, the system can provide personalized lighting settings for each user, meeting the lighting needs of different users in different emotional states and increasing the personalization and intelligence of the system.

[0044] Health and comfort: Different lighting settings have a direct impact on people's emotions and psychology. Adjusting the lighting according to the user's emotional state helps improve the user's mood and mental health, providing a more comfortable living environment. For example, soft lighting can relieve anxiety, while bright lighting can improve alertness and work efficiency.

[0045] Intelligent interaction: By analyzing the tone - emotion coefficient, the system can achieve more natural and intelligent user interaction. Users do not need to manually adjust the lighting settings, and the system can automatically adjust according to voice commands and emotional states, improving the convenience and satisfaction of user interaction.

[0046] Energy conservation and environmental protection: Adjusting the lighting according to the user's emotions and actual needs can avoid unnecessary energy waste. For example, when the user is relaxing and resting, the system can automatically lower the lighting brightness, thus saving electricity and achieving the goal of energy conservation and environmental protection.

[0047] Emotional care: The system provides emotional care and support by perceiving and responding to the user's emotional state, making the intelligent lighting fixture not only a functional device but also an emotional partner in the user's life, enhancing the user's sense of belonging and satisfaction.

[0048] Data - driven optimization: By collecting and analyzing a large amount of user emotion data, the system can continuously optimize the lighting control algorithm and strategy, improve the intelligence level of the lighting fixture, and provide data support for future improvements and innovations.

[0049] Therefore, the application of the tone - emotion coefficient not only enhances the intelligence and humanization of the lighting fixture AI voice control system but also has significant advantages in aspects such as improving user experience, health and comfort, and energy conservation and environmental protection.

[0050] In this embodiment, the specific method for obtaining the tone - emotion coefficient is as follows:

[0051] First, extract the time - domain waveform of the fundamental frequency through voice signal processing technology, and then calculate the fundamental frequency change rate as the frequency feature.

[0052] Extract the volume and intensity through voice signal processing technology, and calculate the energy mean value as the energy feature;

[0053] Extract the speech rate of the voice signal and the duration of the speech segment through voice signal processing technology, and calculate the duration change rate as the duration feature;

[0054] Calculate the tone emotion coefficient through machine learning by integrating the frequency feature, energy feature, and duration feature.

[0055] Among them, the specific calculation formula of the fundamental frequency change rate JP is as follows:

[0056]

[0057] The specific calculation formula of the energy mean value NL is as follows:

[0058]

[0059] The specific calculation formula of the duration change rate SC is as follows:

[0060]

[0061] The specific calculation formula of the tone emotion coefficient is as follows:

[0062]

[0063] In the formula, α is the tone emotion coefficient, N is the total number of data, fo i is the fundamental frequency of the i-th speech frame, YL i is the volume, T i is the time of the i-th speech frame.

[0064] It can be seen from the calculation expression of the tone emotion coefficient that when the value of the tone emotion coefficient is larger, the emotion of the user is lower; on the contrary, when the value of the tone emotion coefficient is larger, the emotion of the user is higher.

[0065] The lexical sentiment coefficient is a quantitative index used to analyze the emotional color in language, and it is calculated by classifying and scoring the emotions of the words in the text. Specifically, it can map each word in the text to a specific emotional dimension in the emotional space and assign it an emotional intensity score based on an emotional dictionary or a trained emotion classification model; by calculating the emotional scores of all the words in the text, the overall emotional tendency of the text can be obtained, so as to more accurately grasp the emotion or emotional state expressed by the text. The application scope of the lexical sentiment coefficient is wide, including fields such as sentiment analysis, public opinion monitoring, and text emotion generation, providing an effective tool and index for deeply understanding and mining the emotional information in text information.

[0066] The lexical sentiment coefficient has the following advantages for analyzing the user's mood state and further controlling the lighting of the lamp:

[0067] Precise emotion analysis: By performing emotion analysis on the words in the voice command or text, the emotional state of the user can be accurately captured, helping the system to more precisely understand the user's mood needs;

[0068] Diverse emotion recognition: The lexical sentiment coefficient can recognize and analyze various emotions contained in the voice, not limited to basic emotional states, but also able to perceive more subtle emotional changes, making the lamp control more rich and detailed;

[0069] Personalized lighting adjustment: According to the user's mood state analyzed by the lexical sentiment coefficient, the lamp can achieve personalized lighting adjustment, providing a customized lighting experience for users in different emotional states, and enhancing user satisfaction and comfort;

[0070] Intelligent emotion interaction: Based on the analysis results of the lexical sentiment coefficient, the lamp can achieve more intelligent emotion interaction. For example, when the user is in a low mood, the lamp can automatically adjust to a soft warm - toned light, creating a warm and peaceful atmosphere, providing emotional support and care for the user;

[0071] Real - time dynamic adjustment: The lexical sentiment coefficient can monitor the user's emotional changes in real time and adjust the light in a timely manner to adapt to the user's current emotional state. This dynamic adjustment can keep the light in sync with the user's mood, enhancing the user experience and emotional connection;

[0072] Improve the quality of life: By perceiving the user's emotional state and adjusting the light, the lamp not only provides basic lighting functions, but also can become an emotional partner in the user's life, creating a more comfortable and warm home environment for the user, and improving the quality of life;

[0073] Therefore, the application of the lexical sentiment coefficient enables the lamp to more intelligently perceive and understand the user's emotional state, thereby achieving more personalized and intelligent lighting control, bringing more emotional experiences and care to the user's life.

[0074] In this embodiment, the specific method for obtaining the lexical sentiment coefficient is as follows:

[0075] Emotional word analysis: Using natural language processing technology, construct an emotional word dictionary, which includes positive and negative emotional words. Then, analyze the user's text input and calculate the quantity and intensity of positive and negative emotional words appearing in the text;

[0076] Emotion classification model: Using emotion analysis algorithms or deep - learning models, classify the user's text into different emotion categories;

[0077] Set the emotional scores and weights for different text emotion data, and calculate the weighted average of the emotional words in all texts to obtain the text emotion coefficient.

[0078] Among them, the specific calculation formula for the word emotional coefficient is as follows:

[0079]

[0080] In the formula, β represents the word emotional coefficient, and x i represents the emotional score of the i-th emotional word, and w i represents the weight of the i-th emotional word.

[0081] It can be seen from the calculation expression of the word emotional coefficient that when the value of the word emotional coefficient is larger, the user's emotion is higher; on the contrary, when the value of the word emotional coefficient is larger, the user's mood is lower.

[0082] The user characteristic influence coefficient is used to consider the personalized influence factors of different user groups on the lighting needs. Different user groups, such as men, women, children, the elderly, young people, etc., may have different preferences and needs for the brightness, color temperature, scene, etc. of the lighting. Therefore, the user characteristic influence coefficient is used to quantify the influence degree of different user groups on the lighting needs, so as to realize personalized lighting adjustment in the intelligent lighting control system.

[0083] The user characteristic influence coefficient has the following advantages for analyzing the user's mood state and further controlling the lighting of the lamp:

[0084] Personalized customization: Considering the characteristics and preferences of different user groups, the user characteristic influence coefficient can help to realize the personalized customization of the lamp, adjust the brightness, color temperature, scene, etc. of the lighting according to factors such as the user's gender, age, and physiological characteristics, and provide a lighting experience that better meets the user's needs;

[0085] Emotional matching: Different user groups may have different needs for the emotional expression of the lighting. For example, the elderly may prefer soft and comfortable lighting, while young people may prefer lively and bright lighting; through the user characteristic influence coefficient, the lighting can be better matched with the user's emotional state, improving the user's emotional experience and comfort;

[0086] Intelligent adjustment: Based on the user characteristic influence coefficient, the lamp can achieve intelligent adjustment, automatically adjust the lighting parameters according to the user's characteristic features and current mood state, and provide more considerate and intelligent lighting services for the user;

[0087] Emotional connection: By considering the user characteristic influence coefficient, the lamp can better establish an emotional connection with the user, making the lighting not only a simple lighting tool, but also an emotional partner in the user's life, providing emotional support and care for the user;

[0088] Improvement of user satisfaction: Personalized lighting adjustment can better meet the needs and expectations of users, enhance user satisfaction and experience, increase users' favorability and desire to use smart lamps, thereby promoting the popularization and application of smart lamps;

[0089] Therefore, the application of the user characteristic influence coefficient enables smart lamps to perceive and understand the user's mood state more intelligently and personalized, thereby realizing more intelligent and user-friendly lighting control and improving the user's quality of life and experience.

[0090] In this embodiment, the specific method for obtaining the user characteristic influence coefficient is as follows:

[0091] Obtain the characteristic factors that record the user's gender, age, and occupation, and represent each characteristic factor with one or more parameters. Gender is represented by a binary variable, age is represented by a continuous variable, and occupation is represented by a categorical variable; obtain a characteristic vector for each user through machine learning;

[0092] Based on the characteristic vector, obtain the emotional state data corresponding to the user. The emotional state of each user can be represented by one or more emotional factors, forming an emotional vector;

[0093] Perform regression analysis on the user's characteristic vector and the corresponding emotional vector to obtain the influence coefficients of different user characteristic factors on the emotional state;

[0094] Among them, the specific calculation formula of the user characteristic influence coefficient is as follows:

[0095] χ = (X T *X) -1 *X T *Y

[0096] In the formula, X is the user characteristic factor matrix, Y is the user emotional state matrix, (X T *X) -1 represents the inverse matrix of X T *X, and χ is the user characteristic influence coefficient.

[0097] It can be seen from the calculation expression of the user characteristic influence coefficient that when the value of the user characteristic influence coefficient is larger, the user's mood is lower; on the contrary, when the value of the user characteristic influence coefficient is larger, the user's mood is higher.

[0098] This embodiment realizes intelligent lamp control through the collaboration of functional modules such as pattern recognition, emotion analysis and intelligent adjustment. Users can select the light adjustment method through voice commands and adaptive mode, which enhances the flexibility of the system and user satisfaction. Emotion analysis and personalized services make light adjustment more intelligent and considerate, improving the quality of life and experience of users. At the same time, through intelligent interaction and emotional care, the system makes smart lamps not only simple lighting equipment, but also emotional partners in the lives of users, enhancing users' recognition and dependence on the system.

[0099] Embodiment 2, a speech analysis module is used to establish a user lighting adaptation model based on machine learning according to speech data, generate a lighting control coefficient, and obtain a first control scheme according to the lighting control coefficient.

[0100] Specifically, the specific calculation formula of the lighting control coefficient is as follows:

[0101]

[0102] Where KZ is the lighting control coefficient, η1 is the correction factor of the tone emotion coefficient, η2 is the correction factor of the vocabulary emotion coefficient, η3 is the correction factor of the user characteristic influence coefficient, α is the tone emotion coefficient, β is the vocabulary emotion coefficient, and χ is the user characteristic influence coefficient.

[0103] A state analysis module is used to obtain current state data, which includes holiday data and time data, and optimize the first control scheme in combination with the lighting control coefficient to obtain a second control scheme;

[0104] Specifically, set the date importance benchmark score Kj 基 ; Get the importance of the current date. The importance of the date is divided into ordinary, holiday, and important date. When the importance of the date is ordinary, Kj1 points are added. When the importance of the date is an important date, Kj2 points are subtracted. When the importance of the date is a holiday, no points are added or subtracted. When the importance of the date is ordinary, Kj2=0, Kj3=0. When the importance of the date is a holiday, Kj1=0, Kj3=0. When the importance of the date is an important date, Kj1=0, Kj1=0. By formula Kj 基 +Kj1-Kj2=Kj 终 , calculate the date score Kj 终 ;

[0105] Set the time base minute and label it as Fj 基, obtain the real-time time. When the real-time time exceeds the preset value A1, subtract Fj1 minutes. When the real-time time is between A1 and A2, add Fj2 minutes. When the real-time time is lower than A2, subtract Fj3 minutes. When the real-time time exceeds the preset value A1, both Fj2 and Fj3 are 0. When the real-time time is between A1 and A2, both Fj1 and Fj3 are 0. When the real-time time is lower than A2, Fj1 and Fj2 are 0; Through the formula Fj 基 −Fj1 + Fj2 − Fj3 = Fj 终 to obtain the time in minutes;

[0106] Assign the importance reference score Kj to the date 基 a correction value θ i , assign the time reference score Fj 基 a correction value δ i , θ i > δ i , θ i + δ i = 1; Through the formula Kj 终 * θ i + Fj 终 * δ i = Ju d , obtain the correction coefficient Ju d .

[0107] Multiply the correction coefficient by the lighting control coefficient to obtain the scheme comparison value;

[0108] Correspond the scheme comparison value with the preset scheme comparison table to obtain the second control scheme;

[0109] In this embodiment, through the combination of the voice analysis module and the status analysis module, the present invention can achieve the adaptive adjustment of intelligent lamps. Specifically, the voice analysis module uses the tone emotion coefficient, the vocabulary emotion coefficient and the user characteristic influence coefficient to establish a lighting adaptation model, generate a lighting control coefficient, and generate a first control scheme accordingly. The status analysis module optimizes the control scheme according to the festival and time data and generates a second control scheme. The technical effect of this system is remarkable. It can dynamically adjust the lighting settings according to the user's mood and the current scene, provide a more personalized and intelligent lighting experience, improve the user's satisfaction and usage experience, and at the same time enhance the flexibility and adaptability of the system.

[0110] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0111] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0112] Those of ordinary skill in the art will realize that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0113] In addition, the functional modules in the various embodiments of this application can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0114] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0115] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. AI voice control system for lamps based on WiFi and Bluetooth dual protocols, characterized by: It includes pattern recognition module, speech reception module, speech analysis module, state analysis module and intelligent adjustment module; A mode recognition module, used to obtain the voice mode of the current lamp, wherein the voice mode includes an adaptive mode and a normal mode; A voice receiving module, used to collect the user's voice data, wherein the voice data includes emotion data, vocabulary data and user data; A speech analysis module, used to establish a user lighting adaptation model based on machine learning according to the speech data, generate a lighting control coefficient, and obtain a first control scheme according to the lighting control coefficient; A state analysis module is used to obtain current state data, which includes holiday data and time data, and optimize the first control scheme in combination with the lighting control coefficient to obtain a second control scheme; The state data includes holiday data and time data, and the first control scheme is optimized in combination with the lighting control coefficient as follows: Set the date importance benchmark score Kj 基 ; Get the importance of the current date. The importance of the date is divided into ordinary, holiday, and important date. When the importance of the date is ordinary, Kj1 points are added. When the importance of the date is an important date, Kj2 points are subtracted. When the importance of the date is a holiday, no points are added or subtracted. When the importance of the date is ordinary, Kj2=0, Kj3=0. When the importance of the date is a holiday, Kj1=0, Kj3=0. When the importance of the date is an important date, Kj1=0, Kj1=0. By formula Kj 基 +Kj1-Kj2=Kj 终 , calculate the date score Kj 终 ; Set the time base minute and label it as Fj 基 , get the real-time time, when the real-time time exceeds the preset value A1, subtract Fj1 points, when the real-time time is between A1 and A2, add Fj2 points, when the real-time time is lower than A2, subtract Fj3 points, when the real-time time exceeds the preset value A1, Fj2 and Fj3 are both 0, when the real-time time is between A1 and A2, Fj1 and Fj3 are both 0, when the real-time time is lower than A2, Fj1 and Fj2 are 0; through the formula Fj 基 -Fj1+Fj c -Fj3=Fj 终 Get time sharing; Assign date importance benchmark score Kj 基 A correction value θ i , giving the time base fraction Fj 基 A correction value δ i ,θ i >δ i ,θ i +δ i =1; through the formula Kj 终 *θ i +Fj 终 *δ i =Ju d , and obtain the correction coefficient Ju d ; Multiply the correction coefficient by the lighting control coefficient to obtain the scheme comparison value; Comparing the scheme control value with the preset scheme control table to obtain a second control scheme; Intelligent adjustment module: used to control the lamp according to the second control scheme when receiving the voice mode of the adaptive mode.

2. The AI ​​voice control system for lamps based on WiFi and Bluetooth dual protocols according to claim 1 is characterized in that: The emotional data includes the emotional coefficient of tone; the vocabulary data includes the emotional coefficient of vocabulary; and the user data includes the user characteristic influence coefficient.

3. The AI ​​voice control system for lamps based on WiFi and Bluetooth dual protocols according to claim 2 is characterized in that: The specific calculation formula of the lighting control coefficient is as follows: Where KZ is the lighting control coefficient, η1 is the correction factor of the tone emotion coefficient, η2 is the correction factor of the vocabulary emotion coefficient, η3 is the correction factor of the user characteristic influence coefficient, α is the tone emotion coefficient, β is the vocabulary emotion coefficient, and χ is the user characteristic influence coefficient.

4. The AI ​​voice control system for lamps based on WiFi and Bluetooth dual protocols according to claim 3 is characterized in that: The specific method for obtaining the tone emotion coefficient is as follows: First, the time domain waveform of the fundamental frequency is extracted through speech signal processing technology, and then the fundamental frequency change rate is calculated as the frequency feature; The volume and intensity are extracted through speech signal processing technology, and the energy mean is calculated as the energy feature; The speech rate and duration of the speech segment of the speech signal are extracted through speech signal processing technology, and the duration change rate is calculated as the duration feature; The tone and emotion coefficient is calculated through machine learning by integrating frequency features, energy features, and duration features.

5. The AI ​​voice control system for lamps based on WiFi and Bluetooth dual protocols according to claim 4 is characterized in that: The specific method for obtaining the vocabulary sentiment coefficient is as follows: Sentiment vocabulary analysis: Use natural language processing technology to build a sentiment vocabulary dictionary that includes positive and negative sentiment words; then analyze the user's text input and calculate the number and intensity of positive and negative sentiment words that appear in the text; Sentiment classification model: Use sentiment analysis algorithms or deep learning models to classify user texts into different sentiment categories; set sentiment scores and weights for different text sentiment data, and take the weighted average of all sentiment words in the text to obtain the text sentiment coefficient.

6. The AI ​​voice control system for lamps based on WiFi and Bluetooth dual protocols according to claim 5 is characterized in that: The specific method for obtaining the user characteristic influence coefficient is as follows: Obtain characteristic factors that record the user's gender, age, and occupation, and represent each characteristic factor with one or more parameters. Gender is represented by a binary variable, age is represented by a continuous variable, and occupation is represented by a categorical variable; obtain a characteristic vector of each user through machine learning; Based on the feature vector, the emotional state data corresponding to the user is obtained. The emotional state of each user can be represented by one or more emotional factors to form an emotional vector; The user's characteristic vector and the corresponding emotion vector are subjected to regression analysis to obtain the influence coefficient of different user characteristic factors on the emotion state.

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