Intelligent shape dynamic controllable sound control canvas system

Abstract

The application discloses a kind of wisdom shape dynamic controllable sound control canvas system, specifically to artificial intelligence technical field, including sound control canvas information acquisition module, text preprocessing module, canvas information analysis module, comprehensive analysis module, comprehensive judgment module and intelligent feedback module.First canvas text corresponding to target canvas area is obtained and preprocessed, and first feature information group is obtained;For obtaining wisdom shape dynamic model, the first feature information group is input into the wisdom shape dynamic model to obtain the second feature information group, and the second feature information group is imported into the comprehensive analysis model to obtain the comprehensive canvas accurate value, the comprehensive canvas accurate value of target canvas area is obtained by presetting the comprehensive canvas threshold value, the comprehensive canvas accurate value and the preset comprehensive canvas threshold value are compared and feedback according to the comprehensive canvas accurate value, which greatly improves the accuracy and comprehensiveness of target building structure design evaluation.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and more specifically, to a smart, dynamically controllable voice-controlled canvas system. Background Technology

[0002] The application of artificial intelligence (AI) technology in intelligent, dynamically controllable, voice-activated canvas systems is becoming increasingly widespread, with a reliable technological foundation. Advances in image recognition and audio processing technologies have further boosted its application. Various advanced sensors and data acquisition devices are constantly emerging, such as high-definition cameras and high-sensitivity microphones, enabling precise data collection from all aspects of the voice-activated canvas system. This data encompasses multi-dimensional information such as image color, pattern shape, sound frequency, and sound intensity, providing rich material for the application of AI in the evaluation of voice-activated canvas systems. Simultaneously, continuous progress in advanced data cleaning and feature extraction technologies has improved the quality and usability of data, creating favorable conditions for training accurate evaluation models.

[0003] The existing evaluation of intelligent, dynamically controllable voice-activated canvas systems includes a data acquisition module, a model processing module, and an effect evaluation module. The data acquisition module uses specialized equipment to acquire relevant data about the canvas system, such as canvas pixel information, dynamic changes in the image, and characteristics of voice commands. The model processing module analyzes the acquired data to comprehensively demonstrate the operational status of the voice-activated canvas system, including analysis of image display effects, voice recognition accuracy, and the timeliness of interactive responses. The effect evaluation module compares the results with preset standards to identify potential problems in the voice-activated canvas system, such as canvas color deviation, misinterpretation of voice commands, and excessive interaction delays.

[0004] However, some shortcomings still exist in practical use. In the data acquisition module, when collecting data from the voice-controlled canvas using various devices, it is easily affected by complex environmental factors, leading to a decrease in data accuracy. For example, changes in ambient light and surrounding noise may affect the acquisition accuracy of high-definition cameras and high-sensitivity microphones, causing errors in the acquired canvas images and sound commands. In the model processing module, when analyzing the acquired data, existing processing technologies may experience biased analysis results and data loss when dealing with complex voice-controlled canvas data, creating data gaps and reducing the data's support for system evaluation. The existing evaluation system lacks indicators that comprehensively consider the overall interactive experience of the voice-controlled canvas, resulting in an incomplete and unobjective evaluation, increasing the difficulty and risk of optimization decisions, and causing considerable trouble for the evaluation of the voice-controlled canvas system.

[0005] Therefore, there is an urgent need for an intelligent, dynamically controllable, voice-controlled canvas system to solve the above problems. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an intelligent, dynamically controllable voice-controlled canvas system, which addresses the problems raised in the background art through the field of artificial intelligence technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a smart, dynamically controllable voice-activated canvas system, comprising a system operation database, a system central processing module, and a user information terminal, and further comprising: a voice-activated canvas information acquisition module, a text preprocessing module, a canvas information analysis module, a comprehensive analysis module, a comprehensive judgment module, and an intelligent feedback module.

[0008] Voice-controlled canvas information acquisition module: used to set different types of multi-source sensors and intelligent operations in the target canvas area, and acquire the first canvas text corresponding to the target canvas area;

[0009] Text preprocessing module: performs preprocessing operations on the first canvas text, obtains the first feature information group corresponding to the first canvas text, determines the first feature information group to be filtered into sub-information, and sequentially numbers each sub-information as i = 1, 2, ..., n;

[0010] Canvas Information Analysis Module: Used to acquire the intelligent dynamic model, input the first feature information group into the intelligent dynamic model to obtain the second feature information group; includes a voice control performance analysis unit, an environmental acoustic analysis unit, a canvas display analysis unit, and a canvas dynamic analysis unit;

[0011] Comprehensive Analysis Module: Used to acquire the comprehensive analysis model, import the second feature information group into the comprehensive analysis model to obtain the precise value of the comprehensive canvas; includes comprehensive analysis units;

[0012] Comprehensive Judgment Module: Obtains the preset comprehensive canvas threshold, compares the precise value of the comprehensive canvas with the preset comprehensive canvas threshold, and transmits the judged digital signal to the intelligent feedback module;

[0013] Intelligent feedback module: Based on the digital signals from the comprehensive judgment module, it sends preset intelligent feedback to the administrators corresponding to the target canvas area.

[0014] Preferably, the first canvas text is denoted as follows: intonation transition frequency as sy, syllable duration ratio as sj, hue offset as sx, pixel flicker density as sm, color saturation change slope as hg, flicker duty cycle of elements in the image as h1, spatial frequency of pattern as hf, hue angle change rate as ts, flicker frequency of image color as tf, curvature of image element movement trajectory as tg, interference fringe density as bk, spectral entropy value of ambient sound as bj, and ambient noise frequency as by.

[0015] Preferably, the voice control performance analysis unit is used to establish a voice control performance analysis model, import the first feature information group processed by the text preprocessing module into the voice control performance analysis model, and calculate the voice control performance coefficient value of each sub-information, specifically expressed as follows:

[0016]

[0017] S i sy represents the voice control performance coefficient value of the i-th sub-information. i-1 sy represents the intonation shift frequency of the (i-1)th sub-information. i sj represents the intonation shift frequency of the i-th sub-information. i sx represents the ratio of syllable duration to the i-th sub-information. i sm represents the hue offset of the i-th sub-information. i represents the pixel flicker density of the i-th sub-information, and n represents the total number of sub-information groups.

[0018] Preferably, the environmental acoustic analysis unit is used to establish an environmental acoustic analysis model, import the first feature information group obtained by the text preprocessing module into the environmental acoustic analysis model, and calculate the environmental acoustic coefficient values ​​of each sub-information, specifically expressed as follows:

[0019]

[0020] H i hg represents the environmental acoustic coefficient value of the i-th sub-information. i hl represents the slope of the color saturation change of the i-th sub-information. i hf represents the blink duty cycle of the element in the frame of the i-th sub-information. i-1 hf represents the spatial frequency of the pattern of the (i-1)th sub-information. i Let n represent the spatial frequency of the pattern of the i-th sub-information, and n represent the total number of sub-information groups.

[0021] Preferably, the canvas display analysis unit is used to establish a canvas display analysis model, import the first feature information group processed by the text preprocessing module into the canvas display analysis model, and calculate the canvas display coefficient value of each sub-information, specifically expressed as follows:

[0022]

[0023] T i ts represents the canvas display coefficient value of the i-th sub-information. i tf represents the rate of change of the hue angle of the i-th sub-information. i-1 The flicker frequency of the screen color in the (i-1)th sub-information, tf i tg represents the flashing frequency of the screen color of the i-th sub-information.i Let represent the curvature of the movement trajectory of the i-th sub-information element, and n represent the number of sub-information elements.

[0024] Preferably, the canvas dynamic analysis unit is used to establish a canvas dynamic analysis model, import the first feature information group processed by the text preprocessing module into the canvas dynamic analysis model, and calculate the canvas dynamic coefficient value of each sub-information, specifically expressed as follows:

[0025]

[0026] B i bk represents the canvas dynamic coefficient value of the i-th sub-information. i Let bj represent the interference fringe density of the i-th sub-information. i The spectral entropy value of the ambient sound of the i-th sub-information is represented by , by i Let represent the environmental noise frequency of the i-th sub-information, and K represent the Boltzmann constant.

[0027] Preferably, the comprehensive analysis unit is used to establish a comprehensive analysis model, import the second feature information group into the comprehensive analysis model, and calculate the precise value of the comprehensive canvas, specifically expressed as follows:

[0028]

[0029] Where P represents the composite canvas precision value, S i H represents the voice control performance coefficient value of the i-th sub-information. i T represents the environmental acoustic coefficient value of the i-th sub-information. i B represents the canvas display coefficient value of the i-th sub-information. i β1, β2, β3, and β4 represent the canvas dynamic coefficients of the i-th sub-information, and all of them are greater than zero. α represents other influencing factors that integrate the canvas precision value.

[0030] Preferably, the preset comprehensive canvas threshold is a precise value of the voice-controlled canvas based on statistical data from previous years; the specific determination is as follows:

[0031] B1: If the comprehensive canvas precision value is greater than the preset comprehensive canvas threshold, a danger signal will be issued;

[0032] B2: If the comprehensive canvas precision value is less than the preset comprehensive canvas threshold, a good signal will be issued.

[0033] Preferably, the preset intelligent feedback methods include: text intelligent feedback, image intelligent feedback, and voice intelligent feedback.

[0034] The technical effects and advantages of this invention are as follows:

[0035] 1. This invention uses different types of multi-source sensors and intelligent operation to set up a target canvas area, obtain the first canvas text corresponding to the target canvas area, and perform text preprocessing to obtain the first feature information group corresponding to the first canvas text. The first feature information group is then filtered into sub-information, which helps to improve the comprehensiveness of the controllable voice-controlled canvas control in the future, and at the same time improves the data accuracy of the controllable voice-controlled canvas evaluation.

[0036] 2. This invention, by acquiring a smart dynamic model and inputting the first feature information group into the smart dynamic model to obtain a second feature information group, can promptly identify problems existing in the controllable voice-controlled canvas, which helps to ensure the intelligent analysis of the controllable voice-controlled canvas and improves the reliability of the controllable voice-controlled canvas evaluation; it is also used to acquire a comprehensive analysis model, by importing the second feature information group into the comprehensive analysis model to obtain the comprehensive canvas accurate value, which helps to present a more intuitive effect and the overall evaluation to managers;

[0037] 3. This invention obtains digital signals through a comprehensive judgment module, which helps improve the accuracy of judgment and remove outliers in controllable voice-controlled canvases. Through a preset intelligent feedback method, the digital signals emitted by the system can be detected in real time. Once a danger signal is received, the system will feed back the processing results to the management personnel, who will then perform intelligent dynamic optimization and adjustment of the target canvas area, which helps improve the comprehensiveness and accuracy of controllable voice-controlled canvases. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the system structure of the present invention.

[0039] Figure 2 This is a schematic diagram of the overall structure of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] As attached Figure 1 The diagram shows a structural schematic of an intelligent, dynamically controllable voice-controlled canvas system, including a system operation database, a system central processing module, and a user information terminal. It also includes a voice-controlled canvas information acquisition module, a text preprocessing module, a canvas information analysis module, a comprehensive analysis module, a comprehensive judgment module, and an intelligent feedback module.

[0042] As attached Figure 2The diagram shown is an overall structural schematic of an intelligent, dynamically controllable voice-controlled canvas system, a system database, a system processor, and a system information terminal.

[0043] The output of the voice-controlled canvas information acquisition module is connected to the input of the text preprocessing module, the output of the text preprocessing module is connected to the input of the canvas information analysis module, the output of the canvas information analysis module is connected to the input of the comprehensive analysis module, the output of the comprehensive analysis module is connected to the input of the comprehensive judgment module, and the output of the comprehensive judgment module is connected to the input of the intelligent feedback module.

[0044] Voice-controlled canvas information acquisition module: used to set different types of multi-source sensors and intelligent operations in the target canvas area, and acquire the first canvas text corresponding to the target canvas area.

[0045] In this embodiment, it should be specifically noted that the first canvas text is denoted as follows: intonation transition frequency is denoted as sy, syllable duration ratio is denoted as sj, hue offset is denoted as sx, pixel flicker density is denoted as sm, color saturation change slope is denoted as hg, the flicker duty cycle of elements in the image is denoted as h1, the spatial frequency of the pattern is denoted as hf, the hue angle change speed is denoted as ts, the flicker frequency of the image color is denoted as tf, the curvature of the movement trajectory of the image element is denoted as tg, the interference fringe density is denoted as bk, the spectral entropy value of the ambient sound is denoted as bj, and the ambient noise frequency is denoted as by.

[0046] In this embodiment, the specific methods and installation locations for acquiring the text on the first canvas are explained as follows: The intonation transition frequency is acquired using a high-precision microphone, converted into a digital signal, and analyzed using speech processing software. The microphone is installed close to the speaker and can clearly receive the speech. The syllable duration ratio acquisition is similar; the speech syllables are first segmented, and then the duration is measured to calculate the ratio. Hue offset is measured using a color sensor or image acquisition device; the initial and changed hue values ​​are subtracted, and the device is directly facing the screen. Pixel flicker density is measured using a high-resolution image sensor with continuous shooting, and pixel brightness changes are analyzed and calculated. The image sensor must be able to capture the entire image. The slope of color saturation change is measured using a color measuring instrument; saturation is continuously measured, and a fitted curve is used to calculate the slope. The instrument must be directly facing the screen, and the environment must be stable. The duty cycle of screen element flicker is measured using an image acquisition device and algorithm, and element brightness changes are analyzed and calculated. The camera must be able to capture the entire image containing the target element. The pattern spatial frequency is determined by analyzing the spectrum using a two-dimensional Fourier transform after acquiring the image with an image sensor. The hue angle change rate is recorded and calculated in real time using a color sensor or related equipment. The equipment must be directly facing the screen and stable. The flicker frequency of the screen colors is calculated using a light sensor based on the number of color changes and the time interval. The sensor must be placed correctly and free from interference, and the camera must be directly facing the screen. The curvature of the movement trajectory of screen elements is captured by a high-speed camera. An algorithm tracks the elements and calculates the values. The camera must be able to record the entire scene, and the shooting angle must be appropriate. The density of interference fringes is measured using an optical interferometer. The spacing is calculated by analyzing the fringe pattern received by a detector, and the instrument is placed on a stable optical platform. The entropy value of the ambient sound spectrum is obtained by collecting sound from a sound level meter and calculating the entropy value using FFT. The meter is installed at the location where the sound needs to be monitored. The frequency of ambient noise is collected by a microphone and analyzed by a spectrum analyzer. The microphone is installed at an effective noise collection location.

[0047] Text preprocessing module: performs preprocessing operations on the first canvas text, obtains the first feature information group corresponding to the first canvas text, determines the first feature information group to be filtered into sub-information, and numbers each sub-information sequentially as i = 1, 2, ..., n.

[0048] In this embodiment, the specific steps for preprocessing the first canvas text of the target building area are as follows:

[0049] A1: Text Cleaning: Remove special characters, punctuation marks, and numbers from the text on the first canvas, convert the case of the text, and unify it to uppercase or lowercase.

[0050] A2: Outlier Detection: Detect and process outliers in the first canvas text after cleaning;

[0051] A3: Feature Extraction: Extract the most representative and informative data from the first canvas text and integrate them into the first feature information group.

[0052] Canvas Information Analysis Module: Used to acquire the intelligent dynamic model, input the first feature information group into the intelligent dynamic model to obtain the second feature information group; includes a voice control performance analysis unit, an environmental acoustic analysis unit, a canvas display analysis unit, and a canvas dynamic analysis unit.

[0053] In this embodiment, it should be specifically noted that the intelligent dynamic model includes: a voice control performance analysis model, an environmental acoustic analysis model, a canvas display analysis model, and a canvas dynamic analysis model.

[0054] In this embodiment, it should be specifically noted that the voice control performance analysis unit is used to establish a voice control performance analysis model. The first feature information group processed by the text preprocessing module is imported into the voice control performance analysis model to calculate the voice control performance coefficient value of each sub-information, specifically expressed as follows:

[0055]

[0056] S i sy represents the voice control performance coefficient value of the i-th sub-information. i-1 sy represents the intonation shift frequency of the (i-1)th sub-information. i sj represents the intonation shift frequency of the i-th sub-information. i sx represents the ratio of syllable duration to the i-th sub-information. i sm represents the hue offset of the i-th sub-information. i represents the pixel flicker density of the i-th sub-information, and n represents the total number of sub-information groups.

[0057] In this embodiment, it should be specifically noted that the environmental acoustic analysis unit is used to establish an environmental acoustic analysis model. The first feature information group obtained from the text preprocessing module is imported into the environmental acoustic analysis model to calculate the environmental acoustic coefficient values ​​of each sub-information, specifically expressed as follows:

[0058]

[0059] H i hg represents the environmental acoustic coefficient value of the i-th sub-information. i hl represents the slope of the color saturation change of the i-th sub-information. i hf represents the blink duty cycle of the element in the frame of the i-th sub-information. i-1 hf represents the spatial frequency of the pattern of the (i-1)th sub-information. i Let n represent the spatial frequency of the pattern of the i-th sub-information, and n represent the total number of sub-information groups.

[0060] In this embodiment, it should be specifically noted that the canvas display analysis unit is used to establish a canvas display analysis model, import the first feature information group processed by the text preprocessing module into the canvas display analysis model, and calculate the canvas display coefficient value of each sub-information, specifically expressed as follows:

[0061]

[0062] T i ts represents the canvas display coefficient value of the i-th sub-information. i tf represents the rate of change of the hue angle of the i-th sub-information. i-1 The flicker frequency of the screen color in the (i-1)th sub-information, tf i tg represents the flashing frequency of the screen color of the i-th sub-information. i Let represent the curvature of the movement trajectory of the i-th sub-information element, and n represent the number of sub-information elements.

[0063] In this embodiment, it should be specifically noted that the canvas dynamic analysis unit is used to establish a canvas dynamic analysis model. The first feature information group processed by the text preprocessing module is imported into the canvas dynamic analysis model to calculate the canvas dynamic coefficient values ​​of each sub-information, specifically expressed as follows:

[0064]

[0065] B i bk represents the canvas dynamic coefficient value of the i-th sub-information. i Let bj represent the interference fringe density of the i-th sub-information. i The spectral entropy value of the ambient sound of the i-th sub-information is represented by , by i Let represent the environmental noise frequency of the i-th sub-information, and K represent the Boltzmann constant.

[0066] Specifically, the first feature information group input into the intelligent dynamic model yields the second feature information group, which includes: S i H represents the voice control performance coefficient value of the i-th sub-information. i The environmental acoustic coefficient value of the i-th sub-information, T i The canvas display coefficient value of the i-th sub-information and B i This represents the canvas dynamic coefficient value of the i-th sub-information.

[0067] Comprehensive Analysis Module: Used to obtain the comprehensive analysis model, import the second feature information group into the comprehensive analysis model to obtain the accurate value of the comprehensive canvas; includes comprehensive analysis unit.

[0068] In this embodiment, it should be specifically noted that the comprehensive analysis unit is used to establish a comprehensive analysis model, import the second feature information group into the comprehensive analysis model, and calculate the precise value of the comprehensive canvas, specifically expressed as follows:

[0069]

[0070] Where P represents the composite canvas precision value, S i H represents the voice control performance coefficient value of the i-th sub-information. i T represents the environmental acoustic coefficient value of the i-th sub-information. i B represents the canvas display coefficient value of the i-th sub-information. i β1, β2, β3, and β4 represent the canvas dynamic coefficients of the i-th sub-information, and all of them are greater than zero. α represents other influencing factors that integrate the canvas precision value.

[0071] Comprehensive Judgment Module: Obtains the preset comprehensive canvas threshold, compares the precise value of the comprehensive canvas with the preset comprehensive canvas threshold, and transmits the judged digital signal to the intelligent feedback module.

[0072] In this embodiment, it should be specifically noted that the preset comprehensive canvas threshold is a precise value of the voice-controlled canvas based on statistical data from previous years; the specific determination is as follows:

[0073] B1: If the comprehensive canvas precision value is greater than the preset comprehensive canvas threshold, a danger signal will be issued;

[0074] B2: If the comprehensive canvas precision value is less than the preset comprehensive canvas threshold, a good signal will be issued.

[0075] Intelligent feedback module: Based on the digital signals from the comprehensive judgment module, it sends preset intelligent feedback to the administrators corresponding to the target canvas area.

[0076] In this embodiment, it should be specifically noted that the preset intelligent feedback methods are: text intelligent feedback, image intelligent feedback, and voice intelligent feedback; and these are sent to relevant management personnel.

[0077] This invention utilizes various types of multi-source sensors and intelligent operation within a target canvas area to acquire the first canvas text corresponding to that area. Text preprocessing is then performed to obtain a first feature information group corresponding to the first canvas text. This first feature information group is further filtered into sub-information, which helps improve the comprehensiveness of subsequent controllable voice-controlled canvas control and enhances the accuracy of data evaluation for the controllable voice-controlled canvas. Subsequently, this information is used to acquire a smart dynamic model. The first feature information group is input into the smart dynamic model to derive a second feature information group, enabling timely detection of problems within the controllable voice-controlled canvas and facilitating subsequent intelligent analysis of the controllable voice-controlled canvas. To ensure reliability, this system helps improve the assessment of controllable voice-controlled canvases; it is used to obtain a comprehensive analysis model, and the second feature information group is imported into the comprehensive analysis model to obtain the accurate comprehensive canvas value, which helps to present a more intuitive effect and a holistic assessment to the management personnel; the digital signal obtained from the comprehensive judgment module helps to improve the accuracy of the judgment of controllable voice-controlled canvases and remove outliers; through the preset intelligent feedback method, the system can detect the digital signals emitted by the system in real time. Once a danger signal is received, the system will feed back the processing results to the management personnel, who will then perform intelligent dynamic optimization and adjustment of the target canvas area, which helps to improve the comprehensiveness and accuracy of controllable voice-controlled canvases.

[0078] Secondly, the accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0079] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart, dynamically controllable, voice-controlled canvas system, characterized in that, This includes the system's operating database, central processing module, and user information terminal, as well as: Voice-controlled canvas information acquisition module: used to set different types of multi-source sensors and intelligent operations in the target canvas area, and acquire the first canvas text corresponding to the target canvas area; Text preprocessing module: performs preprocessing operations on the first canvas text, obtains the first feature information group corresponding to the first canvas text, determines the first feature information group to be filtered into sub-information, and sequentially numbers each sub-information as i = 1, 2, ..., n; Canvas Information Analysis Module: Used to acquire the intelligent dynamic model, input the first feature information group into the intelligent dynamic model to obtain the second feature information group; includes a voice control performance analysis unit, an environmental acoustic analysis unit, a canvas display analysis unit, and a canvas dynamic analysis unit; Comprehensive Analysis Module: Used to acquire the comprehensive analysis model, import the second feature information group into the comprehensive analysis model to obtain the precise value of the comprehensive canvas; includes comprehensive analysis units; Comprehensive Judgment Module: Obtains the preset comprehensive canvas threshold, compares the precise value of the comprehensive canvas with the preset comprehensive canvas threshold, and transmits the judged digital signal to the intelligent feedback module; Intelligent feedback module: Based on the digital signals from the comprehensive judgment module, it sends preset intelligent feedback to the administrators corresponding to the target canvas area.

2. The intelligent, dynamically controllable, voice-controlled canvas system according to claim 1, characterized in that: The first canvas text is denoted as follows: intonation transition frequency as sy, syllable duration ratio as sj, hue offset as sx, pixel flicker density as sm, color saturation change slope as hg, flicker duty cycle of elements in the image as h1, spatial frequency of the pattern as hf, hue angle change rate as ts, flicker frequency of the image color as tf, curvature of the movement trajectory of the image element as tg, interference fringe density as bk, spectral entropy value of ambient sound as bj, and ambient noise frequency as by.

3. The intelligent, dynamically controllable, voice-controlled canvas system according to claim 1, characterized in that: The voice control performance analysis unit is used to establish a voice control performance analysis model. It imports the first feature information group obtained from the text preprocessing module into the voice control performance analysis model and calculates the voice control performance coefficient value of each sub-information, specifically expressed as follows: S i sy represents the voice control performance coefficient value of the i-th sub-information. i-1 sy represents the intonation shift frequency of the (i-1)th sub-information. i sj represents the intonation shift frequency of the i-th sub-information. i sx represents the ratio of syllable duration to the i-th sub-information. i sm represents the hue offset of the i-th sub-information. i represents the pixel flicker density of the i-th sub-information, and n represents the total number of sub-information groups.

4. The intelligent, dynamically controllable, voice-controlled canvas system according to claim 1, characterized in that: The environmental acoustic analysis unit is used to establish an environmental acoustic analysis model. It imports the first feature information group obtained from the text preprocessing module into the environmental acoustic analysis model and calculates the environmental acoustic coefficient values ​​of each sub-information, specifically as follows: H i hg represents the environmental acoustic coefficient value of the i-th sub-information. i hl represents the slope of the color saturation change of the i-th sub-information. i hf represents the blink duty cycle of the element in the frame of the i-th sub-information. i-1 hf represents the spatial frequency of the pattern of the (i-1)th sub-information. i Let n represent the spatial frequency of the pattern of the i-th sub-information, and n represent the total number of sub-information groups.

5. The intelligent, dynamically controllable, voice-controlled canvas system according to claim 1, characterized in that: The canvas display analysis unit is used to establish a canvas display analysis model. It imports the first feature information group obtained from the text preprocessing module into the canvas display analysis model and calculates the canvas display coefficient value for each sub-information, specifically as follows: T i ts represents the canvas display coefficient value of the i-th sub-information. i tf represents the rate of change of the hue angle of the i-th sub-information. i-1 The flicker frequency of the screen color in the (i-1)th sub-information, tf i tg represents the flashing frequency of the screen color of the i-th sub-information. i Let represent the curvature of the movement trajectory of the i-th sub-information element, and n represent the number of sub-information elements.

6. The intelligent, dynamically controllable, voice-controlled canvas system according to claim 1, characterized in that: The canvas dynamic analysis unit is used to establish a canvas dynamic analysis model. It imports the first feature information group obtained from the text preprocessing module into the canvas dynamic analysis model and calculates the canvas dynamic coefficient values ​​for each sub-information, specifically as follows: B i bk represents the canvas dynamic coefficient value of the i-th sub-information. i Let bj represent the interference fringe density of the i-th sub-information. i The spectral entropy value of the ambient sound of the i-th sub-information is represented by , by i Let represent the environmental noise frequency of the i-th sub-information, and K represent the Boltzmann constant.

7. The intelligent, dynamically controllable, voice-controlled canvas system according to claim 1, characterized in that: The comprehensive analysis unit is used to establish a comprehensive analysis model, import the second feature information group into the comprehensive analysis model, and calculate the precise value of the comprehensive canvas, specifically expressed as follows: Where P represents the composite canvas precision value, S i H represents the voice control performance coefficient value of the i-th sub-information. i T represents the environmental acoustic coefficient value of the i-th sub-information. i B represents the canvas display coefficient value of the i-th sub-information. i β1, β2, β3, and β4 represent the canvas dynamic coefficients of the i-th sub-information and are all greater than zero. Here, a represents other influencing factors that integrate the canvas precision value.

8. The intelligent, dynamically controllable, voice-controlled canvas system according to claim 1, characterized in that: The preset comprehensive canvas threshold is a precise value for voice-controlled canvases based on statistical data from previous years; the specific determination is as follows: B1: If the comprehensive canvas precision value is greater than the preset comprehensive canvas threshold, a danger signal will be issued; B2: If the comprehensive canvas precision value is less than the preset comprehensive canvas threshold, a good signal will be issued.

9. The intelligent, dynamically controllable, voice-controlled canvas system according to claim 1, characterized in that: The preset intelligent feedback methods include: text intelligent feedback, image intelligent feedback, and voice intelligent feedback.

Images