Human-computer interaction method based on embedded chip

Abstract

The invention relates to the technical field of man-machine interaction, in particular to a man-machine interaction method based on an embedded chip, which comprises the following steps of: based on user voice input data, converting a voice waveform into a digital signal, performing feature analysis on the signal, converting voice data into characters for output, and generating a textualized processing result. In the invention, through voice data processing and semantic analysis, instructions and demands of a user are accurately understood, real-time visual display and voice feedback are combined, a barrier-free interaction function is realized, the method is more friendly to a vision-limited user, interaction efficiency analysis and optimization feedback are combined, and user experience is improved. The interactive interface is adjusted and optimized according to the operation habit of the user, the brightness, the volume and the operation parameters of the equipment are adjusted in real time by utilizing the environmental condition and the residual electric quantity, the energy consumption of the equipment is reduced, the user interference is reduced by intelligently adjusting the time and the mode of notification, and the working efficiency and the user satisfaction are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of human-computer interaction, and in particular to a human-computer interaction method and device based on an embedded chip. Background Art

[0002] The field of human-computer interaction technology focuses on the design and implementation of interactive interfaces between humans and computer systems. It involves multiple aspects of computer science, behavioral science, design, and engineering. It aims to improve the usability and efficiency of technology by improving the system's usability, accessibility, and interaction efficiency, and to optimize the user experience. It is applied to a variety of desktop systems and industrial control systems. Through user research, interface design, and user testing, it improves the intuitiveness and responsiveness of the interactive interface. It combines touch interfaces, voice control, and gesture recognition to enrich the ways in which users interact with machines, allowing users to interact with technology products or services efficiently and naturally.

[0003] Among them, the human-computer interaction method based on embedded chips uses chips in embedded systems to achieve direct interaction between people and machines. By integrating sensors, display technology and interface design, embedded chips are used to process user input data, and fast feedback and command execution are achieved, thereby optimizing the operating experience and performance of the device, and providing a smooth and intuitive user experience, including improving the interaction quality of portable devices, enhancing the intuitiveness and responsiveness of the user interface, improving the operating efficiency of portable devices, and enhancing the user-friendliness of complex systems. In environments that require rapid response, including industrial automation and smart home systems, effective user interaction methods are provided.

[0004] Traditional human-computer interaction methods are unable to quickly and accurately parse user intentions in complex contexts, resulting in a damaged user experience. They also show limitations in energy management. The lack of intelligent energy regulation causes the device to consume too much energy when not in use. The screen brightness cannot be adjusted according to ambient light and the processor performance cannot be optimized according to usage patterns, affecting the device's battery life and operating performance. In terms of notification management, the lack of a context-aware notification system causes users to receive interfering information at inappropriate times, reducing work efficiency and increasing users' negative feelings about the system. Summary of the invention

[0005] In order to solve the technical problems of poor energy consumption management and interaction efficiency in the prior art, an embodiment of the present invention provides a human-computer interaction method and device based on an embedded chip. The technical solution is as follows: On the one hand, a human-computer interaction method based on an embedded chip is provided, the method comprising: S1: Based on the user's voice input data, the voice waveform is converted into a digital signal, and the signal is analyzed for characteristics, the voice data is converted into text output, and a text processing result is generated; S2: Based on the text processing result and the user input text information, the user intention is identified and a semantic analysis result is generated; S3: Based on the semantic analysis result, execute the user input command, and use visualization to display the user's operation process in real time, combine with speech synthesis to convert text information into speech output, realize barrier-free interaction, and generate interactive interface output settings; S4: Based on the interactive interface output setting, collect user interaction data and analyze the user's task processing efficiency, combine user feedback information, evaluate the quality and efficiency of human-computer interaction, and generate an interaction efficiency analysis result; S5: Based on the interaction efficiency analysis result, using the light sensor and the microphone, monitor the operation status of the device and external environmental factors in real time, adjust the device operation settings, optimize energy use, and generate a device power consumption management configuration; S6: According to the device power consumption management configuration, the urgency of the user's task processing is analyzed in real time, and the priority and delivery method of the notification are adjusted in combination with the real-time scene information, the notification timing is optimized to reduce interference, and the notification setting configuration result is generated.

[0006] As a further solution of the present invention, the text processing results include the text content obtained by speech recognition, the timestamp of the text, and the speech recognition confidence score; the semantic analysis results include the sentence grammatical structure, keyword recognition results, and vocabulary dependency information; the interactive interface output settings include the user interface visual layout, voice feedback volume and speaking speed, and user operation visualization path; the interaction efficiency analysis results include task completion time, number of incorrect operations, and user satisfaction score; the device power consumption management configuration includes adjusted screen brightness settings, processor performance mode, and management settings for background running applications; the notification setting configuration results include notification delivery time, notification priority sorting, and notification display method.

[0007] As a further solution of the present invention, based on the user voice input data, the voice waveform is converted into a digital signal, and the signal is subjected to feature analysis, the voice data is converted into text output, and the steps of generating a text processing result are specifically as follows: S101: Preprocessing the voice data based on the user voice input data, including amplifying the audio signal and filtering out the background noise, to generate an audio preprocessing result; S102: Analyze input voice data based on the audio preprocessing result, convert analog signals into digital signals, and generate digital audio data; S103: Based on the digitized audio data, feature extraction is performed on the voice signal, including recognition of amplitude, frequency and rhythm, and the voice data is converted into text output to generate a text processing result.

[0008] As a further solution of the present invention, based on the text processing result, combined with the user input text information, the steps of identifying the user intention and generating the semantic analysis result are specifically as follows: S201: Collect the text processing results, combine the text information input by the user, identify and mark the keywords in the text, including verbs and nouns, and generate keyword marking data; S202: Analyze the dependency relationship between multiple sentence components based on the keyword tag data, identify the word meaning connection order, and generate context relationship data; S203: Based on the contextual relationship data, adjust and organize the sentence structure, identify the user's intention, including command execution and information query, and generate a semantic analysis result.

[0009] As a further solution of the present invention, based on the semantic analysis result, the user input command is executed, and the user's operation process is displayed in real time by visualization, and the text information is converted into speech output in combination with speech synthesis to achieve barrier-free interaction. The steps of generating the interactive interface output setting are specifically as follows: S301: Based on the semantic analysis result, by analyzing the mapping relationship between multiple input commands and system functions, executing the user's input instruction, and generating an execution command response result; S302: Based on the command execution response result, the executed command action is converted into a visual graphic by using visualization, and displayed in real time on the interactive interface to generate a visual operation interface; S303: Based on the visual operation interface, the text information is converted into speech output by using speech synthesis, the rate and pitch of the speech are adjusted to match the needs of various users, and the interactive interface output settings are generated.

[0010] As a further solution of the present invention, based on the interactive interface output setting, the user's interactive data is collected and the user's task processing efficiency is analyzed, and the quality and efficiency of human-computer interaction are evaluated in combination with user feedback information. The steps of generating the interactive efficiency analysis result are specifically as follows: S401: Based on the interactive interface output setting, collect user operation data in real time, record operation time and error number, and generate user operation data; S402: Analyze the user operation data, calculate the user's task completion speed and error rate, evaluate the operation efficiency, and generate efficiency evaluation information; S403: Based on the efficiency evaluation information and in combination with user feedback information, the interaction efficiency of the human-computer interaction interface is calculated, improvement points are identified and optimized, and an interaction efficiency analysis result is generated.

[0011] As a further solution of the present invention, the specific formula for calculating the interaction efficiency of the human-computer interaction interface is: in, represents the interaction efficiency, Representation Task The weight represents the importance of the target task in user feedback. Indicates completion of task The time required, is the total time of the observation period, is the total number of tasks, indicating the number of tasks considered in the evaluation cycle, Index of the task.

[0012] As a further solution of the present invention, based on the interaction efficiency analysis results, using light sensors and microphones to monitor the operating status of the device and external environmental factors in real time, adjust the device operation settings, optimize energy use, and generate the device power consumption management configuration in the following steps: S501: Based on the interaction efficiency analysis result, use a light sensor and a microphone to monitor the light and noise levels of the environment in real time, and record the current operating status of the device, including the remaining power of the device and the processor occupancy rate, to generate an environment and device status record; S502: Analyze the environment and device status record, adjust the screen brightness and device volume in real time according to the battery power, environment brightness, and background noise, and generate a setting adjustment record; S503: According to the setting adjustment record, taking into account the battery power, adjusting the processor operating speed and the operating parameters of the background application in real time, and generating a device power consumption management configuration.

[0013] As a further solution of the present invention, according to the device power consumption management configuration, the urgency of the user's processing task is analyzed in real time, combined with real-time scene information, the priority and delivery method of the notification are adjusted, and the notification timing is optimized to reduce interference. The steps of generating the notification setting configuration result are specifically: S601: Based on the device power consumption management configuration, combined with the user's real-time activities and application usage frequency, the urgency of the user's task processing is analyzed in real time, the urgency score of the user's task is calculated, and the urgency analysis result is generated; S602: Analyze the types and importance of multiple notification information in real time according to the urgency analysis result, adjust the notification mode of the notification information in combination with the urgency of the task currently being processed by the user, and generate notification mode adjustment parameters; S603: Based on the notification method adjustment parameters, combined with scene information, including conference mode and night mode, the notification parameters are adjusted in real time, including adjusting the volume and display mode, and a notification setting configuration result is generated.

[0014] As a further solution of the present invention, the specific formula for calculating the urgency score of the user task is: in, Represents the urgency score, which is used to measure the urgency of task processing. A score representing the intensity of the user's activity, reflecting the intensity and duration of the user's current activity. The score representing the frequency of application usage, indicating the type of application currently used by the user and the frequency of operation. Represents the device response time, which is the time difference between the user's current task and the system response. It is a dynamically adjusted weight coefficient, which is adjusted according to the current operating efficiency and power consumption of the device. Specially used to adjust the impact of user activity intensity on the urgency score, weight coefficient Used to adjust the impact of application usage frequency on the score, weight coefficient Used to adjust for the inverse effect of device response time on the urgency score.

[0015] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: Through voice data processing and semantic analysis, it can accurately understand the user's instructions and needs, and use the combination of real-time visual display and voice feedback to achieve barrier-free interaction functions, which is more friendly to users with limited vision. Combined with interaction efficiency analysis and optimization feedback, it can adjust and optimize the interaction interface according to the user's operating habits, and use environmental conditions and remaining power to adjust the device's brightness, volume and operating parameters in real time to reduce the device's energy consumption. By intelligently adjusting the timing and method of notifications, it can reduce user interference and improve work efficiency and user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a flow chart of a human-computer interaction method based on an embedded chip provided by an embodiment of the present invention; Figure 2 This is a detailed flow chart of S1 of the present invention; Figure 3 This is a detailed flow chart of S2 of the present invention; Figure 4 This is a detailed flow chart of S3 of the present invention; Figure 5 This is a detailed flow chart of S4 of the present invention; Figure 6This is a detailed flow chart of S5 of the present invention; Figure 7 This is a detailed flow chart of S6 of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0020] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0021] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0022] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0023] The embodiment of the present invention provides a human-computer interaction method based on an embedded chip, such as Figure 1 The flowchart of the human-computer interaction method based on the embedded chip is shown, and the processing flow of the method may include the following steps: S1: Based on the user's voice input data, the voice waveform is converted into a digital signal, and the signal is analyzed for characteristics, the voice data is converted into text output, and a text processing result is generated; S2: Based on the text processing results and combined with the user input text information, identify the user's intention and generate semantic analysis results; S3: Based on the semantic analysis results, execute the user input commands and use visualization to display the user's operation process in real time. Combined with speech synthesis, convert text information into speech output to achieve barrier-free interaction and generate interactive interface output settings; S4: Based on the output settings of the interactive interface, collect user interaction data and analyze the user's task processing efficiency. Combined with user feedback information, evaluate the quality and efficiency of human-computer interaction and generate interaction efficiency analysis results; S5: Based on the results of interaction efficiency analysis, use light sensors and microphones to monitor the operating status of the device and external environmental factors in real time, adjust device operating settings, optimize energy use, and generate device power consumption management configuration; S6: Based on the device power consumption management configuration, the urgency of the user's task processing is analyzed in real time, and combined with the real-time scene information, the priority and delivery method of the notification are adjusted, the notification timing is optimized to reduce interference, and the notification setting configuration result is generated.

[0024] The text processing results include the text content obtained by speech recognition, the timestamp of the text, and the speech recognition confidence score. The semantic analysis results include the sentence grammatical structure, keyword recognition results, and vocabulary dependency information. The interactive interface output settings include the user interface visual layout, voice feedback volume and speaking rate, and user operation visualization path. The interactive efficiency analysis results include task completion time, number of incorrect operations, and user satisfaction score. The device power consumption management configuration includes the adjusted screen brightness setting, processor performance mode, and management settings for background running applications. The notification setting configuration results include the delivery time of notifications, the priority sorting of notifications, and the display method of notifications.

[0025] See also Figure 2 Based on the user's voice input data, the voice waveform is converted into a digital signal, and the signal is analyzed for characteristics, and the voice data is converted into text output. The specific steps for generating the text processing result are: S101: Preprocessing the voice data based on the user voice input data, including amplifying the audio signal and filtering out the background noise, to generate an audio preprocessing result; In sub-step S101, based on the user's voice input, an automatic gain control algorithm is implemented to enhance the amplitude of the voice signal so that the dynamic range of the signal can adapt to the subsequent processing requirements. The AGC algorithm measures the average power of the input signal and compares it with the predetermined target power, and automatically adjusts the gain coefficient to achieve power matching. This step ensures that the signal remains consistent under different recording environments. An adaptive filter, such as a Wiener filter, is applied to filter out background noise. The Wiener filter minimizes the mean square error based on the statistical characteristics of the signal and noise, and effectively recovers the voice signal from the noisy background. The process improves the quality of the voice signal and provides a clear audio foundation for subsequent digitization and feature extraction. The generated audio preprocessing result is a clear voice signal after gain adjustment and noise suppression.

[0026] S102: Analyze the input voice data based on the audio preprocessing result, convert the analog signal into a digital signal, and generate digital audio data; In sub-step S102, based on the audio preprocessing result, the processed audio signal is analyzed in the frequency domain using a fast Fourier transform algorithm. The algorithm converts the time domain signal into the frequency domain, analyzes its spectral components, effectively distinguishes the different frequency components of the speech signal, and performs an analog-to-digital converter process, in which the audio signal at each sampling point is quantized and encoded. The ADC samples the signal according to a preset sampling rate and bit depth to ensure that the digitization accuracy of the audio data meets the subsequent processing requirements. The generated digitized audio data includes a discretized time series and a corresponding signal strength value, which provides the necessary digital format audio data for feature extraction.

[0027] S103: Based on the digitized audio data, feature extraction is performed on the voice signal, including recognition of amplitude, frequency and rhythm, and the voice data is converted into text output to generate a text processing result; In sub-step S103, based on the digitized audio data, the Mel-frequency cepstral coefficient technology is used for feature extraction. The Mel-frequency cepstral coefficient technology is used for feature extraction of speech recognition. By simulating the auditory characteristics of the human ear, the spectral power of the signal is calculated. The Mel-frequency cepstral coefficient processing includes window function processing of the signal and applying discrete cosine transform to extract the logarithmic cepstrum of the spectrum. The target step helps to identify and extract the basic frequency and rhythm in the speech signal, effectively capturing the essential characteristics of the speech. The target features are used to drive the automatic speech recognition system, map the extracted features to the pre-trained language model, and convert the speech signal into text output. The generated text processing result is an accurate text representation of the user's voice command, which provides a basis for semantic analysis and command execution.

[0028] See also Figure 3 Based on the text processing results, combined with the user input text information, the steps of identifying the user's intention and generating the semantic analysis results are as follows: S201: Collect text processing results, combine with text information input by the user, identify and mark keywords in the text, including verbs and nouns, and generate keyword marking data; In sub-step S201, based on the obtained text processing results, a natural language processing framework, such as SpaCy or NLTK, is applied to perform grammatical analysis and part-of-speech tagging. The target tool automatically identifies the core components in the text, including verbs and nouns, through a pre-trained language model. Each word in the text is classified according to its function in the sentence and its part of speech is marked. Verbs such as "start" and "query" are marked as action words, and nouns such as "light" and "information" are marked as entity words. Modifiers and conjunctions are identified to accurately understand the complete intention of the sentence. The target marked keyword data provides the necessary structural information for subsequent dependency analysis. The generated keyword tag data records the function and type of each word in detail, laying the foundation for in-depth text analysis.

[0029] S202: Analyze the dependency relationship between multiple sentence components based on the keyword tag data, identify the word meaning connection order, and generate context relationship data; In sub-step S202, based on the keyword tagging data, a dependency syntactic analysis method, such as Stanford Parser, is used to analyze the dependency relationship between the components in the text. Dependency syntactic analysis reveals the master-slave relationship between the words in the sentence, such as the connection between the subject and the predicate, the dependency between the object and the predicate, and the modification relationship between the attributive and the central word. By analyzing which words are the core of the sentence meaning and which words provide additional information, a dependency structure tree of the sentence is constructed. For example, in the sentence "Turn on the light in the bedroom", "turn on" is the main verb, which directly depends on the execution of the command, while "light" is the object of the command execution, and "bedroom" describes the specific location of the light. The generated contextual relationship data maps the target dependency relationship, reveals the semantic structure of the sentence, and provides an accurate semantic framework for further intent recognition.

[0030] S203: Based on the contextual relationship data, adjust and organize the sentence structure, identify the user's intention, including command execution and information query, and generate a semantic analysis result; In sub-step S203, based on contextual relationship data, intent recognition technology, such as intent classifier or sequence-to-sequence learning model, is used to adjust the structure and semantics of the sentence to ensure the correct expression of semantics. The target model identifies the user's specific intention, such as executing commands or querying information, by analyzing the sentence structure and semantic relationships. For command execution, the model identifies the executed actions and target objects, such as "turn off" and "air conditioning". For information query, the query target and related attributes are parsed, such as "today's weather". This step organizes and optimizes the expression of the sentence, accurately corresponding to the user's operational requirements. The generated semantic parsing results clearly represent the user's intentions and the specific operations performed, ensuring the accurate execution of subsequent actions, greatly improving the system's response efficiency and user satisfaction.

[0031] See also Figure 4 , based on the semantic analysis results, execute the user input command, and use visualization to display the user's operation process in real time. Combined with speech synthesis, convert text information into speech output to achieve barrier-free interaction. The specific steps of generating the interactive interface output settings are: S301: Based on the semantic analysis result, by analyzing the mapping relationship between multiple input commands and system functions, executing the user's input instruction, and generating an execution command response result; In sub-step S301, based on the semantic parsing results, a decision tree algorithm is applied to identify the user's input commands and accurately map them to system functions. The algorithm determines the association between the user's intention and the system's predefined functions by analyzing the keywords and sentence structures in the semantic parsing results, including associating the "turn on the air conditioner" command with the power-on function of the air-conditioning control system. The system calls the corresponding interface to execute this command, such as sending a control signal to the air-conditioning system. During the process, the system monitors the execution status of the command and updates the execution progress of the command in real time to ensure the successful execution of the operation. The generated execution command response result includes confirmation information of the command execution and any execution feedback, ensuring that the user receives clear and timely feedback, thereby improving the user experience and the operating efficiency of the system.

[0032] S302: Based on the command execution response result, the executed command action is converted into a visual graphic by using visualization, and displayed in real time on the interactive interface to generate a visual operation interface; In sub-step S302, based on the response result of the executed command, a graphics processing library such as D3.js or Processing is used to convert the executed command action into a visual graphic representation. For example, if the command involves adjusting environmental equipment, the system will display the state changes of the equipment through dynamic graphics, such as the slider animation of temperature adjustment or the gradual increase in lighting brightness. The target visual elements are updated in real time on the interactive interface to provide users with intuitive operation feedback. Through this visualization technology, users can intuitively see the actual effect of each command, which enhances the intuitiveness and guidance of the interaction. The generated visual operation interface shows the current status of the operation, and also guides users to further interact through visual effects, enhancing users' operational confidence and satisfaction.

[0033] S303: Based on the visual operation interface, using speech synthesis, converting text information into speech output, adjusting the rate and pitch of the speech to match the needs of various users, and generating interactive interface output settings; In sub-step S303, based on the visual operation interface, the system uses speech synthesis technology, such as Google's Text-to-Speech API, to convert text instructions or system feedback into speech output, synthesizes natural and fluent speech by analyzing the semantics and context of the text content, and adjusts the rate and pitch of the speech to adapt to the auditory preferences of different users, including generating slower and clearer speech output for older users for easier understanding, including when the user needs to use both hands to perform other tasks, the speech output can effectively provide auxiliary information, and the generated interactive interface output settings ensure that users can receive clear and accurate instruction execution feedback and system prompts in various usage scenarios, thereby enhancing the system's usability and user satisfaction.

[0034] See also Figure 5 ,Based on the interactive interface output settings, collect user interaction data and analyze the user's task processing efficiency, combine user feedback information, evaluate the quality and efficiency of human-computer interaction, and generate the interaction efficiency analysis results in the following steps: S401: Based on the interactive interface output setting, collect user operation data in real time, record the operation time and error number, and generate user operation data; In sub-step S401, the system uses the data acquisition module to monitor the user's operating behavior in real time based on the interactive interface output settings, and uses data monitoring technologies such as JavaScript listeners and HTML5 data attributes to record each user interaction with the interface, including click events, sliding operations and input data. The timestamp and result status of each action are captured and recorded. The operation time is obtained through the browser's built-in time function to ensure the accuracy of the time. The number of errors is counted through the error handling function triggered by the event. The target function can capture the error type and perform classification statistics. The generated user operation data includes the sequence, duration and error occurrence of the operation, providing basic data for subsequent efficiency evaluation.

[0035] S402: Analyze user operation data, calculate the user's task completion speed and error rate, evaluate operation efficiency, and generate efficiency evaluation information; In sub-step S402, based on user operation data, data processing and statistical analysis are performed using data analysis techniques such as Python's Pandas library. The data is organized into a structured format, the operation time and the number of errors are converted into analyzable numerical data, and the task completion speed is calculated by the inverse of the operation time, expressed as the number of operations completed per minute. The error rate is the ratio of the number of errors to the total number of operations. The target calculation is performed using standard mathematical formulas to ensure the accuracy and objectivity of the analysis. The generated efficiency evaluation information includes the average completion speed and error rate of each user, providing a quantitative indicator for evaluating the user's operation efficiency.

[0036] S403: Based on the efficiency evaluation information and combined with the user feedback information, the interaction efficiency of the human-computer interaction interface is calculated, improvement points are identified and optimized, and an interaction efficiency analysis result is generated; The specific formula for calculating the interaction efficiency of the human-computer interaction interface is: in, represents the interaction efficiency, Representation Task The weight represents the importance of the target task in user feedback. Indicates completion of task The time required, is the total time of the observation period, is the total number of tasks, indicating the number of tasks considered in the evaluation cycle, Index of the task.

[0037] formula Detailed explanation of the formula and the process of formula calculation and derivation: Formula is used to calculate the interaction efficiency of human-computer interaction system , measured by weighting the completion time of each task. The efficiency value reflects the ratio of effective interaction time to total time during the observation period.

[0038] Parameter meaning and setting value: Represents the total number of tasks, assuming it is 5, reflecting the number of tasks completed by the user in one evaluation cycle.

[0039] For the weight of each task, assume , , , , ; Indicates completion of task Time required, assuming Second, Second, Second, Second, Second.

[0040] is the total observation time, assumed to be 120 seconds, reflecting the total time required to complete all tasks.

[0041] Substitute the parameters into the formula for calculation: result It shows that the interaction efficiency of the current human-computer interaction system is 21.7%. The value represents the performance of the system in effective interaction during the observation period. The higher the efficiency, the more effective the system is in response and task processing, and the better the user experience. The analysis provides efficiency evaluation in practical applications for system design and helps developers identify potential improvement points for optimizing the interaction interface.

[0042] See also Figure 6 Based on the results of interaction efficiency analysis, the light sensor and microphone are used to monitor the operating status and external environmental factors of the device in real time, adjust the device operation settings, optimize energy usage, and generate the device power consumption management configuration. The specific steps are as follows: S501: Based on the interaction efficiency analysis result, use the light sensor and microphone to monitor the light and noise level of the environment in real time, and record the current operating status of the device, including the remaining power of the device and the processor occupancy rate, and generate an environment and device status record; In sub-step S501, the system deploys a real-time monitoring module based on the results of the interaction efficiency analysis. By integrating light sensors and microphones, the system detects the light intensity and noise level of the surrounding environment in real time. Through the built-in diagnostic tools of the device, the operating status of the device, including battery power and processor occupancy, is recorded in real time. The process involves data acquisition technology and sensor data processing technology, including the use of Arduino or Raspberry Pi for data reception and preliminary processing. The light intensity and noise level are converted into digital signals through analog signals and analyzed by the system in real time to determine whether the device settings need to be adjusted to adapt to environmental changes. The device status such as battery power is obtained through the device's operating system API, and the processor occupancy is obtained through hardware monitoring software such as CPU-Z. The generated environment and device status records contain detailed environmental parameters and device operating conditions at the monitoring time point, providing data support for intelligent adjustment of device settings.

[0043] S502: Analyze the environment and device status records, adjust the screen brightness and device volume in real time according to the battery power, environment brightness, and background noise, and generate a setting adjustment record; In sub-step S502, based on the environment and device status records, an adaptive adjustment algorithm is used to optimize the device's screen brightness and volume settings. The algorithm automatically adjusts the screen brightness according to the ambient lighting conditions, and uses a feedback control system to ensure that the screen brightness can save power while ensuring user visual comfort. For the device volume, the system adjusts the volume through a simple linear relationship based on the ambient noise level to ensure that the audio output is clear and audible in various environments. The process involves real-time data analysis and control technology, such as control scripts written in Python or JavaScript, which respond quickly when the device detects significant environmental changes. The generated setting adjustment records record in detail the time of each adjustment, the setting values ​​before and after the adjustment, and the corresponding environmental conditions, ensuring the best performance of the device in various environments.

[0044] S503: According to the setting adjustment record, taking into account the battery power, adjusting the processor operating speed and the operating parameters of the background application in real time, and generating a device power consumption management configuration; In sub-step S503, based on the setting adjustment record and the current battery power, the processor operating speed and the operating parameters of the background applications are optimized, and energy management software, such as Windows' power management settings or Android's battery optimization function, is used to adjust the processor's operating strategy, such as reducing the processor's maximum frequency or limiting the background activities of certain high-power applications. Adjustments are made based on the current state of battery power and expected usage needs, aiming to extend the device's usage time while ensuring the performance of core applications. The system monitors the resource usage of background applications and intelligently shuts down or reduces the resource allocation of non-critical applications. The generated device power consumption management configuration describes the adjustments to each setting, including the specific value of the processor speed, the list of restricted background applications, and their status before and after resource use, providing real-time and precise control for device energy management.

[0045] See also Figure 7 , according to the device power consumption management configuration, real-time analysis of the urgency of the user's task processing, combined with real-time scene information, adjust the priority and delivery method of notifications, optimize the timing of notifications to reduce interference, and generate notification setting configuration results. The specific steps are: S601: Based on the device power consumption management configuration, combined with the user's real-time activities and application usage frequency, the urgency of the user's task processing is analyzed in real time, the urgency score of the user's task is calculated, and the urgency analysis result is generated; The specific formula for calculating the urgency score of a user task is: in, Represents the urgency score, which is used to measure the urgency of task processing. A score representing the intensity of the user's activity, reflecting the intensity and duration of the user's current activity. The score representing the frequency of application usage, indicating the type of application currently used by the user and the frequency of operation. Represents the device response time, which is the time difference between the user's current task and the system response. It is a dynamically adjusted weight coefficient, which is adjusted according to the current operating efficiency and power consumption of the device. Specially used to adjust the impact of user activity intensity on the urgency score, weight coefficient Used to adjust the impact of application usage frequency on the score, weight coefficient Used to adjust for the inverse effect of device response time on the urgency score.

[0046] formula: Detailed explanation of the formula and the process of formula calculation and derivation: The formula is used to evaluate the urgency of the task, adjust the device notification strategy according to user behavior and application usage frequency, and optimize the user interaction experience.

[0047] Parameter meaning and setting value: Score the user's activity intensity, assuming 50 represents moderate activity intensity.

[0048] The frequency of application usage is scored, and a value of 70 is assumed to indicate a high frequency of use.

[0049] is the device response time, assuming it is 2 seconds, indicating a fast response.

[0050] is the weight coefficient, assuming , used to adjust the contribution of user activity intensity to the urgency score, , used to adjust the contribution of application usage frequency to the urgency score, , used to adjust the impact of device response time on the urgency score.

[0051] Substitute the parameters into the formula for calculation result This indicates that under the current settings, the urgency score of the user task is 32.86. The result is used to adjust the device power management configuration and message notification settings.

[0052] S602: Analyze the types and importance of multiple notification information in real time according to the urgency analysis result, adjust the notification mode of the notification information in combination with the urgency of the task currently being processed by the user, and generate notification mode adjustment parameters; In sub-step S602, based on the results of the urgency analysis, a priority sorting algorithm, such as a weight-based sorting system, is used to adjust the delivery method of the notification information in real time. The algorithm analyzes the type and importance of the notification, and combines the urgency of the user's current task. For high-priority tasks being processed, the system will reduce the disturbance of non-urgent notifications and give priority to delivering urgent notifications related to the task. The system automatically adjusts the delivery method of notifications, such as sound prompts or visual pop-ups, through user behavior learning to adapt to the user's work environment and personal preferences. The generated notification method adjustment parameters record the notification method and priority of each notification type after adjustment according to the urgency of the task, thereby improving the efficiency and satisfaction of users when handling multiple tasks.

[0053] S603: Based on the notification mode adjustment parameters, combined with the scene information, including the meeting mode and the night mode, the notification parameters are adjusted in real time, including adjusting the volume and the display mode, and a notification setting configuration result is generated; In sub-step S603, the system adjusts parameters based on the notification method, taking into account the user's specific scenarios, such as conference mode or night mode, and uses an environmental perception algorithm to automatically adjust the notification parameters. The algorithm uses built-in sensor data, such as light and sound sensors, to adjust the volume and display mode of the notification according to the brightness and noise level of the environment. In night mode, the notification volume will be automatically reduced, and the notification mode will be changed to vibration or silent to avoid interference. In conference mode, notifications are only displayed on the device lock screen interface to reduce interruptions. The generated notification setting configuration results include notification methods and parameter settings. The target settings are dynamically adjusted according to the user's scenario to provide the most suitable notification experience and ensure effective communication of information without affecting the current environment and activities.

[0054] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0055] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0056] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0057] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0058] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0059] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0060] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0061] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0062] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0063] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0064] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A human-computer interaction method based on an embedded chip, characterized in that: The method comprises: Based on the user's voice input data, the voice waveform is converted into a digital signal, and the signal is analyzed for characteristics, the voice data is converted into text output, and a text processing result is generated; Based on the text processing result, combined with the user input text information, the user intention is identified and a semantic analysis result is generated; Based on the semantic analysis results, execute the user input command, and use visualization to display the user's operation process in real time, combine with speech synthesis to convert text information into speech output, realize barrier-free interaction, and generate interactive interface output settings; Based on the interactive interface output settings, collect user interaction data and analyze the user's task processing efficiency, combine user feedback information, evaluate the quality and efficiency of human-computer interaction, and generate interactive efficiency analysis results; Based on the interaction efficiency analysis results, use light sensors and microphones to monitor the operating status of the device and external environmental factors in real time, adjust device operating settings, optimize energy use, and generate device power consumption management configuration; According to the device power consumption management configuration, the urgency of the user's task processing is analyzed in real time, and the priority and delivery method of the notification are adjusted in combination with the real-time scene information, the notification timing is optimized to reduce interference, and the notification setting configuration result is generated.

2. The human-computer interaction method based on embedded chip according to claim 1, characterized in that: The text processing result includes the text content obtained by speech recognition, the timestamp of the text, and the speech recognition confidence score; the semantic analysis result includes the sentence grammatical structure, keyword recognition results, and vocabulary dependency information; the interactive interface output settings include the user interface visual layout, voice feedback volume and speaking speed, and user operation visualization path; the interaction efficiency analysis results include task completion time, number of incorrect operations, and user satisfaction score; the device power consumption management configuration includes the adjusted screen brightness setting, processor performance mode, and management settings for background running applications; the notification setting configuration results include the delivery time of the notification, the priority sorting of the notification, and the display method of the notification.

3. The human-computer interaction method based on embedded chip according to claim 1, characterized in that: Based on the user's voice input data, the voice waveform is converted into a digital signal, and the signal is analyzed for characteristics, and the voice data is converted into text output. The specific steps for generating the text processing result are as follows: Based on the user's voice input data, the voice data is preprocessed, including audio signal amplification and background noise filtering, to generate an audio preprocessing result; Based on the audio preprocessing result, analyzing the input voice data, converting the analog signal into a digital signal, and generating digitized audio data; Based on the digitized audio data, feature extraction is performed on the voice signal, including recognition of amplitude, frequency and rhythm, and the voice data is converted into text output to generate a text processing result.

4. The human-computer interaction method based on embedded chip according to claim 1, characterized in that: Based on the text processing result, combined with the user input text information, the steps of identifying the user intention and generating the semantic analysis result are as follows: Collect the text processing results, combine with the text information input by the user, identify and mark the keywords in the text, including verbs and nouns, and generate keyword marking data; Based on the keyword tag data, analyzing the dependency relationship between multiple sentence components, identifying the word meaning connection order, and generating contextual relationship data; Based on the contextual relationship data, the sentence structure is adjusted and organized, the user's intention is identified, including command execution and information query, and a semantic analysis result is generated.

5. The human-computer interaction method based on embedded chip according to claim 1, characterized in that: Based on the semantic analysis results, the user input command is executed, and the user's operation process is displayed in real time by visualization. In combination with speech synthesis, the text information is converted into speech output to achieve barrier-free interaction. The steps of generating the interactive interface output setting are specifically as follows: Based on the semantic analysis result, by analyzing the mapping relationship between multiple input commands and system functions, the user's input instructions are executed to generate an execution command response result; Based on the execution command response result, the executed command action is converted into a visual graphic by using visualization, and displayed in real time on the interactive interface to generate a visual operation interface; Based on the visual operation interface, text information is converted into voice output using speech synthesis, the rate and pitch of the voice are adjusted to match the needs of various users, and the interactive interface output settings are generated.

6. The human-computer interaction method based on embedded chip according to claim 1, characterized in that: Based on the interactive interface output setting, the user's interactive data is collected and the user's task processing efficiency is analyzed. In combination with the user feedback information, the quality and efficiency of the human-computer interaction are evaluated. The steps of generating the interactive efficiency analysis result are specifically as follows: Based on the interactive interface output settings, user operation data is collected in real time, operation time and error number are recorded, and user operation data is generated; Analyze the user operation data, calculate the user's task completion speed and error rate, evaluate the operation efficiency, and generate efficiency evaluation information; Based on the efficiency evaluation information and combined with user feedback information, the interaction efficiency of the human-computer interaction interface is calculated, improvement points are identified and optimized, and interaction efficiency analysis results are generated.

7. The human-computer interaction method based on embedded chip according to claim 6, characterized in that: The specific formula for calculating the interaction efficiency of the human-computer interaction interface is: in, represents the interaction efficiency, Indicates the task The weight represents the importance of the target task in user feedback. Indicates completion of task The time required, is the total time of the observation period, is the total number of tasks, indicating the number of tasks considered in the evaluation cycle, Index of the task.

8. The human-computer interaction method based on embedded chip according to claim 1, characterized in that: Based on the interaction efficiency analysis results, the steps of using light sensors and microphones to monitor the operating status and external environmental factors of the device in real time, adjust the device operating settings, optimize energy usage, and generate device power consumption management configuration are as follows: Based on the interaction efficiency analysis results, use a light sensor and a microphone to monitor the light and noise levels of the environment in real time, and record the current operating status of the device, including the remaining power of the device and the processor occupancy rate, to generate an environment and device status record; Analyze the environment and device status records, adjust screen brightness and device volume in real time according to battery power, ambient brightness, and background noise, and generate setting adjustment records; According to the setting adjustment record, the operating speed of the processor and the operating parameters of the background application are adjusted in real time taking into account the battery power, and the device power consumption management configuration is generated.

9. The human-computer interaction method based on embedded chip according to claim 1, characterized in that: According to the device power consumption management configuration, the urgency of the user's processing tasks is analyzed in real time, and the priority and delivery method of the notification are adjusted in combination with the real-time scene information, and the notification timing is optimized to reduce interference. The steps of generating the notification setting configuration result are specifically as follows: Based on the device power consumption management configuration, combined with the user's real-time activities and application usage frequency, the urgency of the user's task processing is analyzed in real time, the urgency score of the user's task is calculated, and the urgency analysis result is generated; According to the urgency analysis result, the types and importance of multiple notification information are analyzed in real time, and the notification mode of the notification information is adjusted in combination with the urgency of the task currently being processed by the user, and a notification mode adjustment parameter is generated; Based on the notification method adjustment parameters, combined with scene information, including conference mode and night mode, the notification parameters are adjusted in real time, including adjusting the volume and display mode, to generate a notification setting configuration result.

10. The human-computer interaction method based on embedded chip according to claim 9, characterized in that: The specific formula for calculating the urgency score of the user task is: in, Represents the urgency score, which is used to measure the urgency of task processing. A score representing the intensity of the user's activity, reflecting the intensity and duration of the user's current activity. The score representing the frequency of application usage, indicating the type of application currently used by the user and the frequency of operation. Represents the device response time, which is the time difference between the user's current task and the system response. It is a dynamically adjusted weight coefficient, which is adjusted according to the current operating efficiency and power consumption of the device. Specially used to adjust the impact of user activity intensity on the urgency score, weight coefficient Used to adjust the impact of application usage frequency on the score, weight coefficient Used to adjust for the inverse effect of device response time on the urgency score.

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