A quantum eloquence training and analysis system based on multi-qubit processing

Abstract

The present invention provides a quantum eloquence training and analysis system based on multi-qubit processing, which relates to the field of eloquence training and analysis, and includes: a data acquisition and preprocessing module, which is used to acquire the multimodal data of the speaker, and preprocess the multimodal data to extract the sound data related to eloquence and emotion; a multi-qubit processing module, which is used to process the extracted sound data using a multi-qubit algorithm based on the multi-qubit hardware deployed to the cloud, and generate processed sound data; a personalized training module, which is used to identify the eloquence problems of the speaker during the speech according to the processed sound data, and generate a personalized training plan. The present invention can improve computing efficiency, evaluation precision and accuracy, effectively integrate and process multimodal data, and provide hardware resources to meet the growing computing needs.

Description

Technical Field

[0001] The present invention relates to the field of eloquence training and analysis, and in particular to a quantum eloquence training and analysis system based on multi-qubit processing. Background Art

[0002] In the traditional field of eloquence training and speech expression, methods based on experience and artificial intelligence algorithms are usually used to analyze speech content and the speaker's eloquence performance. Traditional methods have a series of technical problems, including but not limited to the following aspects:

[0003] 1. Insufficient computing efficiency: Traditional computing methods are slow in processing large-scale data and have difficulty providing real-time analysis and feedback, which is critical in speech and eloquence training.

[0004] 2. Low precision and accuracy: Traditional methods have limited accuracy and precision in speech content and eloquence assessment, and are often affected by noise and data quality.

[0005] 3. Difficulty in processing multimodal data: Speech and eloquence analysis involves multimodal data, including sound, text, and video. Traditional methods find it difficult to effectively integrate and process these multimodal data.

[0006] 4. Limited hardware resources: Classical computer hardware is limited in its ability to handle complex speech content analysis and multimodal data processing, and is unable to meet the growing computing needs. Summary of the invention

[0007] The purpose of the present invention is to provide a quantum eloquence training and analysis system based on multi-qubit processing to solve the problems existing in the prior art.

[0008] To achieve the above object, the present invention provides the following solutions:

[0009] A quantum eloquence training and analysis system based on multi-qubit processing, comprising:

[0010] A data collection and preprocessing module, which is used to collect multimodal data of the speaker, and preprocess the multimodal data to extract sound data related to eloquence and emotion; the multimodal data includes voice data, video data, text data, social media data and online classroom data; the preprocessing process includes denoising;

[0011] A multi-qubit processing module is used to process the extracted sound data using a multi-qubit algorithm based on multi-qubit hardware deployed to the cloud to generate processed sound data; the multi-qubit algorithm includes a quantum machine learning algorithm and a quantum graph algorithm; the quantum machine learning algorithm includes a quantum support vector machine and a quantum neural network; the quantum graph vector includes quantum graph classification and quantum graph clustering; the quantum machine learning algorithm is used to perform speech recognition and sentiment analysis on the extracted sound data; the quantum graph algorithm is used to classify and cluster the speech data;

[0012] The personalized training module is used to identify the eloquence problems of the speaker during the speech according to the processed sound data and generate a personalized training plan.

[0013] Optionally, the data acquisition and preprocessing module specifically includes:

[0014] The multi-source data collection submodule is used to collect the multi-modal data of the speaker;

[0015] A data cleaning submodule, for applying statistical analysis and data rules to capture error values, abnormal values, missing values ​​and duplicates in the multimodal data, and cleaning the multimodal data;

[0016] The data quality assessment submodule is used to analyze the data distribution, data integrity and data changes of the cleaned multimodal data and generate a data quality report; the data quality includes the mean, variance and distribution diagram of the multimodal data;

[0017] The feature extraction and engineering submodule is used to extract sound data related to eloquence and emotion from the multimodal data by using speech processing, natural language processing and computer vision technology, and to perform feature enhancement or feature optimization on the extracted sound data.

[0018] Optionally, the multi-qubit processing module specifically includes: a multi-qubit algorithm sub-module, a multi-quantum computing algorithm optimization sub-module, a multi-qubit hardware management sub-module, a multi-qubit energy consumption optimization sub-module and a multi-qubit performance detection and evaluation module.

[0019] Optionally, the multi-qubit algorithm submodule is used to create different quantum logic gates based on multi-qubit hardware deployed to the cloud, and to process the extracted sound data using a multi-qubit algorithm to generate processed sound data.

[0020] Optionally, the multi-quantum computing algorithm optimization submodule is used to optimize the extracted sound data using an approximate algorithm and a simulation algorithm.

[0021] Optionally, the multi-qubit hardware management submodule specifically includes: cloud computing, edge computing, and hardware management tools;

[0022] The cloud computing is used to provide multi-qubit hardware;

[0023] The edge computing is used to deploy multi-qubit hardware within a set distance range of the speaker;

[0024] The hardware management tool is used to manage and schedule multi-qubit hardware.

[0025] Optionally, the multi-qubit energy consumption optimization submodule is used to use low-temperature quantum computing to reduce the operating temperature of multi-qubit hardware, and to use optical quantum computing technology to reduce the energy consumption of multi-qubit hardware.

[0026] Optionally, the multi-qubit performance detection and evaluation module is used to monitor the performance of the multi-qubit processing module using performance indicators, and to dynamically adjust the performance of the multi-qubit processing module using a dynamic monitoring system.

[0027] Optionally, the personalized training module specifically includes:

[0028] A personalized training plan generation submodule, used to generate a personalized training plan based on the analysis results of the eloquence problem and the level of the speaker;

[0029] A training effect evaluation submodule, for training according to the personalized training plan, analyzing the speaker's real-time speech using speech analysis technology, and scoring the real-time speech according to multiple factors; the multiple factors include pronunciation, fluency, and grammatical accuracy;

[0030] The real-time feedback and adaptive learning submodule is used to analyze the real-time speech according to the score and the training effect, and generate real-time feedback to adjust the personalized training plan.

[0031] Optionally, the multi-qubit processing module also includes: a data parallel processing submodule, which is used to distribute the extracted sound data to multiple processors using a distributed processing algorithm, and to perform parallel processing on the extracted sound data using a graph structure based on a parallel graph algorithm.

[0032] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the embodiments of the present invention solve the problems existing in the traditional computing methods by introducing multi-quantum bit processing technology.

[0033] 1) In the multi-qubit processing module, by making full use of the parallelism of quantum computing and quantum entanglement, the sound data is analyzed and identified in real time. The parallelism of quantum computing allows multiple data points to be processed simultaneously, while quantum entanglement can achieve the association between quantum bits and improve the efficiency of the algorithm. In this way, compared with traditional computing methods, the present invention can process large-scale data more quickly, realize real-time analysis and feedback, and thus improve the efficiency of speech and eloquence training.

[0034] 2) In the data acquisition and preprocessing module, the data is preprocessed to remove noise, standardize sound signals, and improve data quality.

[0035] 3) In the multi-qubit processing module, the parallelism of quantum computing and the characteristics of quantum entanglement make it possible to process multiple types of data at the same time, whether it is sound, text or video, etc., all can be processed in the same quantum computing system. The advantage of quantum computing is that it can process different types of data in a more unified way, improving the integration efficiency of multimodal data. Therefore, the present invention effectively solves the problem that traditional methods are difficult to integrate multimodal data through the application of quantum computing technology, and provides a more comprehensive and efficient processing method for speech and eloquence analysis.

[0036] 4) Use cloud computing technology to deploy multi-qubit hardware to the cloud, so users can use it on demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. 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 paying creative labor.

[0038] Figure 1 This is a structural diagram of the quantum eloquence training and analysis system based on multi-qubit processing provided by the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] The purpose of the present invention is to provide a quantum eloquence training and analysis system based on multi-qubit processing, which can improve computing efficiency, evaluation precision and accuracy, effectively integrate and process multimodal data, and provide hardware resources to meet the growing computing needs.

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] Embodiment 1

[0043] like Figure 1 As shown, the present invention provides a quantum eloquence training and analysis system based on multi-qubit processing, comprising:

[0044] The data collection and preprocessing module 1 is used to collect the speaker's multimodal data, and preprocess the multimodal data to extract sound data related to eloquence and emotion; the multimodal data includes voice data, video data, text data, social media data and online classroom data; the preprocessing process includes denoising.

[0045] In practical applications, the data acquisition and preprocessing module includes a multi-source data acquisition sub-module, a data cleaning sub-module, a data quality assessment sub-module, a feature extraction and engineering sub-module, a privacy protection and compliance sub-module, and a data storage and backup sub-module.

[0046] (I) Structure:

[0047] 1. Multi-source data acquisition submodule:

[0048] Data collector: An access point for various data sources (such as recording devices, cameras, text documents, social media platforms, online classrooms, etc.). Use different interfaces to obtain data.

[0049] Voice data: includes speech samples of speakers obtained from recording devices for eloquence training and analysis.

[0050] Video data: obtained from cameras or other video devices to record the speaker's video speech for eloquence analysis.

[0051] Text data: Data collectors can also extract text information from text documents, which may include speech scripts, lecture notes, etc.

[0052] Social Media Data: Obtain speakers’ social media posts or comments from social media platforms (e.g., Twitter, Facebook, etc.) to understand their online activities and interactions.

[0053] Online classroom data: If eloquence training involves online education or training, the data collector can also obtain the interaction data between students and lecturers from the online classroom platform.

[0054] In general, the data collector is used to collect various types of data, including voice, video, text, and social media data, from speakers and other relevant participants to support the functions of the eloquence training and analysis system. The diversity of this data helps the system provide more comprehensive and personalized eloquence training and analysis services.

[0055] Data source management: information used to manage and maintain data sources, including data source type, data acquisition frequency, data format, data quality, etc.

[0056] 2. Data cleaning submodule:

[0057] Data quality checking: Apply statistical analysis and data rule detection to catch errors, outliers, missing values, and duplicates in the data. Data quality checking uses specific rules to identify problem data.

[0058] Data cleaning algorithms: A set of algorithms, including imputation, outlier handling, and deduplication algorithms, to repair or remove problematic data.

[0059] 3. Data quality assessment submodule:

[0060] Data statistical analysis: Analyze data distribution, data integrity, data changes, etc., and generate data quality reports. The report can include the mean, variance, distribution diagram, etc. of the data.

[0061] Data quality scoring: Apply quantitative metrics to assess data quality, such as data accuracy, consistency, completeness, and availability. These metrics can help determine the trustworthiness of the data.

[0062] 4. Feature extraction and engineering submodule:

[0063] Feature extraction algorithm: Use speech processing, natural language processing and computer vision technology to extract features related to eloquence from multi-source data. This may include audio features, text sentiment features, image features, etc.

[0064] In the feature extraction and engineering submodules, to determine that a feature is related to eloquence, the following conditions need to be met:

[0065] 1) Theoretical basis and domain knowledge: First, the research team needs to have a clear theoretical basis and domain knowledge support. Experts and researchers in the field of eloquence and speech may have identified characteristics related to eloquence performance. For example, domain knowledge can include factors such as voice characteristics, speech fluency, emotional expression, body language, word choice, etc.

[0066] 2) Data analysis and statistical verification: Use a large amount of eloquence data and speech samples to verify the correlation between the feature and eloquence through data analysis and statistical methods. This may include comparison with common features of successful speakers and statistical analysis of the correlation between the feature and eloquence performance. If the feature shows significant differences in successful speakers and individuals with good eloquence performance, then this is a condition that is conducive to determining its correlation with eloquence.

[0067] 3) Machine learning and model validation: Using machine learning methods, especially supervised learning, models can be built to predict eloquence performance using different features as input. The performance of the model can be used as a validation basis for the correlation between features and eloquence. If a feature has a significant influence in the eloquence prediction model, it can be considered to be related to eloquence.

[0068] 4) Cross-validation and external validation: The eloquence relevance of a feature can be verified by cross-validation. The dataset is divided into a training set and a test set, and the consistency of the features is checked by different models and validation methods. External validation, that is, verifying the effectiveness of the feature on an independent eloquence dataset, is also an important condition for determining the relevance of the feature to eloquence.

[0069] 5) User feedback: The eloquence training system may be used by actual users to verify whether the extracted features are truly related to eloquence through user feedback. User feedback and eloquence performance data can be used to adjust and improve feature selection and performance.

[0070] In short, determining the correlation between features and eloquence requires a series of scientific methods, including theoretical support, data analysis, machine learning, and verification. The satisfaction of these conditions will help ensure the effectiveness of the extracted features for eloquence training and analysis.

[0071] Feature engineering tools: used to preprocess, enhance, or optimize features to improve data quality and algorithm performance.

[0072] 5. Privacy protection and compliance submodule:

[0073] Data desensitization technology: used to protect user privacy, including de-identification, encryption, and data masking technologies.

[0074] Compliance checks: Check whether data collection and processing comply with relevant regulations, such as GDPR (General Data Protection Regulation) or HIPAA (Health Insurance Portability and Accountability Act).

[0075] 6. Data storage and backup submodule:

[0076] Data storage strategy: defines the storage structure and format of data for subsequent data access and analysis. This can include relational databases, distributed file systems, or cloud storage.

[0077] Data backup plan: Ensure data security by backing up data regularly to cope with the risk of data loss or catastrophic events.

[0078] (II) Function:

[0079] The function of the data acquisition and preprocessing module is to ensure that the data used is of good quality for subsequent analysis and training. It includes the following aspects:

[0080] Data collection, ensuring that the required information is obtained from multiple sources of data.

[0081] Data cleaning, clearing and correcting errors in data.

[0082] Data quality assessment, evaluating the completeness and accuracy of the data.

[0083] Feature extraction, extracting eloquence and emotion-related features from raw data.

[0084] Privacy protection ensures that users' privacy data is protected.

[0085] Data storage, which safely stores cleaned and processed data for subsequent access.

[0086] (III) Specific implementation methods:

[0087] Multi-source data acquisition submodule: Use APIs or protocols suitable for different data sources to obtain data. This can include APIs of recording devices, APIs of social media platforms, web crawler tools, etc.

[0088] Data cleaning submodule: Use data cleaning algorithms, such as deduplication, outlier detection, missing value processing, etc. Use rules and specifications to check, repair and clean up problematic data.

[0089] Data Quality Assessment Submodule: Apply statistical analysis and data quality indicators, such as measurement of data accuracy and assessment of data completeness.

[0090] Feature extraction and engineering submodule: Use feature extraction tools and algorithms such as speech recognition, text analysis, image processing, etc. Perform feature preprocessing to ensure feature quality.

[0091] Privacy protection and compliance submodule: Use encryption technology and data desensitization tools, while implementing permission management and access control to ensure user privacy is protected.

[0092] Data storage and backup submodule: Select a suitable data storage solution, which can be cloud storage or database, and implement regular backup strategies to ensure data security.

[0093] The goal of this module is to provide high-quality and diverse data sets for subsequent multi-qubit processing to ensure the accuracy and efficiency of the final Eloquent training and analysis results.

[0094] The multi-qubit processing module 2 is used to process the extracted sound data using a multi-qubit algorithm based on the multi-qubit hardware deployed to the cloud to generate processed sound data; the multi-qubit algorithm includes a quantum machine learning algorithm and a quantum graph algorithm; the quantum machine learning algorithm includes a quantum support vector machine and a quantum neural network; the quantum graph vector includes quantum graph classification and quantum graph clustering; the quantum machine learning algorithm is used to perform speech recognition and sentiment analysis on the extracted sound data; the quantum graph algorithm is used to classify and cluster the speech data.

[0095] In practical applications, the multi-qubit processing module includes a multi-qubit algorithm sub-module, a multi-quantum computing algorithm optimization sub-module, a multi-qubit hardware management sub-module, a multi-qubit energy consumption optimization sub-module, a data parallel processing sub-module, and a multi-qubit performance monitoring and evaluation sub-module.

[0096] (I) Structure:

[0097] 1. Multi-qubit algorithm submodule:

[0098] 1.1 Structure: The multi-qubit algorithm submodule is the core of the multi-qubit processing module, including qubits and quantum logic gates.

[0099] 1.2 Function: This submodule is mainly used for real-time analysis and recognition of sound data. Quantum bits perform quantum gate operations to process input data. Quantum machine learning algorithms are used to process tasks such as speech recognition and sentiment analysis, making full use of the parallelism of quantum computing and quantum entanglement to improve efficiency and accuracy. Quantum graph algorithms are used to classify and cluster sound data, using the superposition characteristics of quantum states to improve the parallelism and fault tolerance of the algorithm.

[0100] In practical applications, real-time analysis and recognition of sound data is the core function of the multi-qubit processing module. Sound data can include the speaker's voice record, speech content, sound quality, speaking speed, voice emotion and other information in eloquence training and analysis. This information can be related to eloquence, because eloquence performance is often related to the speaker's pronunciation clarity, expression ability, speaking speed control, emotional expression and other factors.

[0101] Specifically:

[0102] 1) Speech recognition: Quantum bits and quantum logic gates can be used to execute speech recognition algorithms to convert accents, pronunciations, and language content in eloquence training and speeches into text. This is data related to eloquence because it is part of the content of the speech and is crucial to the quality and understanding of the speech.

[0103] 2) Sentiment analysis: Quantum machine learning algorithms can be applied to sound data to analyze the speaker’s emotional expression, such as the positivity, negativity, or neutrality of the emotion, as well as the intensity of the emotion. This is related to eloquence, as the emotional expression of a speech can affect the effectiveness and appeal of the speech.

[0104] 3) Voice quality and speech speed: The parallelism and fast response time of quantum computing can be used to analyze voice quality in real time, including voice clarity, voice quality, speech speed control, etc. These factors are directly related to the quality of eloquence performance.

[0105] Therefore, the sound data is used in the multi-qubit processing module to extract data related to eloquence, including voice content, emotional expression, and voice characteristics. These data can be used in eloquence training and analysis to improve eloquence performance and provide personalized suggestions.

[0106] In practical applications, quantum machine learning algorithms can be implemented through the following specific processing when processing tasks such as speech recognition and sentiment analysis:

[0107] 1) Data preprocessing: First, the collected sound data will undergo data cleaning and preprocessing to remove noise, standardize sound signals, and improve data quality. This includes denoising and eliminating duplicates to ensure the consistency and availability of input data.

[0108] 2) Feature extraction: For sound data, quantum machine learning algorithms will extract relevant features from it, including sound spectrum, pitch, sound intensity, speech speed, etc. These features are processed in a quantum computing environment, making full use of the parallelism of quantum computing and quantum entanglement to accelerate the feature extraction process.

[0109] 3) Speech Recognition: For speech recognition tasks, the extracted sound features will be used to convert the sound into text. Quantum machine learning algorithms will analyze the sound features and map them to text data. This process can benefit from the fast computing speed of quantum computing, allowing for faster speech-to-text conversion.

[0110] 4) Sentiment Analysis: For sentiment analysis tasks, the extracted sound features will be used to analyze the emotional expression in the sound, such as the positivity, negativity or neutrality of the emotion, and the intensity of the emotion. The quantum machine learning algorithm will apply the sentiment analysis model to determine the emotional information by analyzing the sound features.

[0111] 5) Quantum computing parallelism and quantum entanglement: Throughout the process, quantum machine learning algorithms can make full use of quantum computing parallelism and quantum entanglement. This means that it can process multiple features or samples at the same time to speed up the analysis process. The superposition characteristics of quantum states enable the algorithm to execute in multiple states, improving the efficiency and accuracy of calculations.

[0112] Through the above process, quantum machine learning algorithms can achieve efficient speech recognition and sentiment analysis, providing strong support for eloquence training and analysis. The fast processing capabilities of these algorithms and the advantages of quantum computing enable them to play a key role in real-time analysis and recognition of sound data.

[0113] In practical applications, 1) Improve efficiency: The parallelism of quantum computing refers to the ability to process multiple computing states at the same time, rather than executing them one by one. In speech recognition and sentiment analysis tasks, this means that quantum machine learning algorithms can process multiple sound features or multiple samples at the same time, rather than in sequence. This parallelism makes it more efficient to process large amounts of data, especially in real-time analysis and recognition, which can greatly reduce processing time.

[0114] 2) Improved accuracy: Quantum entanglement refers to the non-classical mutual correlation established between two or more quantum bits. In speech recognition and sentiment analysis, quantum entanglement can be used to improve the accuracy of data analysis and recognition. This entangled relationship can capture the complex associations and features in the sound data, helping the algorithm to better understand the sound signal. This improves the accuracy of the analysis and enables the algorithm to better capture the subtle features and emotional expressions of the speech.

[0115] Therefore, the main beneficiaries of taking full advantage of quantum computing's parallelism and quantum entanglement to improve efficiency and accuracy are speech recognition and sentiment analysis. These two tasks are critical to efficiently processing large amounts of data and accurately understanding the meaning of sound, and the advantages of quantum computing can make them more competitive and feasible.

[0116] In practical applications, the application of quantum graph algorithms in sound data classification and clustering tasks is closely related to parallelism and fault tolerance, mainly because the characteristics of quantum computing have a positive impact on these two aspects.

[0117] 1) Improved parallelism: The quantum graph algorithm exploits the properties of quantum state superposition, which means that it can process multiple states or data points simultaneously when processing sound data classification and clustering. Traditional computing methods may need to process each data point in turn, while quantum algorithms can process multiple data points at the same time, thereby achieving higher parallelism. This means that the classification and clustering of sound data can be completed faster, especially when dealing with large-scale data sets. This advantage is particularly obvious.

[0118] 2) Improved fault tolerance: Quantum computing also has a certain degree of fault tolerance, which is due to the entanglement relationship and information redundancy between quantum bits. In sound data classification and clustering, fault tolerance is crucial because the data may contain noise or outliers, which may interfere with the results of traditional computing methods. Quantum graph algorithms can better handle these interferences because they have stronger fault tolerance. Even in the presence of interference or errors, quantum graph algorithms can complete tasks more reliably, improving the robustness of the algorithm.

[0119] Therefore, the application of quantum graph algorithm in sound data classification and clustering makes it a more efficient and reliable method by improving parallelism and fault tolerance. This is very beneficial for real-time analysis and processing of large-scale sound data, especially in eloquence training and analysis systems, which can provide more accurate and efficient services.

[0120] 1.3 Implementation method: Use existing quantum computing hardware or cloud computing resources to create appropriate quantum logic gates, and then develop or apply quantum machine learning and quantum graph algorithms to perform corresponding sound analysis tasks.

[0121] In practical applications, the specific quantum logic gates depend on the quantum computing platform and hardware used. Different quantum computing systems may use different types of quantum logic gates to implement specific quantum operations.

[0122] Some common types of quantum logic gates include:

[0123] 1. X-gate (Pauli-X gate): The X-gate is used to flip between quantum bits on the X-axis. It is equivalent to the logical "NOT" gate in classical computing.

[0124] 2. Z-gate (Pauli-Z gate): The Z-gate is used to perform Z-axis rotation between quantum bits, usually to change the phase. It also plays an important role in quantum coding.

[0125] 3.H gate (Hadamard gate): Hadamard gate is used to create quantum state superposition, which can convert the 0 and 1 states of traditional bits into super-position states, which is used to build quantum parallelism.

[0126] 4. CNOT gate (controlled NOT gate): CNOT gate is used for quantum entanglement between quantum bits. It can realize the interaction between quantum bits.

[0127] 5. T-gate and Toffoli gate: Quantum logic gates are used for phase rotation and multi-bit operations in quantum computing and for building more complex quantum circuits.

[0128] Which quantum logic gates to use and how to configure them depends on the quantum algorithm and task being designed. Therefore, creating appropriate quantum logic gates requires selection and configuration based on specific applications and requirements. As quantum computing technology develops, researchers and engineers will continue to innovate and improve the design and implementation of quantum logic gates.

[0129] 1.4 Technical means:

[0130] 1.4.1 Quantum Machine Learning Algorithms:

[0131] (1) Quantum Support Vector Machine: Convert the traditional support vector machine algorithm into a quantum algorithm and use the advantages of quantum computing to improve the efficiency and accuracy of the algorithm.

[0132] (2) Quantum neural network: Convert traditional neural network algorithms into quantum algorithms and use the parallelism of quantum computing to improve the efficiency of the algorithms.

[0133] 1.4.2 Quantum Graph Algorithm:

[0134] (1) Quantum graph classification: Represent data as a graph structure and use the advantages of quantum computing to perform classification.

[0135] (2) Quantum graph clustering: Represent data as a graph structure and use the advantages of quantum computing to perform clustering.

[0136] 1.5 Specific algorithms and calculation formulas

[0137] (1) Composite quantum feature mapping:

[0138] formula:

[0139] Indicator definition: Ψ(x) represents the mapping function of the quantum state, f i (x) represents the i-th eloquence characteristic function, θ i Represents the corresponding weight parameters. These features may include pitch, volume, speaking speed, etc.

[0140] (2) Quantum entanglement and feature recognition:

[0141] Formula: E ij =<ψ i |ψ j >

[0142] Indicator definition: Ei j Represents the quantum state ψ i and ψ j The degree of entanglement between them is used to characterize the correlation between different eloquence characteristics.

[0143] (3) Quantum decision function

[0144] formula:

[0145] Indicator definition: D(x) is the decision function, αk and yk are parameters determined during the training process, and K is the quantum kernel function used to compare the input x and the training sample x k similarity.

[0146] (4) Quantum Gradient Descent Optimization

[0147] formula:

[0148]

[0149] Indicator definition: θ represents the parameter set, η1 is the learning rate, is the gradient of the quantum algorithm, used to optimize the parameters.

[0150] θ new : represents the updated set of parameters. In quantum computing, these parameters are used to define the state of a quantum bit or the configuration of a quantum gate.

[0151] θ old : represents the parameter set before the update. This is the parameter value at the beginning of the optimization process.

[0152] η1: This is the representation of the learning rate. The learning rate is an important hyperparameter that controls the step size of parameter updates during the optimization process. In quantum computing, an appropriate learning rate can help find the optimal solution efficiently.

[0153] Represents the gradient of a quantum algorithm. The gradient is a vector that indicates the direction in which a function grows fastest in parameter space. In quantum computing, the gradient is calculated to guide the update of parameters to optimize the performance of the algorithm.

[0154] Q(θ): is the objective function of the quantum algorithm, which evaluates the performance of the algorithm based on the current parameter set θ. The form of the objective function depends on the specific application and task.

[0155] These formulas combine the core characteristics of quantum computing (such as quantum state mapping, entanglement, and parallelism) with key indicators of eloquence analysis (such as pitch, volume, and speaking speed), reflecting the innovation and complexity of the invention. Through these advanced quantum computing methods, the accuracy and efficiency of eloquence training and analysis can be effectively improved.

[0156] 1.6 Operation process

[0157] (1) Composite quantum feature mapping: First, based on the speaker’s voice data (such as pitch, volume, and speaking speed), the feature function f is defined. i(x) Calculate the quantum state mapping of each feature. Then, use the parameter θ i To adjust the weight of each feature in the quantum state and generate a composite quantum state representation Ψ(x).

[0158] (2) Quantum entanglement and feature recognition: Entanglement is created between quantum states, and the entanglement is used to measure E ij This helps to identify which eloquence characteristics have a greater impact on speech effectiveness, thereby providing more precise training and evaluation.

[0159] (3) Quantum decision function: K uses the quantum kernel function K to calculate the similarity between the input data and the training sample. Combined with the parameter α obtained by training k and k The speaker's eloquence level is classified by the decision function D(x).

[0160] (3) Quantum gradient descent optimization: During the training process, according to the gradient of the quantum algorithm Adjust the parameters θ to find the most optimized model parameters. This process uses the fast processing power of quantum computing to accelerate the learning process and improve the performance of the model.

[0161] Through these steps, quantum computing methods can efficiently process and analyze complex sound data related to eloquence, providing real-time and accurate training and evaluation feedback.

[0162] 2.Multi-quantum computing algorithm optimization submodule:

[0163] 2.1 Structure: This submodule includes approximate algorithms and simulation algorithms to improve the efficiency and performance of multi-qubit algorithms.

[0164] 2.2 Function: Approximate algorithms can solve complex problems in sound analysis, sacrificing some accuracy for faster analysis speed. Simulation algorithms are used to solve problems with immature quantum hardware to ensure the availability of algorithms.

[0165] 2.3 Implementation method: Select appropriate approximation algorithms and simulation algorithms and integrate them into multi-qubit algorithms as needed to improve their efficiency and performance.

[0166] 2.4 Technical means

[0167] Quantum approximation algorithms:

[0168] (1) Approximate the quantum computing algorithm to reduce its complexity and improve its efficiency.

[0169] Quantum simulation algorithm:

[0170] (2) Simulate quantum computing on classical computers to reduce the demand for quantum computing hardware.

[0171] 2.5 Specific Algorithms and Calculation Formulas

[0172] (1) Advanced Quantum Kernel Transformation

[0173] formula:

[0174]

[0175] Indicator definition: K Q is the quantum kernel function, γ is the kernel transformation parameter, x i ,y i is the eloquence feature of the input data.

[0176] (2) Quantum state optimization:

[0177] formula:

[0178] Indicator definition: |ψ> is the optimized quantum state, α i is the state amplitude and |i> is the ground state.

[0179] (3) Quantum decision tree algorithm:

[0180] formula:

[0181] QDT(x)=∑ paths P(path)·C(path,x)

[0182] Indicator definition: QDT is the quantum decision tree function, P(path) is the path probability, and C(path, x) is the path decision condition.

[0183] 2.6 Operation process

[0184] (1) Advanced quantum kernel transformation: This process first calculates the quantum kernel function K of the input data Q (x,y). This involves calculating the similarity between two sets of eloquence features x and y, with γ as a tuning parameter. This function is used in algorithms such as quantum support vector machines or quantum neural networks to improve the efficiency of identifying eloquence features.

[0185] (2) Quantum state optimization: In this step, the algorithm optimizes the quantum state |ψ> by adjusting the state amplitude α i This optimization helps to accurately represent and process eloquence features and improve the accuracy and performance of the algorithm.

[0186] (3) Quantum decision tree algorithm: Quantum decision tree QDT(x) uses multiple possible decision paths to analyze input data x. This involves calculating the probability P(path) and condition C(path,x) of each path to determine the final classification or prediction result. Through the above process, these formulas and algorithms can deeply analyze and optimize the processing of voice data, thereby providing more efficient and accurate results in eloquence analysis.

[0187] 3. Multi-qubit hardware management submodule:

[0188] 3.1 Structure: This submodule includes cloud computing and edge computing resources, as well as hardware management tools for multi-qubit processing modules.

[0189] 3.2 Function: Cloud computing provides quantum computing hardware resources to meet the needs of multi-qubit processing modules, with flexible allocation capabilities to ensure that real-time processing needs are met. Edge computing deploys quantum computing hardware near users to reduce data transmission delays and is suitable for real-time sound analysis.

[0190] 3.3 Implementation method: Use the resources of cloud computing service providers or build your own cloud computing and edge computing nodes, configure them according to performance requirements, and implement hardware management strategies for multi-qubit processing modules.

[0191] 3.4 Technical means: The specific technical means adopted by the multi-qubit hardware management submodule mainly include:

[0192] Cloud computing technology: deploy multi-qubit hardware resources to the cloud so that users can use them on demand.

[0193] Edge computing technology: deploy multi-qubit hardware near users to reduce data transmission latency.

[0194] Distributed computing technology: distribute data to multiple processors for parallel processing.

[0195] Parallel graph algorithm: Represent data as a graph structure and use the parallelism of the graph for processing.

[0196] 3.5 Calculation formula

[0197] (1) Resource Allocation Optimization Algorithm

[0198] formula:

[0199] R opt (u,υ)=arg min R (α×T(R,u)+β×D(R,υ))

[0200] Indicator definition: Ropt(u,v) represents the optimal resource configuration; T(R,u) represents the computing time under a given resource configuration R; D(R,v) represents the data transmission delay; α and β are trade-off coefficients, which control the impact of computing time and data transmission delay respectively; u and v represent computing requirements and data volume respectively.

[0201] (2) Load balancing and parallel processing algorithms:

[0202] formula:

[0203] Indicator definition: Lbal(n,k) represents the total workload of load balancing on n nodes; W(i) represents the workload on the i-th node; k represents the number of nodes processed in parallel.

[0204] (3) Data transmission and delay optimization strategy:

[0205] formula:

[0206] D min (s,t)=min(∑ p∈Paths C(p,s,t)

[0207] Indicator definition: D min (s, t) represents the minimum data transmission delay from source s to target t; C(p, s, t) represents the communication cost on path p; Paths is the set of possible data transmission paths.

[0208] 3.6 Operation process

[0209] (1) Resource allocation optimization algorithm: First, based on the computing demand u and the data volume v, the algorithm evaluates the computing time T(R,u) and data transmission delay D(R,v) under different resource configurations R. Then, by weighing the computing time and data transmission delay (considering the trade-off coefficients α and β), the optimal resource configuration R is determined. opt (u,v).

[0210] (2) Load balancing and parallel processing algorithm: The system decomposes the computing tasks and distributes them to multiple processing nodes. The workload W(i) of each node is evaluated and the workload is calculated by L bal (n,k), optimize the distribution of tasks on n nodes to ensure balanced load.

[0211] (3) Data transmission and delay optimization strategy: For each data transmission task, the algorithm evaluates the communication cost C(p, s, t) on different paths p. The path with the lowest cost is selected to minimize the data transmission delay D from source s to destination t. min (s,t).

[0212] These processes ensure efficient resource utilization and minimize data transmission delays, thereby optimizing overall system performance while ensuring real-time processing requirements.

[0213] 4. Multi-qubit energy consumption optimization submodule:

[0214] 4.1 Structure: including low-temperature quantum computing and optical quantum computing technologies.

[0215] 4.2 Function: Low-temperature quantum computing reduces the energy consumption of multi-qubit processing hardware, reduces noise and interference through low-temperature environment, and improves processing stability. Optical quantum computing realizes multi-qubit processing in a low-energy way.

[0216] 4.3 Implementation: Select cryogenic or optical quantum computing technologies suitable for specific applications and apply them to reduce the energy consumption of multi-qubit processing modules.

[0217] 4.4 Technical means:

[0218] (1) Low-temperature quantum computing technology: lowering the operating temperature of quantum bits and reducing the energy consumption of quantum bits.

[0219] (2) Photonic quantum computing technology: The energy consumption of optical quantum computing is much lower than that of traditional quantum computing.

[0220] (3) Quantum computing algorithm optimization: Use low-complexity quantum computing algorithms to reduce the energy consumption of quantum computing.

[0221] 4.5 Calculation formula

[0222] (1) Quantum Thermodynamics Model

[0223] formula:

[0224] Indicator definition: E is the total energy of the system, h is Planck's constant, ν i is the frequency of the ith quantum state, kB is the Boltzmann constant, and T is the temperature.

[0225] (2) Photon quantum computing efficiency model:

[0226] formula:

[0227] Indicator definition: η2 represents the efficiency of photon quantum computing, P out ,i and P in ,i are the output power and input power of the i-th photon processing unit respectively; η2 represents the efficiency of photon computing, which is a key indicator used to measure the energy conversion efficiency of photon computing system; ∑ i＝1 m P out,iis the sum of the output powers of all photon processing units; where P out,i is the output power of the i-th photon processing unit; ∑ i＝1 m P in,i is the sum of the input powers of all photon processing units; where P in,i is the input power of the ith photon processing unit; m represents the total number of photon processing units.

[0228] (3) Quantum gate energy consumption optimization:

[0229] Formula: E gate =α1×f+β1×q

[0230] Indicator definition: E gate represents the energy consumption of quantum gate operation, f is the operation frequency, q is the number of quantum bits, and α1 and β1 are adjustment coefficients.

[0231] E gate represents the energy consumption of quantum gate operation; this is an important indicator in quantum computing, which is used to measure the energy required to perform a specific quantum gate operation; α1 is an adjustment coefficient used to quantify the impact of operation frequency on energy consumption; in different quantum computing systems, this coefficient may vary due to different hardware and environment; f represents the operation frequency, that is, the frequency of performing quantum gate operations per unit time; the higher the frequency, the greater the energy consumption; β1 is another adjustment coefficient used to quantify the impact of the number of quantum bits on energy consumption; it reflects the increase in energy consumption when processing more quantum bits; q represents the number of quantum bits, that is, the number of quantum bits involved in the quantum gate operation. The more quantum bits there are, the more energy is usually required to perform the operation.

[0232] 4.6 Operation process

[0233] (1) Quantum thermodynamics model: This model first calculates the total energy E of the quantum system at different temperatures T. This formula is calculated by the frequency ν of the quantum state. i and temperature to determine energy, helping to determine the optimal low-temperature environment to reduce energy consumption.

[0234] (2) Photonic quantum computing efficiency model: This model is used to evaluate and optimize the efficiency η2 of photonic quantum computing. By comparing the input and output power of different photonic quantum processing units, the energy consumption of photonic quantum computing can be optimized.

[0235] (3) Quantum gate energy consumption optimization: This formula is used to calculate the energy consumption E of quantum gate operation gate , considering the operation frequency and the number of qubits. Energy consumption is optimized by adjusting the frequency f and the number of qubits q.

[0236] 5. Data parallel processing module:

[0237] 5.1 Structure: including distributed processing algorithms and parallel graph algorithms.

[0238] 5.2 Function: Distributed processing algorithm distributes sound data to multiple processors for parallel processing to improve processing efficiency. Parallel graph algorithm uses graph structure to process data in parallel to improve parallelism.

[0239] 5.3 Implementation method: Select appropriate distributed processing algorithms or parallel graph algorithms based on the data volume and system architecture characteristics to improve data processing efficiency.

[0240] 5.4 Technical means

[0241] Distributed processing algorithm: divides the sound data into multiple small tasks and assigns these tasks to multiple processors to achieve parallel processing. Distributed processing algorithms can make full use of the computing power of multi-core processors and multiple computers to increase data processing speed.

[0242] Parallel graph algorithm: Use graph data structure to represent sound data and processing tasks, and then use parallel graph algorithm to process data. This method can improve the parallelism of data processing because the nodes in the graph can be processed in parallel without being restricted by the dependencies between data.

[0243] 5.5 Calculation formula

[0244] (1) Dynamic task scheduling algorithm:

[0245] formula:

[0246] Indicator definition: T i represents the total processing time of the i-th processor, M is the total number of tasks, and D ij is the amount of data that the i-th processor processes the j-th task, P ij is the processing speed of the i-th processor for the j-th task.

[0247] (2) Advanced Parallel Graph Algorithms

[0248] formula:

[0249]

[0250] Indicator definition: G eff represents the efficiency of the graph, V is the set of nodes, deg(v) is the degree of node v, and λ is a parameter for adjusting the parallel efficiency.

[0251] 5.6 Operation process

[0252] (1) Dynamic task scheduling algorithm: Operation process: The system first calculates the data volume D of each task.ij and the processing speed P of each processor ij Calculate the total processing time T of each processor i This allows the system to dynamically adjust task allocation to ensure that each processor receives tasks in a balanced manner according to its capabilities and load.

[0253] (2) Advanced parallel graph algorithm: Operation process: The system uses the graph data structure to represent data and tasks, and maximizes the parallel processing efficiency of the entire graph by calculating the degree deg(v) of each node and adjusting the parameter λ. eff This approach improves the parallelism of data processing, allowing different data nodes to be processed simultaneously and independently.

[0254] These two algorithms work together to ensure that the data parallel processing module can achieve efficient, balanced and flexible data processing when processing large-scale and complex sound data.

[0255] 6. Multi-qubit performance monitoring and evaluation submodule:

[0256] 6.1 Structure: including performance indicators and dynamic monitoring system.

[0257] 6.2 Function: Performance indicators are used to evaluate the performance of multi-qubit processing modules, including accuracy, efficiency, and robustness. The dynamic monitoring system monitors the performance of multi-qubit processing modules in real time and makes adjustments when necessary.

[0258] 6.3 Implementation method: Design and implement a performance indicator system and a dynamic monitoring system to ensure the high performance and reliability of the multi-qubit processing module.

[0259] 6.4 Technical means

[0260] Performance indicator definition: Clearly define the performance indicators of the multi-qubit processing module, such as accuracy, efficiency, robustness, etc. These indicators are used to measure the performance of the system.

[0261] Performance monitoring methods: Different performance monitoring methods are used to ensure that the performance of the multi-qubit processing module is traceable during runtime. This includes the use of performance monitoring tools, logging, real-time monitoring, etc.

[0262] Dynamic monitoring technology: Using dynamic monitoring technology, the performance of multi-qubit processing modules can be tracked in real time. This includes real-time sampling, analysis and reporting of performance indicators.

[0263] 6.5 Calculation formula

[0264] (1) Comprehensive Performance Score (CPS) formula:

[0265] CPS=α2·PA+β2·PE+γ·PR-δ·PL

[0266] PA is the accuracy score, which takes into account the accuracy of recognition accuracy and eloquence evaluation; PE is the efficiency score, which measures the processing speed and response time; PR is the robustness score, which measures the system's adaptability to noise and variables; PL is the delay score, which is the delay time of system response and processing; CPS is the comprehensive performance score, which is used to comprehensively evaluate the performance of the multi-qubit processing module; α2 is the weight factor of the accuracy score, which is used to adjust the importance of accuracy in the comprehensive performance score; PA is the accuracy score, which takes into account the accuracy of recognition accuracy and eloquence evaluation; β2 is the weight factor of the efficiency score, which is used to adjust the importance of efficiency in the comprehensive performance score; PE is the efficiency score, which measures the processing speed and response time; γ is the weight factor of the robustness score, which is used to adjust the importance of robustness in the comprehensive performance score; PR is the robustness score, which measures the system's adaptability to noise and variables; δ is the negative weight factor of the delay score, which is used to indicate the negative impact of delay on overall performance; PL is the delay score, which is the delay time of system response and processing.

[0267] (2) Dynamic Adjustment Mechanism (DAM) algorithm:

[0268]

[0269] DAM t+1 ,DAM t : System adjustment at time t+1 and time t.

[0270] θ: Adjustment sensitivity factor.

[0271] W i : The weight of the i-th performance indicator.

[0272] D evi,t : The deviation of the i-th performance indicator at the t-th time.

[0273] 6.6 Operation process

[0274] The operation process includes real-time calculation of the comprehensive performance score (CPS) to evaluate the current performance, where the CPS is composed of accuracy score, efficiency score, robustness score, and delay score, and is comprehensively evaluated in combination with their respective weights. In addition, the dynamic adjustment mechanism (DAM) algorithm is used to dynamically adjust according to the real-time deviation of system performance to optimize system performance. These formulas and algorithms are used in combination to ensure the efficiency and accuracy of the multi-qubit processing module in practical applications.

[0275] Summary: The structure and functions of the multi-qubit processing module include a variety of key technologies and tools, from quantum machine learning and quantum graph algorithms to optimization algorithms, cloud computing, energy consumption optimization technology, parallel processing algorithms and performance monitoring systems. These technologies will provide efficient eloquence training and analysis tools in the speech industry and eloquence expression industry, thereby improving speech performance and improving eloquence level.

[0276] In the multi-qubit processing module, each sub-module is interconnected and works together to achieve efficient multi-qubit processing. The following is the connection and relationship between the modules.

[0277] (1) Multi-qubit algorithm submodule: This submodule is the core of sound data processing and is responsible for real-time analysis and recognition. Its output is the result of quantum algorithm processing, which may include extraction, classification and clustering of speech features. These results will become the basis for subsequent processing.

[0278] (2) Multi-quantum computing algorithm optimization submodule: This submodule focuses on optimizing quantum computing algorithms to improve overall processing efficiency. In conjunction with the multi-qubit algorithm submodule, it may reduce the energy consumption of quantum computing and optimize the performance of the multi-qubit algorithm submodule by providing a quantum algorithm with lower complexity.

[0279] (3) Multi-qubit hardware management submodule: This submodule is responsible for managing the operation and resource allocation of multi-qubit hardware. In conjunction with the multi-qubit energy consumption optimization submodule, it may adjust the working state of the hardware based on the results of energy consumption analysis to achieve more efficient energy utilization.

[0280] (4) Multi-qubit energy consumption optimization submodule: This submodule reduces the energy consumption of multi-qubit processing hardware by adopting cryogenic quantum computing technology and optical quantum computing technology, as well as optimizing quantum computing algorithms. In connection with other submodules, it can provide a more energy-efficient working environment for the multi-qubit algorithm submodule, the multi-quantum computing algorithm optimization submodule, and the multi-qubit hardware management submodule based on energy consumption analysis.

[0281] (5) Data parallel processing submodule: The data parallel processing submodule is used to process data streams in parallel computing. It is related to the multi-qubit algorithm submodule in that it can process multiple input data in parallel, which, combined with the parallelism of quantum computing, improves the overall processing speed.

[0282] (6) Multi-qubit performance monitoring and evaluation submodule: This submodule is responsible for monitoring and evaluating the performance of the multi-qubit processing module. It is connected with other submodules in that it monitors performance, provides feedback information, helps optimize multi-qubit algorithms, algorithm optimization, hardware management, and energy consumption optimization to ensure efficient operation of the system.

[0283] Each submodule is not an independent entity, but completes the multi-qubit processing task together through information transmission and interaction. The present invention optimizes the energy consumption of the multi-qubit processing module through low-temperature quantum computing technology and optical quantum computing technology, as well as the optimization of quantum computing algorithms, solves the high energy consumption problem that may exist in traditional quantum computing, and improves the efficiency and performance of the multi-qubit processing module.

[0284] The personalized training module 3 is used to identify the eloquence problems of the speaker during the speech according to the processed sound data and generate a personalized training plan.

[0285] In practical applications, the personalized training module includes a personalized training module, an eloquence problem identification submodule, a personalized training plan generation submodule, a training effect evaluation submodule, a real-time feedback and adaptive learning submodule, a user interface and interaction submodule, and a data sharing and analysis result communication submodule.

[0286] (I) Structure and function

[0287] 1. Eloquence problem identification submodule:

[0288] Structure: The eloquence question recognition submodule consists of speech recognition, natural language processing, and audio processing submodules.

[0289] Function: Identify possible eloquence problems that speakers may have during a speech, such as repetition, grammatical errors, pronunciation problems, etc.

[0290] Implementation details: Advanced speech recognition technology is used to convert speech content into text. Natural language processing technology is then used to analyze the text to detect grammar and pronunciation errors. Audio processing technology can be used to detect audio quality and clarity.

[0291] 2. Personalized training plan generation submodule:

[0292] Structure: This submodule includes an algorithm engine, learning model and user interface.

[0293] Function: Generate a personalized eloquence training plan based on the analysis results of eloquence problems and the speaker's level.

[0294] Implementation details: The algorithm engine uses machine learning technology to automatically generate a personalized training plan based on the question recognition results and the speaker's historical data. The user interface provides a way for the speaker to view and modify the plan.

[0295] 3. Training effect evaluation submodule:

[0296] Structure: Includes speech analysis, automatic scoring and data storage modules.

[0297] Function: Monitor the speaker's training progress and provide instant scoring and feedback.

[0298] Implementation details: The speech analysis module analyzes the speaker's real-time speech, and the automatic scoring module provides a score for each speech based on multiple factors such as pronunciation, fluency, grammatical accuracy, etc. The historical data storage module is used to record and track the speaker's progress.

[0299] 4. Real-time feedback and adaptive learning submodule:

[0300] Structure: includes feedback generation module and user interface.

[0301] Function: Provides real-time feedback based on training effect evaluation to help speakers improve their performance. Also supports adaptive learning.

[0302] Implementation details: The feedback generation module generates real-time feedback based on the scoring and problem identification results, and the user interface is used to display feedback and record user responses. The system can adjust the training plan based on feedback and user responses to achieve adaptive learning.

[0303] 5. Data sharing and analysis results communication submodule:

[0304] Structure: includes data storage, data analysis and communication modules.

[0305] Function: Supports data sharing and communication between speakers and coaches, and helps monitor training progress.

[0306] Implementation details: The data storage module is used to store historical training data and analysis results. The data analysis module can generate reports and visualize results to support communication between coaches and speakers.

[0307] (II) Specific implementation methods:

[0308] In the field of public speaking and eloquence, the following approaches can be adopted:

[0309] Speech Recognition Technology: Using an advanced speech recognition engine, speech is converted into text to identify possible eloquence problems.

[0310] Machine Learning Algorithm: A machine learning algorithm is used to generate a personalized training plan based on question recognition results and the speaker's historical data.

[0311] Real-time feedback tools: Provide real-time feedback to speakers, supporting user interface and voice interaction.

[0312] Data Sharing Platform: Establish a data sharing platform to facilitate communication between coaches and speakers and share training data and analysis results.

[0313] Adaptive learning: The system can automatically adjust the training plan based on the speaker's progress to meet the needs and ability levels of speakers.

[0314] These implementation methods help to improve speakers' eloquence skills, increase the efficiency of training, and promote development and innovation in the field of eloquence expression.

[0315] Take the speech training system as an example

[0316] 1. Data acquisition and preprocessing module:

[0317] Multi-source data collection submodule: collects multi-source data such as the speaker's voice, video, text, and speech.

[0318] Data cleaning submodule: cleans the data to remove noise and errors.

[0319] Data quality assessment submodule: assess the quality of data and identify data quality issues.

[0320] Feature extraction and engineering submodule: extract sound features, emotional features, etc.

[0321] Privacy protection and compliance submodule: Ensure data privacy and comply with regulations.

[0322] Data storage and backup submodule: Store the cleaned and characterized data in the database.

[0323] 2. Multi-qubit processing module:

[0324] Multi-qubit algorithm submodule: Use quantum machine learning algorithms and quantum graph algorithms to analyze and identify sound data. For example, identify the speaker's speaking speed, tone, emotion, etc.

[0325] Multi-quantum computing algorithm optimization submodule: optimizes quantum computing algorithms to improve analysis efficiency.

[0326] Multi-qubit hardware management submodule: allocates quantum computing resources to ensure high-performance computing.

[0327] Multi-qubit energy consumption optimization submodule: uses low-temperature quantum computing technology or optical quantum computing technology to reduce energy consumption.

[0328] Data parallel processing module: distributes processing tasks to multiple quantum bits for parallel processing to increase speed.

[0329] Multi-qubit performance monitoring and evaluation submodule: real-time monitoring of system performance to ensure high accuracy and efficiency.

[0330] 3. Personalized training module:

[0331] Eloquence problem identification submodule: Based on the results of multi-qubit processing, identify the speaker's eloquence problems, such as repetition, ambiguity, and too fast speed.

[0332] Personalized training plan generation submodule: Generates personalized training plans for speakers and provides suggestions for their eloquence problems.

[0333] Training effect evaluation submodule: evaluate the effect of training and monitor the progress of speakers.

[0334] Real-time Feedback and Adaptive Learning Submodule: Provides real-time feedback to help speakers improve during training.

[0335] User Interface and Interaction Submodule: Provides a user-friendly interface to interact with speakers and share training suggestions.

[0336] Data sharing and analysis results communication submodule: allows speakers to share training results and analysis data and interact with coaches or other speakers.

[0337] The collaborative working process of this system is seamless: the data acquisition and preprocessing module provides cleaned and characterized data to the multi-qubit processing module, which analyzes the data and generates a personalized training plan, while the personalized training module interacts with the speaker through the user interface and provides real-time feedback. The goal of the entire system is to help speakers improve their eloquence and provide more precise and personalized eloquence training suggestions through multi-qubit processing.

Claims

1. A quantum eloquence training and analysis system based on multi-qubit processing, characterized in that: include: A data collection and preprocessing module, which is used to collect multimodal data of the speaker, and preprocess the multimodal data to extract sound data related to eloquence and emotion; the multimodal data includes voice data, video data, text data, social media data and online classroom data; The preprocessing process includes denoising; A multi-qubit processing module, which is used to process the extracted sound data using a multi-qubit algorithm based on multi-qubit hardware deployed to the cloud to generate processed sound data; the multi-qubit algorithm includes a quantum machine learning algorithm and a quantum graph algorithm; the quantum machine learning algorithm includes a quantum support vector machine and a quantum neural network; The quantum graph vector includes quantum graph classification and quantum graph clustering; the quantum machine learning algorithm is used to perform speech recognition and sentiment analysis on the extracted sound data; The quantum graph algorithm is used to classify and cluster the speech data; The multi-qubit processing module specifically includes: a multi-qubit algorithm submodule, a multi-quantum computing algorithm optimization submodule, a multi-qubit hardware management submodule, a multi-qubit energy consumption optimization submodule, and a multi-qubit performance detection and evaluation module; The multi-qubit algorithm submodule is used to create different quantum logic gates based on the multi-qubit hardware deployed to the cloud, and use the multi-qubit algorithm to process the extracted sound data to generate processed sound data; The multi-quantum computing algorithm optimization submodule is used to optimize the extracted sound data using an approximate algorithm and a simulation algorithm; The multi-qubit hardware management submodule specifically includes: cloud computing, edge computing, and hardware management tools; The cloud computing is used to provide multi-qubit hardware; The edge computing is used to deploy multi-qubit hardware within a set distance range of the speaker; The hardware management tool is used to manage and schedule multi-qubit hardware; The multi-qubit energy consumption optimization submodule is used to reduce the operating temperature of multi-qubit hardware by using low-temperature quantum computing, and to reduce the energy consumption of multi-qubit hardware by using optical quantum computing technology; The multi-qubit performance detection and evaluation module is used to monitor the performance of the multi-qubit processing module using performance indicators, and to dynamically adjust the performance of the multi-qubit processing module using a dynamic monitoring system; In the multi-qubit processing module, the sound data is analyzed and recognized in real time by making full use of the parallelism and quantum entanglement of quantum computing; The personalized training module is used to identify the eloquence problems of the speaker during the speech according to the processed sound data and generate a personalized training plan.

2. The quantum eloquence training and analysis system based on multi-qubit processing according to claim 1 is characterized in that: The data acquisition and preprocessing module specifically includes: The multi-source data collection submodule is used to collect the multi-modal data of the speaker; A data cleaning submodule, for applying statistical analysis and data rules to capture error values, abnormal values, missing values ​​and duplicates in the multimodal data, and cleaning the multimodal data; The data quality assessment submodule is used to analyze the data distribution, data integrity and data changes of the cleaned multimodal data and generate a data quality report; the data quality includes the mean, variance and distribution diagram of the multimodal data; The feature extraction and engineering submodule is used to extract sound data related to eloquence and emotion from the multimodal data by using speech processing, natural language processing and computer vision technology, and to perform feature enhancement or feature optimization on the extracted sound data.

3. The quantum eloquence training and analysis system based on multi-qubit processing according to claim 1 is characterized in that: The personalized training module specifically includes: A personalized training plan generation submodule, used to generate a personalized training plan based on the analysis results of the eloquence problem and the level of the speaker; A training effect evaluation submodule, for training according to the personalized training plan, analyzing the speaker's real-time speech using speech analysis technology, and scoring the real-time speech according to multiple factors; the multiple factors include pronunciation, fluency, and grammatical accuracy; The real-time feedback and adaptive learning submodule is used to analyze the real-time speech according to the score and the training effect, and generate real-time feedback to adjust the personalized training plan.

4. The quantum eloquence training and analysis system based on multi-qubit processing according to claim 1 is characterized in that: The multi-qubit processing module also includes: a data parallel processing submodule, which is used to distribute the extracted sound data to multiple processors using a distributed processing algorithm, and based on a parallel graph algorithm, uses a graph structure to perform parallel processing on the extracted sound data.

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