Thyroid cancer postoperative care method based on multi-dimensional sound analysis

Abstract

The invention discloses a thyroid cancer postoperative care method based on multi-dimensional sound analysis, and belongs to the technical field of medical care. Comprising the following steps: step 1, constructing a composite feature vector covering pitch stability, a volume dynamic range, tone change, intonation emotion intensity and resonant cavity coordination; step 2, realizing individualized sound modeling and dynamic reference tracking so as to accurately describe postoperative sound changes; step 3, constructing an adaptive learning model capable of accurately capturing subtle changes of individual sounds; 4, an intelligent early warning mechanism is established, the abnormal vocal cord function is recognized in time, and the sensitivity and specificity of postoperative rehabilitation monitoring are improved; 5, sound feature analysis and clinical rehabilitation data are combined, personalized rehabilitation suggestions are generated, and intelligent optimization of a postoperative rehabilitation scheme is achieved; and step 6, constructing a multi-dimensional sound feature big data platform, and realizing safe storage and intelligent analysis of sound data.

Description

Technical Field

[0001] The present application relates to the field of medical care technology, and more specifically, to a postoperative care method for thyroid cancer using multi-dimensional sound analysis. Background Art

[0002] Thyroid cancer is a common malignant tumor of the endocrine system, and surgery is one of the main treatment options. However, postoperative vocal function may be affected to varying degrees, especially changes in vocal cord function, which can significantly impact a patient's speech ability and quality of life. Therefore, postoperative monitoring and rehabilitation of vocal cord function are crucial.

[0003] Currently, monitoring vocal cord function during postoperative care relies heavily on traditional clinical examination methods, such as laryngoscopy and voice assessment. These methods have limitations, primarily manifested in the following ways: First, they rely on manual judgment, which is highly subjective and unable to accurately capture subtle changes in a patient's voice characteristics in real time; second, individualized voice changes in patients are difficult to accurately model and dynamically track using traditional examination methods; third, guidance for postoperative voice recovery is primarily based on experience, lacking personalized rehabilitation plans based on data analysis; and fourth, existing technologies lack intelligent early warning mechanisms and feedback adjustment functions, making it difficult to achieve real-time, dynamic optimization of the patient's recovery process.

[0004] In recent years, with the advancement of speech signal processing and artificial intelligence technologies, monitoring methods based on acoustic features have gradually entered the medical field, with particular applications in rehabilitation medicine. However, existing technologies have not yet fully integrated multidimensional acoustic feature analysis, personalized acoustic modeling, and adaptive learning models to achieve precise monitoring and optimization during postoperative rehabilitation. They also lack the ability to sensitively capture subtle changes in acoustics and integrate them with clinical data for comprehensive analysis.

[0005] In summary, how to intelligently monitor vocal cord function after thyroid cancer surgery based on multi-dimensional sound characteristics, optimize personalized rehabilitation plans, and achieve accurate feedback in combination with big data platforms has become a technical problem that needs to be solved urgently. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, the present invention aims to provide a multi-dimensional sound analysis method for thyroid cancer postoperative care, including the following steps:

[0007] Step 1: Construct a composite feature vector covering pitch stability, volume dynamic range, timbre variation, intonation emotional intensity, and resonance cavity coordination;

[0008] Step 2: Implement individualized sound modeling and dynamic baseline tracking to accurately characterize postoperative sound changes;

[0009] Step 3: Build an adaptive learning model that can accurately capture subtle changes in individual voices;

[0010] Step 4: Establish an intelligent early warning mechanism to promptly identify vocal cord dysfunction and improve the sensitivity and specificity of postoperative rehabilitation monitoring;

[0011] Step 5: Combine sound feature analysis with clinical rehabilitation data to generate personalized rehabilitation recommendations and achieve intelligent optimization of postoperative rehabilitation plans;

[0012] Step 6: Build a multi-dimensional sound feature big data platform to achieve secure storage and intelligent analysis of sound data.

[0013] Furthermore, the step 1 includes the following steps:

[0014] Using a circular precision microphone array, sound signals were collected in a standardized acoustic isolation room with no acoustic echo and background noise below 20 decibels.

[0015] Preprocess the collected sound signals, including signal denoising, spectrum enhancement and dynamic range compression, to extract pure sound feature information;

[0016] Accurately quantify pitch stability and calculate pitch fluctuation coefficients through frequency differentiation and autocorrelation analysis;

[0017] Use time-frequency analysis to synchronously capture the dynamic range of volume and subtle changes in timbre, and quantitatively describe the sound based on the cepstral coefficients and spectral centroid parameters;

[0018] Based on the micro-dynamic characteristics of the voice, the emotional intensity and emotional type of the intonation are quantified and the emotional characteristic index is established;

[0019] The geometric characteristics and acoustic properties of the vocal tract resonant cavity are analyzed, and linear predictive coding is used to accurately evaluate the resonant cavity coordination and sound resonance characteristics.

[0020] Furthermore, the annular precision microphone array includes at least 6 microphone units, and the spatial resolution of the microphone array is better than 0.5 degrees, and the signal-to-noise ratio is greater than 60 decibels.

[0021] Furthermore, the step 2 includes the following steps:

[0022] Perform dimensionality reduction on the composite feature vector, map the high-dimensional acoustic features to a low-dimensional space, extract the most representative sound features, and establish the core representation of individual sound features;

[0023] Quantify the natural range of fluctuations and abnormal deviations in individual voice characteristics;

[0024] Estimate and update the baseline of individual voice characteristics in real time, quickly respond to and capture subtle changes in voice characteristics, and ensure the real-time and adaptability of the baseline;

[0025] Construct an individual voice feature similarity measurement model to achieve personalized comparison and tracking of voice features;

[0026] Predict and simulate the long-term changing trends of individual voice characteristics to provide predictive insights for postoperative rehabilitation assessment;

[0027] The acoustic features are correlated with the patient's clinical indicators and imaging data to construct a multi-dimensional, personalized comprehensive portrait of the sound features.

[0028] Furthermore, the acoustic features are correlated with the patient's clinical indicators and imaging data to construct a multi-dimensional, personalized comprehensive portrait of the sound features, including the following steps:

[0029] Normalize acoustic features, clinical indicators, and imaging data to eliminate dimension and scale differences and extract key acoustic features;

[0030] Principal component analysis is used to map high-dimensional acoustic features into low-dimensional space, highlighting the most representative and discriminative features;

[0031] Evaluate the ability of acoustic features to explain clinical indicators and establish multimodal feature mapping to reveal the intrinsic connection between sound features and lesion areas and organ structures;

[0032] Based on the extracted core acoustic features and combined with clinical and imaging data, an individualized comprehensive portrait of sound features is established to form a multi-dimensional feature spectrum to quantify the contribution of different features to the patient's health status.

[0033] Furthermore, the step 3 includes the following steps:

[0034] Dynamically capture the correlation between different dimensions of sound features, and achieve deep learning and adaptive modeling of sound micro-change patterns;

[0035] Evaluate the contribution of each acoustic feature dimension to sound changes, automatically learn, and optimize feature weight allocation in real time;

[0036] Quantify the uncertainty of changes in sound characteristics and establish confidence intervals based on probability distributions;

[0037] Adopting a few-shot learning strategy to achieve rapid generalization and accurate capture of emerging sound change patterns;

[0038] Constructing sound features as a dynamically evolving complex network to capture the topological relationships and nonlinear interactions between feature dimensions;

[0039] Build generative models to simulate and predict the potential evolution paths of changes in sound characteristics, providing forward-looking insights and personalized early warnings for sound changes.

[0040] Furthermore, the step 4 includes the following steps:

[0041] Accurately define the normal fluctuation range and abnormal threshold of sound feature changes, and quantify the statistical significance of sound changes;

[0042] Based on the amplitude, persistence, and direction of changes in vocal characteristics, a risk grading model for vocal cord dysfunction is constructed to achieve accurate stratification of the severity of vocal changes;

[0043] Capture potential micro-change trends in vocal characteristics and predict the probability and possibility of future vocal cord dysfunction;

[0044] Learn the normal distribution of individual voice characteristics, identify voice changes that deviate from the individual baseline in real time, and build a highly sensitive anomaly detection mechanism;

[0045] Probabilistically correlate abnormal vocal features with clinical indicators to assess the relevance of vocal changes to potential clinical problems;

[0046] It can achieve accurate identification, risk stratification and personalized early warning of vocal cord dysfunction, and provide intelligent and precise technical support for postoperative rehabilitation monitoring.

[0047] Furthermore, the step 5 includes the following steps:

[0048] Through deep learning attention mechanisms and graph neural networks, we conduct correlation analysis on multi-dimensional clinical data, build individual rehabilitation feature mapping models, and explore the complex relationships between data dimensions.

[0049] Mapping the change patterns of voice characteristics with historical rehabilitation pathways to automatically generate personalized rehabilitation training, voice restoration, and functional exercise plans;

[0050] Evaluate the potential effects of different rehabilitation intervention strategies on vocal cord function recovery and establish a dynamic and quantifiable rehabilitation pathway optimization decision-making mechanism;

[0051] Based on real-time feedback from the patient's voice characteristics, the parameters and intensity of rehabilitation recommendations are dynamically adjusted to achieve closed-loop adaptive optimization;

[0052] Generative adversarial networks are used to simulate the potential impact of different rehabilitation intervention strategies on vocal cord function, providing clinicians with predictions and scenario simulations of personalized rehabilitation plans.

[0053] It integrates personalized recommendations, risk assessment, and program simulation functions, provides an intuitive visual interface, and supports precise rehabilitation intervention decisions driven by sound feature analysis.

[0054] Furthermore, the sound feature change pattern is mapped with the historical rehabilitation path to automatically generate personalized rehabilitation training, voice recovery and functional exercise plans, including the following steps:

[0055] Accurately capture the changing patterns of the patient's voice characteristics to form quantifiable change indicators;

[0056] Mapping the extracted sound change indicators with historical rehabilitation data to identify key rehabilitation pathways and intervention effects through pattern matching and statistical analysis;

[0057] Based on the mapping results, a personalized rehabilitation feature model is constructed using graph neural networks and attention mechanisms to achieve deep integration between different data dimensions.

[0058] Relying on the constructed personalized rehabilitation feature model, targeted rehabilitation training, voice recovery and functional exercise plans are automatically generated, and specific implementation suggestions are output.

[0059] Furthermore, the step 6 includes the following steps:

[0060] Adopting distributed blockchain storage technology to build a decentralized data security architecture, ensuring end-to-end privacy protection of patients' voice feature data and achieving data de-identification;

[0061] Design an intelligent analysis framework based on federated learning to achieve cross-institutional knowledge collaboration while protecting data privacy;

[0062] Construct a multi-dimensional acoustic feature knowledge graph and establish a semantic association network between sound features, clinical indicators and rehabilitation pathways;

[0063] Develop a distributed computing platform based on a microservices architecture to support elastic expansion, high-concurrency processing, and real-time analysis of sound feature data;

[0064] Through fine-grained access control, operation auditing and anonymization technology, standardize data usage processes and balance data openness and privacy protection.

[0065] Compared with the prior art, this application has the following beneficial effects:

[0066] This application is based on multi-dimensional sound feature analysis, combined with individualized sound modeling, adaptive learning and intelligent early warning mechanism, to achieve accurate monitoring of vocal cord function after thyroid cancer surgery, personalized rehabilitation plan optimization and big data-driven intelligent analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flow chart of a multi-dimensional sound analysis postoperative care method for thyroid cancer disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0068] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Throughout the drawings, identical or similar reference numerals represent identical or similar elements or elements having identical or similar functions. The described embodiments are only some, not all, of the embodiments of the present invention.

[0069] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0070] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0071] like Figure 1 As shown, a multi-dimensional sound analysis postoperative nursing method for thyroid cancer comprises the following steps:

[0072] Step 1: Construct a composite feature vector covering pitch stability, volume dynamic range, timbre variation, intonation emotional intensity, and resonance cavity coordination;

[0073] Step 2: Implement individualized sound modeling and dynamic baseline tracking to accurately characterize postoperative sound changes;

[0074] Step 3: Build an adaptive learning model that can accurately capture subtle changes in individual voices;

[0075] Step 4: Establish an intelligent early warning mechanism to promptly identify vocal cord dysfunction and improve the sensitivity and specificity of postoperative rehabilitation monitoring;

[0076] Step 5: Combine sound feature analysis with clinical rehabilitation data to generate personalized rehabilitation recommendations and achieve intelligent optimization of postoperative rehabilitation plans;

[0077] Step 6: Build a multi-dimensional sound feature big data platform to achieve secure storage and intelligent analysis of sound data.

[0078] In this embodiment, step 1 constructs a composite feature vector encompassing multiple dimensions, including pitch stability, volume dynamic range, timbre variation, emotional intensity of intonation, and resonance cavity coordination. This allows for comprehensive capture of the various characteristics of postoperative vocal changes in patients. First, pitch stability reflects the fluctuations in the patient's fundamental frequency during phonation, thereby determining whether the vocal cords are vibrating normally. Volume dynamic range reveals variations in the loudness of the voice and its performance in different contexts, thereby assessing the balance of energy distribution during phonation. Second, timbre variation involves the harmonic structure and spectral characteristics of sound waves, which are important for identifying possible pathological distortions in the patient's voice. Emotional intensity of intonation intuitively reveals subtle differences in the patient's emotional expression, helping to understand the relationship between their psychological state and physiological recovery. Furthermore, resonance cavity coordination, as an indicator reflecting the overall synergistic effect of multiple resonant structures, including the oral, nasal, and pharyngeal cavities, is crucial for assessing the overall function of the vocal mechanism. By integrating these features, not only can a multidimensional and multi-faceted voice feature database be established, but it also provides accurate input data for subsequent personalized modeling, making monitoring of postoperative voice changes more comprehensive and scientific. This method leverages big data and artificial intelligence technologies, integrating statistics with signal processing algorithms to accurately capture instantaneous changes in sound and comprehensively assess the state of sound, providing an objective basis for subsequent rehabilitation assessment and intervention. Furthermore, the construction of this composite feature vector lays a solid foundation for subsequent adaptive learning and early warning mechanisms, playing an irreplaceable and important role in real-time monitoring and dynamic adjustment. It can effectively identify subtle changes in sound and abnormal signals, further promoting the intelligent upgrade of postoperative rehabilitation programs, thereby achieving the dual goals of precision medicine and personalized rehabilitation.

[0079] In this embodiment, step 2 establishes a personalized baseline model by conducting an in-depth analysis of each patient's voice characteristics. This allows each patient to develop a unique voice fingerprint before, after, and throughout their recovery. This model not only captures static voice characteristic data but also tracks dynamic changes in the patient's voice in real time, enabling timely detection of subtle abnormal fluctuations. Dynamic baseline tracking technology utilizes advanced signal processing algorithms and time series analysis methods. By comparing and correcting continuously collected voice data, it effectively eliminates background noise and environmental interference, ensuring that every captured data item reflects the patient's true physiological state. Furthermore, personalized modeling automatically adjusts based on the patient's postoperative recovery progress, ensuring the timeliness and accuracy of baseline data. For example, in the early postoperative period, voice changes may be significant. Dynamic baseline tracking can quickly capture these changes and compare them with the individual baseline to determine whether interventional treatment is necessary. This technology not only improves the accuracy of rehabilitation monitoring but also provides physicians with a quantitative analysis basis, making the diagnosis and treatment process more scientific. Leveraging big data technology and artificial intelligence algorithms, personalized voice modeling can continuously learn and optimize, gradually improving prediction accuracy and response speed, while also providing data support for subsequent intelligent early warning mechanisms.

[0080] In this embodiment, step 3 is a key technical step in accurately capturing subtle changes in the patient's voice. By continuously providing feedback and correcting input data, the adaptive learning model can autonomously adjust parameters in the face of complex and changing sound data, effectively distinguishing between noise, abnormal signals, and natural fluctuations. Based on the principles of deep learning and machine learning, this model employs a neural network structure and a dynamic parameter tuning algorithm. This not only enables strong feature extraction capabilities but also forms a stable prediction mechanism through large-scale data training. In practical applications, the adaptive learning model collects data from the patient's voice in real time, compares each captured frame with historical data, and extracts the most representative abnormal features through nonlinear mapping in a high-dimensional feature space, thereby rapidly identifying potential pathological signals. This not only significantly improves the sensitivity of sound data analysis but also provides timely warnings when abnormal signals occur, providing doctors with early intervention recommendations. More importantly, the model possesses the ability to self-learn and update, gradually optimizing its discrimination criteria and model parameters through continuous practice, resulting in greater stability and robustness in long-term operation. Through the introduction of adaptive learning technology, precise regulation can be carried out based on the individual differences of different patients, and intelligent upgrades of personalized monitoring and rehabilitation plans can be achieved, bringing great technical advantages and clinical value to postoperative rehabilitation.

[0081] In this embodiment, step 4 effectively integrates previously developed technologies, including multi-dimensional acoustic feature vectors, personalized modeling, and adaptive learning models, to monitor the patient's vocal status in real time and identify any abnormal signals that exceed normal fluctuations. This intelligent early warning mechanism relies not only on high-precision sensors and data acquisition devices, but also on efficient data processing and analysis algorithms, enabling rapid identification of abnormal patterns within massive amounts of data. When a sudden change in acoustic parameters or deviation from a predetermined normal range is detected, an alert is immediately triggered, sending a warning message to medical staff via SMS, email, or a dedicated medical monitoring platform, enabling timely intervention. The advantages of this early warning mechanism lie in its high sensitivity and specificity. On the one hand, through continuous optimization of the deep learning algorithm, it can detect extremely subtle acoustic anomalies. On the other hand, through rigorous data validation and multiple cross-comparisons, the false alarm rate is reduced, ensuring the high credibility of each warning. This intelligent early warning mechanism not only improves the real-time nature of postoperative rehabilitation monitoring but also effectively shortens the time between abnormality occurrence and intervention, providing patients with more timely and accurate rehabilitation guidance and further reducing the risk of potential complications.

[0082] In this embodiment, step 5 integrates the multidimensional feature data of the patient's postoperative voice with other clinical indicators (such as physiological parameters, imaging examination results, and medical history records) to construct a multi-level, multi-dimensional data analysis model, enabling a comprehensive and detailed assessment of rehabilitation status. This process requires not only high-quality data acquisition and preprocessing technologies, but also advanced data fusion algorithms to normalize and match data from different sources across multiple dimensions to form a complete patient health profile. Combined with voice feature data, doctors can intuitively observe the patient's rehabilitation progress at various stages of surgery through quantitative indicators, such as the stability of voice recovery, changes in voice intonation and emotional expression, and the recovery of resonant cavity function. Simultaneously, machine learning and intelligent analysis algorithms are used to automatically identify correlations and underlying patterns between voice data and other clinical indicators, and personalized rehabilitation recommendations are generated based on a big data prediction model. This method not only provides customized rehabilitation plans for each patient's specific situation, but also allows for continuous adjustment of treatment plans based on real-time feedback from patients during the rehabilitation process, thereby achieving dynamic optimization. Through this comprehensive analysis, doctors can more accurately assess rehabilitation progress and adjust treatment plans in a timely manner, avoiding the impact of premature or delayed intervention on rehabilitation outcomes.

[0083] In this embodiment, the multi-dimensional sound feature big data platform constructed in step 6 integrates various voice acquisition devices, cloud computing, and big data processing technologies. It not only collects, transmits, and stores massive amounts of sound data in real time, but also systematically classifies, labels, and archives this data. In terms of data security, the platform utilizes high-strength data encryption technology, multiple identity authentication mechanisms, and a comprehensive rights management system to ensure that patients' sound data is effectively protected during the acquisition, transmission, storage, and analysis processes, preventing data leakage and unauthorized access. Furthermore, the platform utilizes distributed storage and cloud computing technologies to achieve rapid processing and efficient analysis of large-scale data, thereby supporting the real-time operation of complex algorithms and the training of deep learning models. The multi-dimensional sound feature big data platform not only provides clinicians and researchers with a vast data resource library, but also reveals the underlying patterns and clinical value hidden behind this massive amount of data through methods such as data mining, pattern recognition, and machine learning. The platform can support a variety of application scenarios, such as dynamic assessment of postoperative rehabilitation outcomes, customization of personalized treatment plans, and intelligent assistance for telemedicine services. By integrating big data and artificial intelligence technologies, the platform not only improves the utilization efficiency and analysis accuracy of sound data, but also provides solid data support and decision-making basis for the entire rehabilitation medical system, realizing closed-loop management of the entire process from data collection, storage to intelligent analysis, and greatly promoting the development of rehabilitation medical informatization and intelligence.

[0084] In summary, this postoperative nursing method for thyroid cancer achieves intelligent management of the entire chain, from sound data collection, personalized modeling, dynamic monitoring, intelligent early warning, to clinical data fusion and big data platform construction, through the organic combination of multiple key steps. Each step technically implements meticulous and comprehensive data processing and analysis, which not only improves the sensitivity and detection accuracy of patients' postoperative sound changes, but also achieves dynamic optimization of treatment plans through personalized rehabilitation recommendations. The overall solution integrates advanced signal processing technology, deep learning algorithms, big data platforms, and secure storage measures, which not only provides a scientific basis for clinical practice, but also brings more accurate and timely rehabilitation monitoring and intervention to patients, fully reflecting the deep integration and collaborative innovation of modern medicine and information technology.

[0085] Furthermore, the step 1 includes the following steps:

[0086] Using a circular precision microphone array, sound signals were collected in a standardized acoustic isolation room with no acoustic echo and background noise below 20 decibels.

[0087] Preprocess the collected sound signals, including signal denoising, spectrum enhancement and dynamic range compression, to extract pure sound feature information;

[0088] Accurately quantify pitch stability and calculate pitch fluctuation coefficients through frequency differentiation and autocorrelation analysis;

[0089] Use time-frequency analysis to synchronously capture the dynamic range of volume and subtle changes in timbre, and quantitatively describe the sound based on the cepstral coefficients and spectral centroid parameters;

[0090] Based on the micro-dynamic characteristics of the voice, the emotional intensity and emotional type of the intonation are quantified and the emotional characteristic index is established;

[0091] The geometric characteristics and acoustic properties of the vocal tract resonant cavity are analyzed, and linear predictive coding is used to accurately evaluate the resonant cavity coordination and sound resonance characteristics.

[0092] In summary, step 1 utilizes a circular precision microphone array to collect sound signals in a standardized low-noise acoustic isolation chamber. Preprocessing includes denoising, spectrum enhancement, and dynamic range compression to accurately quantify pitch stability and fluctuation coefficients. Time-frequency analysis simultaneously captures subtle changes in volume dynamics and timbre, achieving quantitative descriptions using cepstral coefficients and spectral centroids. Furthermore, an intonation affect index is established based on microscopic dynamic features, and linear predictive coding is used to analyze the geometric and acoustic properties of the vocal tract resonant cavity to accurately assess resonance coordination. This integrated approach efficiently extracts and quantifies multidimensional sound features, laying a solid data foundation for subsequent precision sound analysis and applications.

[0093] Furthermore, the annular precision microphone array includes at least 6 microphone units, and the spatial resolution of the microphone array is better than 0.5 degrees, and the signal-to-noise ratio is greater than 60 decibels.

[0094] Furthermore, the pitch stability is precisely quantified and the pitch fluctuation coefficient is calculated through frequency differentiation and autocorrelation analysis, including the following steps:

[0095] Extract the pitch f(t) from the pure sound feature information and construct a continuous time pitch frequency sequence;

[0096] Calculate the mean μ(f) and standard deviation σ(f) of the pitch frequency sequence;

[0097] Perform time differentiation on the pitch frequency sequence and calculate df(t) / dt to obtain the rate of pitch change. df(t) / dt represents the derivative of the pitch f(t) with respect to time t, indicating the rate of pitch change over time. dt is a small increment of time, indicating the unit of change in time resolution, which is used to calculate the derivative of pitch change over time.

[0098] By calculating the autocorrelation function R(τ) = ∫ T f(t)·f(t+τ)dt / [∫ T f(t) 2dt] extracts the periodic characteristics of the pitch signal and evaluates the signal stability and repeatability, where R(τ) is the autocorrelation function, which is used to measure the similarity between the pitch signal and itself at different time delays τ; τ is the time delay, which represents the time offset when calculating the autocorrelation function; T is the total duration of the time window or signal sampling interval, which represents the range of autocorrelation and other signal analysis within this time period; f(t+τ) is the pitch value at time t+τ, which represents the value of the pitch signal after a time delay of τ and is used to calculate the similarity of the signal at different times;

[0099] According to the formula: Pitch fluctuation coefficient = [σ(f)+│df(t) / dt│+∫ T The final pitch fluctuation coefficient is calculated as [R(τ)dτ] / μ(f), which quantifies pitch stability from multiple dimensions, where dτ is a small increment of time delay τ, representing the gradual change of the delay value when calculating the autocorrelation.

[0100] In summary, the above technical solution extracts pitch f(t) from pure sound features, constructs a continuous-time pitch frequency sequence, and calculates its mean and standard deviation. It uses time differentiation to solve df(t) / dt to obtain the pitch change rate, while simultaneously extracting the periodic characteristics of the pitch signal through autocorrelation function analysis. Finally, the pitch fluctuation, change rate, and autocorrelation integral are normalized to obtain the pitch fluctuation coefficient, accurately quantifying pitch stability from multiple dimensions and providing a scientific and comprehensive quantitative indicator for sound signal stability analysis.

[0101] Furthermore, the emotional characteristic index includes at least 6 dimensions, corresponding to pleasure, activation, dominance, tension, comfort and resonance, and adopts a multimodal emotion recognition algorithm.

[0102] Furthermore, the step 2 includes the following steps:

[0103] Perform dimensionality reduction on the composite feature vector, map the high-dimensional acoustic features to a low-dimensional space, extract the most representative sound features, and establish the core representation of individual sound features;

[0104] Quantify the natural range of fluctuations and abnormal deviations in individual voice characteristics;

[0105] Estimate and update the baseline of individual voice characteristics in real time, quickly respond to and capture subtle changes in voice characteristics, and ensure the real-time and adaptability of the baseline;

[0106] Construct an individual voice feature similarity measurement model to achieve personalized comparison and tracking of voice features;

[0107] Predict and simulate the long-term changing trends of individual voice characteristics to provide predictive insights for postoperative rehabilitation assessment;

[0108] The acoustic features are correlated with the patient's clinical indicators and imaging data to construct a multi-dimensional, personalized comprehensive portrait of the sound features.

[0109] In summary, the above technical solution maps high-dimensional composite features to a low-dimensional space through dimensionality reduction, extracting the most representative sound features and establishing an individual core representation, while also quantifying the natural fluctuation range and abnormal deviations. It estimates and updates the individual sound baseline in real time, ensuring rapid capture of subtle changes. It constructs a similarity measurement model for personalized comparison and tracking, and predicts and simulates long-term change trends, providing forward-looking insights for postoperative rehabilitation assessments. Finally, it links acoustic features with clinical and imaging data to construct a multidimensional, personalized comprehensive sound profile, improving the accuracy and practicality of rehabilitation assessments.

[0110] Furthermore, the dimensionality reduction process uses a combination of principal component analysis and manifold learning to compress high-dimensional acoustic features into 3-5 main dimensions.

[0111] Furthermore, the natural fluctuation range and abnormal deviation of individual voice characteristics are quantified, including the following steps:

[0112] The sound feature data before and after surgery are modeled as Gaussian mixture models respectively, and the sound feature before surgery x pre and postoperative voice characteristics x post The probability distributions are expressed as:

[0113] p(x pre )=∑ k＝1 K π k pre N(x pre ∣μ k pre ,Σ k pre );

[0114] p(x post )=∑ k＝1 K π k post N(x post ∣μ k post ,Σ k post );

[0115] In the above formula, p(x pre ) is the preoperative sound feature x pre The probability distribution of K is the number of Gaussian distributions used to fit the sound feature distribution; π k preis the mixing weight of the kth Gaussian component before surgery, which represents the proportion of this component in the overall distribution, satisfying ∑ k＝1 K π k pre =1; N(x pre ∣μ k pre ,Σ k pre ) is the probability density function of the kth Gaussian component before surgery, and its mean is μ k pre , the covariance matrix is ​​Σ k pre ;p(x post ) is the postoperative sound feature x post The probability distribution of π k post is the mixed weight of the kth Gaussian component after surgery, indicating the proportion of this component in the postoperative sound distribution, satisfying ∑ k＝1 K π k post =1; N(x post ∣μ k post ,Σ k post ) The probability density function of the kth Gaussian component after surgery, whose mean is μ k post , the covariance matrix is ​​Σ k post ;

[0116] By analyzing the diagonal elements of the covariance matrix of each component and calculating the standard deviation, the natural fluctuation range is quantified, which is expressed as: range k =√[diag(Σ k )], where Σ k is the covariance matrix of the kth Gaussian component, describing the distribution range and correlation of the component; range k Represents the natural fluctuation range of the kth Gaussian component;

[0117] Calculate the newly collected sound features x new The likelihood L(x new ), when L(x new ) is significantly lower than the preset threshold, it can be determined that there is an abnormal deviation. The formula is: L(x new )=∑ k＝1 K π k N(x new ∣μ k ,Σ k ), where N(x new ∣μk ,Σ k ) represents the new sound feature data point x new The probability density function under the kth Gaussian component; μ k is the mean vector of the kth Gaussian component, indicating the center position of the component; π k is the mixing weight of the kth Gaussian component, which represents the proportion of this component in the overall distribution, satisfying ∑ k＝1 K π k =1.

[0118] In summary, the above technical solution uses a Gaussian mixture model to model preoperative and postoperative sound feature data separately. By analyzing the diagonal elements of the covariance matrix of each Gaussian component, the standard deviation is calculated to quantify the natural range of sound fluctuations. Simultaneously, the model is used to calculate the likelihood of newly collected sound data. When this likelihood is significantly lower than a preset threshold, an abnormal deviation is determined. This technology achieves precise quantification of individual sound feature fluctuations and abnormal changes, providing a reliable mathematical basis and data support for postoperative rehabilitation monitoring.

[0119] Furthermore, the individual sound feature baseline is updated at a frequency of not less than once per hour, and a dynamic estimation algorithm combining exponential smoothing and Kalman filtering is used.

[0120] Furthermore, the acoustic features are correlated with the patient's clinical indicators and imaging data to construct a multi-dimensional, personalized comprehensive portrait of the sound features, including the following steps:

[0121] Normalize acoustic features, clinical indicators, and imaging data to eliminate dimension and scale differences and extract key acoustic features;

[0122] Principal component analysis is used to map high-dimensional acoustic features into low-dimensional space, highlighting the most representative and discriminative features;

[0123] Evaluate the ability of acoustic features to explain clinical indicators and establish multimodal feature mapping to reveal the intrinsic connection between sound features and lesion areas and organ structures;

[0124] Based on the extracted core acoustic features and combined with clinical and imaging data, an individualized comprehensive portrait of sound features is established to form a multi-dimensional feature spectrum to quantify the contribution of different features to the patient's health status.

[0125] In summary, the above technical solution eliminates data dimensionality differences through normalization processing, and uses principal component analysis to reduce the dimensionality of high-dimensional acoustic features and extract the most representative features. By evaluating the explanatory power of acoustic features for clinical indicators and constructing multimodal feature mapping, the intrinsic connection between sound features and lesion areas and organ structures is revealed. Finally, based on the extracted core acoustic features and combined with clinical and imaging data, a personalized, multi-dimensional comprehensive portrait of sound features is constructed, forming a feature spectrum, effectively quantifying the contribution of each feature to the patient's health status, thereby improving the accuracy of individualized diagnosis and treatment and rehabilitation assessment.

[0126] Furthermore, the step 3 includes the following steps:

[0127] Dynamically capture the correlation between different dimensions of sound features, and achieve deep learning and adaptive modeling of sound micro-change patterns;

[0128] Evaluate the contribution of each acoustic feature dimension to sound changes, automatically learn, and optimize feature weight allocation in real time;

[0129] Quantify the uncertainty of changes in sound characteristics and establish confidence intervals based on probability distributions;

[0130] Adopting a few-shot learning strategy to achieve rapid generalization and accurate capture of emerging sound change patterns;

[0131] Constructing sound features as a dynamically evolving complex network to capture the topological relationships and nonlinear interactions between feature dimensions;

[0132] Build generative models to simulate and predict the potential evolution paths of changes in sound characteristics, providing forward-looking insights and personalized early warnings for sound changes.

[0133] In summary, the above technical solution dynamically captures the correlations between various dimensions of acoustic features through deep learning and adaptive modeling, assessing and optimizing the contribution of each feature to sound changes in real time, while quantifying the uncertainty of changes and constructing confidence intervals. It uses few-shot learning to achieve rapid generalization of emerging change patterns, constructs sound features as a dynamically evolving complex network to capture nonlinear topological relationships, and uses generative models to simulate and predict their potential evolution paths, providing strong technical support for personalized early warning and forward-looking insights.

[0134] Furthermore, the dynamic capture of the correlation between different dimensions of sound features and the implementation of deep learning and adaptive modeling of sound micro-change patterns include the following steps:

[0135] For any feature x at time t i (t) and x j(t), the association score is calculated by linear transformation and splicing, and the normalized attention weight α is obtained by softmax ij (t), to reflect the interaction strength among dimensions;

[0136] Use the attention weight to perform weighted summation of all dimension features and update the feature representation through a nonlinear activation function: i (t)=σ feat (∑ j＝1 D α ij (t)·W·x j (t)), forming a more relevant feature vector z(t), where z i (t) represents the updated feature vector of the i-th dimension, the feature representation at time step t; σ feat represents the activation function of the feature update layer; D represents the dimension of the feature vector, that is, the number of features contained in the sound data; W is the weight matrix, which represents the linear transformation matrix between the feature vector and the network layer;

[0137] The updated feature vector z(t) is input into the time series model to capture the time evolution characteristics of the sound micro-changes. The dynamic pattern of the sound is represented by the hidden state h(t): h(t) = RNN(z(t), h(t-1)). Here, RNN is a recurrent neural network used to capture the dynamic pattern of sound features changing over time; h(t-1) represents the hidden state at time step t-1.

[0138] Based on the hidden state h(t), a prediction of future sound characteristics is generated, and dynamic feedback is achieved using the output layer mapping to provide a basis for subsequent rehabilitation monitoring and intervention. The formula is: x^(t+1)=σ out (W h h(t)+b h ), where x^(t+1) is the predicted value of the sound feature at time step t+1; W h is the output layer weight matrix in the RNN, connecting the linear transformation between the hidden state h(t) and the final output x^(t+1); b h is the bias term in RNN, used to adjust the calculation of the output layer; σ out represents the activation function of the output layer.

[0139] In summary, the above solution achieves weighted fusion of sound feature dimensions by calculating the correlation scores and normalized attention weights between features in each dimension at any moment, and updates a more relevant feature vector. This vector is then fed into an RNN to capture dynamic temporal changes, and hidden states are used to generate future feature predictions, thereby enabling deep learning and adaptive modeling of microscopic changes in sound. This technology effectively reveals the nonlinear interactions between sound features, providing forward-looking predictions and dynamic feedback support for rehabilitation monitoring.

[0140] Furthermore, the contribution of each acoustic feature dimension to sound variation is evaluated, and feature weight distribution is automatically learned and optimized in real time. The following steps are included:

[0141] Establish a linear regression model, establish a preliminary mapping relationship between acoustic feature vectors and sound changes, initialize the weights of each feature, and form a basic prediction model;

[0142] Calculate the ratio of the absolute value of each feature weight to the absolute value of the total weight to obtain the contribution rate of each feature to the sound change;

[0143] Calculate the contribution rate C of each feature dimension to the sound change i , that is, the relative contribution of each feature weight, the formula is: C i =|w i ∣ / ∑ r＝1 R ∣w r ∣, where w i represents the weight of the i-th acoustic feature dimension; w r represents the weight of the rth acoustic feature dimension; R represents the total number of dimensions of the acoustic feature, that is, the number of different features contained in the data;

[0144] Adopting the gradient descent method, with mean square error as the loss function, the weight of each feature is updated in real time, and the prediction error is reduced through continuous iteration to achieve automatic learning;

[0145] Combined with newly collected real-time data, the model parameters are continuously adjusted and the weight distribution is dynamically optimized to ensure the model's sensitivity and adaptability to sound changes.

[0146] In summary, the above solution establishes a linear regression model to achieve a preliminary mapping of acoustic features to sound changes, initializes feature weights, and calculates the proportion of each feature's absolute weight to the total weight to quantify its contribution. Subsequently, a gradient descent method is used with mean squared error as the loss function to update and optimize each feature weight in real time, and the model parameters are continuously adjusted based on newly acquired data. This effectively improves the model's sensitivity and adaptability to sound changes, thereby enhancing prediction accuracy and robustness, providing reliable data support for subsequent rehabilitation monitoring.

[0147] Furthermore, the sound features are constructed as a dynamically evolving complex network to capture the topological relationships and nonlinear interactions between feature dimensions, including the following steps:

[0148] Map each feature dimension to a network node, define the edge weights between features through similarity measurement, and construct the network topology structure;

[0149] Build a dynamic feature network that allows node weights and edge connections to change over time to reflect the evolution of sound features;

[0150] Graph neural networks are used to model the nonlinear interactions between features, and feature representations are updated through node information propagation mechanisms.

[0151] By optimizing the network structure and node representation, we enhance our understanding of the topological relationships between features, enabling the network to adaptively adjust its structure to accurately reflect the dynamic relationships between features.

[0152] By learning the temporal relationships and interactions between nodes in the network, the evolution trend of sound features can be predicted in real time, and the network structure can be adjusted through the feedback mechanism to further improve the prediction accuracy.

[0153] In summary, the above solution constructs sound features as a dynamically evolving complex network. By mapping each feature dimension into nodes and defining edge weights using a similarity metric, it captures the topological structure and nonlinear interactions between features. A graph neural network is used to propagate node information and update features, dynamically adjusting the network structure to reflect the temporal evolution of sound features. Real-time prediction is achieved by learning temporal relationships. The introduction of a feedback mechanism further optimizes the network structure and node representation, effectively improving prediction accuracy and providing precise and intelligent data support for sound change monitoring and rehabilitation assessment.

[0154] Furthermore, the generative model adopts a hybrid architecture of variational autoencoder and generative adversarial network to achieve probabilistic generation of sound feature changes and latent space reconstruction.

[0155] Furthermore, the step 4 includes the following steps:

[0156] Accurately define the normal fluctuation range and abnormal threshold of sound feature changes, and quantify the statistical significance of sound changes;

[0157] Based on the amplitude, persistence, and direction of changes in vocal characteristics, a risk grading model for vocal cord dysfunction is constructed to achieve accurate stratification of the severity of vocal changes;

[0158] Capture potential micro-change trends in vocal characteristics and predict the probability and possibility of future vocal cord dysfunction;

[0159] Learn the normal distribution of individual voice characteristics, identify voice changes that deviate from the individual baseline in real time, and build a highly sensitive anomaly detection mechanism;

[0160] Probabilistically correlate abnormal vocal features with clinical indicators to assess the relevance of vocal changes to potential clinical problems;

[0161] It can achieve accurate identification, risk stratification and personalized early warning of vocal cord dysfunction, and provide intelligent and precise technical support for postoperative rehabilitation monitoring.

[0162] In summary, the above solution quantifies the normal fluctuation range and abnormal threshold of vocal feature changes, enabling statistical significance analysis of vocal changes and constructing a risk grading model for vocal cord dysfunction to accurately stratify the severity of vocal changes. Combined with micro-trend prediction technology, it assesses the probability of future abnormalities and detects abnormal changes in real time based on an individual vocal feature baseline. By linking vocal feature abnormalities with clinical indicators through probabilistic modeling, it enables accurate identification, risk assessment, and personalized early warning of vocal cord dysfunction, providing intelligent support for postoperative rehabilitation monitoring.

[0163] Furthermore, the vocal cord dysfunction risk grading model is based on Bayesian networks and Markov chain Monte Carlo methods to accurately quantify the occurrence probability and confidence interval of different risk levels.

[0164] Furthermore, the step 5 includes the following steps:

[0165] Through deep learning attention mechanisms and graph neural networks, we conduct correlation analysis on multi-dimensional clinical data, build individual rehabilitation feature mapping models, and explore the complex relationships between data dimensions.

[0166] Mapping the change patterns of voice characteristics with historical rehabilitation pathways to automatically generate personalized rehabilitation training, voice restoration, and functional exercise plans;

[0167] Evaluate the potential effects of different rehabilitation intervention strategies on vocal cord function recovery and establish a dynamic and quantifiable rehabilitation pathway optimization decision-making mechanism;

[0168] Based on real-time feedback from the patient's voice characteristics, the parameters and intensity of rehabilitation recommendations are dynamically adjusted to achieve closed-loop adaptive optimization;

[0169] Generative adversarial networks are used to simulate the potential impact of different rehabilitation intervention strategies on vocal cord function, providing clinicians with predictions and scenario simulations of personalized rehabilitation plans.

[0170] It integrates personalized recommendations, risk assessment, and program simulation functions, provides an intuitive visual interface, and supports precise rehabilitation intervention decisions driven by sound feature analysis.

[0171] In summary, the above solution utilizes an attention mechanism and graph neural networks to construct an individual rehabilitation feature mapping model, deeply analyzing the complex relationships between multidimensional clinical data. By mapping vocal feature change patterns with historical rehabilitation pathways, personalized rehabilitation training and voice recovery plans are automatically generated. Incorporating a dynamic optimization mechanism, the effectiveness of different intervention strategies is evaluated and rehabilitation parameters are adaptively adjusted based on real-time feedback. A generative adversarial network is used to simulate the impact of rehabilitation strategies on vocal cord function, providing physicians with accurate predictions and scenario simulations.

[0172] Furthermore, the sound feature change pattern is mapped with the historical rehabilitation path to automatically generate personalized rehabilitation training, voice recovery and functional exercise plans, including the following steps:

[0173] Accurately capture the changing patterns of the patient's voice characteristics to form quantifiable change indicators;

[0174] Mapping the extracted sound change indicators with historical rehabilitation data to identify key rehabilitation pathways and intervention effects through pattern matching and statistical analysis;

[0175] Based on the mapping results, a personalized rehabilitation feature model is constructed using graph neural networks and attention mechanisms to achieve deep integration between different data dimensions.

[0176] Relying on the constructed personalized rehabilitation feature model, targeted rehabilitation training, voice recovery and functional exercise plans are automatically generated, and specific implementation suggestions are output.

[0177] In summary, the above solution accurately captures the changing patterns of a patient's voice characteristics, quantifies their fluctuation indicators, and maps and analyzes them against historical rehabilitation data. It utilizes pattern matching and statistical methods to identify key rehabilitation pathways and intervention effects. It then combines graph neural networks with attention mechanisms to construct a personalized rehabilitation feature model, achieving deep integration of multidimensional data. Based on this model, it can automatically generate targeted rehabilitation training, voice restoration, and functional exercise plans, and provide specific implementation recommendations, thereby improving the accuracy and personalization of rehabilitation plans.

[0178] Furthermore, the dynamically adjusted parameters of the rehabilitation recommendations include training intensity, recovery frequency, rest time, and personalized training path, and the adaptive range of the parameter adjustment is within ±20% of the original recommendation.

[0179] Furthermore, the generative adversarial network simulation is based on a conditional generative model, which can simulate the potential physiological and acoustic effects of different rehabilitation interventions on vocal cord function and provide multi-scenario rehabilitation predictions.

[0180] Furthermore, the step 6 includes the following steps:

[0181] Adopting distributed blockchain storage technology to build a decentralized data security architecture, ensuring end-to-end privacy protection of patients' voice feature data and achieving data de-identification;

[0182] Design an intelligent analysis framework based on federated learning to achieve cross-institutional knowledge collaboration while protecting data privacy;

[0183] Construct a multi-dimensional acoustic feature knowledge graph and establish a semantic association network between sound features, clinical indicators and rehabilitation pathways;

[0184] Develop a distributed computing platform based on a microservices architecture to support elastic expansion, high-concurrency processing, and real-time analysis of sound feature data;

[0185] Through fine-grained access control, operation auditing and anonymization technology, standardize data usage processes and balance data openness and privacy protection.

[0186] In summary, the above solution utilizes distributed blockchain storage technology to build a decentralized data security architecture, achieving end-to-end privacy protection and de-identification of acoustic feature data. Integrating this with a federated learning framework enables cross-institutional knowledge collaboration, while simultaneously constructing a multidimensional acoustic feature knowledge graph and establishing semantic associations between acoustic features, clinical indicators, and rehabilitation pathways. A distributed computing platform based on a microservices architecture is developed to support elastic scalability, high-concurrency processing, and real-time analysis of acoustic data. Fine-grained access control, operational auditing, and anonymization technologies ensure a balance between data security and privacy.

[0187] Furthermore, the acoustic feature knowledge graph constructs a dynamic association network between sound features, clinical indicators and rehabilitation pathways through ontological reasoning and semantic association methods, supporting automatic reasoning and semantic retrieval of knowledge.

[0188] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art will appreciate that modifications may be made to the technical solutions described in the above embodiments, or that some of the technical features may be replaced with equivalents; such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A multi-dimensional sound analysis postoperative nursing method for thyroid cancer, characterized in that: The following steps are involved: Step 1: Construct a composite feature vector covering pitch stability, volume dynamic range, timbre variation, intonation emotional intensity, and resonance cavity coordination; Step 2: Implement individualized sound modeling and dynamic baseline tracking to accurately characterize postoperative sound changes; Step 3: Build an adaptive learning model that can accurately capture subtle changes in individual voices; Step 4: Establish an intelligent early warning mechanism to promptly identify vocal cord dysfunction and improve the sensitivity and specificity of postoperative rehabilitation monitoring; Step 5: Combine sound feature analysis with clinical rehabilitation data to generate personalized rehabilitation recommendations and achieve intelligent optimization of postoperative rehabilitation plans; Step 6: Build a multi-dimensional sound feature big data platform to achieve secure storage and intelligent analysis of sound data.

2. The multi-dimensional sound analysis postoperative nursing method for thyroid cancer according to claim 1, characterized in that: The step 1 comprises the following steps: Using a circular precision microphone array, sound signals were collected in a standardized acoustic isolation room with no acoustic echo and background noise below 20 decibels. Preprocess the collected sound signals, including signal denoising, spectrum enhancement and dynamic range compression, to extract pure sound feature information; Accurately quantify pitch stability and calculate pitch fluctuation coefficients through frequency differentiation and autocorrelation analysis; Use time-frequency analysis to synchronously capture the dynamic range of volume and subtle changes in timbre, and quantitatively describe the sound based on the cepstral coefficients and spectral centroid parameters; Based on the micro-dynamic characteristics of the voice, the emotional intensity and emotional type of the intonation are quantified and the emotional characteristic index is established; The geometric characteristics and acoustic properties of the vocal tract resonant cavity are analyzed, and linear predictive coding is used to accurately evaluate the resonant cavity coordination and sound resonance characteristics.

3. The multi-dimensional sound analysis postoperative nursing method for thyroid cancer according to claim 2, characterized in that: The annular precision microphone array includes at least 6 microphone units, and the spatial resolution of the microphone array is better than 0.5 degrees and the signal-to-noise ratio is greater than 60 decibels.

4. The multi-dimensional sound analysis postoperative nursing method for thyroid cancer according to claim 1, characterized in that: The step 2 comprises the following steps: Perform dimensionality reduction on the composite feature vector, map the high-dimensional acoustic features to a low-dimensional space, extract the most representative sound features, and establish the core representation of individual sound features; Quantify the natural range of fluctuations and abnormal deviations in individual voice characteristics; Estimate and update the baseline of individual voice characteristics in real time, quickly respond to and capture subtle changes in voice characteristics, and ensure the real-time and adaptability of the baseline; Construct an individual voice feature similarity measurement model to achieve personalized comparison and tracking of voice features; Predict and simulate the long-term changing trends of individual voice characteristics to provide predictive insights for postoperative rehabilitation assessment; The acoustic features are correlated with the patient's clinical indicators and imaging data to construct a multi-dimensional, personalized comprehensive portrait of the sound features.

5. The multi-dimensional sound analysis postoperative nursing method for thyroid cancer according to claim 1, characterized in that: The acoustic features are correlated with the patient's clinical indicators and imaging data to construct a multi-dimensional, personalized comprehensive profile of the sound features. The following steps are included: Normalize acoustic features, clinical indicators, and imaging data to eliminate dimension and scale differences and extract key acoustic features; Principal component analysis is used to map high-dimensional acoustic features into low-dimensional space, highlighting the most representative and discriminative features; Evaluate the ability of acoustic features to explain clinical indicators and establish multimodal feature mapping to reveal the intrinsic connection between sound features and lesion areas and organ structures; Based on the extracted core acoustic features and combined with clinical and imaging data, an individualized comprehensive portrait of sound features is established to form a multi-dimensional feature spectrum to quantify the contribution of different features to the patient's health status.

6. The multi-dimensional sound analysis postoperative nursing method for thyroid cancer according to claim 1, characterized in that: The step 3 comprises the following steps: Dynamically capture the correlation between different dimensions of sound features, and achieve deep learning and adaptive modeling of sound micro-change patterns; Evaluate the contribution of each acoustic feature dimension to sound changes, automatically learn, and optimize feature weight allocation in real time; Quantify the uncertainty of changes in sound characteristics and establish confidence intervals based on probability distributions; Adopting a few-shot learning strategy to achieve rapid generalization and accurate capture of emerging sound change patterns; Constructing sound features as a dynamically evolving complex network to capture the topological relationships and nonlinear interactions between feature dimensions; Build generative models to simulate and predict the potential evolution paths of changes in sound characteristics, providing forward-looking insights and personalized early warnings for sound changes.

7. The multi-dimensional sound analysis postoperative nursing method for thyroid cancer according to claim 1, characterized in that: The step 4 comprises the following steps: Accurately define the normal fluctuation range and abnormal threshold of sound feature changes, and quantify the statistical significance of sound changes; Based on the amplitude, persistence, and direction of changes in vocal characteristics, a risk grading model for vocal cord dysfunction is constructed to achieve accurate stratification of the severity of vocal changes; Capture potential micro-change trends in vocal characteristics and predict the probability and possibility of future vocal cord dysfunction; Learn the normal distribution of individual voice characteristics, identify voice changes that deviate from the individual baseline in real time, and build a highly sensitive anomaly detection mechanism; Probabilistically correlate abnormal vocal features with clinical indicators to assess the relevance of vocal changes to potential clinical problems; It can achieve accurate identification, risk stratification and personalized early warning of vocal cord dysfunction, and provide intelligent and precise technical support for postoperative rehabilitation monitoring.

8. The multi-dimensional sound analysis postoperative nursing method for thyroid cancer according to claim 1, characterized in that: The step 5 comprises the following steps: Through deep learning attention mechanisms and graph neural networks, we conduct correlation analysis on multi-dimensional clinical data, build individual rehabilitation feature mapping models, and explore the complex relationships between data dimensions. Mapping the change patterns of voice characteristics with historical rehabilitation pathways to automatically generate personalized rehabilitation training, voice restoration, and functional exercise plans; Evaluate the potential effects of different rehabilitation intervention strategies on vocal cord function recovery and establish a dynamic and quantifiable rehabilitation pathway optimization decision-making mechanism; Based on real-time feedback from the patient's voice characteristics, the parameters and intensity of rehabilitation recommendations are dynamically adjusted to achieve closed-loop adaptive optimization; Generative adversarial networks are used to simulate the potential impact of different rehabilitation intervention strategies on vocal cord function, providing clinicians with predictions and scenario simulations of personalized rehabilitation plans. It integrates personalized recommendations, risk assessment, and program simulation functions, provides an intuitive visual interface, and supports precise rehabilitation intervention decisions driven by sound feature analysis.

9. The multi-dimensional sound analysis postoperative nursing method for thyroid cancer according to claim 8, characterized in that: Mapping the sound feature change pattern with the historical rehabilitation path automatically generates personalized rehabilitation training, voice restoration, and functional exercise plans, including the following steps: Accurately capture the changing patterns of the patient's voice characteristics to form quantifiable change indicators; Mapping the extracted sound change indicators with historical rehabilitation data to identify key rehabilitation pathways and intervention effects through pattern matching and statistical analysis; Based on the mapping results, a personalized rehabilitation feature model is constructed using graph neural networks and attention mechanisms to achieve deep integration between different data dimensions. Relying on the constructed personalized rehabilitation feature model, targeted rehabilitation training, voice recovery and functional exercise plans are automatically generated, and specific implementation suggestions are output.

10. The multi-dimensional sound analysis postoperative nursing method for thyroid cancer according to claim 1, characterized in that: The step 6 comprises the following steps: Adopting distributed blockchain storage technology to build a decentralized data security architecture, ensuring end-to-end privacy protection of patients' voice feature data and achieving data de-identification; Design an intelligent analysis framework based on federated learning to achieve cross-institutional knowledge collaboration while protecting data privacy; Construct a multi-dimensional acoustic feature knowledge graph and establish a semantic association network between sound features, clinical indicators and rehabilitation pathways; Develop a distributed computing platform based on a microservices architecture to support elastic expansion, high-concurrency processing, and real-time analysis of sound feature data; Through fine-grained access control, operation auditing and anonymization technology, standardize data usage processes and balance data openness and privacy protection.

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