Intelligent evaluation method and system for anesthesia recovery state

Abstract

The invention discloses an intelligent evaluation method and system for an anesthesia recovery state, and relates to the technical field of anesthesia evaluation. The intelligent evaluation system for the anesthesia recovery state comprises a data acquisition module, a state evaluation module, a recovery sample construction module, a sample evaluation model module, an adjustment strategy generation module, a data statistics and management module and a result output module. According to the method, the multi-dimensional physiological data of the patient is collected in real time through the intelligent equipment, and accurate real-time evaluation of the recovery state of the patient is achieved in combination with the sample evaluation model of segmented matching and long-segment matching; through the dynamically optimized sample area, the samples are subjected to grouping and clustering analysis in combination with the basic information of the patients, the individual features of the patients are dynamically loaded and matched, it is ensured that the evaluation model can adapt to the individual differences of different patients, and the adaptability and robustness of the model are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of anesthesia assessment, and in particular to an intelligent assessment method and system for anesthesia recovery status. Background Art

[0002] With the advancement of modern medical technology, anesthesia technology has become an indispensable part of the surgical process. The anesthesia recovery state of patients after surgery directly affects their life safety and rehabilitation effect. Therefore, the scientific evaluation of the anesthesia recovery state of patients is of great significance. At present, the evaluation of anesthesia recovery state mainly relies on the clinical experience of medical personnel and simple physiological indicator monitoring. However, this traditional method has the following shortcomings: single data reliance, poor real-time performance, lack of personalization, subjectivity of evaluation results, and lack of dynamic adjustment strategies; How to use modern intelligent technology to achieve accurate assessment, dynamic prediction and personalized adjustment of anesthesia recovery status has become an important research direction. In recent years, with the development of big data, machine learning and smart devices, data-driven intelligent assessment methods have provided the possibility to solve the above problems. Summary of the invention

[0003] In view of the above problems, the present invention proposes an intelligent evaluation method and system for anesthesia recovery status, which solves the shortcomings of traditional evaluation methods through an innovative multi-level sample evaluation model, a dynamic category area construction mechanism and a personalized adjustment strategy generation method.

[0004] An intelligent evaluation method for anesthesia recovery status comprises the following steps: Step S1: Data collection, collecting the patient's physiological data in real time through smart devices, and performing denoising, standardization and time alignment on the collected data; the physiological data include but are not limited to heart rate, blood pressure, body temperature, respiratory rate, and blood oxygen saturation; Step S2: single state evaluation, based on the real-time collected physiological data and the patient, extract the patient's physiological characteristics, and evaluate and calculate the patient's current anesthesia recovery state according to the preset evaluation criteria based on the patient's basic information to obtain a real-time recovery value; Step S3: Construction of recovery samples, matching the corresponding recovery stage according to the evaluation results of a single state, and constructing recovery samples according to the results of all single state evaluations; calculating the recovery comparison value by the physiological data of the current recovery stage and the corresponding standard physiological data; the recovery sample finally generated includes the basic information of the patient, each recovery stage and the corresponding multiple sets of physiological data, real-time recovery value and recovery comparison value; Step S4: input the restored sample into the sample evaluation model for evaluation calculation to obtain the sample evaluation result and the restoration prediction result; after the restored sample is input into the sample evaluation model, select the category area and perform segment matching calculation and long segment matching calculation, perform weighted calculation on the two types of calculation results, and obtain the final sample evaluation result and restoration prediction result; Step S5: Recommendation of adjustment strategy: generating a personalized recovery adjustment strategy based on the sample evaluation results and the recovery prediction results; Step S6: Data statistics and management, statistics, storage and management of all patients' recovery samples, sample evaluation results, recovery prediction results and recovery adjustment strategies, support data backup, query and long-term tracking analysis, while ensuring data security and privacy protection.

[0005] As a preferred technical solution of the present invention, the step of calculating the real-time recovery value includes: Extract the real-time collected patient physiological data, denoted as ,in represents the i-th physiological data of the patient; Compare the real-time collected physiological data with the preset healthy physiological data to obtain the difference value of each physiological data item , the calculation formula is: ;in, is the healthy physiological data corresponding to the i-th physiological data item; The difference value for each physiological data item A nonlinear function is used for conversion, such as the following formula: ; The nonlinear function is selected according to the type of physiological data and the size of the difference value. The selection conditions of the four functions in the formula are For continuous changes, For the regression trend, Less than the change threshold and Greater than the change threshold; The difference values ​​of all converted physiological data items are weighted summed to obtain the initial real-time recovery value , the calculation formula is: ,in is the weighting coefficient of the ith physiological data item; Initial real-time recovery value Perform regression analysis and correction to obtain the final real-time recovery value. The calculation formula is: ,in is the preset regression coefficient used to correct the recovery value.

[0006] As a preferred technical solution of the present invention, the construction of the recovery sample includes the following steps: Matching of single state assessment results: According to the real-time recovery value of the patient's single state, the patient's current physiological data is matched with the standard physiological data of the corresponding recovery stage to determine the patient's recovery stage; Calculation of the recovery comparison value: extract the physiological data of all single states in the current recovery stage, calculate the overall average physiological data value and the standard average physiological data value of the standard recovery stage; compare the physiological data standardization of the current recovery stage with that of the standard recovery stage, and calculate the recovery comparison value of the current recovery stage , the calculation formula is: ,in represents the average value of the ith physiological data in the current recovery phase, represents the average value of the ith physiological data in the standard recovery phase, n represents the total number of physiological data, and A represents the adjustment coefficient; A recovery sample of the patient is generated based on the result of a single state assessment and the recovery comparison value of the recovery stage; and the recovery sample is stored in the form of structured data.

[0007] As a preferred technical solution of the present invention, the structure of the sample evaluation model includes: The input layer is used to receive the patient's physiological data and basic information, and convert them into the feature vector form X={x 1 ,x 2 ,…,x n}, where n represents the feature dimension; A pairing layer is used to select a category area according to the basic information of the patient, classify and map the input feature vector X through the mapping function M() of the basic information of the patient, and determine the category area that matches the input patient information; the basic information includes anesthesia type, disease type, age range, gender and chronic disease history; Category area units, including: The segmented matching layer uses a local matching network based on the attention mechanism to extract representative local features in the feature vector, and performs local feature matching on the key physiological indicators of the patient's recovery data to obtain the local matching result R s ; The long segment matching layer, based on the recurrent neural network, globally models the time series features of the input data and obtains the matching results R of the input features in the time dimension. l ; The result fitting layer is used to fit and integrate the matching results of the segment matching layer and the long segment matching layer through the weighting function: F(R s ,R l )=w s *Rs +w l *R l , calculate the intermediate evaluation value R m ; where w s and w l is the weight parameter of the matching result, and satisfies w s +w l =1; The weighted output layer is used to evaluate the intermediate value R m Combined with the specific weight W of the patient's current recovery stage p , generate the final sample evaluation result R f and predicted recovery results P r .

[0008] As a preferred technical solution of the present invention, the acquisition of the category area includes: By preprocessing the historical recovery samples, the collected original sample data is cleaned, denoised, standardized and time-aligned to generate a preprocessed sample set; Based on a combination of one or more attributes in the basic information of the patient, the preprocessed sample set is grouped and divided to form a plurality of sample subsets, wherein the number of samples in each of the grouped sample subsets is greater than a preset threshold; The sample subsets are clustered using a density clustering algorithm, and the number of samples in each cluster category is calculated; Filter the clustering results, and take the sample set whose valid category number in the clustering results exceeds the set value as the valid sample set; the valid category number is the number of samples of the corresponding cluster category exceeding the set ratio; Model training is performed separately for each valid sample set, and a category area model is constructed based on the support vector machine. The corresponding training process includes: Using the physiological data and basic information of the valid sample set as input, the model parameters are optimized to obtain the feature mapping function of each category area model; Use the validation set to evaluate the category area model, optimize the model's hyperparameters, and ensure the accuracy and robustness of the model; Finally, all category area models are combined to form a category area, which is used to select the corresponding category area for calculation in the sample evaluation model according to the basic information of the input patient.

[0009] As a preferred technical solution of the present invention, the acquisition of the weight parameter of the matching result includes: Obtain segment matching results and long segment matching results, use the segment matching layer to extract local features of the input feature X, and generate local matching results R s , the calculation formula is: R s =f s (X,W s), where f s () is the piecewise matching function, W s is the weight parameter of the segment matching layer; The long segment matching layer is used to globally model the overall time series characteristics of the input feature X and generate a global matching result R l , the calculation formula is: R l =f l (X,W l ), where f l () is the long segment matching function, W l is the weight parameter of the long segment matching layer; Calculate the similarity and calculate the segment matching results R respectively s And the long segment matching result R l Similarity with the input feature X; use the cosine similarity function as the similarity measurement function to get the similarity value S s and S l ; For the similarity value S s and S l The weight parameter w of the segment matching result and the long segment matching result is obtained by normalization calculation. s and w l .

[0010] As a preferred technical solution of the present invention, the training of the sample evaluation model includes the following steps: A1. Initialization of the basic model: Construct a basic sample evaluation model including an input layer, a pairing layer, a category area unit, and a weighted output layer. The hidden layer parameters of the category area unit are replaced by the training results of the sample subset, and the parameters of the remaining layers in the basic model are standardly initialized; A2. Independent training of sample areas: Divide and preprocess historical recovery samples to generate sample subsets, and train the hidden layer parameters of the category area units for each sample subset, including: Based on the physiological data and basic information of the sample subset, the parameters of the segment matching layer and the long segment matching layer are trained in the category area unit to generate multiple sets of parameter sets; The trained parameter set is stored according to the category area mapping rules and dynamically loaded into the category area unit as hidden layer parameters during the evaluation process; A3. Training of the overall model: Based on the hidden layer parameters obtained by training the sample subset using the category area unit replacement, the remaining parameters of the sample evaluation model are trained, including: Training of the pairing layer, optimizing the category area selection logic by training the patient's basic information and the category area mapping function; Training of the weighted output layer, adjusting the weight coefficients and bias parameters by minimizing the objective function; A4. Joint optimization of model parameters: By inputting standardized training set samples, the overall model is jointly optimized. The hidden layer parameters in the category unit are fixed, and only the parameters of the pairing layer, result fitting layer, and weighted output layer are optimized to finally obtain the global optimal parameters of the sample evaluation model. A5. Verification and evaluation: The performance of the sample evaluation model is evaluated through the validation set to verify the matching effect of the model in different category areas, and the remaining parameters are fine-tuned based on the verification results.

[0011] As a preferred technical solution of the present invention, the recommendation of the adjustment strategy includes: Based on the sample evaluation results and recovery prediction results, combined with the patient's basic information and physiological data collected in real time, the patient's current key health characteristics are extracted; Calculate the target recovery value R based on the patient's recovery stage and assessment criteria t , the calculation formula is: R t =f g (S p ), where S p is the evaluation standard parameter of the current recovery stage, f g () is the target recovery value calculation function; Compare the patient's current recovery value with the target recovery value and calculate the recovery difference D r , determining the patient's recovery status as normal, delayed, or advanced according to the interval of the value of the recovery difference; According to the patient's recovery status and current recovery stage, combined with the preset recovery adjustment strategy library, a personalized adjustment strategy is generated through the rule engine; Conduct simulation verification of the recommended adjustment strategy and dynamically update the adjustment strategy based on the patient's subsequent physiological data changes; The adjustment strategies are presented in a structured manner and delivered to medical staff or patients through smart devices, providing visual reports including adjustment suggestions, recovery status assessment and predicted trends.

[0012] As a preferred technical solution of the present invention, the data statistics and management include: The patient's recovery samples, sample evaluation results, recovery prediction results and adjustment strategies are stored in layers, specifically including the basic data layer, stage data layer and evaluation result layer; the basic data layer stores the patient's basic information and original physiological data; the stage data layer stores the standardized recovery samples and corresponding recovery stage data; the evaluation result layer stores the output results of the sample evaluation model and the recommended adjustment strategies; Based on the patient's real-time recovery data and historical data, the division logic of the sample area and the construction of the category area are dynamically updated through the incremental learning algorithm; for the newly collected patient data, the similarity between it and the existing category area samples is calculated in real time, and the category area is dynamically adjusted according to the similarity results; for the category areas with uneven data distribution in the sample area, the category division is optimized through merging or splitting strategies to ensure that the number of samples in each category area reaches the set threshold; Provides multi-condition combination query function, and searches by basic information, recovery stage, and evaluation results; supports visual display of query results, including distribution diagrams and trend diagrams of key statistical indicators and corresponding analysis reports; Conduct multi-dimensional statistical analysis based on recovery data, including time series statistics, stage recovery statistics, and analysis of recovery differences among patient groups; The scope of data access is limited through a hierarchical permission control mechanism, so that different users can only access data corresponding to their permission level; Carry out long-term tracking and management of patient recovery data, and analyze the long-term effects of recovery stages and adjustment strategies through data mining algorithms.

[0013] An intelligent evaluation system for anesthesia recovery status includes the following modules: Data acquisition module: used to collect the patient's physiological data in real time through smart devices, and perform denoising, standardization and time alignment on the collected data; State evaluation module: used to extract the patient's physiological characteristics based on the real-time collected physiological data, calculate the patient's current anesthesia recovery state according to the preset evaluation criteria, and generate a real-time recovery value; Recovery sample construction module: used to match the recovery phase according to the single state evaluation results and construct the recovery sample; Sample evaluation model module: used to input the restored samples into the sample evaluation model, select the category area, perform calculations through segment matching and long segment matching, and integrate the matching results to generate sample evaluation results and restoration prediction results; Adjustment strategy generation module: used to generate personalized recovery adjustment strategies based on sample evaluation results and recovery prediction results; Data statistics and management module: used to store, statistically analyze and manage patients’ recovery samples, assessment results, prediction results and adjustment strategies; Result output module: used to present the recovery assessment results, prediction results and adjustment strategies in a visual way, and provide them to medical staff or patients in an interactive form.

[0014] The present invention has the following advantages: The present invention uses intelligent devices to collect patients' multi-dimensional physiological data in real time and combines them with sample evaluation models of segmented matching and long segment matching to achieve accurate real-time evaluation of patients' recovery status; through dynamically optimized sample areas, samples are grouped and clustered in combination with patients' basic information, and individual characteristics of patients are dynamically loaded and matched to ensure that the evaluation model can adapt to the individual differences of different patients, significantly improving the adaptability and robustness of the model.

[0015] The present invention generates personalized recovery adjustment strategies through a preset strategy library and rule engine, combined with the patient's real-time physiological data, including physiological indicator optimization suggestions, medication adjustment plans and life management plans; the strategy recommendation also supports dynamic feedback and optimization, and adjusts the patient's recovery path in real time, overcoming the lack of dynamism in traditional experience-guided strategies.

[0016] The present invention uses a multi-level data statistics and management module to store patient data in layers according to the basic data layer, stage data layer and evaluation result layer, and combines the incremental learning algorithm to optimize the category area construction, thereby achieving efficient management and long-term tracking of data; the use of data encryption and hierarchical authority control technology effectively protects the privacy and security of patient data and meets the high standards of medical data management.

[0017] The present invention can dynamically predict the patient's recovery status through a time series prediction model and an incremental learning algorithm, and analyze the patient's recovery trend in combination with long-term tracking data, providing a reliable basis for medical intervention and solving the problem of the lack of predictive ability of traditional evaluation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art description are briefly introduced below. Obviously, the drawings in the following description are only schematic diagrams of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the provided drawings without creative work. Figure 1 The present invention is a schematic diagram of the structure of an intelligent evaluation system for anesthesia recovery status used in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, rather than all 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.

[0020] Embodiment 1, a method for intelligently evaluating anesthesia recovery status, comprising the following steps: Step S1: Data collection, collecting the patient's physiological data in real time through smart devices, and performing denoising, standardization and time alignment on the collected data; the physiological data include but are not limited to heart rate, blood pressure, body temperature, respiratory rate, and blood oxygen saturation; Step S2: single state evaluation, based on the real-time collected physiological data and the patient, extract the patient's physiological characteristics, and evaluate and calculate the patient's current anesthesia recovery state according to the preset evaluation criteria based on the patient's basic information to obtain a real-time recovery value; The step of calculating the real-time recovery value comprises: Extract the real-time collected patient physiological data, denoted as ,in represents the i-th physiological data of the patient; Compare the real-time collected physiological data with the preset healthy physiological data to obtain the difference value of each physiological data item , the calculation formula is: ;in, is the healthy physiological data corresponding to the i-th physiological data item; The difference value for each physiological data item A nonlinear function is used for conversion, such as the following formula: ; The nonlinear function is selected according to the type of physiological data and the size of the difference value. The selection conditions of the four functions in the formula are For continuous changes, For the regression trend, Less than the change threshold and Greater than the change threshold; The difference values ​​of all converted physiological data items are weighted summed to obtain the initial real-time recovery value , the calculation formula is: ,in is the weighting coefficient of the ith physiological data item; Initial real-time recovery value Perform regression analysis and correction to obtain the final real-time recovery value. The calculation formula is: ,in is the preset regression coefficient used to correct the recovery value.

[0021] Step S3: Construction of recovery samples, matching the corresponding recovery stage according to the evaluation results of a single state, and constructing recovery samples according to the results of all single state evaluations; calculating the recovery comparison value by the physiological data of the current recovery stage and the corresponding standard physiological data; the recovery sample finally generated includes the basic information of the patient, each recovery stage and the corresponding multiple sets of physiological data, real-time recovery value and recovery comparison value; The construction of the recovery sample includes the following steps: Matching of single state assessment results: According to the real-time recovery value of the patient's single state, the patient's current physiological data is matched with the standard physiological data of the corresponding recovery stage to determine the patient's recovery stage; Calculation of the recovery comparison value: extract the physiological data of all single states in the current recovery stage, calculate the overall average physiological data value and the standard average physiological data value of the standard recovery stage; compare the physiological data standardization of the current recovery stage with that of the standard recovery stage, and calculate the recovery comparison value of the current recovery stage , the calculation formula is: ,in represents the average value of the ith physiological data in the current recovery phase, represents the average value of the ith physiological data in the standard recovery phase, n represents the total number of physiological data, and A represents the adjustment coefficient; A recovery sample of the patient is generated based on the result of a single state assessment and the recovery comparison value of the recovery stage; and the recovery sample is stored in the form of structured data.

[0022] Step S4: input the restored sample into the sample evaluation model for evaluation calculation to obtain the sample evaluation result and the restoration prediction result; after the restored sample is input into the sample evaluation model, select the category area and perform segment matching calculation and long segment matching calculation, perform weighted calculation on the two types of calculation results, and obtain the final sample evaluation result and restoration prediction result; The structure of the sample evaluation model includes: The input layer is used to receive the patient's physiological data and basic information, and convert them into the feature vector form X={x 1 ,x 2 ,…,x n}, where n represents the feature dimension; A pairing layer is used to select a category area according to the basic information of the patient, classify and map the input feature vector X through the mapping function M() of the basic information of the patient, and determine the category area that matches the input patient information; the basic information includes anesthesia type, disease type, age range, gender and chronic disease history; Category area units, including: The segmented matching layer uses a local matching network based on the attention mechanism to extract representative local features in the feature vector, and performs local feature matching on the key physiological indicators of the patient's recovery data to obtain the local matching result R s ; The long segment matching layer, based on the recurrent neural network, globally models the time series features of the input data and obtains the matching results R of the input features in the time dimension. l ; The result fitting layer is used to fit and integrate the matching results of the segment matching layer and the long segment matching layer through the weighting function: F(R s ,R l )=w s *R s +w l *R l , calculate the intermediate evaluation value R m ; where w s and w l is the weight parameter of the matching result, and satisfies w s +w l =1; The weighted output layer is used to evaluate the intermediate value R m Combined with the specific weight W of the patient's current recovery stage p , generate the final sample evaluation result R f and predicted recovery results P r .

[0023] The acquisition of the category area includes: By preprocessing the historical recovery samples, the collected original sample data is cleaned, denoised, standardized and time-aligned to generate a preprocessed sample set; Based on a combination of one or more attributes in the basic information of the patient, the preprocessed sample set is grouped and divided to form a plurality of sample subsets, wherein the number of samples in each of the grouped sample subsets is greater than a preset threshold; The sample subsets are clustered using a density clustering algorithm, and the number of samples in each cluster category is calculated; Filter the clustering results, and take the sample set whose valid category number in the clustering results exceeds the set value as the valid sample set; the valid category number is the number of samples of the corresponding cluster category exceeding the set ratio; Model training is performed separately for each valid sample set, and a category area model is constructed based on the support vector machine. The corresponding training process includes: Using the physiological data and basic information of the valid sample set as input, the model parameters are optimized to obtain the feature mapping function of each category area model; Use the validation set to evaluate the category area model, optimize the model's hyperparameters, and ensure the accuracy and robustness of the model; Finally, all category area models are combined to form a category area, which is used to select the corresponding category area for calculation in the sample evaluation model according to the basic information of the input patient.

[0024] The acquisition of the weight parameter of the matching result includes: Obtain segment matching results and long segment matching results, use the segment matching layer to extract local features of the input feature X, and generate local matching results R s , the calculation formula is: R s =f s (X,W s ), where f s () is the piecewise matching function, W s is the weight parameter of the segment matching layer; The long segment matching layer is used to globally model the overall time series characteristics of the input feature X and generate a global matching result R l , the calculation formula is: R l =f l (X,W l ), where f l () is the long segment matching function, W l is the weight parameter of the long segment matching layer; Calculate the similarity and calculate the segment matching results R respectively s And the long segment matching result R l Similarity with the input feature X; use the cosine similarity function as the similarity measurement function to get the similarity value S s and S l ; For the similarity value S s and S l The weight parameter w of the segment matching result and the long segment matching result is obtained by normalization calculation. s and w l .

[0025] The training of the sample evaluation model includes the following steps: A1. Initialization of the basic model: Construct a basic sample evaluation model including an input layer, a pairing layer, a category area unit, and a weighted output layer. The hidden layer parameters of the category area unit are replaced by the training results of the sample subset, and the parameters of the remaining layers in the basic model are standardly initialized; A2. Independent training of sample areas: Divide and preprocess historical recovery samples to generate sample subsets, and train the hidden layer parameters of the category area units for each sample subset, including: Based on the physiological data and basic information of the sample subset, the parameters of the segment matching layer and the long segment matching layer are trained in the category area unit to generate multiple sets of parameter sets; The trained parameter set is stored according to the category area mapping rules and dynamically loaded into the category area unit as hidden layer parameters during the evaluation process; A3. Training of the overall model: Based on the hidden layer parameters obtained by training the sample subset using the category area unit replacement, the remaining parameters of the sample evaluation model are trained, including: Training of the pairing layer, optimizing the category area selection logic by training the patient's basic information and the category area mapping function; Training of the weighted output layer, adjusting the weight coefficients and bias parameters by minimizing the objective function; A4. Joint optimization of model parameters: By inputting standardized training set samples, the overall model is jointly optimized. The hidden layer parameters in the category unit are fixed, and only the parameters of the pairing layer, result fitting layer, and weighted output layer are optimized to finally obtain the global optimal parameters of the sample evaluation model. A5. Verification and evaluation: The performance of the sample evaluation model is evaluated through the validation set to verify the matching effect of the model in different category areas, and the remaining parameters are fine-tuned based on the verification results.

[0026] Step S5: Recommendation of adjustment strategy: generating a personalized recovery adjustment strategy based on the sample evaluation results and the recovery prediction results; The recommended adjustment strategies include: Based on the sample evaluation results and recovery prediction results, combined with the patient's basic information and physiological data collected in real time, the patient's current key health characteristics are extracted; Calculate the target recovery value R based on the patient's recovery stage and assessment criteria t , the calculation formula is: R t =f g (S p ), where S p is the evaluation standard parameter of the current recovery stage, f g () is the target recovery value calculation function; Compare the patient's current recovery value with the target recovery value and calculate the recovery difference D r , determining the patient's recovery status as normal, delayed, or advanced according to the interval of the value of the recovery difference; Based on the patient's recovery status and current recovery stage, combined with the preset recovery adjustment strategy library, a personalized adjustment strategy is generated through the rule engine; the adjustment content includes but is not limited to: physiological index adjustment suggestions, such as target ranges for respiratory rate, heart rate or blood pressure; drug use suggestions, including dosage adjustment or medication timing; life management suggestions, including personalized plans for diet, exercise, and rest; Conduct simulation verification on the recommended adjustment strategy, and dynamically update the adjustment strategy according to the patient's subsequent physiological data changes; specifically, correct the recovery difference and strategy recommendation content in real time according to the subsequent collected physiological data; if the patient's recovery value does not approach the target value for multiple consecutive periods, recalculate the adjustment strategy; The adjustment strategies are presented in a structured manner and delivered to medical staff or patients through smart devices, providing visual reports including adjustment suggestions, recovery status assessment and predicted trends.

[0027] Step S6: Data statistics and management, statistics, storage and management of all patients' recovery samples, sample evaluation results, recovery prediction results and recovery adjustment strategies, support data backup, query and long-term tracking analysis, while ensuring data security and privacy protection.

[0028] The data statistics and management include: B1. The patient's recovery samples, sample evaluation results, recovery prediction results and adjustment strategies are stored in layers, specifically including the basic data layer, stage data layer and evaluation result layer; The basic data layer stores the patient's basic information and original physiological data; the stage data layer stores the standardized recovery samples and corresponding recovery stage data; the evaluation result layer stores the output results of the sample evaluation model and the recommended adjustment strategies; All data is stored and transmitted through encryption algorithms, and distributed database technology is used to achieve high reliability and disaster recovery backup functions; B2. Dynamic optimization construction of sample area: Based on the patient's real-time recovery data and historical data, the sample area division logic and category area construction are dynamically updated through incremental learning algorithms; For newly collected patient data, the similarity between it and existing category area samples is calculated in real time, and the category area is dynamically adjusted according to the similarity results; For category areas with uneven data distribution in the sample area, optimize the category division through merging or splitting strategies to ensure that the number of samples in each category area reaches the set threshold; B3.Data query and retrieval: Provides multi-condition combination query function, supporting retrieval by patient attributes (such as gender, age, disease type), recovery stage, assessment results, etc. Supports visual display of query results, including distribution charts and trend charts of key statistical indicators and corresponding analysis reports; B4. Statistics and Analysis: Conduct multi-dimensional statistical analysis based on recovery data, including time series statistics, stage recovery statistics, and analysis of recovery differences among patient groups; Provide dynamic analysis reports on data distribution within the sample area, and count the number of samples in each category area, the mean value and fluctuation range of key physiological indicators; B5.Data Privacy and Rights Management: The scope of data access is limited through a hierarchical permission control mechanism, so that different users can only access data corresponding to their permission level; each access, modification and download operation of the data is logged to ensure the traceability of data operations; anonymization and differential privacy technologies are used to protect the privacy of patient data to avoid leakage of sensitive information; B6. Long-term tracking and forecasting optimization: Carry out long-term tracking and management of patient recovery data, and analyze the long-term effects of recovery stages and adjustment strategies through data mining algorithms; Use time series prediction models to predict patient recovery trends and dynamically adjust prediction model parameters to adapt to new data; Provide time span analysis of patient data in sample areas to ensure that the construction of category areas is consistent with long-term data distribution.

[0029] Example 2, an intelligent evaluation system for anesthesia recovery status, see Figure 1 As shown, it includes the following modules: Data acquisition module: used to collect the patient's physiological data in real time through smart devices, and perform denoising, standardization and time alignment on the collected data; State evaluation module: used to extract the patient's physiological characteristics based on the real-time collected physiological data, calculate the patient's current anesthesia recovery state according to the preset evaluation criteria, and generate a real-time recovery value; Recovery sample construction module: used to match the recovery phase according to the single state evaluation results and construct the recovery sample; Sample evaluation model module: used to input the restored samples into the sample evaluation model, select the category area, perform calculations through segment matching and long segment matching, and integrate the matching results to generate sample evaluation results and restoration prediction results; Adjustment strategy generation module: used to generate personalized recovery adjustment strategies based on sample evaluation results and recovery prediction results; Data statistics and management module: used to store, statistically analyze and manage patients’ recovery samples, assessment results, prediction results and adjustment strategies; Result output module: used to present the recovery assessment results, prediction results and adjustment strategies in a visual way, and provide them to medical staff or patients in an interactive form.

[0030] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent evaluation method for anesthesia recovery status, characterized in that: The following steps are involved: Step S1: Data collection, collecting the patient's physiological data in real time through smart devices, and performing denoising, standardization and time alignment on the collected data; the physiological data include but are not limited to heart rate, blood pressure, body temperature, respiratory rate, and blood oxygen saturation; Step S2: single state evaluation, based on the real-time collected physiological data and the patient, extract the patient's physiological characteristics, and evaluate and calculate the patient's current anesthesia recovery state according to the preset evaluation criteria based on the patient's basic information to obtain a real-time recovery value; Step S3: Construction of recovery samples, matching the corresponding recovery stages according to the evaluation results of the single states, and constructing recovery samples according to the evaluation results of all single states; The recovery comparison value is obtained by calculating the physiological data of the current recovery stage and the corresponding standard physiological data; the recovery sample finally generated includes the basic information of the patient, each recovery stage and the corresponding multiple sets of physiological data, the real-time recovery value and the recovery comparison value; Step S4: input the restored sample into the sample evaluation model for evaluation calculation to obtain the sample evaluation result and the restoration prediction result; After the restored sample is input into the sample evaluation model, the category area is selected and segment matching calculation and long segment matching calculation are performed, and the two types of calculation results are weighted to obtain the final sample evaluation result and restoration prediction result; Step S5: Recommendation of adjustment strategy: generating a personalized recovery adjustment strategy based on the sample evaluation results and the recovery prediction results; Step S6: Data statistics and management, statistics, storage and management of all patients' recovery samples, sample evaluation results, recovery prediction results and recovery adjustment strategies, support data backup, query and long-term tracking analysis, while ensuring data security and privacy protection.

2. The method for intelligently evaluating anesthesia recovery status according to claim 1, characterized in that: The step of calculating the real-time recovery value comprises: Extract the real-time collected patient physiological data, denoted as ,in represents the i-th physiological data of the patient; Compare the real-time collected physiological data with the preset healthy physiological data to obtain the difference value of each physiological data item , the calculation formula is: ;in, is the healthy physiological data corresponding to the i-th physiological data item; The difference value for each physiological data item A nonlinear function is used for conversion, such as the following formula: ； The nonlinear function is selected according to the type of physiological data and the size of the difference value. The selection conditions of the four functions in the formula are For continuous changes, For the regression trend, Less than the change threshold and Greater than the change threshold; The difference values ​​of all converted physiological data items are weighted summed to obtain the initial real-time recovery value , the calculation formula is: ,in is the weighting coefficient of the ith physiological data item; Initial real-time recovery value Perform regression analysis and correction to obtain the final real-time recovery value. The calculation formula is: ,in is the preset regression coefficient used to correct the recovery value.

3. The method for intelligently evaluating anesthesia recovery status according to claim 1, characterized in that: The construction of the recovery sample includes the following steps: Matching of single state assessment results: According to the real-time recovery value of the patient's single state, the patient's current physiological data is matched with the standard physiological data of the corresponding recovery stage to determine the patient's recovery stage; Calculation of the recovery comparison value: extract the physiological data of all single states in the current recovery stage, calculate the overall average physiological data value and the standard average physiological data value of the standard recovery stage; compare the physiological data standardization of the current recovery stage with that of the standard recovery stage, and calculate the recovery comparison value of the current recovery stage , the calculation formula is: ,in represents the average value of the ith physiological data in the current recovery phase, represents the average value of the ith physiological data in the standard recovery phase, n represents the total number of physiological data, and A represents the adjustment coefficient; A recovery sample of the patient is generated based on the result of a single state assessment and the recovery comparison value of the recovery stage; and the recovery sample is stored in the form of structured data.

4. The method for intelligently evaluating anesthesia recovery status according to claim 1, characterized in that: The structure of the sample evaluation model includes: The input layer is used to receive the patient’s physiological data and basic information, and convert them into the feature vector form X={x1,x2,…,x n }, where n represents the feature dimension; A pairing layer is used to select a category area according to the basic information of the patient, classify and map the input feature vector X through the mapping function M() of the basic information of the patient, and determine the category area that matches the input patient information; the basic information includes anesthesia type, disease type, age range, gender and chronic disease history; Category area units, including: The segmented matching layer uses a local matching network based on the attention mechanism to extract representative local features in the feature vector, and performs local feature matching on the key physiological indicators of the patient's recovery data to obtain the local matching result R s ; The long segment matching layer, based on the recurrent neural network, globally models the time series features of the input data and obtains the matching results R of the input features in the time dimension. l ; The result fitting layer is used to fit and integrate the matching results of the segment matching layer and the long segment matching layer through the weighting function: F(R s ,R l )=w s *R s +w l *R l , calculate the intermediate evaluation value R m ; where w s and w l is the weight parameter of the matching result, and satisfies w s +w l =1; The weighted output layer is used to evaluate the intermediate value R m Combined with the specific weight W of the patient's current recovery stage p , generate the final sample evaluation result R f and predicted recovery results P r .

5. The method for intelligently evaluating anesthesia recovery status according to claim 4, characterized in that: The acquisition of the category area includes: By preprocessing the historical recovery samples, the collected original sample data is cleaned, denoised, standardized and time-aligned to generate a preprocessed sample set; Based on a combination of one or more attributes in the basic information of the patient, the preprocessed sample set is grouped and divided to form a plurality of sample subsets, wherein the number of samples in each of the grouped sample subsets is greater than a preset threshold; The sample subsets are clustered using a density clustering algorithm, and the number of samples in each cluster category is calculated; Filter the clustering results, and take the sample set whose valid category number in the clustering results exceeds the set value as the valid sample set; the valid category number is the number of samples of the corresponding cluster category exceeding the set ratio; Model training is performed separately for each valid sample set, and a category area model is constructed based on the support vector machine. The corresponding training process includes: Using the physiological data and basic information of the valid sample set as input, the model parameters are optimized to obtain the feature mapping function of each category area model; Use the validation set to evaluate the category area model, optimize the model's hyperparameters, and ensure the accuracy and robustness of the model; Finally, all category area models are combined to form a category area, which is used to select the corresponding category area for calculation in the sample evaluation model according to the basic information of the input patient.

6. The method for intelligently evaluating anesthesia recovery status according to claim 4, characterized in that: The acquisition of the weight parameter of the matching result includes: Obtain segment matching results and long segment matching results, use the segment matching layer to extract local features of the input feature X, and generate local matching results R s , the calculation formula is: R s =f s (X,W s ), where f s () is the piecewise matching function, W s is the weight parameter of the segment matching layer; The long segment matching layer is used to globally model the overall time series characteristics of the input feature X and generate a global matching result R l , the calculation formula is: R l =f l (X,W l ), where f l () is the long segment matching function, W l is the weight parameter of the long segment matching layer; Calculate the similarity and calculate the segment matching results R respectively s And the long segment matching result R l Similarity with the input feature X; use the cosine similarity function as the similarity measurement function to get the similarity value S s and S l ; For the similarity value S s and S l The weight parameter w of the segment matching result and the long segment matching result is obtained by normalization calculation. s and w l .

7. The method for intelligently evaluating anesthesia recovery status according to claim 4, characterized in that: The training of the sample evaluation model includes the following steps: A1. Initialization of the basic model: Construct a basic sample evaluation model including an input layer, a pairing layer, a category area unit, and a weighted output layer. The hidden layer parameters of the category area unit are replaced by the training results of the sample subset, and the parameters of the remaining layers in the basic model are standardly initialized; A2. Independent training of sample areas: Divide and preprocess historical recovery samples to generate sample subsets, and train the hidden layer parameters of the category area units for each sample subset, including: Based on the physiological data and basic information of the sample subset, the parameters of the segment matching layer and the long segment matching layer are trained in the category area unit to generate multiple sets of parameter sets; The trained parameter set is stored according to the category area mapping rules and dynamically loaded into the category area unit as hidden layer parameters during the evaluation process; A3. Training of the overall model: Based on the hidden layer parameters obtained by training the sample subset using the category area unit replacement, the remaining parameters of the sample evaluation model are trained, including: Training of the pairing layer, optimizing the category area selection logic by training the patient's basic information and the category area mapping function; Training of the weighted output layer, adjusting the weight coefficients and bias parameters by minimizing the objective function; A4. Joint optimization of model parameters: By inputting standardized training set samples, the overall model is jointly optimized. The hidden layer parameters in the category unit are fixed, and only the parameters of the pairing layer, result fitting layer, and weighted output layer are optimized to finally obtain the global optimal parameters of the sample evaluation model. A5. Verification and evaluation: The performance of the sample evaluation model is evaluated through the validation set to verify the matching effect of the model in different category areas, and the remaining parameters are fine-tuned based on the verification results.

8. The method for intelligently evaluating anesthesia recovery status according to claim 1, characterized in that: The recommended adjustment strategies include: Based on the sample evaluation results and recovery prediction results, combined with the patient's basic information and physiological data collected in real time, the patient's current key health characteristics are extracted; Calculate the target recovery value R based on the patient's recovery stage and assessment criteria t , the calculation formula is: R t =f g (S p ), where S p is the evaluation standard parameter of the current recovery stage, f g () is the target recovery value calculation function; Compare the patient's current recovery value with the target recovery value and calculate the recovery difference D r , determining the patient's recovery status as normal, delayed, or advanced according to the interval of the value of the recovery difference; According to the patient's recovery status and current recovery stage, combined with the preset recovery adjustment strategy library, a personalized adjustment strategy is generated through the rule engine; Conduct simulation verification of the recommended adjustment strategy and dynamically update the adjustment strategy based on the patient's subsequent physiological data changes; The adjustment strategies are presented in a structured manner and delivered to medical staff or patients through smart devices, providing visual reports including adjustment suggestions, recovery status assessment and predicted trends.

9. The method for intelligently evaluating anesthesia recovery status according to claim 1, characterized in that: The data statistics and management include: The patient's recovery samples, sample evaluation results, recovery prediction results and adjustment strategies are stored in layers, specifically including the basic data layer, stage data layer and evaluation result layer; the basic data layer stores the patient's basic information and original physiological data; the stage data layer stores the standardized recovery samples and corresponding recovery stage data; the evaluation result layer stores the output results of the sample evaluation model and the recommended adjustment strategies; Based on the patient's real-time recovery data and historical data, the division logic of the sample area and the construction of the category area are dynamically updated through the incremental learning algorithm; for the newly collected patient data, the similarity between it and the existing category area samples is calculated in real time, and the category area is dynamically adjusted according to the similarity results; for the category areas with uneven data distribution in the sample area, the category division is optimized through merging or splitting strategies to ensure that the number of samples in each category area reaches the set threshold; Provides multi-condition combination query function, and searches by basic information, recovery stage, and evaluation results; supports visual display of query results, including distribution diagrams and trend diagrams of key statistical indicators and corresponding analysis reports; Conduct multi-dimensional statistical analysis based on recovery data, including time series statistics, stage recovery statistics, and analysis of recovery differences among patient groups; The scope of data access is limited through a hierarchical permission control mechanism, so that different users can only access data corresponding to their permission level; Carry out long-term tracking and management of patient recovery data, and analyze the long-term effects of recovery stages and adjustment strategies through data mining algorithms.

10. An intelligent evaluation system for anesthesia recovery status, characterized in that: The system applies an intelligent evaluation method for anesthesia recovery status according to any one of claims 1 to 9, and comprises the following modules: Data acquisition module: used to collect the patient's physiological data in real time through smart devices, and perform denoising, standardization and time alignment on the collected data; State evaluation module: used to extract the patient's physiological characteristics based on the real-time collected physiological data, calculate the patient's current anesthesia recovery state according to the preset evaluation criteria, and generate a real-time recovery value; Recovery sample construction module: used to match the recovery phase according to the single state evaluation results and construct the recovery sample; Sample evaluation model module: used to input the restored samples into the sample evaluation model, select the category area, perform calculations through segment matching and long segment matching, and integrate the matching results to generate sample evaluation results and restoration prediction results; Adjustment strategy generation module: used to generate personalized recovery adjustment strategies based on sample evaluation results and recovery prediction results; Data statistics and management module: used to store, statistically analyze and manage patients’ recovery samples, assessment results, prediction results and adjustment strategies; Result output module: used to present the recovery assessment results, prediction results and adjustment strategies in a visual way, and provide them to medical staff or patients in an interactive form.

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