Patient health data management system and method based on edge computing

Abstract

The invention discloses a patient health data management system and method based on edge computing, and relates to the technical field of edge computing, and the system comprises an edge computing node, a cloud server, a data transmission module and a system optimization module. Comprising the following steps that 1, medical equipment transmits health data of a patient to an edge computing node in real time through a data acquisition module; 2, the data acquisition module sends the data to a data processing module, a deep learning algorithm is used for analyzing data features, and various formulas are applied for processing and optimization; 3, the processed data are encrypted and stored through a data storage and encryption module, and integrity verification is carried out regularly; 4, the edge computing node transmits required data to a cloud server, and a data transmission module is responsible for dynamically adjusting a transmission strategy according to network conditions; and 5, the cloud server analyzes and mines the multi-source data, and outputs feedback information to further optimize a processing algorithm and model parameters of the edge computing node.

Description

Technical Field

[0001] The present invention relates to the field of edge computing technology, and in particular to a patient health data management system and method based on edge computing. Background Art

[0002] Edge computing is a distributed computing architecture that transfers the demand for data processing and analysis from centralized data centers to the edge of devices. In the modern medical industry, real-time monitoring and management of patient health data is crucial. However, with the advancement of information technology, the medical and health industry faces the challenge of collecting, processing and analyzing a large amount of data. The traditional cloud computing model has the following disadvantages:

[0003] 1. In the traditional model, data needs to be uploaded to the cloud for processing, resulting in delays in real-time monitoring and decision-making, affecting the timing of patient treatment;

[0004] 2. With the introduction of data privacy protection regulations, storing patient health data in the cloud is vulnerable to cyber attacks and data leaks;

[0005] 3. Existing technologies have problems such as poor compatibility, scattered data, poor real-time performance, low network resource utilization efficiency, and inability to conduct in-depth analysis and optimization of data;

[0006] Therefore, in response to the above problems, there is an urgent need for a patient health data management system based on edge computing to improve data processing efficiency, protect data privacy, and achieve more flexible system integration. Summary of the invention

[0007] The purpose of the present invention is to provide a patient health data management system and method based on edge computing to solve the problems raised in the above-mentioned background technology.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0009] A patient health data management system based on edge computing, including edge computing nodes, cloud servers, data transmission modules and system optimization modules;

[0010] The edge computing node includes a data acquisition module, a data processing module, a data storage and encryption module, and a communication module; the data acquisition module collects patient health data in real time and transmits the data to the data processing module; the data processing module uses a deep learning algorithm to analyze data features based on the data collected by the data acquisition module, and applies various formulas for processing and optimization; the data storage and encryption module: encrypts and stores the data processed by the data processing module through the data storage and encryption module, and performs integrity verification regularly; the communication module is responsible for data transmission between the cloud server and other edge computing nodes;

[0011] The cloud server includes a storage unit and a data analysis and feedback unit. The storage unit is used to store patient health data from multiple edge computing nodes. The data analysis and feedback unit: the cloud server analyzes and mines data from multiple sources and outputs feedback information to further optimize the processing algorithms and model parameters of the edge computing nodes.

[0012] The data transmission module: The edge computing node transmits the required data to the cloud server, and the data transmission module is responsible for dynamically adjusting the transmission strategy according to the network conditions;

[0013] The system optimization module continuously optimizes the data processing and analysis model based on the data analysis and feedback unit feedback mechanism.

[0014] A further improvement of the technical solution of the present invention is that the data acquisition module includes a sensor interface unit and a data format processing unit. The sensor interface unit connects a variety of sensor devices through a standardized interface to collect the patient's health data from various medical devices in real time, and marks the collected data as X={X1, X2..., X i}, where X i represents the i-th data, i is a positive integer, and the data format processing unit has a data parsing function to convert multiple data formats from different devices into a unified format for subsequent processing.

[0015] A further improvement of the technical solution of the present invention is that the data processing module includes data cleaning, feature extraction and model prediction for the collected data:

[0016] The data cleaning and preprocessing uses variance calculation for denoising and smoothing. The specific formula is as follows:

[0017]

[0018] Where Var(X) represents the variance of sample data X, N represents the number of data points collected; x i represents the collected health data value of the i-th data point, μ represents the mean of the sample data, that is,

[0019] The feature extraction uses a convolutional neural network to extract features from the collected data to identify potential health states. The specific formula is as follows:

[0020]

[0021] where y i,jRepresents the output of the convolution operation, x represents the input data matrix, ω represents the convolution kernel or filter, which is used to determine the feature extraction method, M and N represent the height and width of the convolution kernel, and i and j represent the position index in the current feature map.

[0022] A further improvement of the technical solution of the present invention is that the model prediction uses a deep learning model for prediction, and calculates the mean square error loss to evaluate the model performance. The specific formula is as follows:

[0023]

[0024] Where L represents the loss function, y represents the true value vector, that is, the actual health data of each sample, represents the model prediction value vector, n represents the number of samples, y i , They represent the true value of the ith sample and the predicted value of the ith sample respectively.

[0025] A further improvement of the technical solution of the present invention is that the data storage and encryption module locally stores the processed data and ensures the security and integrity of the data. The data storage and encryption module includes an encryption storage unit and an integrity verification unit. The encryption storage unit uses the advanced encryption standard to encrypt the stored data to protect patient privacy. The integrity verification unit uses a hash function to perform integrity verification on the stored data to ensure that the data has not been tampered with.

[0026] The data storage and encryption module is specifically analyzed as follows:

[0027] The formula for encrypting a storage unit is as follows:

[0028] C Ciphertext =E(K,P)

[0029] Among them C Ciphertext =E(K,P) represents ciphertext, E represents encryption algorithm, K represents key, and P represents unencrypted original data;

[0030] The integrity verification formula is as follows:

[0031] H(x)

[0032] Where H represents the hash function and x represents the input data.

[0033] A further improvement of the technical solution of the present invention is that the communication module is responsible for data transmission between the cloud server and other edge computing nodes.

[0034] A further improvement of the technical solution of the present invention is that the data transmission module transmits data from other edge computing nodes to the cloud server through the hub function of the communication module, and the data transmission module dynamically adjusts the data transmission parameters according to the network conditions and calculates the transmission efficiency using the channel capacity formula. The specific formula is as follows:

[0035]

[0036] Where C represents channel capacity, B represents bandwidth, P represents signal power, and N represents noise power.

[0037] A further improvement of the technical solution of the present invention is that the cloud server includes a storage unit and a data analysis and feedback unit. The storage unit provides a large-scale storage space to store patient health data transmitted from multiple edge computing nodes through a data transmission module. The data analysis and feedback unit performs centralized analysis on data from multiple edge computing nodes, uses machine learning to predict trends, feeds back information to the edge computing nodes based on the analysis results, and adjusts and optimizes data processing algorithms and model parameters to improve the overall performance of the system.

[0038] A further improvement of the technical solution of the present invention is that the system optimization module continuously optimizes the data processing and analysis model through a feedback mechanism to ensure that the system has adaptive learning capabilities, while supporting the access and data integration of multiple medical devices to ensure the flexibility and applicability of the system.

[0039] A further improvement of the technical solution of the present invention is: a patient health data management system based on edge computing, used to implement a patient health data management method based on edge computing as described in any one of claims 1-9, comprising the following steps:

[0040] Step 1: Medical equipment transmits patient health data to the edge computing node in real time through the data acquisition module;

[0041] Step 2: The data acquisition module sends the data to the data processing module, uses deep learning algorithms to analyze data features, and applies various formulas for processing and optimization;

[0042] Step 3: The processed data is encrypted and stored through the data storage and encryption module, and integrity verification is performed regularly;

[0043] Step 4: The edge computing node transmits the required data to the cloud server, and the data transmission module is responsible for dynamically adjusting the transmission strategy according to network conditions;

[0044] Step 5: The cloud server analyzes and mines data from multiple sources and outputs feedback information to further optimize the processing algorithms and model parameters of the edge computing nodes.

[0045] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0046] 1. The present invention provides an innovative patient health data management system and method based on edge computing. The present invention can perform real-time processing at the location where data is generated, reduce the delay of data transmission, improve the response speed, and realize real-time monitoring of the patient's health status;

[0047] 2. The patient health data management system and method based on edge computing provided by the present invention effectively reduces the risk of data being stolen during transmission by processing and storing sensitive health data on edge devices, and enhances the privacy protection of patient data;

[0048] 3. The patient health data management system and method based on edge computing provided by the present invention performs preliminary processing on the data at the edge computing node and only sends the necessary information to the cloud, which significantly reduces the use of bandwidth and improves the utilization efficiency of network resources;

[0049] 4. The patient health data management system and method based on edge computing provided by the present invention have good scalability through the present invention, and can be integrated with different types of medical devices and applications to meet diverse medical needs;

[0050] 5. The patient health data management system and method based on edge computing provided by the present invention can provide intelligent support for medical decision-making and improve doctors' work efficiency and diagnostic accuracy by conducting in-depth analysis of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0052] Figure 1 A flow chart of a patient health data management system and method based on edge computing of the present invention;

[0053] Figure 2 An inclusion diagram of edge computing nodes of a patient health data management system based on edge computing according to the present invention; DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Examples, such as Figure 1 and Figure 2 As shown, a patient health data management system based on edge computing includes an edge computing node, a cloud server, a data transmission module and a system optimization module;

[0056] The edge computing node includes a data acquisition module, a data processing module, a data storage and encryption module, and a communication module; the data acquisition module collects patient health data in real time and transmits the data to the data processing module; the data processing module uses a deep learning algorithm to analyze data features based on the data collected by the data acquisition module, and applies various formulas for processing and optimization; the data storage and encryption module: encrypts and stores the data processed by the data processing module through the data storage and encryption module, and performs integrity verification regularly; the communication module is responsible for data transmission between the cloud server and other edge computing nodes;

[0057] The data collection module collects patient health data such as heart rate, blood pressure, blood sugar level, etc., and transmits the data to the edge computing node. Various medical devices include heart rate monitors, blood sugar meters, etc.

[0058] The cloud server includes a storage unit, a data analysis and feedback unit. The storage unit is used to store patient health data from multiple edge computing nodes. The data analysis and feedback unit: the cloud server analyzes and mines data from multiple sources, and outputs feedback information to further optimize the processing algorithms and model parameters of the edge computing nodes.

[0059] Data transmission module: The edge computing node transmits the required data to the cloud server. The data transmission module is responsible for dynamically adjusting the transmission strategy according to network conditions.

[0060] The system optimization module continuously optimizes the data processing and analysis model based on the data analysis and feedback unit feedback mechanism.

[0061] The data acquisition module includes a sensor interface unit and a data format processing unit. The sensor interface unit connects a variety of sensor devices through a standardized interface, collects the patient's health data from various medical devices in real time, and marks the collected data as X = {X1, X2..., X i}, where X iIt represents the i-th data, i is a positive integer, and the data format processing unit has a data parsing function, which converts various data formats from different devices into a unified format for subsequent processing.

[0062] The data processing module includes data cleaning, feature extraction and model prediction of the collected data:

[0063] Data cleaning and preprocessing use variance calculation for denoising and smoothing. The specific formula is as follows:

[0064]

[0065] Where Var(X) represents the variance of sample data X, N represents the number of data points collected; x i represents the collected health data value of the i-th data point, μ represents the mean of the sample data, that is,

[0066] When dealing with health data, variance is used to measure the variation in the data. In simple terms, it helps us see how far each data point is from the overall mean. Through this calculation, we can clean up those fluctuations that may be accidental and ensure that subsequent analysis is more accurate.

[0067] Feature extraction uses convolutional neural network to extract features from collected data to identify potential health status. The specific formula is as follows:

[0068]

[0069] where y i,j Represents the output of the convolution operation, x represents the input data matrix, that is, a large amount of patient data information is collected, ω represents the convolution kernel or filter, which is used to determine the feature extraction method, M and N represent the height and width of the convolution kernel, and i and j represent the position index in the current feature map.

[0070] Convolutional neural networks are used to extract important features from health data. Through convolution operations, the input matrix (such as time series or image data to collect data information from a large number of patients) is operated with the filter to generate an output matrix with important features for automatic analysis and prediction of health status.

[0071] Among them, filters are an important tool in convolutional neural networks, mainly used to extract features from input data. The main function of the filter is to "scan" the input data and extract specific features by performing operations (usually convolution operations) on the input data. For example, in image processing, filters can be used to detect edges, textures or other visual features.

[0072] Model prediction uses a deep learning model to make predictions and calculates the mean square error loss to evaluate model performance. The specific formula is as follows:

[0073]

[0074] Where L represents the loss function, y represents the true value vector, that is, the actual health data of each sample, represents the model prediction value vector, n represents the number of samples, y i , They represent the true value of the ith sample and the predicted value of the ith sample respectively.

[0075] By evaluating the degree of difference between the model's predicted value and the true value, and by calculating the squared difference between the predicted value and the true value and taking the average, the loss function helps optimize the model performance and make it more accurate in predicting health data. This is a method used to measure the gap between the predicted results and the actual situation. It tells us how accurate the prediction is by comparing our predicted health status with the actual health status. This method helps us continuously adjust the model to make it more reliable in future predictions.

[0076] The data storage and encryption module stores the processed data locally and ensures the security and integrity of the data. The data storage and encryption module includes an encryption storage unit and an integrity verification unit. The encryption storage unit uses the Advanced Encryption Standard to encrypt the stored data to protect patient privacy. The integrity verification unit uses a hash function to perform integrity verification on the stored data to ensure that the data has not been tampered with.

[0077] The data storage and encryption module is specifically analyzed as follows:

[0078] The formula for encrypting a storage unit is as follows:

[0079] C Ciphertext =E(K,P)

[0080] Among them C Ciphertext =E(K,P) represents ciphertext, E represents encryption algorithm, K represents key, and P represents unencrypted original data;

[0081] Generate a random key K, ensure that the encrypted data P is a multiple of the AES block size, perform necessary padding, and use the AES algorithm to perform encryption operations to obtain the ciphertext C Ciphertext , the ciphertext C Ciphertext To store for later decryption, select Advanced Encryption Standard (AES), which is formula C Ciphertext=E(K,P), generate and securely manage encryption keys, generate key K through a random number generator, and use a hardware security module (HSM) for storage; when encrypting data, input the plaintext data to be encrypted into the encryption algorithm to generate ciphertext, and store the ciphertext in the database; when decrypting, verify the user's authority to obtain the key, and then decrypt the ciphertext into plaintext. Through this process, the security and privacy protection of sensitive data during storage and transmission are ensured. Encrypted storage is an effective means to protect data security. By implementing a strong encryption mechanism and management process, data leakage can be effectively prevented, user privacy can be protected, compliance requirements can be met, and the trust of the system can be improved. In the medical and health data management system, this mechanism is particularly important to ensure that the patient's health information is always safe and reliable.

[0082] The integrity verification formula is as follows:

[0083] H(x)

[0084] Where H represents the hash function and x represents the input data.

[0085] Use the SHA-256 algorithm to calculate the hash value H(x) of the data, and store the generated hash value together with the encrypted data. When the data integrity needs to be verified, recalculate the hash value and compare it with the stored hash value. By comparing the recalculated hash value with the original stored hash value, the integrity of the data can be confirmed. If the two are the same, the data has not been modified; if they are different, it means that the data may have been tampered with. The hash value is an effective security mechanism. Even in the event of a data leak, it is difficult for an attacker to restore the original data. The hash value can ensure that only authorized users can verify the integrity of the data. Each time the data is accessed or processed, hash verification can be used as an audit mechanism to ensure the security and integrity of the data during use.

[0086] The communication module is responsible for data transmission between the cloud server and other edge computing nodes.

[0087] The data transmission module transmits data from other edge computing nodes to the cloud server through the hub function of the communication module. The data transmission module dynamically adjusts the data transmission parameters according to the network conditions and uses the channel capacity formula to calculate the transmission efficiency. The specific formula is as follows:

[0088]

[0089] Where C represents channel capacity, B represents bandwidth, P represents signal power, and N represents noise power.

[0090] The cloud server includes a storage unit and a data analysis and feedback unit. The storage unit provides large-scale storage space to store patient health data transmitted from multiple edge computing nodes through data transmission modules. The data analysis and feedback unit conducts centralized analysis of data from multiple edge computing nodes, uses machine learning to predict trends, and feeds back information to the edge computing nodes based on the analysis results, adjusts and optimizes data processing algorithms and model parameters to improve the overall performance of the system.

[0091] Cloud servers have powerful computing capabilities and can conduct in-depth analysis of large amounts of health data collected centrally.

[0092] The system optimization module continuously optimizes the data processing and analysis models through a feedback mechanism to ensure that the system has adaptive learning capabilities. It also supports the access and data integration of multiple medical devices to ensure the flexibility and applicability of the system.

[0093] A patient health data management method based on edge computing, used to implement a patient health data management system based on edge computing according to any one of claims 1 to 9, comprising the following steps:

[0094] Step 1: Medical equipment transmits patient health data to the edge computing node in real time through the data acquisition module;

[0095] Step 2: The data acquisition module sends the data to the data processing module, uses deep learning algorithms to analyze data features, and applies various formulas for processing and optimization;

[0096] Step 3: The processed data is encrypted and stored through the data storage and encryption module, and integrity verification is performed regularly;

[0097] Step 4: The edge computing node transmits the required data to the cloud server, and the data transmission module is responsible for dynamically adjusting the transmission strategy according to network conditions;

[0098] Step 5: The cloud server analyzes and mines data from multiple sources and outputs feedback information to further optimize the processing algorithms and model parameters of the edge computing nodes.

[0099] Working principle: The staff connects the medical equipment and data acquisition module including the sensor interface unit to various sensor devices through standardized interfaces, collects the patient's health data from various medical devices in real time, and marks the collected data as X = {X1, X2..., X i}, where X iRepresents the i-th data, i is a positive integer, the data format processing unit has the data parsing function, converts multiple data formats from different devices into a unified format for subsequent processing, and transmits the patient's health data to the edge computing node in real time through the data acquisition module. The data acquisition module sends the data to the data processing module, which includes data cleaning, feature extraction and model prediction for the collected data, and uses variance calculation for denoising and smoothing by using data cleaning and preprocessing. The specific formula is as follows:

[0100]

[0101] Where Var(X) represents the variance of sample data X, N represents the number of data points collected; x i represents the collected health data value of the i-th data point, μ represents the mean of the sample data, that is,

[0102] Feature extraction uses convolutional neural network to extract features from collected data to identify potential health status. The specific formula is as follows:

[0103]

[0104] where y i,j Represents the output of the convolution operation, x represents the input data matrix, ω represents the convolution kernel or filter, which is used to determine the feature extraction method, M and N represent the height and width of the convolution kernel, i and j represent the position index in the current feature map,

[0105] Model prediction uses a deep learning model to make predictions and calculates the mean square error loss to evaluate model performance. The specific formula is as follows:

[0106]

[0107] Where L represents the loss function, y represents the true value vector, that is, the actual health data of each sample, represents the model prediction value vector, n represents the number of samples, y i , They represent the true value of the i-th sample and the predicted value of the i-th sample respectively. The data features are analyzed using deep learning algorithms, and various formulas are applied for processing and optimization. The processed data is encrypted and stored through the data storage and encryption module, and integrity verification is performed regularly. When data transmission is required, the communication module is responsible for data transmission between the cloud server and other edge computing nodes. The data transmission module transmits data from other edge computing nodes to the cloud server through the hub function of the communication module. The data transmission module dynamically adjusts the data transmission parameters according to the network conditions, and uses the channel capacity formula to calculate the transmission efficiency. The specific formula is as follows:

[0108]

[0109] Where C represents channel capacity, B represents bandwidth, P represents signal power, and N represents noise power. The cloud server then includes a storage unit and a data analysis and feedback unit. The storage unit provides large-scale storage space to store patient health data transmitted from multiple edge computing nodes through data transmission modules. The data analysis and feedback unit centrally analyzes data from multiple edge computing nodes, uses machine learning to predict trends, and feeds back information to edge computing nodes based on the analysis results, adjusts and optimizes data processing algorithms and model parameters to improve the overall performance of the system. The cloud server analyzes and mines data from multiple sources, and outputs feedback information to further optimize the processing algorithms and model parameters of edge computing nodes. Model parameters, the system optimization module continuously optimizes the data processing and analysis model through the feedback mechanism to ensure that the system has adaptive learning capabilities, while supporting the access and data integration of multiple medical devices to ensure the flexibility and applicability of the system. This technical solution collects and processes patient health data in real time through edge computing nodes. In the data processing module, a deep learning algorithm is used to analyze the data, and multiple formulas are applied to extract data features, assess losses, and optimize results. The data is protected by encryption technology to ensure that it is not illegally accessed during transmission and storage. In addition, the system has self-learning capabilities, and continuously optimizes the model through feedback to improve overall performance, further improving the system's work efficiency and usage effects.

[0110] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A patient health data management system based on edge computing, characterized by: Includes edge computing nodes, cloud servers, data transmission modules and system optimization modules; The edge computing node includes a data acquisition module, a data processing module, a data storage and encryption module and a communication module; the data acquisition module collects patient health data in real time and transmits the data to the data processing module; the data processing module uses a deep learning algorithm to analyze data features based on the data collected by the data acquisition module, and applies various formulas for processing and optimization; the data storage and encryption module: encrypts and stores the data processed by the data processing module through the data storage and encryption module, and performs integrity verification regularly; The communication module is responsible for data transmission between the cloud server and other edge computing nodes; The cloud server includes a storage unit, a data analysis and feedback unit, the storage unit is used to store patient health data from multiple edge computing nodes, and the data analysis and feedback unit: the cloud server analyzes and mines data from multiple sources and outputs feedback information to further optimize the processing algorithm and model parameters of the edge computing nodes; The data transmission module: The edge computing node transmits the required data to the cloud server, and the data transmission module is responsible for dynamically adjusting the transmission strategy according to the network conditions; The system optimization module continuously optimizes the data processing and analysis model based on the data analysis and feedback unit feedback mechanism.

2. A patient health data management system based on edge computing according to claim 1, characterized in that: The data acquisition module includes a sensor interface unit and a data format processing unit. The sensor interface unit connects a variety of sensor devices through a standardized interface, collects the patient's health data from various medical devices in real time, and marks the collected data as X=X1, X2..., X i , where X i represents the i-th data, i is a positive integer, and the data format processing unit has a data parsing function to convert multiple data formats from different devices into a unified format for subsequent processing.

3. A patient health data management system based on edge computing according to claim 1, characterized in that: The data processing module includes data cleaning, feature extraction and model prediction for the collected data: The data cleaning and preprocessing uses variance calculation for denoising and smoothing. The specific formula is as follows: Where Var(X) represents the variance of sample data X, N represents the number of data points collected; x i represents the collected health data value of the i-th data point, μ represents the mean of the sample data, that is, The feature extraction uses a convolutional neural network to extract features from the collected data to identify potential health states. The specific formula is as follows: where y i,j Represents the output of the convolution operation, x represents the input data matrix, ω represents the convolution kernel or filter, which is used to determine the feature extraction method, M and N represent the height and width of the convolution kernel, and i and j represent the position index in the current feature map.

4. A patient health data management system based on edge computing according to claim 3, characterized in that: The model prediction uses a deep learning model to make predictions and calculates the mean square error loss to evaluate the model performance. The specific formula is as follows: Where L represents the loss function, y represents the true value vector, that is, the actual health data of each sample, represents the model prediction value vector, n represents the number of samples, They represent the true value of the ith sample and the predicted value of the ith sample respectively.

5. A patient health data management system based on edge computing according to claim 1, characterized in that: The data storage and encryption module stores the processed data locally and ensures the security and integrity of the data. The data storage and encryption module includes an encryption storage unit and an integrity verification unit. The encryption storage unit uses the advanced encryption standard to encrypt the stored data to protect the privacy of the patient. The integrity verification unit uses a hash function to perform integrity verification on the stored data to ensure that the data has not been tampered with: The data storage and encryption module is specifically analyzed as follows: The formula of the encryption storage unit is as follows: C Ciphertext =E(K,P) Among them C Ciphertext =E(K,P) represents ciphertext, E represents encryption algorithm, K represents key, and P represents unencrypted original data; The integrity verification formula is as follows: H(x) Where H represents the hash function and x represents the input data.

6. A patient health data management system based on edge computing according to claim 1, characterized in that: The communication module is responsible for data transmission between the cloud server and other edge computing nodes.

7. A patient health data management system based on edge computing according to claim 1, characterized in that: The data transmission module transmits data from other edge computing nodes to the cloud server through the hub function of the communication module. The data transmission module dynamically adjusts the data transmission parameters according to the network conditions and calculates the transmission efficiency using the channel capacity formula. The specific formula is as follows: Where C represents channel capacity, B represents bandwidth, P represents signal power, and N represents noise power.

8. The patient health data management system based on edge computing according to claim 1, characterized in that: The cloud server includes a storage unit and a data analysis and feedback unit. The storage unit provides a large-scale storage space to store patient health data transmitted from multiple edge computing nodes through a data transmission module. The data analysis and feedback unit performs centralized analysis on data from multiple edge computing nodes, uses machine learning to predict trends, feeds back information to the edge computing nodes based on the analysis results, and adjusts and optimizes data processing algorithms and model parameters to improve the overall performance of the system.

9. The patient health data management system based on edge computing according to claim 1, characterized in that: The system optimization module continuously optimizes the data processing and analysis models through a feedback mechanism to ensure that the system has adaptive learning capabilities, while supporting the access and data integration of multiple medical devices to ensure the flexibility and applicability of the system.

10. A patient health data management method based on edge computing, used to implement a patient health data management system based on edge computing as described in any one of claims 1 to 9, comprising the following steps: Step 1: Medical equipment transmits patient health data to the edge computing node in real time through the data acquisition module; Step 2: The data acquisition module sends the data to the data processing module, uses deep learning algorithms to analyze data features, and applies various formulas for processing and optimization; Step 3: The processed data is encrypted and stored through the data storage and encryption module, and integrity verification is performed regularly; Step 4: The edge computing node transmits the required data to the cloud server, and the data transmission module is responsible for dynamically adjusting the transmission strategy according to network conditions; Step 5: The cloud server analyzes and mines data from multiple sources and outputs feedback information to further optimize the processing algorithms and model parameters of the edge computing nodes.

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