Peritoneal dialysis data management system and method based on cloud

Abstract

The invention belongs to the technical field of data management, and discloses a cloud-based peritoneal dialysis data management system and method. The system comprises a cloud data center and a plurality of edge computing gateways, the plurality of edge computing gateways are all in communication connection with the cloud data center, and the cloud data center is in communication connection with a plurality of external query terminals. The method comprises the following steps: receiving a peritoneal dialysis file uploaded by a data acquisition device; performing data analysis on the peritoneal dialysis file to obtain initial peritoneal dialysis data; performing data extraction on the initial peritoneal dialysis data to obtain formatted peritoneal dialysis data; encrypting the formatted peritoneal dialysis data to obtain first encrypted peritoneal dialysis data, and uploading the first encrypted peritoneal dialysis data to a cloud data center for cloud storage. According to the invention, the problems of low data processing efficiency, poor accuracy, large labor cost input, poor data circulation, low data value, difficulty in local storage, large hardware cost input and low data security in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data management, and in particular relates to a cloud-based peritoneal dialysis data management system and method. Background Art

[0002] With the rapid development of computer and communication technology, the foundation for the digital transmission of peritoneal dialysis reports has been laid, and the realization of digital medical systems has become an unstoppable trend in modern medicine. With the advancement of digitization and informatization in the medical field, the management and storage of massive data has become a top priority.

[0003] In the existing technology, due to the constraints of regional imbalance in the development of medical modernization and the short time for the promotion of digital technology, the peritoneal dialysis data of various regions, hospitals and even departments are often managed manually, with low data processing efficiency, poor accuracy and high labor costs. Then, there is also the problem that the data formats of peritoneal dialysis data vary greatly and cannot be managed and used in a unified manner, resulting in poor circulation of peritoneal dialysis data and low data value. Finally, based on the huge medical system, the local storage of the massive peritoneal dialysis data generated is difficult, the hardware cost investment is large, there is a risk of leakage, and the data security is low. Summary of the invention

[0004] In order to solve the problems of low data processing efficiency, poor accuracy, high manpower cost, poor data circulation, low data value, difficulty in local storage, high hardware cost and low data security in the prior art, the present invention aims to provide a cloud-based peritoneal dialysis data management system and method.

[0005] The technical solution adopted by the present invention is:

[0006] A cloud-based peritoneal dialysis data management system includes a cloud data center, several edge computing gateways and several data acquisition devices. The cloud data center is respectively communicated with the edge computing gateways in several data management areas, and the cloud data center is communicated with several external query terminals. Each edge computing gateway is respectively communicated with the several data acquisition devices in the data management area.

[0007] Furthermore, the edge computing gateway includes a file receiving port, a data parsing module, a data extraction module, an encryption processing module, a data upload module and a storage module. The storage module is respectively connected to the file receiving port, the data parsing module, the data extraction module, the encryption processing module and the data upload module, and the data upload module is communicatively connected to the cloud data center.

[0008] Furthermore, the cloud data center includes a data management allocation server, a data query allocation server, several functional servers and several data servers. The data management allocation server and the data query allocation server are respectively connected to several functional servers, and the data management allocation server is respectively communicated with several edge computing gateways, the data query allocation server is respectively communicated with several query terminals, each functional server is respectively connected to several data servers, and distributed connections are adopted between several data servers.

[0009] Furthermore, the cloud data center is also connected to a trusted institution.

[0010] A cloud-based peritoneal dialysis data management method is applied to a peritoneal dialysis data management system. The system includes a cloud data center, several edge computing gateways, and several data acquisition devices. The cloud data center is respectively connected to the edge computing gateways in several data management areas, and the cloud data center is connected to several external query terminals. Each edge computing gateway is respectively connected to the data acquisition devices in the data management area. The method includes the following steps:

[0011] Using a data collection device, collecting the user's peritoneal dialysis file, and transmitting the peritoneal dialysis file to an edge computing gateway in a data management area;

[0012] Using the edge computing gateway, the peritoneal dialysis file is parsed, extracted, and encrypted to obtain the first encrypted peritoneal dialysis data, which is then uploaded to the cloud data center.

[0013] Using a cloud data center, decrypting and analyzing the first encrypted peritoneal dialysis data to obtain the first decrypted peritoneal dialysis data and a first data analysis result, and performing distributed storage;

[0014] Using the cloud data center, the corresponding second decrypted peritoneal dialysis data and the second data analysis result are extracted according to the query data uploaded by the query terminal, and returned to the corresponding query terminal.

[0015] Furthermore, using the edge computing gateway, the peritoneal dialysis file is parsed, extracted and encrypted to obtain the first encrypted peritoneal dialysis data, and uploaded to the cloud data center, including the following steps:

[0016] Using the edge computing gateway, receiving the peritoneal dialysis file sent by the data acquisition device, and performing data analysis on the peritoneal dialysis file to obtain initial peritoneal dialysis data;

[0017] Data extraction was performed on the initial peritoneal dialysis data to obtain formatted peritoneal dialysis data;

[0018] Encrypting the formatted peritoneal dialysis data to obtain first encrypted peritoneal dialysis data;

[0019] The first encrypted peritoneal dialysis data is uploaded to the cloud data center.

[0020] Further, using a cloud data center, decrypting and analyzing the first encrypted peritoneal dialysis data to obtain the first decrypted peritoneal dialysis data and the first data analysis result, and performing distributed storage, includes the following steps:

[0021] Receiving first encrypted post-peritoneal dialysis data uploaded by the edge computing gateway, decrypting the first encrypted post-peritoneal dialysis data, and obtaining first decrypted post-peritoneal dialysis data;

[0022] Using the data analysis model, performing data analysis on the first decrypted peritoneal dialysis data to obtain a first data analysis result;

[0023] Using the blockchain network, the first decrypted peritoneal dialysis data and the first data analysis result are distributedly stored, and the blockchain address and the first data analysis result are recorded in a distributed ledger.

[0024] Furthermore, the data analysis model is constructed based on the RF-Attention-MLP algorithm, and the data analysis model includes a key feature extraction module constructed based on the RF algorithm, an attention weight module constructed based on the Attention mechanism, and a data analysis module constructed based on the MLP algorithm, which are connected in sequence.

[0025] Further, using a blockchain network, distributively storing the first decrypted peritoneal dialysis data and the first data analysis result, and recording the blockchain address and the first data analysis result in a distributed ledger, comprises the following steps:

[0026] Assign identities to several data nodes in the blockchain network to obtain a storage layer consisting of several storage nodes and a consensus layer consisting of several consensus nodes;

[0027] According to the replica mechanism, data sharding is performed on the first decrypted peritoneal dialysis data to obtain a plurality of data shards including the replica shards;

[0028] According to the distributed storage rules, several data shards are sent one by one to several storage nodes of the storage layer of the blockchain network;

[0029] Use the storage node to locally store the received data shards and obtain the corresponding blockchain address;

[0030] Generate corresponding transaction data based on blockchain address and timestamp, and send the transaction data to the consensus layer;

[0031] The consensus node of the consensus layer that receives the transaction data is used as the master node, and the legitimacy of the transaction data is verified based on the master node;

[0032] After the legality verification is passed, the consensus algorithm is used to reach a consensus on the transaction data. After the consensus is passed, the real-time transaction data is converted into data blocks and linked to the chain;

[0033] The blockchain address and the first data analysis result are recorded in the distributed ledger, and the distributed ledger copies of all data nodes are updated according to the distributed ledger.

[0034] Further, using the cloud data center, according to the query data uploaded by the query terminal, extracting the corresponding second decrypted peritoneal dialysis data and the second data analysis result, and returning them to the corresponding query terminal, comprises the following steps:

[0035] Using the cloud data center, receiving the query data uploaded by the query user of the query terminal;

[0036] According to the query data, searching and matching is performed in the distributed ledger to obtain the second blockchain address and the second data analysis result of the corresponding second decrypted peritoneal dialysis data;

[0037] According to the second blockchain address, extracting the corresponding second decrypted peritoneal dialysis data in the blockchain network;

[0038] The second decrypted peritoneal dialysis data is encrypted to obtain second encrypted peritoneal dialysis data, and the second encrypted peritoneal dialysis data and the second data analysis result are returned to the corresponding query terminal.

[0039] The beneficial effects of the present invention are:

[0040] The present invention provides a cloud-based peritoneal dialysis data management system and method, which uses an edge computing gateway to automatically process local peritoneal dialysis files, avoids the use of manual methods, improves the processing efficiency and accuracy of peritoneal dialysis data, and reduces human cost investment. By performing data analysis and data extraction on peritoneal dialysis files, it can adapt to peritoneal dialysis data of different data types and different data formats, improves the practicality of the system, obtains formatted peritoneal dialysis data with a unified data format, improves the circulation and data value of peritoneal dialysis data, and provides a data basis for the regional development balance and digital technology promotion of the medical system. A cloud data center is used to provide cloud storage, which facilitates the unified management of peritoneal dialysis data, avoids the problem of difficulty in storing massive peritoneal dialysis data locally, reduces hardware cost investment, and uses cloud data centers and blockchain technology to ensure data security and transparency, reduce the risk of data leakage, and through the distributed storage mechanism of blockchain, not only the reliability of data storage is improved, but also the data management efficiency is optimized. The cloud data center also provides a peritoneal dialysis data query function, improves the functionality of the system and method, and improves the user experience.

[0041] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a structural block diagram of the cloud-based peritoneal dialysis data management system in the present invention.

[0043] Figure 2 It is a flowchart of the cloud-based peritoneal dialysis data management method of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0045] Embodiment 1:

[0046] like Figure 1 As shown, this embodiment provides a cloud-based peritoneal dialysis data management system, including a cloud data center, a plurality of edge computing gateways and a plurality of data acquisition devices, the cloud data center is respectively connected to the edge computing gateways in the plurality of data management areas, and the cloud data center is connected to the plurality of external query terminals, and each edge computing gateway is respectively connected to the plurality of data acquisition devices in the data management area;

[0047] A data collection device, used to collect the user's peritoneal dialysis files and transmit the peritoneal dialysis files to the edge computing gateway in the data management area;

[0048] The edge computing gateway is used to receive the peritoneal dialysis file uploaded by the data acquisition device, perform data analysis, data extraction and encryption processing on the peritoneal dialysis file, obtain the first encrypted peritoneal dialysis data, and upload it to the cloud data center;

[0049] The cloud data center is used to decrypt and analyze the first encrypted peritoneal dialysis data, obtain the first decrypted peritoneal dialysis data and the first data analysis result, and perform distributed storage; according to the query data uploaded by the query terminal, extract the corresponding second decrypted peritoneal dialysis data and the second data analysis result, and return them to the corresponding query terminal.

[0050] Preferably, the edge computing gateway includes a file receiving port, a data parsing module, a data extraction module, an encryption processing module, a data upload module and a storage module, the storage module is respectively connected to the file receiving port, the data parsing module, the data extraction module, the encryption processing module and the data upload module, and the data upload module is communicatively connected to the cloud data center;

[0051] A file receiving port, used to receive the peritoneal dialysis file uploaded by the data acquisition device, and cache the peritoneal dialysis file to the storage module;

[0052] A data analysis module, used for extracting the peritoneal dialysis files cached by the storage module, performing data analysis on the peritoneal dialysis files to obtain initial peritoneal dialysis data, and caching the initial peritoneal dialysis data to the storage module;

[0053] A data extraction module, used for extracting the initial peritoneal dialysis data cached by the storage module, performing data extraction on the initial peritoneal dialysis data to obtain formatted peritoneal dialysis data, and caching the formatted peritoneal dialysis data to the storage module;

[0054] an encryption processing module, used for extracting the formatted peritoneal dialysis data cached by the storage module, encrypting the formatted peritoneal dialysis data to obtain first encrypted peritoneal dialysis data, and storing the first encrypted peritoneal dialysis data in the storage module;

[0055] A data uploading module, used for extracting the first encrypted post-peritoneal dialysis data stored in the storage module, and uploading the first encrypted post-peritoneal dialysis data to a cloud data center for cloud storage;

[0056] The storage module is used to cache the peritoneal dialysis files received by the file receiving port, the initial peritoneal dialysis data obtained by the data parsing module, and the formatted peritoneal dialysis data extracted by the data extraction module, and after the cache period is reached, clear the cached peritoneal dialysis files, initial peritoneal dialysis data, and formatted peritoneal dialysis data, and store the first encrypted peritoneal dialysis data generated by the encryption processing module.

[0057] Preferably, the cloud data center includes a data management allocation server, a data query allocation server, several functional servers and several data servers, the data management allocation server and the data query allocation server are respectively connected to several functional servers, and the data management allocation server is respectively communicated with several edge computing gateways, the data query allocation server is respectively communicated with several query terminals, each functional server is respectively connected to several data servers, and a distributed connection is adopted between several data servers;

[0058] a data management allocation server, configured to receive the first encrypted post-peritoneal dialysis data uploaded by the edge computing gateway, decrypt the first encrypted post-peritoneal dialysis data to obtain first decrypted post-peritoneal dialysis data, perform data analysis on the first decrypted post-peritoneal dialysis data to obtain a first data analysis result, schedule any functional server as a data management server, and send the first decrypted post-peritoneal dialysis data and the first data analysis result to the data management server;

[0059] The data query allocation server is used to receive the query data uploaded by the query user of the query terminal, schedule any functional server as the data query server, and send the query data to the data query server; and receive the second decrypted post-peritoneal dialysis data and the second data analysis result returned by the data query server, encrypt the second decrypted post-peritoneal dialysis data to obtain the second encrypted post-peritoneal dialysis data, and return the second encrypted post-peritoneal dialysis data and the second data analysis result to the corresponding query terminal;

[0060] The functional server is configured to accept the scheduling of the data management allocation server, be converted into a data management server, receive the first decrypted post-peritoneal dialysis data and the first data analysis result as the data management server, and store them in the blockchain network; and accept the scheduling of the data query allocation server, be converted into a data query server, receive the query data as the data query server, extract the corresponding second decrypted post-peritoneal dialysis data and the second data analysis result in the blockchain network according to the query data, and return them to the corresponding data query allocation server;

[0061] The data server is used to serve as a data node, to form a blockchain network with other distributedly connected data servers, to perform distributed storage on the first encrypted peritoneal dialysis data and the first data analysis result sent by the data management server, and to record the corresponding first blockchain address and the first data analysis result in the distributed ledger.

[0062] Preferably, the cloud data center is also connected to a trusted institution;

[0063] The trusted institution is used to generate keys according to the attribute information of the edge computing gateway, obtain a public-private key pair, send the private key in the public-private key pair to the corresponding edge computing gateway, and send the public key in the public-private key pair to the cloud data center.

[0064] Embodiment 2:

[0065] like Figure 2 As shown, this embodiment provides a cloud-based peritoneal dialysis data management method, which is applied to a peritoneal dialysis data management system. The system includes a cloud data center, a plurality of edge computing gateways, and a plurality of data acquisition devices. The cloud data center is respectively connected to the edge computing gateways in the data management area for communication, and the cloud data center is connected to a plurality of external query terminals for communication. Each edge computing gateway is respectively connected to the plurality of data acquisition devices in the data management area for communication. The method includes the following steps:

[0066] S1: Use a data acquisition device to collect the user's peritoneal dialysis files, and transmit the peritoneal dialysis files to the edge computing gateway in the data management area;

[0067] Peritoneal dialysis files include basic patient information, dialysis prescription information, laboratory test data, dialysis adequacy assessment results, and other data;

[0068] S2: Using the edge computing gateway, performing data analysis, data extraction, and encryption processing on the peritoneal dialysis file to obtain the first encrypted peritoneal dialysis data, and uploading it to the cloud data center, including the following steps:

[0069] S2-1: Using the edge computing gateway to receive the peritoneal dialysis file sent by the data acquisition device, and performing data analysis on the peritoneal dialysis file to obtain initial peritoneal dialysis data, including the following steps:

[0070] S2-1-1: decompressing the initial peritoneal dialysis file to obtain a decompressed peritoneal dialysis file;

[0071] S2-1-2: Obtain the suffix name of each initial peritoneal dialysis data in the decompressed peritoneal dialysis file;

[0072] S2-1-3: performing a data analysis on the decompressed peritoneal dialysis file according to the suffix name to obtain initial peritoneal dialysis data of different data types; the data types of the initial peritoneal dialysis data include structured data types and unstructured data types;

[0073] S2-1-4: performing secondary data analysis on initial peritoneal dialysis data of different data types to obtain initial peritoneal dialysis data in different data formats;

[0074] S2-1-5: The data formats of initial peritoneal dialysis data include information retrieval format, database format, and web page format for structured data types, and text format, image format, and video format for unstructured data types;

[0075] S3-1: extracting the initial peritoneal dialysis data to obtain formatted peritoneal dialysis data, including the following steps:

[0076] S3-1-1: Extract the initial peritoneal dialysis data of the structured data type using the data warehouse technology (Extract-Load-Transform, ETL) tool to obtain the first peritoneal dialysis data;

[0077] S3-1-2: Use the data extraction model to extract the initial peritoneal dialysis data of the unstructured data type to obtain the second peritoneal dialysis data;

[0078] The data extraction model is built based on deep learning algorithms. For example, for data extraction of text data and image data, the data extraction model is built based on the Convolutional Neural Network (CNN) algorithm, which can realize the extraction of most unstructured data.

[0079] S3-1-3: performing data conversion on the first peritoneal dialysis data and the second peritoneal dialysis data to obtain formatted peritoneal dialysis data with a unified data format;

[0080] S3-1-3: extracting the initial peritoneal dialysis data in text format from the unstructured data type using a text data extraction model to obtain the second peritoneal dialysis data corresponding to the initial peritoneal dialysis data in text format;

[0081] S4-1: encrypting the formatted peritoneal dialysis data to obtain first encrypted peritoneal dialysis data;

[0082] S5-1: uploading the first encrypted peritoneal dialysis data to the cloud data center;

[0083] The formula is:

[0084] M u =E(SK u ,m u )

[0085] Where M u is the first encrypted peritoneal dialysis data; E(*) is the asymmetric encryption function; m u To format peritoneal dialysis data; SK u is the encryption key of the edge computing gateway u;

[0086] S3: Using a cloud data center, decrypting and analyzing the first encrypted peritoneal dialysis data to obtain the first decrypted peritoneal dialysis data and the first data analysis result, and performing distributed storage, including the following steps:

[0087] S3-1: receiving first encrypted post-peritoneal dialysis data uploaded by the edge computing gateway, decrypting the first encrypted post-peritoneal dialysis data, and obtaining first decrypted post-peritoneal dialysis data;

[0088] The formula is:

[0089] m' u =E - (PK u ,M u )

[0090] In the formula, m u For the first decryption of peritoneal dialysis data; E - (*) is an asymmetric decryption function; PK u is the decryption key of the edge computing gateway u;

[0091] S3-2: using the data analysis model, performing data analysis on the first decrypted peritoneal dialysis data to obtain a first data analysis result;

[0092] The data analysis model is built based on the Random Forest (RF)-Attention-Multilayer Perceptron (MLP) algorithm, and the data analysis model includes a key feature extraction module built based on the RF algorithm, an attention weight module built based on the Attention mechanism, and a data analysis module built based on the MLP algorithm, which are connected in sequence;

[0093] The key feature extraction module uses the internal Classification And Regression Tree (CART) to screen the key features of the input data, extract several key features related to the data prediction label, and predict the label based on several key features; the attention weight module splices several key features through the preset attention weight value to improve the influence of some important key features; the data analysis module analyzes the spliced ​​features and outputs the predicted label;

[0094] S3-3: Using a blockchain network, distributively storing the first decrypted peritoneal dialysis data and the first data analysis result, and recording the blockchain address and the first data analysis result in a distributed ledger, including the following steps:

[0095] S3-3-1: Assign identities to several data nodes in the blockchain network to obtain a storage layer consisting of several storage nodes and a consensus layer consisting of several consensus nodes, including the following steps:

[0096] S3-3-1-1: Collect the consensus honor value of each data node in the cloud data center, divide the data nodes with the highest consensus honor value into consensus nodes, and build a consensus layer based on the consensus nodes;

[0097] The formula is:

[0098]

[0099] In the formula, C i is the consensus reputation value of data node i; i is the data node indicator; is the initial reputation value; α1 is the weight value of the consensus verification index; R(i) is the consensus verification index; α2 is the weight value of the storage resource index; P(i) is the storage resource index; I is the total number of data nodes;

[0100]

[0101] In the formula, ε is the number of times data node i successfully participates in consensus; e is a natural constant;

[0102]

[0103] Where B is the maximum value of the storage resource index greater than 1; P max is the storage resource rating of data node i; P v is the storage resource index threshold of data node i; the lower the storage resource index, the worse the storage performance, the higher the reputation value, and the easier it is for the data node to be selected as a consensus node;

[0104] S3-3-1-2: Screen the data nodes except the consensus layer, remove the data nodes whose storage resource index is lower than the storage resource index threshold, divide the screened data nodes into storage nodes, and build a storage layer based on the storage nodes;

[0105] S3-3-2: According to the replica mechanism, the first decrypted peritoneal dialysis data is fragmented to obtain a plurality of data fragments including the replica fragment;

[0106] S3-3-3: According to the distributed storage rules, several data shards are sent one by one to several storage nodes of the storage layer of the blockchain network;

[0107] S3-3-4: Use the storage node to locally store the received data shards and obtain the corresponding blockchain address;

[0108] S3-3-5: Generate corresponding transaction data based on blockchain address and timestamp, and send the transaction data to the consensus layer;

[0109] S3-3-6: The consensus node of the consensus layer that receives the transaction data is used as the master node, and the legitimacy of the transaction data is verified based on the master node;

[0110] S3-3-7: After the legitimacy verification is passed, the consensus algorithm is used to reach consensus on the transaction data. After the consensus is passed, the real-time transaction data is converted into data blocks and linked to the chain;

[0111] S3-3-8: Record the blockchain address and the first data analysis result in the distributed ledger, and update the distributed ledger copies of all data nodes according to the distributed ledger;

[0112] S4: using the cloud data center, extracting the corresponding second decrypted post-peritoneal dialysis data and the second data analysis result according to the query data uploaded by the query terminal, and returning them to the corresponding query terminal, including the following steps:

[0113] S4-1: using the cloud data center to receive query data uploaded by the query user of the query terminal;

[0114] S4-2: According to the query data, searching and matching is performed in the distributed ledger to obtain the second blockchain address and the second data analysis result of the corresponding second decrypted peritoneal dialysis data;

[0115] S4-3: extracting corresponding second decrypted peritoneal dialysis data in the blockchain network according to the second blockchain address;

[0116] S4-4: encrypting the second decrypted peritoneal dialysis data to obtain second encrypted peritoneal dialysis data, and returning the second encrypted peritoneal dialysis data and the second data analysis result to the corresponding query terminal;

[0117] The formula is:

[0118] M' u =E(SK u ,m" u )

[0119] In the formula, M' u is the second encrypted peritoneal dialysis data; E(*) is the asymmetric encryption function; m" u For the second decryption of peritoneal dialysis data; SK u is the encryption key of edge computing gateway u.

[0120] The present invention provides a cloud-based peritoneal dialysis data management system and method, which uses an edge computing gateway to automatically process local peritoneal dialysis files, avoids the use of manual methods, improves the processing efficiency and accuracy of peritoneal dialysis data, and reduces human cost investment. By performing data analysis and data extraction on peritoneal dialysis files, it can adapt to peritoneal dialysis data of different data types and different data formats, improves the practicality of the system, obtains formatted peritoneal dialysis data with a unified data format, improves the circulation and data value of peritoneal dialysis data, and provides a data basis for the regional development balance and digital technology promotion of the medical system. A cloud data center is used to provide cloud storage, which facilitates the unified management of peritoneal dialysis data, avoids the problem of difficulty in storing massive peritoneal dialysis data locally, reduces hardware cost investment, and uses cloud data centers and blockchain technology to ensure data security and transparency, reduce the risk of data leakage, and through the distributed storage mechanism of blockchain, not only the reliability of data storage is improved, but also the data management efficiency is optimized. The cloud data center also provides a peritoneal dialysis data query function, improves the functionality of the system and method, and improves the user experience.

[0121] The present invention is not limited to the above optional implementations, and anyone can derive other various forms of products under the enlightenment of the present invention. The above specific implementations should not be understood as limiting the scope of protection of the present invention. The scope of protection of the present invention should be based on the definition in the claims, and the description can be used to interpret the claims.

Claims

1. A cloud-based peritoneal dialysis data management system, characterized by: It includes a cloud data center, several edge computing gateways and several data acquisition devices. The cloud data center is respectively communicated with the edge computing gateways in several data management areas, and the cloud data center is communicated with several external query terminals. Each of the edge computing gateways is respectively communicated with the several data acquisition devices in the data management area.

2. A cloud-based peritoneal dialysis data management system according to claim 1, characterized in that: The edge computing gateway includes a file receiving port, a data parsing module, a data extraction module, an encryption processing module, a data upload module and a storage module. The storage module is respectively connected to the file receiving port, the data parsing module, the data extraction module, the encryption processing module and the data upload module, and the data upload module is communicatively connected to the cloud data center.

3. The cloud-based peritoneal dialysis data management system according to claim 1, characterized in that: The cloud data center includes a data management allocation server, a data query allocation server, several functional servers and several data servers. The data management allocation server and the data query allocation server are respectively connected to several functional servers, and the data management allocation server is respectively communicated with several edge computing gateways, and the data query allocation server is respectively communicated with several query terminals. Each of the functional servers is respectively connected to several data servers, and distributed connections are adopted between the several data servers.

4. A cloud-based peritoneal dialysis data management system according to claim 3, characterized in that: The cloud data center is also connected to a trusted institution.

5. A cloud-based peritoneal dialysis data management method, applied to the peritoneal dialysis data management system according to any one of claims 1 to 4, wherein the system comprises a cloud data center, a plurality of edge computing gateways and a plurality of data acquisition devices, wherein the cloud data center is respectively connected to the edge computing gateways in the plurality of data management areas in communication, and the cloud data center is connected to a plurality of external query terminals in communication, and each of the edge computing gateways is respectively connected to the plurality of data acquisition devices in the data management area in communication, characterized in that: The method comprises the following steps: Using a data collection device, collecting the user's peritoneal dialysis file, and transmitting the peritoneal dialysis file to an edge computing gateway in a data management area; Using the edge computing gateway, the peritoneal dialysis file is parsed, extracted, and encrypted to obtain the first encrypted peritoneal dialysis data, which is then uploaded to the cloud data center. Using a cloud data center, decrypting and analyzing the first encrypted peritoneal dialysis data to obtain the first decrypted peritoneal dialysis data and a first data analysis result, and performing distributed storage; Using the cloud data center, the corresponding second decrypted peritoneal dialysis data and the second data analysis result are extracted according to the query data uploaded by the query terminal, and returned to the corresponding query terminal.

6. A cloud-based peritoneal dialysis data management method according to claim 5, characterized in that: Using the edge computing gateway, the peritoneal dialysis file is parsed, extracted, and encrypted to obtain the first encrypted peritoneal dialysis data, which is then uploaded to the cloud data center, including the following steps: Using the edge computing gateway, receiving the peritoneal dialysis file sent by the data acquisition device, and performing data analysis on the peritoneal dialysis file to obtain initial peritoneal dialysis data; Data extraction was performed on the initial peritoneal dialysis data to obtain formatted peritoneal dialysis data; Encrypting the formatted peritoneal dialysis data to obtain first encrypted peritoneal dialysis data; The first encrypted peritoneal dialysis data is uploaded to the cloud data center.

7. The cloud-based peritoneal dialysis data management method according to claim 5, characterized in that: Using a cloud data center, decrypting and analyzing the first encrypted peritoneal dialysis data to obtain the first decrypted peritoneal dialysis data and the first data analysis result, and performing distributed storage, includes the following steps: Receiving first encrypted post-peritoneal dialysis data uploaded by the edge computing gateway, decrypting the first encrypted post-peritoneal dialysis data, and obtaining first decrypted post-peritoneal dialysis data; Using the data analysis model, performing data analysis on the first decrypted peritoneal dialysis data to obtain a first data analysis result; Using the blockchain network, the first decrypted peritoneal dialysis data and the first data analysis result are distributedly stored, and the blockchain address and the first data analysis result are recorded in a distributed ledger.

8. The cloud-based peritoneal dialysis data management method according to claim 7, characterized in that: The data analysis model is constructed based on the RF-Attention-MLP algorithm, and the data analysis model includes a key feature extraction module constructed based on the RF algorithm, an attention weight module constructed based on the Attention mechanism, and a data analysis module constructed based on the MLP algorithm, which are connected in sequence.

9. The cloud-based peritoneal dialysis data management method according to claim 7, characterized in that: Using a blockchain network, distributively storing the first decrypted peritoneal dialysis data and the first data analysis result, and recording the blockchain address and the first data analysis result in a distributed ledger, including the following steps: Assign identities to several data nodes in the blockchain network to obtain a storage layer consisting of several storage nodes and a consensus layer consisting of several consensus nodes; According to the replica mechanism, data sharding is performed on the first decrypted peritoneal dialysis data to obtain a plurality of data shards including the replica shards; According to the distributed storage rules, several data shards are sent one by one to several storage nodes of the storage layer of the blockchain network; Use the storage node to locally store the received data shards and obtain the corresponding blockchain address; Generate corresponding transaction data based on blockchain address and timestamp, and send the transaction data to the consensus layer; The consensus node of the consensus layer that receives the transaction data is used as the master node, and the legitimacy of the transaction data is verified based on the master node; After the legality verification is passed, the consensus algorithm is used to reach a consensus on the transaction data. After the consensus is passed, the real-time transaction data is converted into data blocks and linked to the chain; The blockchain address and the first data analysis result are recorded in the distributed ledger, and the distributed ledger copies of all data nodes are updated according to the distributed ledger.

10. The cloud-based peritoneal dialysis data management method according to claim 7, characterized in that: Using a cloud data center, according to the query data uploaded by the query terminal, extracting the corresponding second decrypted post-peritoneal dialysis data and the second data analysis result, and returning them to the corresponding query terminal, comprises the following steps: Using the cloud data center, receiving the query data uploaded by the query user of the query terminal; According to the query data, searching and matching is performed in the distributed ledger to obtain the second blockchain address and the second data analysis result of the corresponding second decrypted peritoneal dialysis data; According to the second blockchain address, extracting the corresponding second decrypted peritoneal dialysis data in the blockchain network; The second decrypted peritoneal dialysis data is encrypted to obtain second encrypted peritoneal dialysis data, and the second encrypted peritoneal dialysis data and the second data analysis result are returned to the corresponding query terminal.

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