Medical data processing method and system based on edge computing and blockchain

Abstract

The application provides a medical data processing method and system based on edge computing and block chain, which mainly comprises the following steps: data collection, collecting the data of different patients; feature extraction, identifying the features related to the patient state from the obtained data; patient state monitoring, detecting the main changes of the patient state by using the identified features, determining and sharing the data with the block chain network; the data sender uploads the data to the nearby block chain manager in the form of transaction; the block chain manager assigns different priorities and channels to the collected transactions according to the urgency of the transaction; the block chain manager distributes the unverified blocks to the selected validators as the manager of the validators; the validators verify, trigger the consensus process between the validators, and insert the verified blocks into the block chain. The application can effectively realize large-scale medical data processing, reduce network load and quickly respond to emergency events.

Description

Technical Field

[0001] This invention relates to a medical data processing method and system based on edge computing and blockchain, belonging to the field of edge computing and blockchain technology. Background Technology

[0002] With the continuous development of the healthcare field and the acceleration of digitalization, efficient medical data processing across different entities has become a global focus. Modern healthcare involves data covering all aspects, from patient health status to medical records, diagnosis, treatment, and prognosis. This data originates from various entities such as hospitals, clinics, laboratories, and insurance companies, making efficient medical data processing across these entities crucial. Automating most healthcare functions and providing efficient healthcare services can make a key contribution to the development of smart healthcare. Emerging technologies such as edge computing and blockchain can turn this vision into reality. These technologies can effectively collect, process, and exchange medical data.

[0003] However, the sheer volume of information at the edge presents a significant challenge in reducing network load and substantially decreasing the amount of information that needs to be shared on the blockchain. Secondly, in emergency situations, how to initiate rapid alerts and notifications to facilitate effective analysis and avoid wasting doctors' time is also crucial.

[0004] In view of this, it is indeed necessary to propose a medical data processing method and system based on edge computing and blockchain to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a medical data processing method and system based on edge computing and blockchain, which can improve the efficiency of large-scale medical data processing.

[0006] To achieve the above objectives, this invention provides a medical data processing method based on edge computing and blockchain, mainly comprising the following steps:

[0007] S1. Data collection: Collecting information from different patients;

[0008] S2. Feature extraction: Identify features related to the patient's condition from the acquired data;

[0009] S3. Patient status monitoring: Detects major changes in patient status using identified features and identifies data to be shared with the blockchain network.

[0010] S4. The data sender uploads the data to a nearby blockchain manager in the form of a transaction;

[0011] S5: The blockchain manager assigns different priorities and channels to the collected transactions based on their urgency.

[0012] S6. The blockchain manager, acting as the manager of the validators, distributes unverified blocks to the selected validators.

[0013] S7. The validator performs verification, triggers the consensus process among validators, and inserts the verified block into the blockchain.

[0014] As a further improvement of the present invention, in S1, the patient's data includes the patient's electroencephalogram (EEG), body temperature, and blood pressure.

[0015] As a further improvement of the present invention, in S2, the maximum value of each channel of the collected patient data is... Minimum value average value variance Root mean square R i and kurtosis K i As a feature, let the number of EEG channels be n (i∈{1,2,…,n}) and the number of samples be M (m∈{1,2,…,M}), where

[0016] average value:

[0017] variance:

[0018] Root mean square:

[0019] kurtosis:

[0020] As a further improvement of the present invention, in S3, the index θ is defined. i To obtain clear classification rules:

[0021]

[0022] As a further improvement to the present invention, the quantitative index β is defined as {β1,β2,…,β}. i ,…,β n},

[0023]

[0024] in,

[0025] As a further improvement of the present invention, α is defined as the threshold for evaluating the main changes, and γ is defined as the final result state.

[0026]

[0027] Among them, [α] +=max(0,α), ‖p‖0 represents the 0 norm.

[0028] As a further improvement of the present invention, in S3, when γ = (1), that is, when a significant change is detected, the blockchain network will share an emergency notification and the original data that needs further investigation through the blockchain; when γ = (2), that is, when a slight or no change is detected, the blockchain network will only share the obtained features.

[0029] To achieve the above objectives, the present invention also provides a medical data processing system based on edge computing and blockchain, which applies the medical data processing method based on edge computing and blockchain as described above.

[0030] As a further improvement of the present invention, the medical data processing system based on edge computing and blockchain includes a local network and a blockchain network, wherein the local network includes: IoT devices connected to the patient, professional public health institutions, hospitals, primary healthcare institutions, other healthcare institutions, and IoT devices connected to the patient within the local network.

[0031] As a further improvement of the present invention, the blockchain network includes: a data transmitter, a blockchain manager, a validator, and a multi-channel blockchain, wherein the multi-channel blockchain includes a first channel, a second channel, and a third channel, wherein the first channel is used for urgent data, the second channel is used for non-urgent data that requires a high level of security, and the third channel is used for normal data;

[0032] Define α as the threshold for evaluating the main changes, and define γ as the final result state.

[0033]

[0034] Among them, [α] + =max(0,α),‖p‖0 represents the 0 norm, β is the quantitative indicator. When γ=(1), that is, when a major change is detected, the data will go through the first channel. When γ=(2), that is, when a slight or no change is detected, if the data is high-level data, it will go through the second channel; otherwise, it will go through the third channel.

[0035] The beneficial effects of this invention are: it can effectively realize large-scale medical data processing, reduce network load, and quickly respond to emergencies. Attached Figure Description

[0036] Figure 1 This is a system architecture diagram of the medical data processing system based on edge computing and blockchain according to the present invention.

[0037] Figure 2This is a flowchart illustrating the medical data processing method based on edge computing and blockchain of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] It should be noted that, in order to avoid obscuring the present invention with unnecessary details, only the structures and / or processing steps closely related to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0040] Additionally, it should be noted that the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0041] like Figure 1 and Figure 2 As shown, this invention discloses a medical data processing method and system based on edge computing and blockchain. The medical data processing system consists of various electronic medical entities, and its basic function is to monitor, promote and maintain people's health.

[0042] The system architecture is as follows: Figure 1 As shown, it consists of two main networks: a local network and a blockchain network. For scalability, e-health entities collect and process healthcare-related data from the local network, and then share this important information through the blockchain network. Shared data is verified by various entities within the blockchain and stored locally; the blockchain itself is a trusted entity with significant storage and computing power.

[0043] The local network extends from data sources located on or around patients to the local healthcare service system, such as primary healthcare institutions. The local network comprises the following main components: IoT devices connected to patients, specialized public health institutions, hospitals, primary healthcare institutions, other healthcare institutions, and IoT devices connected to patients within the local network.

[0044] Internet of Things (IoT) devices connected to patients are used to monitor health status and activity in smart assistive environments. Examples include: body area sensor networks (i.e., implantable or wearable sensors that measure different biosignals and vital signs), smartphones, IP cameras, and external medical and non-medical devices.

[0045] Specialized public health institutions specifically include disease prevention and control systems, maternal and child health care systems, pre-hospital emergency care systems, blood collection and supply systems, and occupational disease prevention and control systems. Their main role is to strengthen the coordination and monitoring of the quality and effectiveness of healthcare services with different healthcare entities, and to enhance the professional guidance and collaborative work between higher-level and lower-level disease control institutions.

[0046] Hospitals, which are divided into public and private hospitals, are an important part of the medical data exchange system.

[0047] Primary healthcare institutions refer to township health centers, community health service centers, village clinics, medical offices, outpatient departments, and clinics. They primarily provide basic health services such as prevention, healthcare, and disease management; diagnosis and treatment of common and frequently occurring diseases; rehabilitation, nursing, and palliative care services for some diseases; and receive patients referred from hospitals.

[0048] Other medical and health institutions, including mental health institutions and other medical institutions, set up independent medical testing, pathological diagnosis, medical imaging, hemodialysis, health check-up and other departments, and establish cooperative relationships with hospitals and primary healthcare service institutions to achieve regional resource sharing.

[0049] IoT devices connected to patients within a local network play a crucial role in monitoring their condition. The local healthcare system provides essential medical services to local patients, records their status, and offers timely emergency services when needed. The local network handles data storage and applies sophisticated data analytics, enabling various healthcare entities to share vital health-related information. Therefore, leveraging edge computing capabilities, each entity can verify the authenticity and integrity of medical data within the local network before sharing it on a blockchain.

[0050] In terms of blockchain networks, the core is a multi-channel data sharing architecture based on blockchain, enabling secure access, processing, and sharing of medical data among different e-health entities. Blockchain is indeed particularly suitable for secure medical data exchange because of its immutability and decentralized nature.

[0051] A blockchain network mainly consists of: 1. a data transmitter; 2. a blockchain manager (BM); and 3. a validator.

[0052] First, data senders upload their data to the nearby BM in the form of "transactions." Then, BM, acting as the validator manager, distributes unverified blocks to validators for verification, triggering a consensus process among validators, and inserts the verified blocks into the blockchain. Thus, BM acts as the leader, and validators as followers, collaboratively completing the block verification task.

[0053] The blockchain network also includes a multi-channel blockchain, where each channel corresponds to a separate transaction chain, which can be used to support data access and private communication between channel users. This architecture allows for the efficient handling of medical events at different levels.

[0054] The blockchain has three channels: the first channel (channel 1) is used for urgent data (such as emergency notifications), the second channel (channel 2) is used for non-urgent data that requires a high level of security, and the third channel (channel 3) is used for normal data.

[0055] The purpose of proposing a multi-channel blockchain architecture is based on the fact that when there is minimal trust between participating entities (or when the generated transaction is not urgent), it is ideal to spend more time verifying and securing transactions. On the other hand, when participating entities share a high level of trust, or when the nature of the generated transaction is urgent, enforcing high security will unnecessarily reduce transaction throughput. This is particularly evident in healthcare applications, where supporting rapid response in emergency situations is a primary goal of emergency care.

[0056] Therefore, urgent data (i.e. data requiring minimal latency) should be given the highest priority and processed on less restricted blockchains, i.e., using the minimum number of validators.

[0057] The medical data processing method includes the following steps:

[0058] S1: Data collection, collecting information from different patients;

[0059] Electronic medical devices collect medical-related data from local networks. This data includes patient information such as electroencephalograms (EEGs) and routine observations like body temperature and blood pressure. EEGs are a comprehensive external manifestation of the overall activity of brain nerve cells, including ion exchange and metabolism. In-depth research into the characteristics of EEGs will advance the exploration and research of the human brain and enhance its diagnostic capabilities. EEG recording devices have evolved from initially recording only one or two channels to later 6-channel and 8-channel EEG machines. Currently, clinically commonly used EEG machines include 16-channel, 32-channel, and 64-channel models.

[0060] S2: Feature extraction, identifying specific features from the acquired data that are rich in information and relevant to the patient's condition;

[0061] Doctors often find it difficult to distinguish and detect changes in the collected electroencephalogram (EEG) data, especially the maximum values ​​of each channel in the patient's data. Minimum value average value variance Root mean square R iand kurtosis K i As a feature, let the number of EEG channels be n (i∈{1,2,…,n}) and the number of samples be M (m∈{1,2,…,M}), where

[0062] average value:

[0063] variance:

[0064] Root mean square:

[0065] kurtosis:

[0066] S3: Patient status monitoring, which uses identified features to detect major changes in patient status and determine data to be shared with the blockchain network;

[0067] Define an index θ using the features generated above. i To obtain clear classification rules and thus reveal the main changes in the acquired data, this indicator integrates the following characteristics:

[0068]

[0069] Define an index β = {β1, β2, ..., β} i ,…,β n}

[0070] in, Define an α as a threshold for evaluating the main changes, which can be dynamically adjusted according to the actual situation (e.g., α = 20% or α = 30%). Also define a γ as the final result state.

[0071]

[0072] Among them, [α] + =max(0,α), ‖p‖0 represents the 0 norm.

[0073] The edge node can optimize the shared content on the blockchain network based on the detected changes by detecting major changes in the patient's condition using the identified features. When γ = (1), i.e., when a major change (i.e., an emergency) is detected, it will share an emergency notification and raw data that may require further investigation through the blockchain. When γ = (2), i.e., when a minor or no change is detected, it will only share the obtained features.

[0074] Furthermore, in S3, major changes in the patient's condition are detected using identified features. Based on the detected changes, the edge node can optimize the shared content on the blockchain network. In the case of detecting significant changes (i.e., emergencies), it will share emergency notifications and raw data that may require further investigation via the blockchain; in the case of detecting minor or no changes, it will only share the acquired features.

[0075] S4: Data senders upload their data to a nearby BM in the form of "transactions".

[0076] S5: BM assigns different priorities and access levels to the collected transactions based on their urgency.

[0077] BM assigns different priorities and channels to the collected transactions based on their urgency. Data obtained from different entities should be processed differently according to their urgency and security level. For example, urgent data (i.e., data requiring minimal latency) should be given the highest priority and processed using a less restricted blockchain, i.e., using the minimum number of validators. However, for data with high security requirements, a fully restricted blockchain should be used. For normal data, i.e., data requiring both latency and security, the blockchain configuration can be further optimized. When γ = (1), i.e., when a significant change is detected, the data will go through the first channel. When γ = (2), i.e., when a slight or no change is detected, if the data has a high security level, it will go through the second channel; otherwise, it will go through the third channel.

[0078] S6: Then, BM, as the manager of the validators, distributes the unvalidated blocks to selected validators (such as hospitals, which have sufficient computing and storage resources).

[0079] S7: Validators perform verification, triggering a consensus process among validators, and insert the verified block into the blockchain. BM acts as the leader, and validators act as followers, working together to complete the block verification task.

[0080] In summary, this invention proposes a system and method for medical data processing based on edge computing and blockchain technologies to effectively achieve large-scale medical data processing. While ensuring the secure transmission of medical data, on the one hand, at the edge, changes in the collected data are detected, significantly reducing the amount of information that needs to be shared on the blockchain, thereby alleviating network load and enabling rapid response to emergencies. On the other hand, within the blockchain network, a multi-channel blockchain is considered, allocating different priorities and channels to collected transactions based on their urgency, thereby promoting effective analysis without wasting the time of doctors and patients.

[0081] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A medical data processing method based on edge computing and blockchain, characterized in that, The main steps include: S1. Data collection: Collecting information from different patients; S2. Feature extraction: Identify features related to the patient's condition from the acquired data; S3. Patient status monitoring: Detects major changes in patient status using identified features and identifies data to be shared with the blockchain network. S4. The data sender uploads the data to a nearby blockchain manager in the form of a transaction; S5: The blockchain manager assigns different priorities and channels to the collected transactions based on their urgency. S6. The blockchain manager, acting as the manager of the validators, distributes unverified blocks to the selected validators. S7. The validator performs verification, triggers the consensus process among validators, and inserts the verified block into the blockchain; In S2, the collected patient data includes the patient's electroencephalogram (EEG), and the maximum value of each channel of the collected patient's EEG is recorded. Minimum value average value variance Root mean square R i and kurtosis K i As a feature, let n be the number of EEG channels, i∈{1,2,…,n}, and M be the number of samples, m∈{1,2,…,M}, where average value: variance: Root mean square: kurtosis: In S3, the index θ is defined. i To obtain clear classification rules: Define the quantitative index β={β1,β2,…,β i ,…,β n }, in, Define α as the threshold for evaluating the main changes, and define γ as the final result state. Among them, [α] + =max(0,α), where ||p||0 represents the 0 norm; In S3, when γ = (1), i.e., when a significant change is detected, the blockchain network will share an emergency notification and the original data that needs further investigation through the blockchain; when γ = (2), i.e., when a slight or no change is detected, the blockchain network will only share the obtained features. There are three channels in the blockchain. The first channel is used for urgent data, the second channel is used for non-urgent data that requires a high level of security, and the third channel is used for normal data. When γ = (1), the data will go through the first channel. When γ = (2), if the data is high-security data, it will go through the second channel; otherwise, it will go through the third channel.

2. The medical data processing method based on edge computing and blockchain according to claim 1, characterized in that: In S1, the patient's data includes the patient's electroencephalogram (EEG), body temperature, and blood pressure.

3. A medical data processing system based on edge computing and blockchain, characterized in that: The medical data processing method based on edge computing and blockchain as described in any one of claims 1-2 is applied; It includes a local network and a blockchain network, wherein the blockchain network includes: a data transmitter, a blockchain manager, a validator, and a multi-channel blockchain.

4. The medical data processing system based on edge computing and blockchain according to claim 3, characterized in that: The local network includes: IoT devices connected to patients, professional public health institutions, hospitals, primary healthcare institutions, other healthcare institutions, and IoT devices connected to patients within the local network.

5. The medical data processing system based on edge computing and blockchain according to claim 4, characterized in that: The multi-channel blockchain includes a first channel, a second channel, and a third channel. The first channel is used for urgent data, the second channel is used for non-urgent data that requires a high level of security, and the third channel is used for normal data. Define α as the threshold for evaluating the main changes, and define γ as the final result state. Among them, [α] + =max(0,α),‖p‖0 represents the 0 norm, β is the quantitative indicator. When γ=(1), that is, when a major change is detected, the data will go through the first channel. When γ=(2), that is, when a slight or no change is detected, if the data is high-level data, it will go through the second channel; otherwise, it will go through the third channel.

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