CN112289437B - Diabetes adjuvant therapy cloud platform system based on edge computing framework - Google Patents
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Abstract
The invention provides a diabetes adjuvant therapy cloud platform system based on an edge computing framework, and relates to the field of public health. The diabetes adjuvant therapy cloud platform system based on the edge computing architecture comprises a hospital body, a community body and a patient, wherein the hospital body is a hospital-community-patient, the patient is a terminal, the community, namely a community medical center, is an edge node, the hospital is a cloud center, the edge nodes form an edge computing layer, the edge nodes are independent of one another, and the edge nodes are interconnected with HIS, LIS, EMR and PACS data. Through edge calculation, data synchronization and model sharing between edge nodes and cloud nodes are achieved, data processing time delay, communication overhead and system overload risks are reduced, a clustering algorithm is introduced to identify diabetes patients according to classes, local sensitivity of data is highlighted on the basis of overall data characteristics, and clustered diabetes patients are enabled to be more similar and diagnosis and treatment modes of the clustered diabetes patients are enabled to have more reference values.
Description
Technical Field
The invention relates to the field of public health, in particular to a diabetes adjuvant therapy cloud platform system based on an edge computing architecture.
Background
The diabetes has the characteristics of long course time and complicated complications, the diabetes course monitoring and the complications management are used as keys for diabetes diagnosis, curative effect evaluation and treatment scheme adjustment at home and abroad, and the cloud platform can analyze and apply various diagnosis and treatment and physical examination data of the diabetes in a comprehensive manner on the diabetes course monitoring and the complications management, so that the cloud platform has natural advantages.
With the advent of 5G and the development of the internet of things (IoT), over 500 billions of terminal devices were networked by the year 2020. Considering the challenges of bandwidth consumption, network delay, data privacy protection and the like, under the scenes of huge data quantity, sensitivity to processing delay and sensitivity to data privacy of smart cities, smart medical treatment, smart manufacturing, smart homes and the like, more than half of data generated by terminal equipment needs to be analyzed and processed nearby at the edge side of the terminal equipment or the network, and a centralized cloud only processes computing tasks with high computing resource requirements and low real-time requirements.
The new pattern brought by the edge computing architecture utilizes the edge nodes to not only carry out task scheduling, storage, network management and the like on edge equipment, but also provide a set of complete safety isolation mechanism to prevent tasks dynamically scheduled on the same edge node from being influenced mutually; the central node belongs to a management platform and is responsible for managing a large number of small data centers formed by edge equipment, namely when a cluster formed by terminal equipment, an access gateway and the like is used as a small data center one by one, each edge node does not run a single task any more and becomes a general computing node capable of dynamically executing various tasks of the node to be scheduled.
By introducing edge calculation, the technical scheme of the customized service support environment can be directly constructed according to the abundance and the functional requirements of resources in different scenes, so that the resource occupation is reduced, and the response speed is increased. For example, a large number of clinical departments or hospital units directly access the cloud center, which may cause excessive load of the cloud center, network congestion, and long patient visit time, the system response efficiency can be improved through the edge nodes and the edge management platform, and the waiting time of the patient can be saved.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a diabetes adjuvant therapy cloud platform system based on an edge computing framework under the medical conjuncted background, and solves the problems that the existing diabetes patient has long treatment waiting time, and the existing system has high prediction accuracy and is to be improved in treating complex complications.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: an edge computing architecture-based diabetes adjuvant therapy cloud platform system comprises a hospital body, a community and a patient, wherein the hospital body is a hospital-community-patient, the patient is a terminal, the community, namely a community medical center, is an edge node, the hospital is a cloud center, the edge node forms an edge computing layer, the edge nodes are independent of one another, the edge nodes are interconnected with data of HIS, LIS, EMR and PACS, the cloud center is matched with Yun Jiedian and the edge nodes, yun Jiedian forms a cloud computing layer, the terminal-edge computing layer-cloud computing layer are interconnected through a network and address application is carried out through a webpage and an app, a doctor communication platform is additionally arranged in the webpage and the app, the doctor communication platform autonomously calls diabetes therapy related documents and experimental data and stores the diabetes therapy related documents and the experimental data, can call the data in the cloud computing layer as required after permission is obtained, and can communicate opinions through a table, and the communication opinions can be autonomously stored by the doctor communication platform and can be called by the cloud computing layer;
the edge node acquires medical information of a patient, establishes diagnosis and treatment records for the diagnosis and treatment records of the patient, preprocesses the diagnosis and treatment records and the medical information after a data trigger platform in the edge node is filtered and triggered, then uploads the diagnosis and treatment records to a hospital cloud node, and then shares the diagnosis and treatment records in a cloud computing layer, wherein information transmission in the edge node uses QoS (quality of service) constraint, the cloud computing layer takes data gathered by the edge computing layer as a basic data set, then selects a proper calculation standard of the similarity of patients with diabetes according to the data characteristics of the data set, and reselects if the calculation standard is not met according to a main evaluation index which meets the standard as a contour coefficient, and if the calculation standard is met, then carries out cluster analysis on the data set according to the calculation standard of the similarity, then, aiming at the continuity of highly-complicated diabetes courses in the hospital body, the change of the courses and the internal relation are fully considered by utilizing time sequence, machine learning or collaborative filtering to carry out coupling and multi-parameter optimization, a diabetes course trend and an auxiliary diagnosis and treatment prediction model are designed and shared, the edge node makes the cloud computing layer formulate a diagnosis and treatment and rehabilitation scheme for the diabetes course trend, the auxiliary diagnosis and treatment prediction model and the medical information of the patient designed by the patient, stores the relevant data of the diagnosis and treatment of the patient in the cloud node to participate in the next model training, and the patient can obtain the diagnosis and treatment and rehabilitation scheme from the terminal layer and carry out information interaction with the medical staff of the edge node.
Preferably, the cloud computing layer is used for storing the diabetes big data processed by the edge nodes and training the data model.
Preferably, the edge computing layer is formed by interconnection of edge computing network devices with certain computing and storing capabilities, and is mainly used for receiving and processing medical data from the terminal layer, uploading medical information and diagnosis and treatment records to the cloud computing layer, and realizing global information sharing.
Preferably, the diabetes patient similarity calculation standard may be replaced with a standard corresponding to cosine similarity and euclidean distance.
Preferably, before the diagnosis and rehabilitation scheme is generated by the edge node, model verification needs to be performed on a diabetes course trend and an auxiliary diagnosis and treatment prediction model designed for a patient by using a public data set and an experimental environment.
Preferably, the edge node and the cloud center have unique static addresses, and the terminal patient registers for application in a webpage and an app through terminal equipment.
Preferably, the medical information is data information about diabetes acquired by the patient through one of a community medical center, a hospital and a self-detection mode.
Preferably, the diabetes course trend and auxiliary diagnosis and treatment prediction model is analyzed and checked by the staff of hospitals and scientific research institutions under the guidance of doctors with the authority of a doctor communication platform, and the analysis and check result is stored as a secondary item of the diabetes course trend and auxiliary diagnosis and treatment prediction model.
(III) advantageous effects
The invention provides a diabetes adjuvant therapy cloud platform system based on an edge computing architecture. The method has the following beneficial effects:
1. according to the invention, data synchronization and model sharing between the edge nodes and the cloud nodes are realized based on edge computing, so that data heterogeneity and system hardware difference of each data source are shielded, and data processing time delay, communication overhead and system overload risk are reduced.
2. According to the invention, a clustering algorithm is introduced to identify the diabetics according to classes, and the local sensitivity of data is highlighted on the basis of the overall characteristics of the data, so that the clustered diabetics are more similar, and the diagnosis and treatment modes of the clustered diabetics have more reference values.
3. According to the method, based on the clustering result and the data sensitivity, the coupling and multi-parameter optimization of the basic algorithm are realized, the algorithm parameter selection is ensured to meet the requirement of a data set, and the accuracy of model prediction is improved.
4. According to the invention, the doctor communication platform is used as the intermediate diabetes diagnosis communication platform, and the communication result is used as the basis of the system, so that the reliability of the auxiliary treatment of the special diabetes is improved.
Drawings
FIG. 1 is a diagram of the data architecture of the present invention;
fig. 2 is a model diagram of diagnosis and treatment prediction according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment is as follows:
as shown in fig. 1-2, an embodiment of the present invention provides a diabetes adjuvant therapy cloud platform system based on an edge computing architecture, including a hospital body, a hospital-community-patient, a patient terminal, a community, namely a community medical center, an edge node, a hospital cloud center, an edge computing layer composed of edge nodes, and each edge node being independent of each other, the edge nodes being interconnected with HIS, LIS, EMR, PACS data, the cloud center matching Yun Jiedian and the edge nodes, yun Jiedian constituting a cloud computing layer, the terminal-edge computing layer-cloud computing layer being interconnected using a network, and addressing application via a web page and app, a physician communication platform being additionally provided in the web page and app, the physician communication platform autonomously calling relevant diabetes therapy documents, experimental data and storing them, after obtaining the authority, being able to call data in the cloud computing layer as needed and exchange opinions by the cloud computing layer, the communication opinions being autonomously saved by the physician communication platform and being able to be called by the cloud computing layer;
the edge nodes collect medical information of patients, establish diagnosis and treatment records for the diagnosis and treatment records of the patients, preprocess the diagnosis and treatment records and the medical information after a data trigger platform in the edge nodes is triggered in a filtering way and then upload the diagnosis and treatment records and the medical information to a hospital cloud node, the data trigger platform can prevent data aggregation which affects a central node even possibly causes dirty data of the central node when service in the edge computing nodes is failed or abnormal in data trigger, a cloud node early warning mechanism is set in the cloud node to ensure data trigger and edge cloud synchronization, then the data trigger and the edge cloud are shared in a cloud computing layer, information transmission in the edge nodes uses QoS constraint so as to shield data isomerism and system hardware difference of each data source, realize processing and fusion of data of each edge node, and reduce risks such as data processing delay, communication overhead and system overload, providing data support for the construction of a subsequent diabetes data model, wherein a cloud computing layer takes data gathered by an edge computing layer as a basic data set, then selecting a proper diabetes patient similarity computing standard (cosine similarity and Euclidean distance) according to the data characteristics of the data set, taking main evaluation indexes of whether the data set meets the standard as contour coefficients, reselecting if the data set does not meet the standard, carrying out cluster analysis on the data set according to the similarity computing standard if the data set meets the standard, then, fully considering course change and internal relation by using time series, machine learning or collaborative filtering according to the continuity of highly concurrent diabetes courses in a hospital body, carrying out coupling and multi-parameter optimization, designing a diabetes course trend and an auxiliary diagnosis and treatment prediction model, sharing the models, improving the clinical prediction and accuracy of diabetes and the effectiveness of diagnosis and treatment rehabilitation, and assisting clinical diagnosis and early warning of potential blood sugar risk crowds, the purpose of reducing blood sugar risks is achieved, the edge nodes set a diagnosis and rehabilitation scheme according to the diabetes course trend designed by the cloud computing layer for a patient, assist a diagnosis and treatment prediction model and patient medical information, relevant data of diagnosis and treatment of the patient are stored in the cloud nodes, and participate in next model training, the patient can obtain the diagnosis and treatment and rehabilitation scheme from the terminal layer and perform information interaction with medical staff of the edge nodes, whether the treatment corresponding to the diagnosis and rehabilitation scheme is symptoms of the patient is judged, when an accident happens in the treatment process, the medical staff of the edge nodes can conduct analysis and diagnosis from a doctor communication platform rapidly to deal with the accident, the diagnosis and treatment and rehabilitation scheme is regenerated according to analysis and diagnosis results of the doctor communication platform in the period, the whole content forms a closed loop, and various diabetes patients can be improved.
The cloud computing layer has the function of storing the diabetes big data processed by the edge nodes and training the data model.
The edge computing layer is formed by interconnection of edge computing network devices (such as switches, routers and common servers based on home-made processors) with certain computing and storage capacities, and is mainly used for receiving and processing medical data from the terminal layer and uploading medical information and diagnosis and treatment records to the cloud computing layer to achieve global information sharing.
Before the diagnosis and rehabilitation scheme is generated by the edge node, model verification needs to be carried out on the diabetes course trend and the auxiliary diagnosis and treatment prediction model designed for the patient by utilizing a public data set and an experimental environment, so that the correctness of the data integration and aggregation framework and the diabetes auxiliary diagnosis and treatment prediction model is proved.
The edge node and the cloud center are provided with unique static addresses, and the terminal patient registers and applies in a webpage and an app through the terminal device.
The medical information is data information about diabetes acquired by a patient through one of community medical centers, hospitals and self-detection modes.
The diabetes course trend and auxiliary diagnosis and treatment prediction model is analyzed and checked by the staff of hospitals and scientific research institutions under the guidance of doctors with the authority of a doctor communication platform, and the analysis and check result is stored as a secondary item of the diabetes course trend and auxiliary diagnosis and treatment prediction model.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. A diabetes adjuvant therapy cloud platform system based on an edge computing architecture comprises a hospital body and is characterized in that: the hospital body is a hospital-community-patient, the patient is a terminal, the community, namely a community medical center, is an edge node, the hospital is a cloud center, the edge node forms an edge computing layer, the edge nodes are independent of one another, the edge node is interconnected with HIS, LIS, EMR and PACS data, the cloud center is matched with Yun Jiedian and the edge node, yun Jiedian forms a cloud computing layer, the terminal-edge computing layer-cloud computing layer are interconnected through a network and address application is carried out through a webpage and app, a doctor communication platform is additionally arranged in the webpage and the app, the doctor communication platform autonomously calls diabetes treatment related documents and experimental data and stores the data, the data in the cloud computing layer can be called as required after permission is obtained, and can be used for presenting communication opinions, and the communication opinions are autonomously stored by the doctor communication platform and can be called by the cloud computing layer;
the method comprises the steps that an edge node acquires medical information of a patient, establishes diagnosis and treatment records for the patient, preprocesses the diagnosis and treatment records and the medical information after the filtration and the triggering of a data triggering platform in the edge node, uploads the diagnosis and treatment records and the medical information to a hospital cloud node, and then shares the diagnosis and treatment records in a cloud computing layer, information transmission in the edge node uses QoS (quality of service) constraint, the cloud computing layer takes data gathered by the edge computing layer as a basic data set, then selects a proper diabetes patient similarity calculation standard according to the data characteristics of the data set, performs coupling and multi-parameter optimization according to whether main evaluation indexes meet the standard are contour coefficients or not, designs a diabetes course trend and an auxiliary diagnosis and treatment prediction model according to the similarity calculation standard if the similarity calculation standard meets the requirement, and shares the edge node, fully considers the disease course change and the internal relation by using a time sequence, machine learning or collaborative filtering according to the continuity of high-concurrence diabetes courses in a conjuncted body, designs a diabetes course trend and the auxiliary diagnosis and treatment prediction model and the medical information, designs a diabetes course trend and the auxiliary diagnosis and treatment prediction model, and the patient information, and the cloud computing layer makes a diagnosis and treatment information related health care information interactive cloud computing layer to acquire diagnosis and treatment information of the diagnosis and treatment related health care information of the patient, and a diagnosis and treatment information interactive scheme, and a rehabilitation terminal node, and a rehabilitation training scheme can be related to participate in the diagnosis and a rehabilitation information interaction scheme of the patient; the cloud computing layer has the function of storing the diabetes big data processed by the edge nodes and the training data model; the edge computing layer is formed by interconnection of edge computing network equipment with certain computing and storing capabilities, is mainly used for receiving and processing medical data from the terminal layer, and uploads medical information and diagnosis and treatment records to the cloud computing layer, so that global information sharing is realized;
the similarity calculation standard of the diabetic patient can be replaced with a standard corresponding to cosine similarity and Euclidean distance;
before the edge node generates the diagnosis and rehabilitation scheme, model verification is carried out on the diabetes course trend and the auxiliary diagnosis and treatment prediction model designed for the patient by utilizing a public data set and an experimental environment.
2. The cloud platform system for diabetes adjuvant therapy based on edge computing architecture of claim 1, wherein: the edge nodes and the cloud center are provided with unique static addresses, and the terminal patient registers and applies in a webpage and an app through terminal equipment.
3. The cloud platform system for diabetes adjuvant therapy based on edge computing architecture of claim 1, wherein: the medical information is data information about diabetes acquired by a patient through one of community medical centers, hospitals and self-detection modes.
4. The cloud platform system for diabetes adjuvant therapy based on edge computing architecture of claim 1, wherein: the diabetes course trend and auxiliary diagnosis and treatment prediction model is analyzed and checked by the staff of hospitals and scientific research institutions under the guidance of doctors with the authority of a doctor communication platform, and the analysis and check result is stored as a secondary item of the diabetes course trend and auxiliary diagnosis and treatment prediction model.
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