WO2019192118A1 - 基于边缘计算的健康监测方法、装置、设备及存储介质 - Google Patents

基于边缘计算的健康监测方法、装置、设备及存储介质 Download PDF

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Publication number
WO2019192118A1
WO2019192118A1 PCT/CN2018/100147 CN2018100147W WO2019192118A1 WO 2019192118 A1 WO2019192118 A1 WO 2019192118A1 CN 2018100147 W CN2018100147 W CN 2018100147W WO 2019192118 A1 WO2019192118 A1 WO 2019192118A1
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edge node
data
auxiliary data
cloud server
indicator
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PCT/CN2018/100147
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English (en)
French (fr)
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王义文
王健宗
肖京
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平安科技（深圳）有限公司
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Publication of WO2019192118A1 publication Critical patent/WO2019192118A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/02—Details
    • H04L12/16—Arrangements for providing special services to substations
    • H04L12/18—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/02—Details
    • H04L12/16—Arrangements for providing special services to substations
    • H04L12/18—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast
    • H04L12/1881—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast with schedule organisation, e.g. priority, sequence management
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/02—Details
    • H04L12/16—Arrangements for providing special services to substations
    • H04L12/18—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast
    • H04L12/1859—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast adapted to provide push services, e.g. data channels

Definitions

  • the present invention relates to the field of artificial intelligence, and in particular, to a method, device, device and storage medium for health monitoring based on edge calculation.
  • a health monitoring method based on edge calculation comprising:
  • a health monitoring device based on edge calculation comprising:
  • a detecting module configured to detect first data of a physical indicator of the user
  • the sending module is configured to send the first data of the physical indicator to a cloud server that communicates with the edge node;
  • the sending module is further configured to send a request broadcast of the auxiliary data in the target time period for requesting the physical indicator;
  • An obtaining module configured to acquire the auxiliary data according to the response instruction of the request broadcast
  • a determining module configured to determine, according to the auxiliary data and the first data, a level of the physical indicator by using an indicator risk level analysis model, and send the level of the physical indicator to the cloud server.
  • An electronic device comprising a memory and a processor, the memory for storing at least one instruction, the processor for executing the at least one instruction to implement the edge calculation based health monitoring in any of the embodiments method.
  • a non-volatile readable storage medium storing at least one instruction that, when executed by a processor, implements edge-based health monitoring in any of the embodiments method.
  • the present invention detects first data of a user's physical index; when determining that the first data of the physical indicator is normal, transmitting the first data of the physical indicator to a cloud server that communicates with an edge node Transmitting a request broadcast of the auxiliary data requesting the target time period of the physical indicator when the first data abnormality of the physical indicator is determined; acquiring the auxiliary data based on the response instruction of the request broadcast; The auxiliary data and the first data are determined by an indicator risk level analysis model, and the level of the physical indicator is sent to the cloud server.
  • the invention can realize data sharing between edge nodes, reduce the computing burden of the cloud server, reduce the transit time of the data center under the original cloud computing model, and improve the real-time performance of the data processing.
  • FIG. 1 is an application environment diagram of a preferred embodiment of an edge calculation based health monitoring method embodying the present invention.
  • FIG. 2 is a flow chart of a first preferred embodiment of the edge computing based health monitoring method of the present invention.
  • FIG. 3 is a flow chart of a second preferred embodiment of the edge computing based health monitoring method of the present invention.
  • FIG. 4 is a block diagram of a program of a preferred embodiment of the edge computing based health monitoring device of the present invention.
  • FIG. 5 is a block diagram showing a preferred embodiment of a health monitoring device based on edge calculation in at least one example of the present invention.
  • FIG. 1 is an application environment diagram of a preferred embodiment of an edge calculation based health monitoring method embodying the present invention.
  • the application environment diagram includes a cloud server and a plurality of edge nodes.
  • Each edge node corresponds to one medical device. Since the purpose of each medical device may be the same or different, the types of body indicators measured by each edge node are the same or different.
  • Each edge node is in communication with the cloud server, and each edge node has edge computing capabilities.
  • Each edge node can monitor the user's body and perform edge calculation based on the monitored indicator data to determine the category of the indicator data (eg, normal category, light category, medium category, etc.), and the indicator data category Sent to the cloud server.
  • the category of the indicator data eg, normal category, light category, medium category, etc.
  • Each of the edge nodes communicates with the cloud server through a network
  • the network includes, but is not limited to, the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), and the like.
  • VPN virtual private network
  • An edge computing-based health monitoring method using an electronic device is described in detail in conjunction with the following embodiments.
  • FIG. 2 is a flow chart of a first preferred embodiment of the edge computing-based health monitoring method of the present invention. The order of the steps in the flowchart may be changed according to different requirements, and some steps may be omitted.
  • the edge node detects first data of a physical indicator of the user.
  • one edge node corresponds to one medical device
  • the medical device includes, but is not limited to, a wearable medical device, a hospital detecting medical device.
  • the wearable medical device is a monitoring instrument worn on the body part of the user.
  • the edge node corresponds to a smart wristband, and the smart wristband is arranged with some small electrodes, which can transmit a weak current through the skin. The sweat glands are then measured for irritation.
  • the hospital detecting medical device includes, but is not limited to, various types of instruments for acquiring user body index data. For example, assay devices, sensing devices, and the like. The present invention does not impose any restrictions on medical devices.
  • the medical device may be used differently because of the medical device corresponding to each edge node.
  • the detected physical indicators are different.
  • a sphygmomanometer measures blood pressure and an ECG monitor measures heart rate data.
  • the first data of each of the body indicators includes a plurality of data.
  • the first data measured by the sphygmomanometer includes the highest blood pressure, the lowest blood pressure, the stable blood pressure, and the like.
  • the edge node determines whether the first data of the physical indicator is normal.
  • each data of the body metrics measured by each medical device is configured with a range of normal values.
  • the first data measured by the sphygmomanometer includes the highest blood pressure, the lowest blood pressure, the stable blood pressure, and the like. The highest blood pressure ranges from 100 to 120 units, the lowest blood pressure ranges from 50 to 70 units, and so on.
  • the first data of the physical indicator is abnormal when the data of the first data of the physical indicator is not within the normal value range corresponding to the one data, further determining the The cause of the abnormality of the physical index, thereby further judging the category of the physical indicator, that is, performing S23.
  • the edge node sends the first data of the physical indicator to a cloud server that is in communication with the edge node.
  • the cloud server stores a communication address of each edge node and a body indicator measured by each edge node.
  • the communication address includes, but is not limited to, an IP address and the like.
  • the edge node's communication address is utilized to achieve edge connection, edge coordination, and data sharing between the edge nodes of the two edge nodes.
  • the cloud server further stores a monitoring record of each user at the edge node, including, but not limited to, the first data monitored, the level of the monitored body indicator, and the like.
  • the edge node When it is determined that the first data of the physical indicator is abnormal, the edge node sends a request broadcast for requesting auxiliary data within the target time period of the physical indicator.
  • the target time period represents a certain time period before the current time, such as the previous week, the first three days, and the like.
  • the request broadcast includes, but is not limited to: an identifier of the auxiliary data, a user identifier corresponding to the auxiliary data, and the like.
  • the identification of the auxiliary data includes, but is not limited to, the name of the auxiliary data (such as blood routine five items, etc.) and the like for identifying other forms of representation of the auxiliary data.
  • the user identifier corresponding to the auxiliary data is used to identify the identity information of the user, for example, the name of the user, the ID card of the user, the social security card number of the user, and the like.
  • the blood lipid index can be obtained by the request broadcast, so that the integration and sharing of the resources can be realized, thereby preventing the user from repeating the inspection again.
  • the same time with reference to a variety of data, it can also provide users with accurate medical diagnosis.
  • the request broadcast is encrypted and transmitted.
  • the requesting broadcast by the edge node to request the auxiliary data includes, but is not limited to, a combination of one or more of the following:
  • the edge node sends the request broadcast to the cloud server to cause the cloud server to look up the auxiliary data from the stored monitoring record.
  • the edge node sends the request broadcast to the cloud server to cause the cloud server to find a target edge node that stores the auxiliary data.
  • the edge node searches for an edge node matching the auxiliary data based on the stored physical indicators corresponding to the edge nodes, and sends a request broadcast to make the matching based on the communication address of the matched edge node.
  • the edge node looks up the auxiliary data. Since each edge node can also save the body indicator corresponding to each edge node and the communication address of each edge node, each edge node also saves the records of the respective monitoring, so the edge node can directly go to the edge of the matching.
  • the node sends a request broadcast, which reduces the storage pressure of the cloud center and increases the data sharing of each edge node.
  • the cloud server communicates with three edge nodes, edge node A, edge node B, and edge node C. If the edge node A needs the auxiliary data, and the edge node C has the auxiliary data, the edge node A may directly request the auxiliary data from the edge node C.
  • the edge node acquires an edge node in the target area of the edge node based on the stored location information of each edge node, and then based on the body indicator corresponding to the edge node in the target area, in the target area An edge node corresponding to the auxiliary data is filtered in the edge node, and a request broadcast is sent based on the communication address of the filtered edge node to cause the filtered edge node to search for the auxiliary data. In this way, finding the required edge nodes in a certain area can reduce the search time and improve the real-time monitoring.
  • the cloud server communicates with three edge nodes, an edge node A, an edge node B, and an edge node C, wherein the edge node A and the edge node C are in the same region. If the edge node A needs the auxiliary data, the edge node A requests the auxiliary data from the edge node C.
  • the edge node acquires the auxiliary data based on the response instruction of the request broadcast.
  • acquiring the auxiliary data based on the response instruction of the request broadcast includes, but is not limited to, one or a combination of the following:
  • the response instruction includes a target edge node that includes the auxiliary data sent by the cloud server, acquire a communication address of the target edge node, and send request information to the target edge node to enable the The target edge node sends the auxiliary data, and when the target edge node verifies the request information, receives the auxiliary data sent by the target edge node.
  • the prompt information including the health risk alert is output to enable the user to follow the check.
  • the cloud server connects a plurality of edge nodes, and the plurality of edge nodes are geographically distributed, but have respective physical locations and network structures. Moreover, edge-to-point edge connection methods can be implemented between edge nodes, thereby realizing data sharing and ensuring real-time edge calculation on edge nodes.
  • the edge node determines a level of the physical indicator by using an indicator risk level analysis model.
  • the indicator risk level analysis model includes, but is not limited to, a Support Vector Machine (SVM) model.
  • SVM Support Vector Machine
  • the training risk sample corresponding to the physical index is used to train the indicator risk level analysis model, and the training process is as follows:
  • Different levels of training sample data of the physical indicators are configured, and different levels of training sample data are distributed to different folders.
  • the training sample data of the first-level risk level is distributed to the first folder; the training sample data of the second-level risk level is distributed to the second folder; and the training sample data of the third-level risk level is distributed to the third folder.
  • the training sample data of the four-level risk level is distributed to the fourth folder. Extracting a first preset ratio from each of the different folders, for example, 70% of the training sample data is used as training data for training of a support vector machine (SVM) model, and the remaining second preset ratios are taken from different folders. For example, 30% of the training sample data is used as test data to verify the accuracy of the generated SVM model.
  • SVM support vector machine
  • the generated SVM model accuracy is less than the preset accuracy rate, for example, 99%, the number of acquisitions of the training sample data of different levels of the body indicator is increased, and the generation process of the SVM model is repeated until the generated SVM model accuracy rate is generated. Greater than or equal to the preset accuracy, for example, 99%.
  • the edge node sends the level of the physical indicator to the cloud server. Further, each edge node sends the first data of the monitored physical indicators and the level of the monitored physical indicators to the cloud server, so that the cloud server performs comprehensive evaluation of the indicators to comprehensively evaluate the health status of the user.
  • each edge node corresponds to one medical device, and the types of body indicator types measured by each edge node are the same or different.
  • Each edge node is in communication with the cloud server, and each edge node has edge computing capabilities.
  • Each edge node can monitor the user's body and perform edge calculation based on the monitored indicator data to determine the category of the indicator data (eg, normal category, light category, medium category, etc.), and the indicator data category
  • the data is sent to the cloud server, and the data sharing between the edge nodes is implemented, the computing burden of the cloud server is reduced, the transit time of the data center under the original cloud computing model is reduced, and the real-time performance of the data processing is improved.
  • FIG. 3 it is a flowchart of a second preferred embodiment of the edge computing based health monitoring method of the present invention.
  • the order of the steps in the flowchart may be changed according to different requirements, and some steps may be omitted.
  • S30 to S36 correspond to S20 to S26 in the first preferred embodiment, respectively, and will not be described in detail herein.
  • the cloud server acquires a level of a physical indicator corresponding to each edge node of the at least one edge node.
  • each edge node is geographically distributed, and a user can perform various physical indicators at a plurality of edge nodes, for example, in a hospital for blood test, and B hospital for B. Super check and so on.
  • the monitoring data of each edge node to the user is sent to the cloud server.
  • the cloud server includes, but is not limited to, a server designated by a user, a server of an insurance institution, and the like.
  • the cloud server determines a health state level of the user by using a health state level analysis model based on a level of the physical indicator corresponding to each edge node.
  • the health status level analysis model includes, but is not limited to, a Support Vector Machine (SVM) model.
  • SVM Support Vector Machine
  • the health state level analysis is trained by using sample data of each body indicator under each health state level, and the training process is as follows:
  • Configure sample data for each physical indicator under each health status level and distribute sample data of different health status levels to different folders.
  • the sample data of the first-level health status level is distributed to the first folder; the sample data of the second-level health status level is distributed to the second folder; and the sample data of the third-level health status level is distributed to the third folder.
  • the sample data of the four-level health status level is distributed to the fourth folder. Extracting a first preset ratio from each of the different folders, for example, 70% of the sample data is used as training data for training of a support vector machine (SVM) model, and the remaining second preset ratios are taken from different folders. For example, 30% of the sample data is used as test data to verify the accuracy of the generated SVM model.
  • SVM support vector machine
  • the generated SVM model accuracy is less than the preset accuracy rate, for example, 99%, the number of sample data of each body indicator under each health state level is increased, and the generation process of the SVM model is repeated until the generated SVM model is accurate.
  • the rate is greater than or equal to the preset accuracy, for example, 99%.
  • the cloud server acquires monitoring data of physical indicators of each user of each edge node, and when the cloud server detects that the number of users whose physical indicators are abnormal exceeds a threshold value, the warning information is output to adopt prevention and control. Measures. For example, the number of users who have detected the flu indicator in the cloud server has increased sharply over a period of time, indicating that the flu outbreak requires prevention and control measures.
  • each edge node corresponds to one medical device, and the types of body indicator types measured by each edge node are the same or different.
  • Each edge node is in communication with the cloud server, and each edge node has edge computing capabilities.
  • Each edge node can monitor the user's body and perform edge calculation based on the monitored indicator data to determine the category of the indicator data (eg, normal category, light category, medium category, etc.), and the indicator data category
  • the data is sent to the cloud server, and the data sharing between the edge nodes is implemented, the computing burden of the cloud server is reduced, the transit time of the data center under the original cloud computing model is reduced, and the real-time performance of the data processing is improved.
  • the edge calculation-based health monitoring device 4 includes, but is not limited to, one or more of the following modules: a detection module 40, a determination module 41, a transmission module 42, an acquisition module 43, a determination module 44, and a training module 45.
  • the term "module” as used herein refers to a series of computer readable instruction segments that can be executed by a processor of the edge computing based health monitoring device 4 and that are capable of performing fixed functions, which are stored in a memory. The function of each module will be detailed in the subsequent embodiments.
  • the detecting module 40 detects first data of a physical indicator of the user.
  • one edge node corresponds to one medical device
  • the medical device includes, but is not limited to, a wearable medical device, a hospital detecting medical device.
  • the wearable medical device is a monitoring instrument worn on the body part of the user.
  • the edge node corresponds to a smart wristband, and the smart wristband is arranged with some small electrodes, which can transmit a weak current through the skin. The sweat glands are then measured for irritation.
  • the hospital detecting medical device includes, but is not limited to, various types of instruments for acquiring user body index data. For example, assay devices, sensing devices, and the like. The present invention does not impose any restrictions on medical devices.
  • the medical device may be used differently because of the medical device corresponding to each edge node.
  • the detected physical indicators are different.
  • a sphygmomanometer measures blood pressure and an ECG monitor measures heart rate data.
  • the first data of each of the body indicators includes a plurality of data.
  • the first data measured by the sphygmomanometer includes the highest blood pressure, the lowest blood pressure, the stable blood pressure, and the like.
  • the determining module 41 determines whether the first data of the physical indicator is normal.
  • Each data of the physical indicators measured by each medical device is configured with a normal range of values.
  • the first data measured by the sphygmomanometer includes the highest blood pressure, the lowest blood pressure, the stable blood pressure, and the like. The highest blood pressure ranges from 100 to 120 units, the lowest blood pressure ranges from 50 to 70 units, and so on.
  • the determining module 41 determines that the first data of the body indicator is abnormal, then It is necessary to further determine the cause of the abnormality of the physical index, thereby further determining the category of the physical indicator.
  • the determining module 41 determines that the first data of the physical indicator is normal.
  • the sending module 42 transmits the first data of the body indicator to a cloud server in communication with the edge node.
  • the cloud server stores a communication address of each edge node and a body indicator measured by each edge node.
  • the communication address includes, but is not limited to, an IP address and the like.
  • the edge node's communication address is utilized to achieve edge connection, edge coordination, and data sharing between the edge nodes of the two edge nodes.
  • the cloud server further stores a monitoring record of each user at the edge node, including, but not limited to, the first data monitored, the level of the monitored body indicator, and the like.
  • the transmitting module 42 transmits a request broadcast requesting auxiliary data within the target time period of the physical indicator.
  • the target time period represents a certain time period before the current time, such as the previous week, the first three days, and the like.
  • the request broadcast includes, but is not limited to: an identifier of the auxiliary data, a user identifier corresponding to the auxiliary data, and the like.
  • the identification of the auxiliary data includes, but is not limited to, the name of the auxiliary data (such as blood routine five items, etc.) and the like for identifying other forms of representation of the auxiliary data.
  • the user identifier corresponding to the auxiliary data is used to identify the identity information of the user, for example, the name of the user, the ID card of the user, the social security card number of the user, and the like.
  • the blood lipid index can be obtained by the request broadcast, so that the integration and sharing of the resources can be realized, thereby preventing the user from repeating the inspection again.
  • the same time with reference to a variety of data, it can also provide users with accurate medical diagnosis.
  • the request broadcast is encrypted and transmitted.
  • the sending of the request broadcast requesting the auxiliary data by the sending module 42 includes, but is not limited to, a combination of one or more of the following:
  • each edge node can also save the body indicator corresponding to each edge node and the communication address of each edge node, each edge node also saves the records of the respective monitoring, so the edge node can directly go to the edge of the matching.
  • the node sends a request broadcast, which reduces the storage pressure of the cloud center and increases the data sharing of each edge node.
  • the cloud server communicates with three edge nodes, edge node A, edge node B, and edge node C. If the edge node A needs the auxiliary data, and the edge node C has the auxiliary data, the edge node A may directly request the auxiliary data from the edge node C.
  • edge nodes in the target area of the edge node based on the stored location information of each edge node, and filtering the edge nodes in the target area based on the body indicators corresponding to the edge nodes in the target area.
  • An edge node corresponding to the auxiliary data and based on a communication address of the filtered edge node, transmitting a request broadcast to cause the filtered edge node to look up the auxiliary data. In this way, finding the required edge nodes in a certain area can reduce the search time and improve the real-time monitoring.
  • the cloud server communicates with three edge nodes, an edge node A, an edge node B, and an edge node C, wherein the edge node A and the edge node C are in the same region. If the edge node A needs the auxiliary data, the edge node A requests the auxiliary data from the edge node C.
  • the obtaining module 43 acquires the auxiliary data based on the response instruction of the request broadcast.
  • the obtaining module 43 acquires the auxiliary data according to the response instruction of the request broadcast, including but not limited to one or a combination of the following:
  • the response instruction includes a target edge node that includes the auxiliary data sent by the cloud server, acquire a communication address of the target edge node, and send request information to the target edge node to enable the The target edge node sends the auxiliary data, and when the target edge node verifies the request information, receives the auxiliary data sent by the target edge node.
  • the prompt information including the health risk alert is output to enable the user to follow the check.
  • the cloud server connects a plurality of edge nodes, and the plurality of edge nodes are geographically distributed, but have respective physical locations and network structures. Moreover, edge-to-point edge connection methods can be implemented between edge nodes, thereby realizing data sharing and ensuring real-time edge calculation on edge nodes.
  • the determining module 44 determines the level of the physical indicator using an indicator risk level analysis model.
  • the indicator risk level analysis model includes, but is not limited to, a Support Vector Machine (SVM) model.
  • SVM Support Vector Machine
  • the training module 45 uses the training samples corresponding to the physical indicators to train the index risk level analysis model, and the training process is as follows:
  • Different levels of training sample data of the physical indicators are configured, and different levels of training sample data are distributed to different folders.
  • the training sample data of the first-level risk level is distributed to the first folder; the training sample data of the second-level risk level is distributed to the second folder; and the training sample data of the third-level risk level is distributed to the third folder.
  • the training sample data of the four-level risk level is distributed to the fourth folder. Extracting a first preset ratio from each of the different folders, for example, 70% of the training sample data is used as training data for training of a support vector machine (SVM) model, and the remaining second preset ratios are taken from different folders. For example, 30% of the training sample data is used as test data to verify the accuracy of the generated SVM model.
  • SVM support vector machine
  • the generated SVM model accuracy is less than the preset accuracy rate, for example, 99%, the number of acquisitions of the training sample data of different levels of the body indicator is increased, and the generation process of the SVM model is repeated until the generated SVM model accuracy rate is generated. Greater than or equal to the preset accuracy, for example, 99%.
  • the sending module 42 sends the level of the physical indicator to the cloud server. Further, each edge node sends the first data of the monitored physical indicators and the level of the monitored physical indicators to the cloud server, so that the cloud server performs comprehensive evaluation of the indicators to comprehensively evaluate the health status of the user.
  • each edge node corresponds to one medical device, and the types of body indicator types measured by each edge node are the same or different.
  • Each edge node is in communication with the cloud server, and each edge node has edge computing capabilities.
  • Each edge node can monitor the user's body and perform edge calculation based on the monitored indicator data to determine the category of the indicator data (eg, normal category, light category, medium category, etc.), and the indicator data category
  • the data is sent to the cloud server, and the data sharing between the edge nodes is implemented, the computing burden of the cloud server is reduced, the transit time of the data center under the original cloud computing model is reduced, and the real-time performance of the data processing is improved.
  • the edge computing based health monitoring device 4 further includes one or more modules located in the cloud server: a data acquisition module 46, a level determination module 47, and a model training module 48.
  • the data acquisition module 46 acquires a level of a physical indicator corresponding to each edge node of the at least one edge node.
  • each edge node is geographically distributed, and a user can perform various physical indicators at a plurality of edge nodes, for example, in a hospital for blood test, and B hospital for B. Super check and so on.
  • the monitoring data of each edge node to the user is sent to the cloud server.
  • the cloud server includes, but is not limited to, a server designated by a user, a server of an insurance institution, and the like.
  • the level determination module 47 determines the health status level of the user using the health status level analysis model based on the level of the physical indicator corresponding to each edge node.
  • the health status level analysis model includes, but is not limited to, a Support Vector Machine (SVM) model.
  • SVM Support Vector Machine
  • model training module 48 uses the sample data of each body indicator under each health state level to train the health state level analysis.
  • the training process is as follows:
  • Configure sample data for each physical indicator under each health status level and distribute sample data of different health status levels to different folders.
  • the sample data of the first-level health status level is distributed to the first folder; the sample data of the second-level health status level is distributed to the second folder; and the sample data of the third-level health status level is distributed to the third folder.
  • the sample data of the four-level health status level is distributed to the fourth folder. Extracting a first preset ratio from each of the different folders, for example, 70% of the sample data is used as training data for training of a support vector machine (SVM) model, and the remaining second preset ratios are taken from different folders. For example, 30% of the sample data is used as test data to verify the accuracy of the generated SVM model.
  • SVM support vector machine
  • the generated SVM model accuracy is less than the preset accuracy rate, for example, 99%, the number of sample data of each body indicator under each health state level is increased, and the generation process of the SVM model is repeated until the generated SVM model is accurate.
  • the rate is greater than or equal to the preset accuracy, for example, 99%.
  • the data obtaining module 46 acquires monitoring data of the physical indicators of each of the plurality of users by the edge node, and outputs the warning information to adopt when the cloud server detects that the number of users whose physical indicators are abnormal exceeds the threshold value.
  • Prevention and control measures For example, the number of users who have detected the flu indicator in the cloud server has increased sharply over a period of time, indicating that the flu outbreak requires prevention and control measures.
  • each edge node corresponds to one medical device, and the types of body indicator types measured by each edge node are the same or different.
  • Each edge node is in communication with the cloud server, and each edge node has edge computing capabilities.
  • Each edge node can monitor the user's body and perform edge calculation based on the monitored indicator data to determine the category of the indicator data (eg, normal category, light category, medium category, etc.), and the indicator data category
  • the data is sent to the cloud server, and the data sharing between the edge nodes is implemented, the computing burden of the cloud server is reduced, the transit time of the data center under the original cloud computing model is reduced, and the real-time performance of the data processing is improved.
  • the above-described integrated unit implemented in the form of a software program module can be stored in a non-volatile readable storage medium.
  • the software program module described above is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to perform the method of each embodiment of the present invention. Part of the steps.
  • the electronic device 5 comprises at least one transmitting device 51, at least one memory 52, at least one processor 53, at least one receiving device 54, and at least one communication bus.
  • the communication bus is used to implement connection communication between these components.
  • the electronic device 5 corresponds to an edge node and is in communication with the cloud server.
  • the electronic device 5 is a device capable of automatically performing numerical calculation and/or information processing according to an instruction set or stored in advance, and the hardware includes, but not limited to, a microprocessor, an application specific integrated circuit (ASIC). ), Field-Programmable Gate Array (FPGA), Digital Signal Processor (DSP), embedded devices, etc.
  • the electronic device 5 may also comprise a network device and/or a user device.
  • the network device includes, but is not limited to, a single network server, a server group composed of multiple network servers, or a cloud computing-based cloud composed of a large number of hosts or network servers, where the cloud computing is distributed computing.
  • a super virtual computer consisting of a group of loosely coupled computers.
  • the electronic device 5 can be, but is not limited to, any electronic product that can interact with a user through a keyboard, a touch pad, or a voice control device, such as a tablet, a smart phone, or a personal digital assistant (Personal Digital Assistant). , PDA), smart wearable devices, camera equipment, monitoring equipment and other terminals.
  • a keyboard e.g., a keyboard
  • a touch pad e.g., a touch pad
  • a voice control device such as a tablet, a smart phone, or a personal digital assistant (Personal Digital Assistant). , PDA), smart wearable devices, camera equipment, monitoring equipment and other terminals.
  • PDA Personal Digital Assistant
  • the network in which the electronic device 5 and the cloud server are located includes, but is not limited to, the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), and the like.
  • the Internet includes, but is not limited to, the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), and the like.
  • VPN virtual private network
  • the receiving device 54 and the sending device 51 may be wired transmission ports, or may be wireless devices, for example, including antenna devices, for performing data communication with other devices.
  • the memory 52 is configured to store program code.
  • the memory 52 may be a circuit having a storage function, such as a RAM (Random-Access Memory), a FIFO (First In First Out), or the like, which is not in a physical form in the integrated circuit.
  • the memory 52 may also be a memory having a physical form, such as a memory stick, a TF card (Trans-flash Card), a smart media card, a secure digital card, a flash memory card.
  • Storage devices such as (flash card) and the like.
  • the processor 53 can include one or more microprocessors, digital processors.
  • the processor can invoke program code stored in the memory to perform related functions, and the processor can invoke program code stored in the memory to perform related functions.
  • the various modules described in FIG. 3 are program code stored in the memory and executed by the processor to implement an edge calculation based health monitoring method.
  • the processor 53 also known as a central processing unit (CPU), is a very large-scale integrated circuit, which is a computing core (Core) and a control unit (Control Unit).
  • the processor 53 may invoke program code stored in the memory 52 to perform related functions.
  • the various modules described in FIG. 3 are program code stored in the memory 52 and executed by the processor 53 to implement an edge calculation based health monitoring method.
  • Embodiments of the present invention also provide a non-volatile readable storage medium having stored thereon computer instructions that, when executed by an edge-based health monitoring device including one or more processors, are based on edges
  • the calculated health monitoring device performs an edge calculation based health monitoring method as described in the method embodiments above.
  • the memory stores a plurality of instructions to implement an edge calculation-based health monitoring method
  • the processor may execute the plurality of instructions to: detect a user's physical indicator. First data; when determining that the first data of the physical indicator is normal, transmitting the first data of the physical indicator to a cloud server in communication with the edge node; when determining that the first data of the physical indicator is abnormal Sending a request broadcast of the assistance data requesting the target time period of the physical indicator; acquiring the auxiliary data based on the response instruction of the request broadcast; and utilizing the indicator risk level based on the auxiliary data and the first data
  • the analysis model determines a level of the physical indicator, and sends the level of the physical indicator to the cloud server.
  • the integrated circuit of the present invention is installed in an electronic device, and causes the electronic device to perform the following functions: detecting first data of the user's physical index; and when determining that the first data of the physical index is normal, the physical indicator is Sending a data to a cloud server in communication with the edge node; when determining that the first data of the body indicator is abnormal, transmitting a request broadcast requesting assistance data within the target time period of the body indicator; broadcasting based on the request And responding to the instruction, acquiring the auxiliary data; determining, according to the auxiliary data and the first data, a level of the physical indicator by using an indicator risk level analysis model, and sending the level of the physical indicator to the cloud server.
  • the functions that can be implemented by the edge computing-based health monitoring method in any of the embodiments can be installed in an electronic device by using the integrated circuit of the present invention, so that the electronic device can perform the edge computing-based health monitoring method according to any embodiment.
  • the functions that can be implemented are not described in detail here.
  • the disclosed apparatus may be implemented in other ways.
  • the device embodiments described above are merely illustrative.
  • the division of the unit is only a logical function division.
  • there may be another division manner for example, multiple units or components may be combined or may be Integrate into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be electrical or otherwise.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a non-volatile readable storage medium.
  • the technical solution of the present invention which is essential or contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product stored in a storage medium.
  • a number of instructions are included to cause a computer device (which may be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention.
  • the foregoing storage medium includes: a U disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and the like. .

Abstract

本发明提供一种基于边缘计算的健康监测方法：检测用户的身体指标的第一数据；当确定所述身体指标的第一数据正常时，将所述身体指标的第一数据发送至与边缘节点相通信的云服务器；当确定所述身体指标的第一数据异常时，发送请求所述身体指标的目标时间段内的辅助数据的请求广播；基于所述请求广播的响应指令，获取所述辅助数据；基于所述辅助数据及所述第一数据，利用指标风险等级分析模型确定所述身体指标的级别，将所述身体指标的级别发送至所述云服务器。本发明还提供一种基于边缘计算的健康监测装置、电子设备及存储介质。本发明能实现边缘节点之间的数据共享，减少云服务器的计算负担，提高数据处理的实时性。

Description

基于边缘计算的健康监测方法、装置、设备及存储介质
本申请要求于2018年04月04日提交中国专利局，申请号为201810301873.4发明名称为“基于边缘计算的健康监测方法、装置、设备及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及人工智能领域，尤其涉及一种基于边缘计算的健康监测方法、装置、设备及存储介质。
背景技术
医疗设备应用将会越来越广泛，而现有的医疗设备只具备数据记录功能而不具备实时分析功能。而且每个医疗设备都是独立的个体，无法实现医疗设备之间的数据通信。虽然基于物联网医疗的云架构能将多个医疗设备之间进行关联，但目前的云计算大多采用集中式管理的方法，这使云服务创造出较高的经济效益，而在万物互联的背景下，应用服务需要低延时、高可靠性以及数据安全，而传统云计算在对身体状态监测上无法满足这些需求。
发明内容
鉴于以上内容，有必要提供一种基于边缘计算的健康监测方法、装置、设备及存储介质，能减少云服务器的计算负担，降低了原有云计算模型下的数据中心的中转时间，提高数据处理的实时性。
一种基于边缘计算的健康监测方法，所述方法包括：
检测用户的身体指标的第一数据；
当确定所述身体指标的第一数据正常时，将所述身体指标的第一数据发送至与边缘节点相通信的云服务器；
当确定所述身体指标的第一数据异常时，发送请求所述身体指标的目标时间段内的辅助数据的请求广播；
基于所述请求广播的响应指令，获取所述辅助数据；
基于所述辅助数据及所述第一数据，利用指标风险等级分析模型确定所述身体指标的级别，将所述身体指标的级别发送至所述云服务器。
一种基于边缘计算的健康监测装置，所述装置包括：
检测模块，用于检测用户的身体指标的第一数据；
当确定所述身体指标的第一数据正常时，发送模块，用于将所述身体指标的第一数据发送至与边缘节点相通信的云服务器；
当确定所述身体指标的第一数据异常时，所述发送模块还用于发送请求所述身体指标的目标时间段内的辅助数据的请求广播；
获取模块，用于基于所述请求广播的响应指令，获取所述辅助数据；
确定模块，用于基于所述辅助数据及所述第一数据，利用指标风险等级分析模型确定所述身体指标的级别，将所述身体指标的级别发送至所述云服务器。
一种电子设备，所述电子设备包括存储器及处理器，所述存储器用于存储至少一个指令，所述处理器用于执行所述至少一个指令以实现任意实施例中所述基于边缘计算的健康监测方法。
一种非易失性可读存储介质，所述非易失性可读存储介质存储有至少一个指令，所述至少一个指令被处理器执行时实现任意实施例中所述基于边缘计算的健康监测方法。
由以上技术方案可知，本发明检测用户的身体指标的第一数据；当确定所述身体指标的第一数据正常时，将所述身体指标的第一数据发送至与边缘节点相通信的云服务器；当确定所述身体指标的第一数据异常时，发送请求所述身体指标的目标时间段内的辅助数据的请求广播；基于所述请求广播的响应指令，获取所述辅助数据；基于所述辅助数据及所述第一数据，利用指标风险等级分析模型确定所述身体指标的级别，将所述身体指标的级别发送至所述云服务器。本发明能实现边缘节点之间的数据共享，减少云服务器的计算负担，降低了原有云计算模型下的数据中心的中转时间，提高数据处理的实时性。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据提供的附图获得其他的附图。
图1是实现本发明的基于边缘计算的健康监测方法的较佳实施例的应用环境图。
图2是本发明基于边缘计算的健康监测方法的第一较佳实施例的流程图。
图3是本发明基于边缘计算的健康监测方法的第二较佳实施例的流程图。
图4是本发明基于边缘计算的健康监测装置的较佳实施例的程序模块图。
图5是本发明至少一个实例中基于边缘计算的健康监测设备的较佳实施例的结构示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合附图和具体实施方式对本发明作进一步详细的说明。
为了使本技术领域的人员更好地理解本发明方案，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分的实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本发明保护的范围。
本发明的说明书和权利要求书及上述附图中的术语“第一”、“第二”和“第三”等是用于区别不同对象，而非用于描述特定顺序。此外，术语“包括”以及它们任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、系统、产品或设备没有限定于已列出的步骤或单元，而是可选地还包括没有列出的步骤或单元，或可选地还包括对于这些过程、方法、 产品或设备固有的其它步骤或单元。
如图1所示，图1是实现本发明的基于边缘计算的健康监测方法的较佳实施例的应用环境图。所述应用环境图包括云服务器及多个边缘节点。每个边缘节点对应一个医疗设备，由于每个医疗设备的用途可能相同或者不同，因此，每个边缘节点测量的身体指标种类的种类相同或者不同。每个边缘节点与所述云服务器相通信，且每个边缘节点具有边缘计算能力。每个边缘节点能对用户的身体进行监测，并基于监测的指标数据进行边缘计算，确定指标数据的类别(例如，正常类别、轻度类别、中度类别等等)，将所述指标数据类别发送至所述云服务器。
其中每个边缘节点与所述云服务器通过网络相通信，所处的网络包括，但不限于互联网、广域网、城域网、局域网、虚拟专用网络(Virtual Private Network，VPN)等。
结合以下实施例详述利用电子设备实现基于边缘计算的健康监测方法。
如图2所示，是本发明基于边缘计算的健康监测方法的第一较佳实施例的流程图。根据不同的需求，该流程图中步骤的顺序可以改变，某些步骤可以省略。
S20、所述边缘节点检测用户的身体指标的第一数据。
在本发明的可选实施例中，一个边缘节点对应一个医疗设备，医疗设备包括，但不限于：穿戴式医疗设备、医院检测医疗设备。所述穿戴式医疗设备是在用户身体部位随身穿戴的监测仪器，例如，所述边缘节点对应一个智能腕带，所述智能腕带上布置有一些小型的电极，可透过皮肤传导微弱电流，然后测量汗腺受刺激情况。所述医院检测医疗设备包括，但不限于各类获取用户身体指标数据的仪器。例如，化验设备、感测设备等等。本发明对医疗设备不做任何限制。
由于每个边缘节点对应的一个医疗设备，医疗设备的用途可能是不同的，当医疗设备不同时，检测的身体指标也不同。例如，血压计测量血压、心电监护仪测量心率数据。其中每个身体指标的第一数据包括多项数据。例如血压计测量的第一数据中包括最高血压、最低血压、稳定血压等等。
S21、所述边缘节点判断所述身体指标的第一数据是否正常。
在本发明的可选实施例中，每个医疗设备测量的身体指标的每项数据都配置有正常值范围。例如血压计测量的第一数据中包括最高血压、最低血压、稳定血压等等。最高血压范围为100至120单位、最低血压范围为50-70个单位等等。
优选地，当所述身体指标的第一数据中各项数据有一项数据不在所述一项数据对应的正常值范围内时，确定所述身体指标的第一数据异常，则需要进一步确定所述身体指标异常的原因，从而进一步判断身体指标的类别，即执行S23。
当所述身体指标的第一数据中各项数据都在各项数据对应的正常值范围内时，确定所述身体指标的第一数据正常，即执行S22。
S22、当确定所述身体指标的第一数据正常时，所述边缘节点将所述身体 指标的第一数据发送至与所述边缘节点相通信的云服务器。
优选地，所述云服务器存储每个边缘节点的通信地址、每个边缘节点测量的身体指标。所述通信地址包括，但不限于：IP地址等等。利用边缘节点的通信地址，从而实现两个边缘节点的边缘连接，边缘协同，边缘节点之间的数据共享。
优选地，所述云服务器还存储每个用户在边缘节点处的监测记录，所述监测记录包括，但不限于：监测的第一数据，监测的身体指标的级别等等。
S23、当确定所述身体指标的第一数据异常时，所述边缘节点发送请求所述身体指标的目标时间段内的辅助数据的请求广播。
优选地，所述目标时间段表示当前时间以前的某一时间段内，如前一周，前三天等等。
优选地，所述请求广播包括，但不限于：所述辅助数据的标识、所述辅助数据对应的用户标识等等。所述辅助数据的标识包括，但不限于：所述辅助数据的名称(如血常规五项等等)等等用于标识所述辅助数据的其他表现形式。所述辅助数据对应的用户标识用于标识用户的身份信息，例如，用户的名字、用户的身份证、用户的社保卡号等等。例如，当利用血压即测量出用户的需呀偏高时，为了进一步诊断用户的病情，需要了解血脂的粘稠情况，则可以请求与血脂指标相关的数据。若用户在目标时间段内检测过血脂指标，则可以通过所述请求广播的方式获取所述血脂指标，这样可以实现资源的整合和共享，从而避免用户再次重复检查等等。同时参考多种数据，也能为用户提供准确的医疗诊断。
进一步的，为了保证用户数据的隐私性，对所述请求广播进行加密传送。
优选地，所述边缘节点发送请求所述辅助数据的请求广播包括，但不限于以下一种或者多种的组合：
(1)所述边缘节点向所述云服务器发送所述请求广播以使所述云服务器从存储的监测记录中查找所述辅助数据。
(2)所述边缘节点向所述云服务器发送所述请求广播以使所述云服务器查找存储所述辅助数据的目标边缘节点。
(3)所述边缘节点基于存储的各个边缘节点对应的身体指标，查找与所述辅助数据匹配的边缘节点，并基于所述匹配的边缘节点的通信地址，发送请求广播以使所述匹配的边缘节点查找所述辅助数据。由于每个边缘节点也可以保存每个边缘节点对应的身体指标及每个边缘节点的通信地址，每个边缘节点也保存各自监测的记录，因此，所述边缘节点可以直接向所述匹配的边缘节点发送请求广播，这种方式从而减少云中心的存储压力，及增加各个边缘节点的数据共享。
例如，云服务器与三个边缘节点相通信，边缘节点A、边缘节点B、边缘节点C。若边缘节点A需要所述辅助数据，且所述边缘节点C有所述辅助数据，则所述边缘节点A可以直接向所述边缘节点C请求所述辅助数据。
(4)所述边缘节点基于存储的各个边缘节点的位置信息，获取所述边缘节点的目标区域内的边缘节点，再基于目标区域内的边缘节点对应的身体指 标，在所述目标区域内的边缘节点中筛选与所述辅助数据对应的边缘节点，并基于所述筛选的边缘节点的通信地址，发送请求广播以使所述筛选的边缘节点查找所述辅助数据。这样在一定区域内查找需要的边缘节点，可以减少搜索时间，提高实时性的监测。
例如，云服务器与三个边缘节点相通信，边缘节点A、边缘节点B、边缘节点C，其中边缘节点A与边缘节点C在同一区域范围内。若边缘节点A需要所述辅助数据，则所述边缘节点A向所述边缘节点C请求所述辅助数据。
S24，所述边缘节点基于所述请求广播的响应指令，获取所述辅助数据。
优选地，基于所述请求广播的响应指令，获取所述辅助数据包括，但不限于以下一种或者多种的组合：
(1)当所述响应指令中包括所述云服务器查找的所述辅助数据时，从所述响应指令中获取所述辅助数据。
(2)当所述响应指令中包括所述云服务器发送的包含所述辅助数据的目标边缘节点时，获取所述目标边缘节点的通信地址，向所述目标边缘节点发送请求信息以使所述目标边缘节点发送所述辅助数据，当所述目标边缘节点对所述请求信息验证通过后，接收所述目标边缘节点发送的所述辅助数据。
(3)当所述匹配的边缘节点发送的响应指令中包括所述辅助数据时，从所述响应指令中获取所述辅助数据。
(4)当所述筛选的边缘节点发送的响应指令中包括所述辅助数据时，从所述响应指令中获取所述辅助数据。
进一步地，当所有边缘节点没有所述辅助数据时，输出包含健康风险提示的提示信息以使用户跟踪检查。
在上述实施例，云服务器连接多个边缘节点，多个边缘节点之间在地理上是分布的，但具有各自的物理位置和网络结构。而且边缘节点之间能实现类似于点对点的边缘连接方式，从而实现数据的共享，也保证边缘节点上的边缘计算的实时性。
S25、基于所述辅助数据及所述第一数据，所述边缘节点利用指标风险等级分析模型确定所述身体指标的级别。
优选地，指标风险等级分析模型包括，但不限于：支持向量机(Support Vector Machine，SVM)模型。
进一步地，利用所述身体指标对应的训练样本，训练所述指标风险等级分析模型，训练过程如下：
配置所述身体指标的不同级别的训练样本数据，将不同级别的训练样本数据分发到不同的文件夹里。例如，一级风险等级的训练样本数据的分发到第一文件夹里；二级风险等级的训练样本数据分发到第二文件夹里；三级风险等级的训练样本数据分发到第三文件夹里；四级风险等级的训练样本数据分发到第四文件夹里。从不同文件夹下各提取第一预设比例，例如，70％的训练样本数据作为训练数据进行支持向量机(SVM)模型的训练，从不同文件夹下各取剩下的第二预设比例，例如，30％的训练样本数据作为测试数据以 对生成的SVM模型进行准确性验证。
若生成的SVM模型准确率小于预设准确率，例如，99％，则增加所述身体指标的不同级别的训练样本数据的获取数量，重复上述SVM模型的生成过程，直到生成的SVM模型准确率大于等于预设准确率，例如，99％。
S26，所述边缘节点将所述身体指标的级别发送至所述云服务器。进一步地，每个边缘节点将监测的身体指标的第一数据及监测的身体指标的级别发送至所述云服务器以使所述云服务器进行综合各项指标综合评估用户的健康状态。
通过上述实施例，每个边缘节点对应一个医疗设备，每个边缘节点测量的身体指标种类的种类相同或者不同。每个边缘节点与所述云服务器相通信，且每个边缘节点具有边缘计算能力。每个边缘节点能对用户的身体进行监测，并基于监测的指标数据进行边缘计算，确定指标数据的类别(例如，正常类别、轻度类别、中度类别等等)，将所述指标数据类别发送至所述云服务器，并且边缘节点之间还能实现数据共享，减少云服务器的计算负担，降低了原有云计算模型下的数据中心的中转时间，提高数据处理的实时性。
如图3所示，是本发明基于边缘计算的健康监测方法的第二较佳实施例的流程图。根据不同的需求，该流程图中步骤的顺序可以改变，某些步骤可以省略。
S30至S36分别与第一较佳实施例中的S20至S26对应，在此不再详述。
S37、所述云服务器获取用户在至少一个边缘节点中每个边缘节点对应的身体指标的级别。
在发明的可选实施例中，每个边缘节点在地理位置上是分布的，一个用户可以在多个边缘节点处进行各项身体指标的检查，例如在A医院化验血常规，B医院做B超检查等等。每个边缘节点对用户的监测数据发送至所述云服务器。
例如，所述云服务器包括，但不限于：用户指定医院的服务器、保险机构的服务器等等。
S38、所述云服务器基于每个边缘节点对应的身体指标的级别，利用健康状态等级分析模型确定用户的健康状态级别。
优选地，健康状态等级分析模型包括，但不限于：支持向量机(Support VectorMachine，SVM)模型。
进一步地，利用各个健康状态级别下的各个身体指标的样本数据，训练所述健康状态等级分析，训练过程如下：
配置各个健康状态级别下的各个身体指标的样本数据，将不同健康状态级别的样本数据分发到不同的文件夹里。例如，一级健康状态等级的样本数据的分发到第一文件夹里；二级健康状态等级的样本数据分发到第二文件夹里；三级健康状态等级的样本数据分发到第三文件夹里；四级健康状态等级的样本数据分发到第四文件夹里。从不同文件夹下各提取第一预设比例，例如，70％的样本数据作为训练数据进行支持向量机(SVM)模型的训练，从不 同文件夹下各取剩下的第二预设比例，例如，30％的样本数据作为测试数据以对生成的SVM模型进行准确性验证。
若生成的SVM模型准确率小于预设准确率，例如，99％，则增加各个健康状态级别下的各个身体指标的样本数据的获取数量，重复上述SVM模型的生成过程，直到生成的SVM模型准确率大于等于预设准确率，例如，99％。
优选地，所述云服务器获取每个边缘节点对多个用户的身体指标的监测数据，当所述云服务器监测到一项身体指标异常的用户数超过数量阈值时，输出警示信息以采用防控措施。例如，所述云服务器监测到流感指标的用户数在一段时间内暴增，说明流感爆发，需要采取防控措施。
通过上述实施例，每个边缘节点对应一个医疗设备，每个边缘节点测量的身体指标种类的种类相同或者不同。每个边缘节点与所述云服务器相通信，且每个边缘节点具有边缘计算能力。每个边缘节点能对用户的身体进行监测，并基于监测的指标数据进行边缘计算，确定指标数据的类别(例如，正常类别、轻度类别、中度类别等等)，将所述指标数据类别发送至所述云服务器，并且边缘节点之间还能实现数据共享，减少云服务器的计算负担，降低了原有云计算模型下的数据中心的中转时间，提高数据处理的实时性。
如图4所示，本发明基于边缘计算的健康监测装置的第一较佳实施例的程序模块图。所述基于边缘计算的健康监测装置4包括，但不限于以下一个或者多个模块：检测模块40、判断模块41、发送模块42、获取模块43、确定模块44及训练模块45。本发明所称的模块是指一种能够被基于边缘计算的健康监测装置4的处理器所执行并且能够完成固定功能的一系列计算机可读指令段，其存储在存储器中。关于各模块的功能将在后续的实施例中详述。
所述检测模块40检测用户的身体指标的第一数据。
在本发明的可选实施例中，一个边缘节点对应一个医疗设备，医疗设备包括，但不限于：穿戴式医疗设备、医院检测医疗设备。所述穿戴式医疗设备是在用户身体部位随身穿戴的监测仪器，例如，所述边缘节点对应一个智能腕带，所述智能腕带上布置有一些小型的电极，可透过皮肤传导微弱电流，然后测量汗腺受刺激情况。所述医院检测医疗设备包括，但不限于各类获取用户身体指标数据的仪器。例如，化验设备、感测设备等等。本发明对医疗设备不做任何限制。
由于每个边缘节点对应的一个医疗设备，医疗设备的用途可能是不同的，当医疗设备不同时，检测的身体指标也不同。例如，血压计测量血压、心电监护仪测量心率数据。其中每个身体指标的第一数据包括多项数据。例如血压计测量的第一数据中包括最高血压、最低血压、稳定血压等等。
所述判断模块41判断所述身体指标的第一数据是否正常。每个医疗设备测量的身体指标的每项数据都配置有正常值范围。例如血压计测量的第一数据中包括最高血压、最低血压、稳定血压等等。最高血压范围为100至120单位、最低血压范围为50-70个单位等等。
优选地，当所述身体指标的第一数据中各项数据有一项数据不在所述一项数据对应的正常值范围内时，所述判断模块41确定所述身体指标的第一 数据异常，则需要进一步确定所述身体指标异常的原因，从而进一步判断身体指标的类别。
当所述身体指标的第一数据中各项数据都在各项数据对应的正常值范围内时，所述判断模块41确定所述身体指标的第一数据正常。
当确定所述身体指标的第一数据正常时，所述发送模块42将所述身体指标的第一数据发送至与所述边缘节点相通信的云服务器。
优选地，所述云服务器存储每个边缘节点的通信地址、每个边缘节点测量的身体指标。所述通信地址包括，但不限于：IP地址等等。利用边缘节点的通信地址，从而实现两个边缘节点的边缘连接，边缘协同，边缘节点之间的数据共享。
优选地，所述云服务器还存储每个用户在边缘节点处的监测记录，所述监测记录包括，但不限于：监测的第一数据，监测的身体指标的级别等等。
当确定所述身体指标的第一数据异常时，所述发送模块42发送请求所述身体指标的目标时间段内的辅助数据的请求广播。
优选地，所述目标时间段表示当前时间以前的某一时间段内，如前一周，前三天等等。
优选地，所述请求广播包括，但不限于：所述辅助数据的标识、所述辅助数据对应的用户标识等等。所述辅助数据的标识包括，但不限于：所述辅助数据的名称(如血常规五项等等)等等用于标识所述辅助数据的其他表现形式。所述辅助数据对应的用户标识用于标识用户的身份信息，例如，用户的名字、用户的身份证、用户的社保卡号等等。例如，当利用血压即测量出用户的需呀偏高时，为了进一步诊断用户的病情，需要了解血脂的粘稠情况，则可以请求与血脂指标相关的数据。若用户在目标时间段内检测过血脂指标，则可以通过所述请求广播的方式获取所述血脂指标，这样可以实现资源的整合和共享，从而避免用户再次重复检查等等。同时参考多种数据，也能为用户提供准确的医疗诊断。
进一步的，为了保证用户数据的隐私性，对所述请求广播进行加密传送。
优选地，所述发送模块42发送请求所述辅助数据的请求广播包括，但不限于以下一种或者多种的组合：
(1)向所述云服务器发送所述请求广播以使所述云服务器从存储的监测记录中查找所述辅助数据。
(2)向所述云服务器发送所述请求广播以使所述云服务器查找存储所述辅助数据的目标边缘节点。
(3)基于存储的各个边缘节点对应的身体指标，查找与所述辅助数据匹配的边缘节点，并基于所述匹配的边缘节点的通信地址，发送请求广播以使所述匹配的边缘节点查找所述辅助数据。由于每个边缘节点也可以保存每个边缘节点对应的身体指标及每个边缘节点的通信地址，每个边缘节点也保存各自监测的记录，因此，所述边缘节点可以直接向所述匹配的边缘节点发送请求广播，这种方式从而减少云中心的存储压力，及增加各个边缘节点的数据共享。
例如，云服务器与三个边缘节点相通信，边缘节点A、边缘节点B、边缘节点C。若边缘节点A需要所述辅助数据，且所述边缘节点C有所述辅助数据，则所述边缘节点A可以直接向所述边缘节点C请求所述辅助数据。
(4)基于存储的各个边缘节点的位置信息，获取所述边缘节点的目标区域内的边缘节点，再基于目标区域内的边缘节点对应的身体指标，在所述目标区域内的边缘节点中筛选与所述辅助数据对应的边缘节点，并基于所述筛选的边缘节点的通信地址，发送请求广播以使所述筛选的边缘节点查找所述辅助数据。这样在一定区域内查找需要的边缘节点，可以减少搜索时间，提高实时性的监测。
例如，云服务器与三个边缘节点相通信，边缘节点A、边缘节点B、边缘节点C，其中边缘节点A与边缘节点C在同一区域范围内。若边缘节点A需要所述辅助数据，则所述边缘节点A向所述边缘节点C请求所述辅助数据。
所述获取模块43基于所述请求广播的响应指令，获取所述辅助数据。
优选地，所述获取模块43基于所述请求广播的响应指令，获取所述辅助数据包括，但不限于以下一种或者多种的组合：
(1)当所述响应指令中包括所述云服务器查找的所述辅助数据时，从所述响应指令中获取所述辅助数据。
(2)当所述响应指令中包括所述云服务器发送的包含所述辅助数据的目标边缘节点时，获取所述目标边缘节点的通信地址，向所述目标边缘节点发送请求信息以使所述目标边缘节点发送所述辅助数据，当所述目标边缘节点对所述请求信息验证通过后，接收所述目标边缘节点发送的所述辅助数据。
(3)当所述匹配的边缘节点发送的响应指令中包括所述辅助数据时，从所述响应指令中获取所述辅助数据。
(4)当所述筛选的边缘节点发送的响应指令中包括所述辅助数据时，从所述响应指令中获取所述辅助数据。
进一步地，当所有边缘节点没有所述辅助数据时，输出包含健康风险提示的提示信息以使用户跟踪检查。
在上述实施例，云服务器连接多个边缘节点，多个边缘节点之间在地理上是分布的，但具有各自的物理位置和网络结构。而且边缘节点之间能实现类似于点对点的边缘连接方式，从而实现数据的共享，也保证边缘节点上的边缘计算的实时性。
基于所述辅助数据及所述第一数据，所述确定模块44利用指标风险等级分析模型确定所述身体指标的级别。
优选地，指标风险等级分析模型包括，但不限于：支持向量机(Support Vector Machine，SVM)模型。
进一步地，训练模块45利用所述身体指标对应的训练样本，训练所述指标风险等级分析模型，训练过程如下：
配置所述身体指标的不同级别的训练样本数据，将不同级别的训练样本数据分发到不同的文件夹里。例如，一级风险等级的训练样本数据的分发到 第一文件夹里；二级风险等级的训练样本数据分发到第二文件夹里；三级风险等级的训练样本数据分发到第三文件夹里；四级风险等级的训练样本数据分发到第四文件夹里。从不同文件夹下各提取第一预设比例，例如，70％的训练样本数据作为训练数据进行支持向量机(SVM)模型的训练，从不同文件夹下各取剩下的第二预设比例，例如，30％的训练样本数据作为测试数据以对生成的SVM模型进行准确性验证。
若生成的SVM模型准确率小于预设准确率，例如，99％，则增加所述身体指标的不同级别的训练样本数据的获取数量，重复上述SVM模型的生成过程，直到生成的SVM模型准确率大于等于预设准确率，例如，99％。
所述发送模块42将所述身体指标的级别发送至所述云服务器。进一步地，每个边缘节点将监测的身体指标的第一数据及监测的身体指标的级别发送至所述云服务器以使所述云服务器进行综合各项指标综合评估用户的健康状态。
通过上述实施例，每个边缘节点对应一个医疗设备，每个边缘节点测量的身体指标种类的种类相同或者不同。每个边缘节点与所述云服务器相通信，且每个边缘节点具有边缘计算能力。每个边缘节点能对用户的身体进行监测，并基于监测的指标数据进行边缘计算，确定指标数据的类别(例如，正常类别、轻度类别、中度类别等等)，将所述指标数据类别发送至所述云服务器，并且边缘节点之间还能实现数据共享，减少云服务器的计算负担，降低了原有云计算模型下的数据中心的中转时间，提高数据处理的实时性。
在可选实施例中，所述基于边缘计算的健康监测装置4还包括位于云服务器中的一个或者多个模块：数据获取模块46、级别确定模块47、模型训练模块48。
所述数据获取模块46获取用户在至少一个边缘节点中每个边缘节点对应的身体指标的级别。
在发明的可选实施例中，每个边缘节点在地理位置上是分布的，一个用户可以在多个边缘节点处进行各项身体指标的检查，例如在A医院化验血常规，B医院做B超检查等等。每个边缘节点对用户的监测数据发送至所述云服务器。
例如，所述云服务器包括，但不限于：用户指定医院的服务器、保险机构的服务器等等。
所述级别确定模块47基于每个边缘节点对应的身体指标的级别，利用健康状态等级分析模型确定用户的健康状态级别。
优选地，健康状态等级分析模型包括，但不限于：支持向量机(Support Vector Machine，SVM)模型。
进一步地，模型训练模块48利用各个健康状态级别下的各个身体指标的样本数据，训练所述健康状态等级分析，训练过程如下：
配置各个健康状态级别下的各个身体指标的样本数据，将不同健康状态级别的样本数据分发到不同的文件夹里。例如，一级健康状态等级的样本数据的分发到第一文件夹里；二级健康状态等级的样本数据分发到第二文件夹 里；三级健康状态等级的样本数据分发到第三文件夹里；四级健康状态等级的样本数据分发到第四文件夹里。从不同文件夹下各提取第一预设比例，例如，70％的样本数据作为训练数据进行支持向量机(SVM)模型的训练，从不同文件夹下各取剩下的第二预设比例，例如，30％的样本数据作为测试数据以对生成的SVM模型进行准确性验证。
若生成的SVM模型准确率小于预设准确率，例如，99％，则增加各个健康状态级别下的各个身体指标的样本数据的获取数量，重复上述SVM模型的生成过程，直到生成的SVM模型准确率大于等于预设准确率，例如，99％。
优选地，所述数据获取模块46获取每个边缘节点对多个用户的身体指标的监测数据，当所述云服务器监测到一项身体指标异常的用户数超过数量阈值时，输出警示信息以采用防控措施。例如，所述云服务器监测到流感指标的用户数在一段时间内暴增，说明流感爆发，需要采取防控措施。
通过上述实施例，每个边缘节点对应一个医疗设备，每个边缘节点测量的身体指标种类的种类相同或者不同。每个边缘节点与所述云服务器相通信，且每个边缘节点具有边缘计算能力。每个边缘节点能对用户的身体进行监测，并基于监测的指标数据进行边缘计算，确定指标数据的类别(例如，正常类别、轻度类别、中度类别等等)，将所述指标数据类别发送至所述云服务器，并且边缘节点之间还能实现数据共享，减少云服务器的计算负担，降低了原有云计算模型下的数据中心的中转时间，提高数据处理的实时性。
上述以软件程序模块的形式实现的集成的单元，可以存储在一个非易失性可读取存储介质中。上述软件程序模块存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或处理器(processor)执行本发明每个实施例所述方法的部分步骤。
如图5所示，电子设备5包括至少一个发送装置51、至少一个存储器52、至少一个处理器53、至少一个接收装置54以及至少一个通信总线。其中，所述通信总线用于实现这些组件之间的连接通信。所述电子设备5对应一个边缘节点，并与云服务器相通信。
所述电子设备5是一种能够按照事先设定或存储的指令，自动进行数值计算和/或信息处理的设备，其硬件包括但不限于微处理器、专用集成电路(Application Specific Integrated Circuit，ASIC)、可编程门阵列(Field－Programmable Gate Array，FPGA)、数字处理器(Digital Signal Processor，DSP)、嵌入式设备等。电子设备5还可包括网络设备和/或用户设备。其中，所述网络设备包括但不限于单个网络服务器、多个网络服务器组成的服务器组或基于云计算(Cloud Computing)的由大量主机或网络服务器构成的云，其中，云计算是分布式计算的一种，由一群松散耦合的计算机集组成的一个超级虚拟计算机。
所述电子设备5可以是，但不限于任何一种可与用户通过键盘、触摸板或声控设备等方式进行人机交互的电子产品，例如，平板电脑、智能手机、个人数字助理(Personal Digital Assistant，PDA)、智能式穿戴式设备、摄像设备、监控设备等终端。
所述电子设备5及所述云服务器所处的网络包括，但不限于互联网、广域网、城域网、局域网、虚拟专用网络(Virtual Private Network，VPN)等。
其中，所述接收装置54和所述发送装置51可以是有线发送端口，也可以为无线设备，例如包括天线装置，用于与其他设备进行数据通信。
所述存储器52、用于存储程序代码。所述存储器52可以是集成电路中没有实物形式的具有存储功能的电路，如RAM(Random-Access Memory，随机存取存储器)、FIFO(First In First Out，)等。或者，所述存储器52也可以是具有实物形式的存储器，如内存条、TF卡(Trans-flash Card)、智能媒体卡(smart media card)、安全数字卡(secure digital card)、快闪存储器卡(flash card)等储存设备等等。
所述处理器53可以包括一个或者多个微处理器、数字处理器。所述处理器可调用所述存储器中存储的程序代码以执行相关的功能，所述处理器可调用所述存储器中存储的程序代码以执行相关的功能。例如，图3中所述的各个模块是存储在所述存储器中的程序代码，并由所述处理器所执行，以实现一种基于边缘计算的健康监测方法。所述处理器53又称中央处理器(CPU，Central Processing Unit)，是一块超大规模的集成电路，是运算核心(Core)和控制核心(Control Unit)。
在其他实施例中，所述处理器53可调用所述存储器52中存储的程序代码以执行相关的功能。例如，图3中所述的各个模块是存储在所述存储器52中的程序代码，并由处理器53所执行，以实现一种基于边缘计算的健康监测方法。
本发明实施例还提供一种非易失性可读存储介质，其上存储有计算机指令，所述指令当被包括一个或多个处理器的基于边缘计算的健康监测设备执行时，使基于边缘计算的健康监测设备执行如上文方法实施例所述的基于边缘计算的健康监测方法。
优选地，结合图2及图3所示，所述存储器存储多个指令以实现一种基于边缘计算的健康监测方法，所述处理器可执行所述多个指令从而实现：检测用户的身体指标的第一数据；当确定所述身体指标的第一数据正常时，将所述身体指标的第一数据发送至与边缘节点相通信的云服务器；当确定所述身体指标的第一数据异常时，发送请求所述身体指标的目标时间段内的辅助数据的请求广播；基于所述请求广播的响应指令，获取所述辅助数据；基于所述辅助数据及所述第一数据，利用指标风险等级分析模型确定所述身体指标的级别，将所述身体指标的级别发送至所述云服务器。
以上说明的本发明的特征性的手段可以通过集成电路来实现，并控制实现上述任意实施例中所述基于边缘计算的健康监测方法的功能。即，本发明的集成电路安装于电子设备中，使电子设备发挥如下功能：检测用户的身体指标的第一数据；当确定所述身体指标的第一数据正常时，将所述身体指标的第一数据发送至与边缘节点相通信的云服务器；当确定所述身体指标的第一数据异常时，发送请求所述身体指标的目标时间段内的辅助数据的请求广播；基于所述请求广播的响应指令，获取所述辅助数据；基于所述辅助数据及所述第一数据，利用指标风险等级分析模型确定所述身体指标的级别，将所述身体指标的级别发送至所 述云服务器。
在任意实施例中所述基于边缘计算的健康监测方法所能实现的功能都能通过本发明的集成电路安装于电子设备中，使电子设备发挥任意实施例中所述基于边缘计算的健康监测方法所能实现的功能，在此不再详述。
需要说明的是，对于前述的各方法实施例，为了简单描述，故将其都表述为一系列的动作组合，但是本领域技术人员应该知悉，本发明并不受所描述的动作顺序的限制，因为依据本发明，某些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作和模块并不一定是本发明所必须的。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其他实施例的相关描述。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置，可通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本发明的各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个非易失性可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可为个人计算机、服务器或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、移动硬盘、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。

Claims (20)

  1. 一种基于边缘计算的健康监测方法，其特征在于，所述方法包括：
    检测用户的身体指标的第一数据；
    当确定所述身体指标的第一数据正常时，将所述身体指标的第一数据发送至与边缘节点相通信的云服务器；
    当确定所述身体指标的第一数据异常时，发送请求所述身体指标的目标时间段内的辅助数据的请求广播；
    基于所述请求广播的响应指令，获取所述辅助数据；
    基于所述辅助数据及所述第一数据，利用指标风险等级分析模型确定所述身体指标的级别，将所述身体指标的级别发送至所述云服务器。
  2. 如权利要求1所述的基于边缘计算的健康监测方法，其特征在于，所述发送请求所述身体指标的目标时间段内的辅助数据的请求广播包括以下一种或者多种的组合：
    向所述云服务器发送所述请求广播以使所述云服务器从存储的监测记录中查找所述辅助数据；
    向所述云服务器发送所述请求广播以使所述云服务器查找存储所述辅助数据的目标边缘节点；
    基于存储的各个边缘节点对应的身体指标，查找与所述辅助数据匹配的边缘节点，并基于所述匹配的边缘节点的通信地址，发送请求广播以使所述匹配的边缘节点查找所述辅助数据；
    基于存储的各个边缘节点的位置信息，获取所述边缘节点的目标区域内的边缘节点，再基于目标区域内的边缘节点对应的身体指标，在所述目标区域内的边缘节点中筛选与所述辅助数据对应的边缘节点，并基于所述筛选的边缘节点的通信地址，发送请求广播以使所述筛选的边缘节点查找所述辅助数据。
  3. 如权利要求2所述的基于边缘计算的健康监测方法，其特征在于，所述基于所述请求广播的响应指令，获取所述辅助数据包括以下一种或者多种的组合：
    当所述响应指令中包括所述云服务器查找的所述辅助数据时，从所述响应指令中获取所述辅助数据；
    当所述响应指令中包括所述云服务器发送的包含所述辅助数据的目标边缘节点时，获取所述目标边缘节点的通信地址，向所述目标边缘节点发送请求信息以使所述目标边缘节点发送所述辅助数据，当所述目标边缘节点对所述请求信息验证通过后，接收所述目标边缘节点发送的所述辅助数据；
    当所述匹配的边缘节点发送的响应指令中包括所述辅助数据时，从所述响应指令中获取所述辅助数据；
    当所述筛选的边缘节点发送的响应指令中包括所述辅助数据时，从所述响应指令中获取所述辅助数据。
  4. 如权利要求3所述的基于边缘计算的健康监测方法，其特征在于，所述方法还包括：
    当所有边缘节点没有所述辅助数据时，输出包含健康风险提示的提示信息以使用户跟踪检查。
  5. 如权利要求1所述的基于边缘计算的健康监测方法，其特征在于，所述云服务器获取用户在至少一个边缘节点中每个边缘节点对应的身体指标的级别；
    所述云服务器基于每个边缘节点对应的身体指标的级别，利用健康状态等级分析模型确定用户的健康状态级别。
  6. 如权利要求1所述的基于边缘计算的健康监测方法，其特征在于，所述云服务器获取每个边缘节点对多个用户的身体指标的监测数据，当所述云服务器监测到一项身体指标异常的用户数超过数量阈值时，输出警示信息以采用防控措施。
  7. 如权利要求1所述的基于边缘计算的健康监测方法，其特征在于，各个边缘节点与云服务器在地理位置上是分布式的。
  8. 一种基于边缘计算的健康监测装置，其特征在于，所述装置包括：
    检测模块，用于检测用户的身体指标的第一数据；
    当确定所述身体指标的第一数据正常时，发送模块，用于将所述身体指标的第一数据发送至与边缘节点相通信的云服务器；
    当确定所述身体指标的第一数据异常时，所述发送模块还用于发送请求所述身体指标的目标时间段内的辅助数据的请求广播；
    获取模块，用于基于所述请求广播的响应指令，获取所述辅助数据；
    确定模块，用于基于所述辅助数据及所述第一数据，利用指标风险等级分析模型确定所述身体指标的级别，将所述身体指标的级别发送至所述云服务器。
  9. 一种电子设备，其特征在于，所述电子设备包括存储器及处理器，所述存储器用于存储至少一个指令，所述处理器用于执行所述至少一个指令以实现以下步骤：
    检测用户的身体指标的第一数据；
    当确定所述身体指标的第一数据正常时，将所述身体指标的第一数据发送至与边缘节点相通信的云服务器；
    当确定所述身体指标的第一数据异常时，发送请求所述身体指标的目标时间段内的辅助数据的请求广播；
    基于所述请求广播的响应指令，获取所述辅助数据；
    基于所述辅助数据及所述第一数据，利用指标风险等级分析模型确定所述身体指标的级别，将所述身体指标的级别发送至所述云服务器。
  10. 如权利要求9所述的电子设备，其特征在于，所述发送请求所述身体指标的目标时间段内的辅助数据的请求广播包括以下一种或者多种的组合：
    向所述云服务器发送所述请求广播以使所述云服务器从存储的监测记录中查找所述辅助数据；
    向所述云服务器发送所述请求广播以使所述云服务器查找存储所述辅助数据的目标边缘节点；
    基于存储的各个边缘节点对应的身体指标，查找与所述辅助数据匹配的边缘节点，并基于所述匹配的边缘节点的通信地址，发送请求广播以使所述匹配的 边缘节点查找所述辅助数据；
    基于存储的各个边缘节点的位置信息，获取所述边缘节点的目标区域内的边缘节点，再基于目标区域内的边缘节点对应的身体指标，在所述目标区域内的边缘节点中筛选与所述辅助数据对应的边缘节点，并基于所述筛选的边缘节点的通信地址，发送请求广播以使所述筛选的边缘节点查找所述辅助数据。
  11. 如权利要求10所述的电子设备，其特征在于，所述基于所述请求广播的响应指令，获取所述辅助数据包括以下一种或者多种的组合：
    当所述响应指令中包括所述云服务器查找的所述辅助数据时，从所述响应指令中获取所述辅助数据；
    当所述响应指令中包括所述云服务器发送的包含所述辅助数据的目标边缘节点时，获取所述目标边缘节点的通信地址，向所述目标边缘节点发送请求信息以使所述目标边缘节点发送所述辅助数据，当所述目标边缘节点对所述请求信息验证通过后，接收所述目标边缘节点发送的所述辅助数据；
    当所述匹配的边缘节点发送的响应指令中包括所述辅助数据时，从所述响应指令中获取所述辅助数据；
    当所述筛选的边缘节点发送的响应指令中包括所述辅助数据时，从所述响应指令中获取所述辅助数据。
  12. 如权利要求11所述的电子设备，其特征在于，所述处理器还用于执行所述至少一个指令以实现以下步骤：
    当所有边缘节点没有所述辅助数据时，输出包含健康风险提示的提示信息以使用户跟踪检查。
  13. 如权利要求9所述的电子设备，其特征在于，所述云服务器获取用户在至少一个边缘节点中每个边缘节点对应的身体指标的级别；
    所述云服务器基于每个边缘节点对应的身体指标的级别，利用健康状态等级分析模型确定用户的健康状态级别。
  14. 如权利要求9所述的电子设备，其特征在于，所述云服务器获取每个边缘节点对多个用户的身体指标的监测数据，当所述云服务器监测到一项身体指标异常的用户数超过数量阈值时，输出警示信息以采用防控措施。
  15. 一种非易失性可读存储介质，其特征在于，所述非易失性可读存储介质存储有至少一个指令，所述至少一个指令被处理器执行时实现以下步骤：
    检测用户的身体指标的第一数据；
    当确定所述身体指标的第一数据正常时，将所述身体指标的第一数据发送至与边缘节点相通信的云服务器；
    当确定所述身体指标的第一数据异常时，发送请求所述身体指标的目标时间段内的辅助数据的请求广播；
    基于所述请求广播的响应指令，获取所述辅助数据；
    基于所述辅助数据及所述第一数据，利用指标风险等级分析模型确定所述身体指标的级别，将所述身体指标的级别发送至所述云服务器。
  16. 如权利要求15所述的存储介质，其特征在于，所述发送请求所述身体指标的目标时间段内的辅助数据的请求广播包括以下一种或者多种的组合：
    向所述云服务器发送所述请求广播以使所述云服务器从存储的监测记录中查找所述辅助数据；
    向所述云服务器发送所述请求广播以使所述云服务器查找存储所述辅助数据的目标边缘节点；
    基于存储的各个边缘节点对应的身体指标，查找与所述辅助数据匹配的边缘节点，并基于所述匹配的边缘节点的通信地址，发送请求广播以使所述匹配的边缘节点查找所述辅助数据；
    基于存储的各个边缘节点的位置信息，获取所述边缘节点的目标区域内的边缘节点，再基于目标区域内的边缘节点对应的身体指标，在所述目标区域内的边缘节点中筛选与所述辅助数据对应的边缘节点，并基于所述筛选的边缘节点的通信地址，发送请求广播以使所述筛选的边缘节点查找所述辅助数据。
  17. 如权利要求16所述的存储介质，其特征在于，所述基于所述请求广播的响应指令，获取所述辅助数据包括以下一种或者多种的组合：
    当所述响应指令中包括所述云服务器查找的所述辅助数据时，从所述响应指令中获取所述辅助数据；
    当所述响应指令中包括所述云服务器发送的包含所述辅助数据的目标边缘节点时，获取所述目标边缘节点的通信地址，向所述目标边缘节点发送请求信息以使所述目标边缘节点发送所述辅助数据，当所述目标边缘节点对所述请求信息验证通过后，接收所述目标边缘节点发送的所述辅助数据；
    当所述匹配的边缘节点发送的响应指令中包括所述辅助数据时，从所述响应指令中获取所述辅助数据；
    当所述筛选的边缘节点发送的响应指令中包括所述辅助数据时，从所述响应指令中获取所述辅助数据。
  18. 如权利要求17所述的存储介质，其特征在于，所述至少一个指令被所述处理器执行时还实现以下步骤：
    当所有边缘节点没有所述辅助数据时，输出包含健康风险提示的提示信息以使用户跟踪检查。
  19. 如权利要求15所述的存储介质，其特征在于，所述云服务器获取用户在至少一个边缘节点中每个边缘节点对应的身体指标的级别；
    所述云服务器基于每个边缘节点对应的身体指标的级别，利用健康状态等级分析模型确定用户的健康状态级别。
  20. 如权利要求15所述的存储介质，其特征在于，所述云服务器获取每个边缘节点对多个用户的身体指标的监测数据，当所述云服务器监测到一项身体指标异常的用户数超过数量阈值时，输出警示信息以采用防控措施。
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CN109542457B (zh) * 2018-11-21 2022-02-01 四川长虹电器股份有限公司 一种边缘计算网络的分布式应用分发部署的系统及方法
CN110251098A (zh) * 2019-06-28 2019-09-20 深圳尚一互联技术有限公司 一种基于ble4.0的检测生猪健康状态的方法及系统
CN110867105B (zh) * 2019-10-23 2020-12-04 重庆特斯联智慧科技股份有限公司 一种基于边缘计算的家庭学习监督方法和系统
CN111212023A (zh) * 2019-10-30 2020-05-29 浙江中控自动化仪表有限公司 一种基于边缘计算网关的远程站间数据通讯方法
CN111212264B (zh) * 2019-12-27 2021-08-17 中移（杭州）信息技术有限公司 基于边缘计算的图像处理方法、装置、及存储介质
CN113132176B (zh) * 2019-12-31 2024-02-02 华为云计算技术有限公司 一种控制边缘节点的方法、节点及边缘计算系统
CN113314203A (zh) * 2020-02-27 2021-08-27 中移(成都)信息通信科技有限公司 医疗影像数据存储系统、方法、装置、设备及介质
CN111818173B (zh) * 2020-07-21 2023-06-09 郑州大学 基于主动大数据感知的用药提醒系统与方法
CN112133423A (zh) * 2020-08-28 2020-12-25 北京世纪互联宽带数据中心有限公司 基于边缘计算的医疗数据处理方法、装置及网络设备
CN112233816A (zh) * 2020-10-27 2021-01-15 贵州精准医疗电子有限公司 健康监测方法、装置及计算机可读介质
CN114895701B (zh) * 2022-04-18 2023-04-25 深圳织算科技有限公司 一种无人机巡检方法及系统

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150164376A1 (en) * 2013-12-17 2015-06-18 International Mobile Iot Corp System and device for healthcare monitoring
CN105581774A (zh) * 2014-10-22 2016-05-18 长天科技股份有限公司 遵循蓝牙低功耗通信协议的生理信息监测系统
CN105631186A (zh) * 2014-11-07 2016-06-01 中国移动通信集团公司 一种体征数据管理方法、装置和系统

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090204427A1 (en) * 2004-12-27 2009-08-13 Anuthep Benja-Athon Adaptable compositions of health information
EP1423046B2 (en) * 2001-08-13 2015-03-25 Novo Nordisk A/S Portable device of communicating medical data information
WO2016160841A1 (en) * 2015-03-30 2016-10-06 Zoll Medical Corporation Clinical data handoff in device management and data sharing
CN107133486A (zh) * 2017-06-07 2017-09-05 佛山市凡卡家具有限公司 一种基于健康监测智能枕头的辅助医疗云管理系统

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150164376A1 (en) * 2013-12-17 2015-06-18 International Mobile Iot Corp System and device for healthcare monitoring
CN105581774A (zh) * 2014-10-22 2016-05-18 长天科技股份有限公司 遵循蓝牙低功耗通信协议的生理信息监测系统
CN105631186A (zh) * 2014-11-07 2016-06-01 中国移动通信集团公司 一种体征数据管理方法、装置和系统

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