Information directional sharing method and system based on intelligent wearable device

Abstract

The invention is suitable for the technical field of directional sharing, and particularly relates to an information directional sharing method and system based on an intelligent wearable device, and the method comprises the steps: delimiting an activity range of a user, positioning a deployment position of an edge device, obtaining a pairing request of the intelligent wearable device, building a link, and reading a unique identifier, a unique identifier, a corresponding relation between a user and the intelligent wearable device are configured, at least two target terminals are determined, and a communication link between the edge device and the target terminals is established; and obtaining health data of the user based on the wearable device. By constructing the communication link, risks can be quickly identified, corresponding processing can be performed in time, the safety of the competition is greatly enhanced, the communication network performance is optimized by calculating the fluency of the communication link, and meanwhile, the abnormal conditions of the competitors are timely and quickly sent to rescue personnel, so that the rescue efficiency is greatly improved, and the safety of the competition is improved. And the safety guarantee of the match is enhanced.

Description

Technical Field

[0001] This invention relates to the field of targeted information sharing technology, and in particular to a method and system for targeted information sharing based on smart wearable devices. Background Technology

[0002] With economic development, more and more grassroots sports events are coming into people's view, such as marathons, fun runs and various ball games. However, since the participants are generally ordinary people, sudden long-term high-intensity exercise may cause the amount of exercise to exceed the body's capacity, leading to dehydration or even fainting and other accidents.

[0003] Therefore, during the event, health data of participants can be collected by distributing smart wearable devices (such as smartwatches, smart glasses, and health bracelets) and shared with medical personnel at the event site to monitor participants' health and prevent the aforementioned unexpected situations from occurring. However, when processing health data, the health data of multiple participants will be shared to the same medical personnel's terminal, which may lead to delays in data transmission and processing, making it impossible to respond promptly to the participants' physical conditions and increasing medical risks. Therefore, "how to perform targeted multicasting of participants' health data" is the technical problem that this invention aims to solve. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for targeted information sharing based on smart wearable devices, in order to solve the problem of "how to perform targeted multicast of participants' health data" mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for targeted information sharing based on smart wearable devices, the method comprising: Define the user's activity range, locate the deployment location of the edge device, obtain the pairing request of the smart wearable device and establish a link, read the unique identifier, configure the correspondence between the unique identifier, the user and the smart wearable device, determine the target terminal, wherein the number of target terminals is at least two, and build a communication link between the edge device and the target terminal. Based on wearable devices, the system acquires users' health data, creates data blocks that correspond one-to-one with each user, transfers the health data into the data blocks, links the data blocks to obtain a data block chain, splits the health data into multiple individual items, determines the normal fluctuation range of each individual item, defines the individual items whose health data exceeds the normal fluctuation range as abnormal items, defines the data blocks corresponding to the abnormal items as fluctuating data blocks, and pops the fluctuating data blocks to the top of the data block chain. The smoothness of each communication link is calculated and arranged in descending order of smoothness. Communication links with smoothness greater than a set value are marked as target links. A network topology management architecture is constructed and a load balancing mechanism is embedded to transfer the fluctuation blocks and target links into the network topology management architecture. When the health data exceeds the threshold, the health data is sliced, dangerous segments are selected, the dangerous segments are multicast using the target link, and the network topology management architecture is reloaded.

[0006] Furthermore, the steps of "delineating the user's activity range, locating the deployment location of the edge device, obtaining the pairing request from the smart wearable device, and establishing a connection" include: The abnormal item is divided into several levels, and the priority of each level is configured. The priority is synchronized to the fluctuation blocks, and a scheduling mechanism is introduced into the block chain.

[0007] Furthermore, the method also includes: The load of the edge device is monitored to determine the real-time load and a load threshold is configured. If the real-time load exceeds the load threshold, the interconnection channel between the edge devices is activated for load balancing. The health data received from the edge device is uploaded to the cloud to build a distributed AI model, which consists of an edge device part and a cloud part.

[0008] Furthermore, the step of "establishing a communication link between the edge device and the target terminal" includes: A self-monitoring mechanism is embedded in the edge device to preprocess the received health data; The self-monitoring mechanism is triggered to find missing items in the health data, identify a unique identifier, and generate a supplementary data collection instruction. The supplementary sampling instruction is sent to the corresponding smart wearable device via the link.

[0009] Furthermore, the step of "acquiring user health data based on wearable devices, creating data blocks that correspond one-to-one with the user, transferring the health data into the data blocks, and linking the data blocks to obtain a data block chain" includes: Collect user's motion data and correct any abnormal items; The system iterates through the pre-built scenario template library to find the target template, fills the exception item into the target template, generates a message summary, and pushes it to the corresponding smart wearable device.

[0010] Furthermore, the step of "calculating the smoothness of each communication link" includes: Determine the evaluation metrics for the communication link, wherein the evaluation metrics include: latency, bandwidth, and packet loss rate; Configure the weight of each evaluation indicator, and use the comprehensive evaluation formula to calculate the smoothness of each communication link; The comprehensive evaluation formula is as follows: ; Where S represents fluency, L represents latency, B represents bandwidth, and P represents packet loss rate; , and These are the weights corresponding to latency, bandwidth, and packet loss rate, respectively.

[0011] Furthermore, the step of "constructing a network topology management architecture and embedding a load balancing mechanism to transfer the fluctuation blocks and target links into the network topology management architecture" includes: Integrate redundant links into the network topology management architecture and issue permission to enable redundant links to the fluctuation block; A deep learning model is constructed, the motion data is fed into the deep learning model, and the predicted value of the health data is output. The predicted value is then used to make advance corrections to the network topology management architecture.

[0012] Furthermore, the step of "selecting dangerous segments and multicasting the dangerous segments using the target link" includes: The source of the dangerous segment is traced back, the number of the source segments is calculated, and the same number of target links are selected to obtain the emergency links; The source is defined as the sender of the emergency link.

[0013] Furthermore, the system includes: The module is used to define the user's activity range, locate the deployment location of the edge device, obtain the pairing request of the smart wearable device and establish a link, read the unique identifier, configure the correspondence between the unique identifier, the user and the smart wearable device, determine the target terminal, wherein the number of target terminals is at least two, and establish a communication link between the edge device and the target terminal. The pop-up module is used to acquire the user's health data based on the wearable device, create a data block that corresponds one-to-one with the user, transfer the health data into the data block, link the data blocks to obtain a data block chain, split the health data into multiple items, determine the normal fluctuation range of each item, define the item whose health data exceeds the normal fluctuation range as an abnormal item, define the data block corresponding to the abnormal item as a fluctuating data block, and pop the fluctuating data block to the top of the data block chain. The transfer module is used to calculate the smoothness of each communication link and arrange them in descending order of smoothness. Communication links with smoothness greater than a set value are marked as target links. A network topology management architecture is constructed and a load balancing mechanism is embedded to transfer the fluctuation blocks and target links into the network topology management architecture. The reconstruction module is used to slice the health data when the read health data exceeds the threshold, select dangerous segments, multicast the dangerous segments using the target link, and reload the network topology management architecture.

[0014] Furthermore, the construction module includes: A partitioning unit is used to divide the single abnormal item into several levels and configure the priority of each level; An introduction unit is used to synchronize the priority to the fluctuation blocks and introduce a scheduling mechanism into the block chain; A preprocessing unit is used to embed a self-monitoring mechanism in the edge device to preprocess the received health data; The identification unit is used to trigger the self-monitoring mechanism, find the missing items in the health data, identify the unique identifier, and generate a supplementary sampling instruction. The sending unit is used to send the supplementary sampling instruction to the corresponding smart wearable device via the link.

[0015] Compared with the prior art, the beneficial effects of the present invention are: By establishing links, the health of participants can be monitored, while significantly reducing the latency and cost of data transmission to the cloud, ensuring the timeliness of medical rescue. By identifying abnormal items, abnormal fluctuations in participants' physical indicators can be monitored, thereby promptly detecting health problems and preparing for rescue in advance. At the same time, it can also optimize competition strategies and ensure the smooth running of the competition. By building communication links, risks can be quickly identified and dealt with in a timely manner, greatly enhancing the safety of the competition. By calculating the smoothness of communication links, the performance of communication networks can be optimized, and abnormal situations of participants can be sent to medical personnel in a timely and rapid manner, greatly improving rescue efficiency and enhancing the safety of the competition. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0017] Figure 1 This is a flowchart illustrating a method for targeted information sharing based on smart wearable devices, as provided in an embodiment of the present invention.

[0018] Figure 2 This is a first sub-flowchart of the information-oriented sharing method based on smart wearable devices provided in an embodiment of the present invention.

[0019] Figure 3 This is a second sub-flow diagram of the information-oriented sharing method based on smart wearable devices provided in an embodiment of the present invention.

[0020] Figure 4 This is a third sub-flow diagram of the information-oriented sharing method based on smart wearable devices provided in an embodiment of the present invention.

[0021] Figure 5 The fourth sub-flowchart of the information-oriented sharing method based on smart wearable devices provided in the embodiments of the present invention.

[0022] Figure 6 This is a block diagram of an information-oriented sharing system based on smart wearable devices, provided in an embodiment of the present invention.

[0023] Figure 7 This is a block diagram of the components of the information-oriented sharing system based on smart wearable devices provided in an embodiment of the present invention.

[0024] Figure 8 This is a block diagram of the pop-up module in an information-oriented sharing system based on smart wearable devices provided in an embodiment of the present invention.

[0025] Figure 9 This is a block diagram of the transfer module in the information-oriented sharing system based on smart wearable devices provided in an embodiment of the present invention.

[0026] Figure 10 This is a block diagram of the reconstructing module in an information-oriented sharing system based on smart wearable devices, provided in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0028] In Example 1, Figure 1 The implementation flow of the information-oriented sharing method based on smart wearable devices provided in this embodiment of the invention is illustrated below, and is described in detail below: S100: Define the user's activity range, locate the deployment location of the edge device, obtain the pairing request of the smart wearable device and establish a link, read the unique identifier, configure the correspondence between the unique identifier, the user and the smart wearable device, determine the target terminal, wherein the number of target terminals is at least two, and build a communication link between the edge device and the target terminal.

[0029] Define the user's activity area, which is the competition venue area, such as a stadium, playground, or square. Deploy edge devices within the activity area. Edge devices are computing devices located close to the data source that can receive and process data from smart wearable devices and perform preliminary analysis on-site without transmitting the data to the cloud. At the same time, edge devices can also send suspicious data to the medical staff's terminal for decision-making.

[0030] It should be noted that in this embodiment, the user can be not only a participant, but also a referee or on-site logistics personnel.

[0031] Establish a link between the edge device and the smart wearable device. When processing data from the smart wearable device, the edge device needs to read out the unique identifier. Each smart wearable device has a unique identifier. After the edge device performs preliminary processing on the data from the smart wearable device, it also needs to send any suspicious or abnormal data to the target terminal. The target terminal can be a medical staff terminal, a participant's family member terminal, or a terminal pre-bound by the participant. The target terminal must include at least two medical staff terminals.

[0032] S200: Based on the wearable device, acquire the user's health data, create a data block that corresponds one-to-one with the user, transfer the health data into the data block, link the data blocks to obtain a data block chain, split the health data into multiple individual items, determine the normal fluctuation range of each individual item, define the individual item whose health data exceeds the normal fluctuation range as an abnormal individual item, define the data block corresponding to the abnormal individual item as a fluctuating data block, and pop the fluctuating data block to the top of the data block chain.

[0033] During the competition, smart wearable devices are used to collect users' health data. A data block is created for each user, and the user's health data is transferred into the corresponding data block. The data blocks of each user are linked to obtain a data block chain. The data block is the basic unit for storing data. In this embodiment, it is mainly used to store users' health data. The data block chain is the collection of health data of all users.

[0034] Once health data is received, it is broken down into multiple individual items. Based on common sense about physiology and by consulting existing data, the normal fluctuation range corresponding to each individual item can be determined. Individual items that exceed the fluctuation range are defined as abnormal items.

[0035] In real life, if a user's health data contains an abnormal item, it does not necessarily require immediate action from medical staff. This may just be a normal fluctuation in physiological indicators. However, it is necessary to pay close attention to this user. Specifically, the fluctuation data corresponding to this user should be popped to the top of the data block chain and monitored using a high-smoothness communication link.

[0036] S300: Calculate the smoothness of each communication link and arrange them in descending order of smoothness. Mark the communication links with smoothness greater than the set value as target links, construct a network topology management architecture, and embed a load balancing mechanism to transfer the fluctuation blocks and target links into the network topology management architecture.

[0037] Communication links with smoothness greater than a set value are defined as target links. The set value is predetermined by the competition organizer and is determined based on the number of participants and the bandwidth of edge devices. The network topology management architecture is mainly used to manage communication links, and the load balancing mechanism is mainly used to evenly distribute traffic in communication links with low smoothness to other multiple communication links. The network topology management architecture is used to manage fluctuation blocks, communication links, and target links.

[0038] When a user's health data fluctuates, the part of the communication link with a smoothness greater than a set value is selected as the target link. Using the target link, the user's real-time health data is transmitted to the target terminal so that medical staff and the user's family can promptly report any abnormalities. Each user whose health data fluctuates corresponds to one target link.

[0039] S400: When the health data read exceeds the threshold, the health data is sliced, dangerous segments are selected, the dangerous segments are multicast using the target link, and the network topology management architecture is reloaded.

[0040] If, during the processing of health data, a single item in the health data of an edge device exceeds a threshold, the health data within the time period before and after exceeding the threshold is selected to obtain dangerous segments. Based on the number of dangerous segments, the same number of target links are selected from the front of the communication links, and these target links are used to multicast the dangerous segments.

[0041] For example, in a game, the health data of three users, A, B, and C, fluctuates. Based on the smoothness of the connection, three target links are selected, establishing a one-to-one correspondence between these three users and the three target links. Each user's health data is then sent to the target terminal via the corresponding target link. However, as the game progresses, user A's health data exceeds a threshold. At this point, the communication link with the highest smoothness is selected, and user A's health data is sent to the target terminal via this link. Simultaneously, load balancing is performed on all communication links to ensure load balance.

[0042] In Example 2, Figure 2 The present invention illustrates the implementation flow of an information-oriented sharing method based on smart wearable devices, as provided in an embodiment of the present invention. The following details the steps of "defining the user's activity range, locating the deployment location of the edge device, obtaining the pairing request of the smart wearable device, and establishing a connection": S101: Divide the abnormal item into several levels and configure the priority of each level.

[0043] Determine the importance of each item; for example, assign different levels of importance to physiological indicators such as blood pressure, heart rate, and cholesterol, and divide the items into several levels according to their importance, with each level corresponding to a priority.

[0044] S102: Synchronize the priority to the fluctuation block and introduce a scheduling mechanism into the block chain.

[0045] The priority corresponding to each item is synchronized to the corresponding fluctuation block. At the same time, the position of the fluctuation block in the block chain is adjusted, and an adjustment mechanism is introduced. The adjustment mechanism is the specific method of scheduling the communication link according to the different priorities. For example, for a high-priority fluctuation block, a communication link with higher smoothness should be selected for transmission.

[0046] In Example 3, Figure 2 The implementation flow of the information-oriented sharing method based on smart wearable devices provided by an embodiment of the present invention is illustrated. The step of "establishing a communication link between the edge device and the target terminal" is described in detail below: S103: Embed a self-monitoring mechanism in the edge device to preprocess the received health data.

[0047] Embed a self-monitoring mechanism into edge devices, which is a method for evaluating the completeness and authenticity of health data; use edge devices to preprocess the received health data.

[0048] S104: Trigger the self-monitoring mechanism to find missing items in the health data, identify a unique identifier, and generate a supplementary data collection instruction.

[0049] By utilizing a self-monitoring mechanism, health data can be analyzed to identify whether any data items are missing. For example, some values ​​in a patient's physiological indicators such as body temperature, blood pressure, and blood sugar may not have been correctly recorded or stored. By comparing the data with standard templates or expected data models, missing items can be identified, and supplementary data collection instructions can be generated.

[0050] S105: The supplementary sampling instruction is sent to the corresponding smart wearable device via the link.

[0051] The unique identifier is traced back, and the missing item is pushed to the corresponding smart wearable device.

[0052] In Example 4, Figure 3 The following illustrates the implementation flow of the information-oriented sharing method based on smart wearable devices provided by an embodiment of the present invention. The steps of "obtaining user health data based on the wearable device, creating data blocks corresponding one-to-one with the user, transferring the health data into the data blocks, and linking the data blocks to obtain a data block chain" are described in detail below: S201: Collect the user's motion data and correct the abnormal items.

[0053] Acquire user exercise data and correct any abnormal items; for example, if a user's health data exceeds the normal fluctuation range at a certain moment, the reason may be that the user has experienced dehydration, insufficient energy, or other conditions; however, such abnormal items do not require additional attention after appropriate processing.

[0054] S202: From the pre-built scene template library, traverse to the target template, fill the exception item into the target template, generate a message summary, and push it to the corresponding smart wearable device.

[0055] Build a scenario template library, select the target template based on the attributes of the abnormal item, generate a message summary, and push the message summary to the corresponding smart wearable device; where the attributes are the specific categories of the abnormal item, such as body temperature, blood pressure, and blood sugar.

[0056] In Example 5, Figure 4The implementation flow of the information-oriented sharing method based on smart wearable devices provided by an embodiment of the present invention is illustrated. The step of "calculating the smoothness of each communication link" is described in detail below: S301: Determine the evaluation metrics of the communication link, wherein the evaluation metrics include: latency, bandwidth and packet loss rate.

[0057] Extract evaluation metrics for the communication link.

[0058] S302: Configure the weight of each evaluation index, and calculate the smoothness of each communication link using the comprehensive evaluation formula; The comprehensive evaluation formula is as follows: ; Where S represents fluency, L represents latency, B represents bandwidth, and P represents packet loss rate; , and These are the weights corresponding to latency, bandwidth, and packet loss rate, respectively.

[0059] Each evaluation indicator is assigned a corresponding weight, and the evaluation indicator and its corresponding weight are transferred into the comprehensive evaluation formula to calculate the smoothness of each communication link; the higher the smoothness, the better the data transmission capability of the communication link.

[0060] In Example 6, Figure 4 The present invention illustrates the implementation flow of an information-oriented sharing method based on smart wearable devices. The following details the steps of "constructing a network topology management architecture and embedding a load balancing mechanism to transfer the fluctuation blocks and target links into the network topology management architecture": S303: Integrate redundant links into the network topology management architecture and issue permission to enable redundant links to the fluctuation block.

[0061] Integrating redundant links into the network topology management architecture refers to configuring one or more backup links in the network topology management architecture so that when a communication link fails, it can switch to a backup link to ensure the continuity of data transmission.

[0062] In addition to serving as a backup, redundant links can also be used for load balancing of the target link when there are many fluctuations.

[0063] S304: Construct a deep learning model, input the motion data into the deep learning model, output the predicted value of the health data, and use the predicted value to make advance corrections to the network topology management architecture.

[0064] The motion data is input into a pre-built deep learning model. Based on the user's motion data, the predicted value of the user's health data is determined so that the network topology management architecture can be corrected in advance. Specifically, the correction involves retrieving the target link in advance as a backup.

[0065] In Example 7, Figure 5 The implementation flow of the information-oriented sharing method based on smart wearable devices provided by an embodiment of the present invention is illustrated below. The step of "selecting dangerous segments and multicasting the dangerous segments using the target link" is described in detail below: S401: Trace back to the source of the dangerous segment, calculate the number of sources, select the same number of target links, and obtain the emergency links.

[0066] When an edge device receives a dangerous segment, it traces back to the source of the dangerous segment, which is the specific smart wearable device. It counts the number of sources and selects the target links with the same number, defining them as emergency links.

[0067] S402: Define the source as the sender of the emergency link.

[0068] The source is identified as the sender of the emergency link. In this embodiment, if two users' health data contain dangerous segments during the competition, the two segments with the highest smoothness are selected from the target links and identified as emergency links. The dangerous segments are then transmitted using the emergency links.

[0069] In Example 8, unlike Example 1, the method further includes: The load of the edge device is monitored to determine the real-time load and a load threshold is configured. If the real-time load exceeds the load threshold, the interconnection channel between the edge devices is activated for load balancing. The health data received from the edge device is uploaded to the cloud to build a distributed AI model, which consists of an edge device part and a cloud part.

[0070] The load of edge devices is monitored to determine the real-time load of each edge device and the load threshold of each edge device. If the real-time load of an edge device exceeds the load threshold, load balancing is performed on the edge devices through the pre-built interconnection channel. When an edge device receives health data, it uploads the received health data to the cloud and processes the health data using a distributed AI model jointly built by the cloud and the edge devices.

[0071] Figure 6This diagram illustrates the structural block diagram of an information-oriented sharing system based on smart wearable devices provided in an embodiment of the present invention. The information-oriented sharing system 1 based on smart wearable devices includes: The module 11 is used to define the user's activity range, locate the deployment location of the edge device, obtain the pairing request of the smart wearable device and establish a link, read the unique identifier, configure the correspondence between the unique identifier, the user and the smart wearable device, determine the target terminal, wherein the number of target terminals is at least two, and establish a communication link between the edge device and the target terminal. The pop-up module 12 is used to acquire the user's health data based on the wearable device, create a data block that corresponds one-to-one with the user, transfer the health data into the data block, link the data blocks to obtain a data block chain, split the health data into multiple items, determine the normal fluctuation range of each item, define the item whose health data exceeds the normal fluctuation range as an abnormal item, define the data block corresponding to the abnormal item as a fluctuating data block, and pop the fluctuating data block to the top of the data block chain. The module 13 is used to calculate the smoothness of each communication link and arrange them in descending order of smoothness. Communication links with smoothness greater than a set value are marked as target links. A network topology management architecture is constructed and a load balancing mechanism is embedded to transfer the fluctuation blocks and target links into the network topology management architecture. The reconstruction module 14 is used to slice the health data when the health data exceeds the threshold, select dangerous segments, multicast the dangerous segments using the target link, and reload the network topology management architecture.

[0072] Figure 7 This diagram illustrates the structural block diagram of an information-oriented sharing system based on smart wearable devices provided in an embodiment of the present invention. The building module 11 includes: The partitioning unit 111 is used to divide the abnormal item into several levels and configure the priority of each level; The introduction unit 112 is used to synchronize the priority to the fluctuation block and introduce a scheduling mechanism into the block chain; The preprocessing unit 113 is used to embed a self-monitoring mechanism in the edge device to preprocess the received health data; The identification unit 114 is used to trigger the start of the self-monitoring mechanism, find the missing items in the health data, identify the unique identifier, and generate a supplementary sampling instruction. The sending unit 115 is used to send the supplementary sampling instruction to the corresponding smart wearable device via the link.

[0073] Figure 8This diagram illustrates the structural composition of an information-oriented sharing system based on smart wearable devices provided in an embodiment of the present invention. The pop-up module 12 includes: The correction unit 121 is used to collect the user's motion data and correct the abnormal items. The push unit 122 is used to traverse the target template from the pre-built scene template library, fill the abnormal item into the target template, generate a message summary, and push it to the corresponding smart wearable device.

[0074] Figure 9 This diagram illustrates the structural block diagram of an information-oriented sharing system based on smart wearable devices provided in an embodiment of the present invention. The transfer module 13 includes: The determining unit 131 is used to determine the evaluation indicators of the communication link, wherein the evaluation indicators include: latency, bandwidth and packet loss rate; The calculation unit 132 is used to configure the weight of each evaluation index and calculate the smoothness of each communication link using a comprehensive evaluation formula. The comprehensive evaluation formula is as follows: ; Where S represents fluency, L represents latency, B represents bandwidth, and P represents packet loss rate; , and These are the weights corresponding to latency, bandwidth, and packet loss rate, respectively.

[0075] The issuing unit 133 is used to integrate redundant links into the network topology management architecture and issue the permission to enable redundant links to the fluctuation block; Output unit 134 is used to construct a deep learning model, input the motion data into the deep learning model, output the predicted value of health data, and use the predicted value to make advance corrections to the network topology management architecture.

[0076] Figure 10 This diagram illustrates the structural composition of an information-oriented sharing system based on smart wearable devices provided in an embodiment of the present invention. The reconfiguration module 14 includes: Unit 141 is used to trace back the source of the dangerous segment, calculate the number of sources, select the same number of target links, and obtain the emergency link; Definition unit 142 is used to define the source as the sender of the emergency link.

[0077] The building module 11 is mainly used to complete step S100, the pop-up module 12 is mainly used to complete step S200, the transfer module 13 is mainly used to complete step S300, and the reconstruction module 14 is mainly used to complete step S400. The dividing unit 111 is mainly used to complete step S101, the introducing unit 112 is mainly used to complete step S102, the preprocessing unit 113 is mainly used to complete step S103, the identification unit 114 is mainly used to complete step S104, and the sending unit 115 is mainly used to complete step S105. The correction unit 121 is mainly used to complete step S201, and the push unit 122 is mainly used to complete step S202. The determining unit 131 is mainly used to complete step S301, the calculation unit 132 is mainly used to complete step S302, the issuing unit 133 is mainly used to complete step S303, and the output unit 134 is mainly used to complete step S304. Unit 141 is mainly used to complete step S401, and unit 142 is mainly used to complete step S402.

[0078] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for targeted information sharing based on smart wearable devices, characterized in that, The method includes: Define the user's activity range, locate the deployment location of the edge device, obtain the pairing request of the smart wearable device and establish a link, read the unique identifier, configure the correspondence between the unique identifier, the user and the smart wearable device, determine the target terminal, wherein the number of target terminals is at least two, and build a communication link between the edge device and the target terminal. Based on wearable devices, the system acquires users' health data, creates data blocks that correspond one-to-one with each user, transfers the health data into the data blocks, links the data blocks to obtain a data block chain, splits the health data into multiple individual items, determines the normal fluctuation range of each individual item, defines the individual items whose health data exceeds the normal fluctuation range as abnormal items, defines the data blocks corresponding to the abnormal items as fluctuating data blocks, and pops the fluctuating data blocks to the top of the data block chain. The smoothness of each communication link is calculated and arranged in descending order of smoothness. Communication links with smoothness greater than a set value are marked as target links. A network topology management architecture is constructed and a load balancing mechanism is embedded to transfer the fluctuation blocks and target links into the network topology management architecture. When the health data exceeds the threshold, the health data is sliced, dangerous segments are selected, the dangerous segments are multicast using the target link, and the network topology management architecture is reloaded.

2. The information-oriented sharing method based on smart wearable devices according to claim 1, characterized in that, The steps of "delineating the user's activity range, locating the deployment location of the edge device, obtaining the pairing request from the smart wearable device, and establishing a connection" include: The abnormal item is divided into several levels, and the priority of each level is configured. The priority is synchronized to the fluctuation blocks, and a scheduling mechanism is introduced into the block chain.

3. The information-oriented sharing method based on smart wearable devices according to claim 1, characterized in that, The method further includes: The load of the edge device is monitored to determine the real-time load and a load threshold is configured. If the real-time load exceeds the load threshold, the interconnection channel between the edge devices is activated for load balancing. The health data received from the edge device is uploaded to the cloud to build a distributed AI model, which consists of an edge device part and a cloud part.

4. The information-oriented sharing method based on smart wearable devices according to claim 1, characterized in that, The step of "establishing a communication link between the edge device and the target terminal" includes: A self-monitoring mechanism is embedded in the edge device to preprocess the received health data; The self-monitoring mechanism is triggered to find missing items in the health data, identify a unique identifier, and generate a supplementary data collection instruction. The supplementary sampling instruction is sent to the corresponding smart wearable device via the link.

5. The information-oriented sharing method based on smart wearable devices according to claim 4, characterized in that, The steps of "acquiring user health data based on wearable devices, creating data blocks that correspond one-to-one with users, transferring the health data into the data blocks, and linking the data blocks to obtain a data block chain" include: Collect user's motion data and correct any abnormal items; The system iterates through the pre-built scenario template library to find the target template, fills the exception item into the target template, generates a message summary, and pushes it to the corresponding smart wearable device.

6. The information-oriented sharing method based on smart wearable devices according to claim 1, characterized in that, The step of "calculating the smoothness of each communication link" includes: Determine the evaluation metrics for the communication link, wherein the evaluation metrics include: latency, bandwidth, and packet loss rate; Configure the weight of each evaluation indicator, and use the comprehensive evaluation formula to calculate the smoothness of each communication link; The comprehensive evaluation formula is as follows: ； Where S represents fluency, L represents latency, B represents bandwidth, and P represents packet loss rate; , and These are the weights corresponding to latency, bandwidth, and packet loss rate, respectively.

7. The information-oriented sharing method based on smart wearable devices according to claim 5, characterized in that, The step of "constructing a network topology management architecture and embedding a load balancing mechanism to transfer the fluctuation blocks and target links into the network topology management architecture" includes: Integrate redundant links into the network topology management architecture and issue permission to enable redundant links to the fluctuation block; A deep learning model is constructed, the motion data is fed into the deep learning model, and the predicted value of the health data is output. The predicted value is then used to make advance corrections to the network topology management architecture.

8. The information-oriented sharing method based on smart wearable devices according to claim 1, characterized in that, The step of "selecting dangerous segments and multicasting the dangerous segments using the target link" includes: The source of the dangerous segment is traced back, the number of the source segments is calculated, and the same number of target links are selected to obtain the emergency links; The source is defined as the sender of the emergency link.

9. An information-oriented sharing system based on smart wearable devices, characterized in that, The system includes: The module is used to define the user's activity range, locate the deployment location of the edge device, obtain the pairing request of the smart wearable device and establish a link, read the unique identifier, configure the correspondence between the unique identifier, the user and the smart wearable device, determine the target terminal, wherein the number of target terminals is at least two, and establish a communication link between the edge device and the target terminal. The pop-up module is used to acquire the user's health data based on the wearable device, create a data block that corresponds one-to-one with the user, transfer the health data into the data block, link the data blocks to obtain a data block chain, split the health data into multiple items, determine the normal fluctuation range of each item, define the item whose health data exceeds the normal fluctuation range as an abnormal item, define the data block corresponding to the abnormal item as a fluctuating data block, and pop the fluctuating data block to the top of the data block chain. The transfer module is used to calculate the smoothness of each communication link and arrange them in descending order of smoothness. Communication links with smoothness greater than a set value are marked as target links. A network topology management architecture is constructed and a load balancing mechanism is embedded to transfer the fluctuation blocks and target links into the network topology management architecture. The reconstruction module is used to slice the health data when the read health data exceeds the threshold, select dangerous segments, multicast the dangerous segments using the target link, and reload the network topology management architecture.

10. The information-oriented sharing system based on intelligent wearable devices according to claim 9, characterized in that, The construction module includes: A partitioning unit is used to divide the single abnormal item into several levels and configure the priority of each level; An introduction unit is used to synchronize the priority to the fluctuation blocks and introduce a scheduling mechanism into the block chain; A preprocessing unit is used to embed a self-monitoring mechanism in the edge device to preprocess the received health data; The identification unit is used to trigger the self-monitoring mechanism, find the missing items in the health data, identify the unique identifier, and generate a supplementary sampling instruction. The sending unit is used to send the supplementary sampling instruction to the corresponding smart wearable device via the link.

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