Distributed home user health monitoring system based on Internet of Things and cloud interaction

Abstract

The invention relates to a distributed home user health monitoring system based on Internet of Things and cloud interaction, comprising the following steps: a monitoring network is constructed by a plurality of distributed monitoring nodes together, each distributed monitoring node comprises a monitoring pad and a control box thereof, the monitoring pad is used for collecting related information of a user through a multi-element sensor, and the control box is used for controlling the monitoring pad; the control box is used for uploading the collected related information of the user; the cloud server is used for receiving the related information uploaded by each distributed monitoring node and performing the following processing on the related information uploaded by each distributed monitoring node: refining the related information to obtain screened data; performing health monitoring on the screening data according to a preset judgment condition to obtain a monitoring result; and visually displaying the IP equipment associated with the current distributed monitoring node according to the monitoring result. According to the invention, the health monitoring use experience of home users can be greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field related to health monitoring, and in particular to a distributed home user health monitoring system based on the Internet of Things and cloud interaction. Background Art

[0002] Home vital signs monitoring devices refer to devices used in home environments to monitor the physiological parameters of individuals or family members. The original intention of designing such devices is to improve the efficiency of health management and disease prevention, so that users can more conveniently understand their health status and seek medical treatment in a timely manner when necessary.

[0003] At present, the home user health monitoring systems on the market are often independent of each other, and the data between them cannot provide a reference basis. Therefore, once there is an abnormality, it is impossible to provide users with some more appropriate solutions based on historical conditions in advance. In addition, for distributed home user health monitoring systems, the cloud needs to process a large amount of sampled data. Considering the limited computing power of the cloud and the excessive redundancy of sampled data, if all data is analyzed and processed directly through the cloud, it is very likely that the analysis results will be biased and unable to meet the needs of users. In addition, the home user health monitoring systems on the market are relatively troublesome to deploy, and are not friendly to groups with limited mobility or restrictions such as the elderly and children. Summary of the invention

[0004] The purpose of the present invention is to solve at least one of the deficiencies of the prior art and to provide a distributed home user health monitoring system based on the Internet of Things and cloud interaction.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: Specifically, a distributed home user health monitoring system based on the Internet of Things and cloud interaction is proposed, including the following: The monitoring network is constructed by multiple distributed monitoring nodes, each of which includes: A monitoring mat and a control box thereof, wherein the monitoring mat is fixedly arranged on a hard bed board and electrically connected to the control box, the control box is connected to the mains, the monitoring mat is used to collect relevant information of the user through a multi-sensor, and the control box is used to upload the collected relevant information of the user; The cloud server is used to receive the relevant information uploaded by each distributed monitoring node, and perform the following processing on the relevant information uploaded by each distributed monitoring node: Refining the relevant information to obtain screening data; Performing health monitoring on the screening data according to preset judgment conditions to obtain monitoring results; The monitoring results are visualized for the IP devices associated with the current distributed monitoring node.

[0006] Further, specifically, the relevant information includes real-time vital sign parameters, namely heart rate, respiratory rate, and body movement data.

[0007] Further, specifically, the relevant information is refined to obtain screening data, including: For any type of data in the relevant information, Step 210: Assume that the element in the uploaded data set is , where T∈[1,N], N represents the maximum number of sampling times in the uploaded data; Step 220: Definition is the uploaded data of the pre-th sampling to be judged, and the value range of variables c and c is [1, pre], that is, For the uploaded data sampled for the cth time, the dynamic threshold is set to M, and the initial values ​​of the independent variable j, j and c are both 1; Step 230: Determine the size relationship between pre and M. If pre is less than M, find to The mean of is used as the judgment standard. If pre is not less than M, The value of is used as the judgment standard; Step 240: Perform refined operations according to preset rules; Step 250: when j≤M, j is incremented by 1 and the process goes to step 220; when j>M, j=1 and the process goes to step 260; Step 260, when c≤pre, increase the value of c by M, and go to step 220 to execute; when c>pre, go to step 270 to execute; Step 270, outputting the processed data as screening data for data of this type; The above steps 210 to 270 are performed for each type of data in the relevant information, so as to refine the relevant information and obtain the filtered data.

[0008] Further, specifically, the refinement operation performed according to the preset rules in step 240 includes: Calculate the upper limit of change And the lower limit value of change ,in, ; ; In the formula, Representation calculation to The median value of the data set, max[] means the maximum value in the data set, and min[] means the minimum value in the data set; Calculate standard and The difference is recorded as the first value, and the calculation The difference between the first value and the standard is recorded as the second value. If there is only one negative value between the first value and the second value, calculate , and The numerical difference is to take the absolute value of difference to obtain C, C1 and C2, and only retain the data corresponding to the middle value among C, C1 and C2; If there are 2 negative values ​​or 0 negative values ​​in the first value and the second value, calculate , and The numerical difference is obtained by taking the difference and taking the absolute value to obtain C', C1' and C2'. At this time, only the data corresponding to the maximum value among C', C1' and C2' is retained.

[0009] Further, specifically, the screening data is subjected to health monitoring according to preset judgment conditions to obtain monitoring results, The filtered data is judged according to a preset discrimination threshold. When the proportion of a certain type of data in the filtered data that exceeds the preset discrimination threshold of the type of data to the total number of data of the type reaches a preset proportion value, it is judged that there is an abnormality in the type of data. The above judgment is performed on all types of data in turn to obtain the monitoring result.

[0010] Furthermore, it also includes, When any type of data has anomalies, the filtered data and the data before refinement are combined to form log data and sent to the administrator for subsequent user inquiry and analysis.

[0011] Furthermore, the system also includes: The response plan push module is used for the abnormal data situation of the current user, calculates the similarity of the historical data uploaded by any distributed monitoring node in the monitoring network, and finds the historical data with the highest similarity, and pushes the response plan corresponding to the historical data with the highest similarity to the current user.

[0012] The beneficial effects of the present invention are: The present invention proposes a distributed home user health monitoring system based on the Internet of Things and cloud interaction. On the one hand, by integrating the detection module into the form of a monitoring pad and a monitoring box, and separating the detection box from the monitoring box, it is convenient to install the monitoring pad on the bed board while avoiding the risk of electric shock, and it is very convenient to only place the monitoring pad and complete the plugging and unplugging between the monitoring box, which is especially convenient for use by the elderly and children. In addition, each monitoring pad layout is used as a monitoring node to jointly construct a monitoring network, so that the historical situations encountered in the monitoring network can be used to push response plans to the current user, which is convenient for user use. In addition, the cloud server will also perform fine processing on each type of data uploaded by each monitoring node, which can make the data for abnormal judgment more representative, and thus make the final result more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other features of the present disclosure will become more apparent by describing in detail the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings of the present disclosure represent the same or similar elements. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure. For those skilled in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work. In the accompanying drawings: Figure 1 The figure shows a schematic diagram of the installation of a distributed monitoring node in the distributed home user health monitoring system based on the Internet of Things and cloud interaction of the present invention; Figure 2 Shown is a schematic diagram of two monitoring pads connected in series in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0014] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention, so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments in this application and the features in the embodiments can be combined with each other without conflict. The same reference numerals used throughout the drawings indicate the same or similar parts.

[0015] Example 1, reference Figure 1 as well as Figure 2 The present invention proposes a distributed home user health monitoring system based on the Internet of Things and cloud interaction, including the following: The monitoring network is constructed by multiple distributed monitoring nodes, each of which includes: A monitoring mat and a control box thereof, wherein the monitoring mat is fixedly arranged on a hard bed board and electrically connected to the control box, the control box is connected to the mains, the monitoring mat is used to collect relevant information of the user through a multi-sensor, and the control box is used to upload the collected relevant information of the user; The cloud server is used to receive the relevant information uploaded by each distributed monitoring node, and perform the following processing on the relevant information uploaded by each distributed monitoring node: Refining the relevant information to obtain screening data; Performing health monitoring on the screening data according to preset judgment conditions to obtain monitoring results; The monitoring results are visualized for the IP devices associated with the current distributed monitoring node.

[0016] In this embodiment 1, on the one hand, by integrating the detection module into the form of a monitoring pad and a monitoring box, and separating the detection box from the monitoring box, the risk of electric shock can be avoided while the monitoring pad is installed on the bed board conveniently; on the other hand, each monitoring pad arrangement location is used as a monitoring node to jointly construct a monitoring network, so that the historical situations encountered in the monitoring network can be used to push response plans to the current user, which is convenient for user use. In addition, the cloud server will also perform fine processing on each type of data uploaded by each monitoring node, which can make the data for abnormal judgment more representative, and thus make the final result more accurate.

[0017] Specifically, in actual application, the sleep monitor uses professional sensors to collect the user's physiological parameters in multiple dimensions, including heart rate, respiratory rate, body movement and other data, and uses algorithms to analyze the user's sleep status: including awake, asleep, out of bed, etc. In conjunction with the software system, it can monitor the elderly's real-time dynamics including physiological parameters, sleep status, abnormal conditions, and long-term tracking of the elderly's physiological data, sleep and rest habits, so as to better arrange nursing human resources and improve nursing quality and efficiency.

[0018] The specific installation process is as follows: 1. Confirm whether the bed frame and mattress are suitable. The bed frame and bed board must be a hard surface. If placed on a slatted frame, the width of the slatted frame must be greater than the width of the monitoring pad (7cm); 2. Install the SIM card. With the phone turned off, insert the IoT card (metal side facing down, notched corner facing outward) into the SIM card slot. Push it in with your fingernail until you hear a "click" and the edge of the SIM card is in the slot, indicating that the card is in place.

[0019] How to remove the SIM card: Use your fingernail to push the SIM card inwards. When you hear a "click", release your finger and the SIM card will pop out partially. Then, hold the SIM card and pull it out.

[0020] 3. Place the monitoring pad After lifting the mattress, place the monitoring pad in the middle of the bed frame, 50 to 60 cm away from the head of the bed (approximately 1.5 times the length of the monitoring pad).

[0021] Two monitoring pad connection methods: Connect the connecting cable of one monitoring pad to the middle USB port of another monitoring pad, and then place it horizontally on the bed board (this method can be used to effectively deal with the situation in some scenarios where the bed is too large).

[0022] 4. Connect the control box and monitoring pad and turn on the power Plug the monitoring mat connection cable and adapter power cable into the control box, and then plug the adapter into the socket. If the network indicator light is on, it means that the power is on and the machine is turned on successfully. If the monitoring mat indicator light is always green, it means that the monitoring mat is plugged in tightly. If it is not lit, it means that the monitoring mat is not plugged in tightly.

[0023] 5. Close the bottom hatch After aligning the bayonet of the bottom hatch cover with the monitoring pad connection cable and the power cable, push the bottom hatch cover inwards. When you hear a "click", it indicates that the bottom hatch cover is in place.

[0024] How to remove the bottom hatch cover: After pressing the buckle, the bottom hatch cover will be pushed open, and then you can remove the bottom hatch cover in the direction of the arrow.

[0025] 6.Fix the monitoring mat and control box If installing on a lifting bed, please stick the adhesive backing on the back of the monitoring mat and then fix it on the bed board.

[0026] 7. Place the mattress Place the mattress on top of the device.

[0027] As a preferred embodiment of the present invention, specifically, the relevant information includes real-time vital sign parameters, namely heart rate, respiratory rate, and body movement data.

[0028] As a preferred embodiment of the present invention, specifically, the relevant information is refined to obtain screening data, including: For any type of data in the relevant information, Step 210: Assume that the element in the uploaded data set is , where T∈[1,N], N represents the maximum number of sampling times in the uploaded data; Step 220: Definition is the uploaded data of the pre-th sampling to be judged, and the value range of variables c and c is [1, pre], that is, For the uploaded data sampled for the cth time, the dynamic threshold is set to M, and the initial values ​​of the independent variable j, j and c are both 1; Step 230: Determine the size relationship between pre and M. If pre is less than M, find to The mean of is used as the judgment standard. If pre is not less than M, The value of is used as the judgment standard; Step 240: Perform refined operations according to preset rules; Step 250: when j≤M, j is incremented by 1 and the process goes to step 220; when j>M, j=1 and the process goes to step 260; Step 260, when c≤pre, increase the value of c by M, and go to step 220 to execute; when c>pre, go to step 270 to execute; Step 270, outputting the processed data as screening data for data of this type; The above steps 210 to 270 are performed for each type of data in the relevant information, so as to refine the relevant information and obtain the filtered data.

[0029] As a preferred embodiment of the present invention, specifically, the refinement operation performed according to the preset rules in step 240 includes: Calculate the upper limit of change And the lower limit value of change ,in, ; ; In the formula, Representation calculation to The median value of the data set, max[] means the maximum value in the data set, and min[] means the minimum value in the data set; Calculate standard and The difference is recorded as the first value, and the calculation The difference between the first value and the standard is recorded as the second value. If there is only one negative value between the first value and the second value, calculate , and The numerical difference is to take the absolute value of difference to obtain C, C1 and C2, and only retain the data corresponding to the middle value among C, C1 and C2; If there are 2 negative values ​​or 0 negative values ​​in the first value and the second value, calculate , and The numerical difference is obtained by taking the difference and taking the absolute value to obtain C', C1' and C2'. At this time, only the data corresponding to the maximum value among C', C1' and C2' is retained.

[0030] In this preferred embodiment, by processing the uploaded data in the above manner, less representative data can be eliminated. On the one hand, the amount of redundant data in the distributed system is reduced to save cloud server computing power. On the other hand, since the remaining data is more representative, the final analysis results can be more accurate to a certain extent.

[0031] As a preferred embodiment of the present invention, specifically, the screening data is subjected to health monitoring according to preset judgment conditions to obtain monitoring results. The filtered data is judged according to a preset discrimination threshold. When the proportion of a certain type of data in the filtered data that exceeds the preset discrimination threshold of the type of data to the total number of data of the type reaches a preset proportion value, it is judged that there is an abnormality in the type of data. The above judgment is performed on all types of data in turn to obtain the monitoring result.

[0032] As a preferred embodiment of the present invention, it also includes: When any type of data has anomalies, the filtered data and the data before refinement are combined to form log data and sent to the administrator for subsequent user inquiry and analysis.

[0033] In this preferred embodiment, considering that subsequent users may have questions about the monitoring data, both forms of data are sent to the administrator so that the administrator can analyze and quickly find the problem.

[0034] As a preferred embodiment of the present invention, the system further includes: The response plan push module is used for abnormal data of the current user, calculates the similarity of historical data uploaded by any distributed monitoring node in the monitoring network, finds the historical data with the highest similarity, and pushes the response plan corresponding to the historical data with the highest similarity to the current user. In this preferred embodiment, considering that it is impossible to answer questions of all users with monitoring abnormalities in a timely manner due to limited manpower in the distributed monitoring system, the similarity algorithm is used to find the historical optimal solution to first inform the user to take corresponding measures, and then follow up later, which can greatly improve the user experience of the application.

[0035] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0036] If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or system that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0037] Although the description of the present invention has been quite detailed and specifically describes several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but should be regarded as providing a broad possible interpretation of these claims in view of the prior art by reference to the appended claims, thereby effectively covering the intended scope of the present invention. In addition, the above description of the present invention is based on the embodiments foreseeable by the inventor, and its purpose is to provide a useful description, and those non-substantial changes to the present invention that have not yet been foreseen may still represent equivalent changes to the present invention.

[0038] The above is only a preferred embodiment of the present invention. The present invention is not limited to the above implementation. As long as the technical effect of the present invention is achieved by the same means, it should belong to the protection scope of the present invention. Within the protection scope of the present invention, its technical scheme and / or implementation method can have various modifications and changes.

Claims

1. A distributed home user health monitoring system based on the Internet of Things and cloud interaction, characterized in that: Includes the following: The monitoring network is constructed by multiple distributed monitoring nodes, each of which includes: A monitoring mat and a control box thereof, wherein the monitoring mat is fixedly arranged on a hard bed board and electrically connected to the control box, the control box is connected to the mains, the monitoring mat is used to collect relevant information of the user through a multi-sensor, and the control box is used to upload the collected relevant information of the user; The cloud server is used to receive the relevant information uploaded by each distributed monitoring node, and perform the following processing on the relevant information uploaded by each distributed monitoring node: Refining the relevant information to obtain screening data; Performing health monitoring on the screening data according to preset judgment conditions to obtain monitoring results; The monitoring results are visualized for the IP devices associated with the current distributed monitoring node.

2. The distributed home user health monitoring system based on the Internet of Things and cloud interaction according to claim 1 is characterized in that: Specifically, the relevant information includes real-time vital sign parameters, namely heart rate, respiratory rate, and body movement data.

3. The distributed home user health monitoring system based on the Internet of Things and cloud interaction according to claim 2 is characterized in that: Specifically, the relevant information is refined to obtain screening data, including: For any type of data in the relevant information, Step 210: Assume that the element in the uploaded data set is , where T∈[1,N], N represents the maximum number of sampling times in the uploaded data; Step 220: Definition is the uploaded data of the pre-th sampling to be judged, and the value range of variables c and c is [1, pre], that is, For the uploaded data sampled for the cth time, the dynamic threshold is set to M, and the initial values ​​of the independent variable j, j and c are both 1; Step 230: Determine the size relationship between pre and M. If pre is less than M, find to The mean of is used as the judgment standard. If pre is not less than M, The value of is used as the judgment standard; Step 240: Perform refined operations according to preset rules; Step 250: when j≤M, j is incremented by 1 and the process goes to step 220; when j>M, j=1 and the process goes to step 260; Step 260, when c≤pre, increase the value of c by M, and go to step 220 to execute; when c>pre, go to step 270 to execute; Step 270, outputting the processed data as screening data for data of this type; The above steps 210 to 270 are performed for each type of data in the relevant information, so as to refine the relevant information and obtain the filtered data.

4. The distributed home user health monitoring system based on the Internet of Things and cloud interaction according to claim 3 is characterized in that: Specifically, the refinement operation performed according to the preset rules in step 240 includes: Calculate the upper limit of change And the lower limit value of change ,in, ； ； In the formula, Representation calculation to The median value of the data set, max[] means the maximum value in the data set, and min[] means the minimum value in the data set; Calculate standard and The difference is recorded as the first value, and the calculation The difference between the first value and the standard is recorded as the second value. If there is only one negative value between the first value and the second value, calculate , and The numerical difference is to take the absolute value of difference to obtain C, C1 and C2, and only retain the data corresponding to the middle value among C, C1 and C2; If there are 2 negative values ​​or 0 negative values ​​in the first value and the second value, calculate , and The numerical difference is obtained by taking the difference and taking the absolute value to obtain C', C1' and C2'. At this time, only the data corresponding to the maximum value among C', C1' and C2' is retained.

5. The distributed home user health monitoring system based on the Internet of Things and cloud interaction according to claim 1 is characterized in that: Specifically, the screening data is subjected to health monitoring according to preset judgment conditions to obtain monitoring results. The filtered data is judged according to a preset discrimination threshold. When the proportion of a certain type of data in the filtered data that exceeds the preset discrimination threshold of the type of data to the total number of data of the type reaches a preset proportion value, it is judged that there is an abnormality in the type of data. The above judgment is performed on all types of data in turn to obtain the monitoring result.

6. The distributed home user health monitoring system based on the Internet of Things and cloud interaction according to claim 5 is characterized in that: Also includes, When any type of data has anomalies, the filtered data and the data before refinement are combined to form log data and sent to the administrator for subsequent user inquiry and analysis.

7. The distributed home user health monitoring system based on the Internet of Things and cloud interaction according to claim 1 is characterized in that: The system further comprises, The response plan push module is used for the abnormal data situation of the current user, calculates the similarity of the historical data uploaded by any distributed monitoring node in the monitoring network, and finds the historical data with the highest similarity, and pushes the response plan corresponding to the historical data with the highest similarity to the current user.

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