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

Abstract

The application relates to a distributed home user health monitoring system based on an Internet of Things and cloud interaction, which comprises the following: a monitoring network is jointly constructed by multiple distributed monitoring nodes, wherein each distributed monitoring node comprises a monitoring mat and a control box thereof, the monitoring mat is used for collecting relevant information of a user through a multi-element sensor, and the control box is used for uploading the collected relevant information of the user; a cloud server is used for receiving the relevant information uploaded by each distributed monitoring node, and the relevant information uploaded by each distributed monitoring node is processed as follows: the relevant information is refined to obtain screening data; the screening data is subjected to health monitoring according to a preset judgment condition to obtain a monitoring result; and the monitoring result is used for visually displaying an IP device associated with the current distributed monitoring node. The application can greatly improve the use experience of home user health monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of health monitoring, and particularly relates to a distributed home user health monitoring system based on Internet of Things and cloud interaction. BACKGROUND

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

[0003] The current home user health monitoring systems on the market are often independent of each other, and the data between them cannot provide reference basis, so once there is an anomaly, it is impossible to provide some more appropriate solutions for the user in advance according to the historical situation. In addition, for the distributed home user health monitoring system, the cloud needs to process a large amount of sampling data. Considering the limited cloud computing power and the excessive redundancy of the sampling data, if all data is directly analyzed and processed through the cloud, it is likely to result in a large deviation of the analysis result, which cannot meet the needs of the user. In addition, the current home user health monitoring systems on the market are troublesome to lay out and are not friendly to the elderly, children and other groups with limited mobility or restrictions. SUMMARY

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

[0005] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0006] Specifically, a distributed home user health monitoring system based on Internet of Things and cloud interaction is proposed, which comprises the following:

[0007] A monitoring network is jointly constructed by multiple distributed monitoring nodes, wherein each distributed monitoring node comprises,

[0008] A monitoring mat and a control box thereof, the monitoring mat is fixedly arranged on a hard bed board and electrically connected to the control box, the control box is connected to a commercial power supply, the monitoring mat is used to collect relevant information of a user through a multi-element sensor, and the control box is used to upload the collected relevant information of the user;

[0009] A cloud server is used to receive the relevant information uploaded by each distributed monitoring node, and the relevant information uploaded by each distributed monitoring node is processed as follows,

[0010] The relevant information is refined to obtain screening data;

[0011] The screening data is subjected to health monitoring according to preset judgment conditions to obtain a monitoring result;

[0012] The monitoring result is visually displayed on an IP device associated with the current distributed monitoring node.

[0013] Further, specifically, the related information includes real-time vital sign parameters, i.e., heart rate, respiratory rate, and body movement data.

[0014] Further, specifically, the related information is subjected to fine processing to obtain the screening data, including,

[0015] For any type of data in the related information,

[0016] Step 210, assuming that the uploaded data set includes elements , where T ∈ [1, N], N represents the maximum sampling number in the uploaded data;

[0017] Step 220, defining as the uploaded data to be judged for the pre-th sampling, and the variable c, the value range of c is [1, pre], i.e. as the uploaded data for the c-th sampling, and setting a dynamic threshold value as M, and the independent variable j, the initial values of j and c are both 1;

[0018] Step 230, judging the size relationship between pre and M, if pre is smaller than M, then the mean value of is taken as the discrimination standard standard, and if pre is not smaller than M, then the value of is taken as the discrimination standard standard;

[0019] Step 240, performing fine processing according to preset rules;

[0020] Step 250, when j ≤ M, j is increased by 1, and the process goes to step 220; when j > M, j = 1, and the process goes to step 260;

[0021] Step 260, when c ≤ pre, the value of c is increased by M, and the process goes to step 220; when c > pre, the process goes to step 270;

[0022] Step 270, outputting the processed data as the screening data of the type of data;

[0023] The above steps 210 to 270 are performed for each type of data in the related information, so as to fine process the related information to obtain the screening data.

[0024] Further, specifically, the fine processing according to preset rules in step 240 includes,​

[0025] upper limit of change and lower limit of change wherein,

[0026]

[0027]

[0028] wherein, indicates the median value of to max[] indicates the maximum value in the data set, and min[] indicates the minimum value in the data set;

[0029] the difference between standard and is recorded as a first value, and the difference between and standard is recorded as a second value, if there is only one negative value in the first value and the second value, respectively, the value difference between , and is obtained by taking the absolute value of the difference, and only the data corresponding to the middle value of C, C1 and C2 is retained;

[0030] if there are two negative values or no negative values in the first value and the second value, respectively, the value difference between , and is obtained by taking the absolute value of the difference, and only the data corresponding to the maximum value of C', C1' and C2' is retained.

[0031] Further, specifically, the screening data is subjected to health monitoring according to a preset judgment condition to obtain a monitoring result,

[0032] the screening data is subjected to judgment according to a preset discrimination threshold, when the proportion of the data quantity of a certain type of data exceeding the preset discrimination threshold of the type of data to the total quantity of the type of data reaches a preset proportion value, it is judged that the type of data is abnormal, and the above judgment is sequentially performed on all types of data to obtain the monitoring result.

[0033] Further, the system further comprises,

[0034] when any type of data is abnormal, the screening data and the data before its refinement are jointly formed into log data and sent to the administrator for subsequent user inquiry and analysis.

[0035] Further, the system further comprises,

[0036] ​​The coping scheme pushing 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, finds out the highest similarity historical data, and pushes the coping scheme corresponding to the highest similarity historical data to the current user.

[0037] The present application has the following beneficial effects:

[0038] The present application provides a distributed home user health monitoring system based on Internet of Things and cloud interaction, which on one hand, through the form of monitoring pad and monitoring box involved in the detection module, and the detection box is electrically separated from the monitoring pad, can avoid the risk of electric shock while installing the monitoring pad on the bed board, and only needs to place the monitoring pad to complete the plug-in between the monitoring pad and the monitoring box, which is very convenient, especially for the use of the old and young groups; on the other hand, each monitoring pad is arranged as a monitoring node to jointly construct a monitoring network, so that the historical situation encountered in the monitoring network can be used to push the coping scheme to the current user, which is convenient for the user to use, and the cloud server can also finely process each kind of data uploaded by each monitoring node, so that the data for abnormal judgment is more representative, and the final result is more accurate. BRIEF DESCRIPTION OF DRAWINGS

[0039] The above and other features of the present disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which like reference numerals denote like elements or components, and in which:

[0040] Figure 1 Fig. 1 shows the installation schematic diagram of a distributed monitoring node in the distributed home user health monitoring system based on Internet of Things and cloud interaction of the present application;

[0041] Figure 2 Fig. 2 shows the schematic diagram of two monitoring pads connected in series in one preferred mode of the present application. DETAILED DESCRIPTION

[0042] The concept, specific structure and generated technical effects of the present application will be described clearly and completely in combination with the embodiments and the drawings, so as to fully understand the purpose, scheme and effect of the present application. It should be noted that the embodiments and the features in the embodiments in the present application can be combined with each other without conflict. The same reference numerals in the drawings indicate the same or similar parts.

[0043] Embodiment 1, refer to Figure 1 and Figure 2The application provides a distributed home user health monitoring system based on Internet of Things and cloud interaction, and comprises the following:

[0044] A monitoring network is jointly constructed by multiple distributed monitoring nodes, wherein each distributed monitoring node comprises,

[0045] A monitoring mat and a control box thereof, the monitoring mat is fixedly arranged on a hard bed plate and electrically connected to the control box, the control box is connected to a commercial power supply, the monitoring mat is used for collecting relevant information of a user through a multi-element sensor, and the control box is used for uploading the collected relevant information of the user;

[0046] A cloud server is used for receiving the relevant information uploaded by each distributed monitoring node, and performing the following processing on the relevant information uploaded by each distributed monitoring node,

[0047] The relevant information is finely processed to obtain screening data;

[0048] The screening data is subjected to health monitoring according to a preset judgment condition to obtain a monitoring result;

[0049] The monitoring result is visually displayed on an IP device associated with the current distributed monitoring node.

[0050] In the embodiment 1, on the one hand, the detection module is arranged in the form of a monitoring mat and a monitoring box, and the detection box is electrically separated from the monitoring box, so that the monitoring mat can be conveniently installed on the bed plate while the risk of electric shock is avoided; on the other hand, each monitoring mat is arranged as a monitoring node to jointly construct a monitoring network, so that the historical conditions encountered in the monitoring network can be used to push a coping scheme to the current user, and the user is facilitated to use, and the cloud server also finely processes each kind of data uploaded by each monitoring node, so that the data for abnormal judgment is more representative, and the final result is more accurate.

[0051] Specifically, in actual application, the sleep monitor collects physiological parameters of a user in multiple dimensions through professional sensors, including heart rate, respiratory rate, body movement and other data, analyzes the sleep state of the user through an algorithm: including wakefulness, sleep, bed leaving and the like. In cooperation with a software system, the real-time dynamics of the old person can be monitored, including physiological parameters, sleep state, abnormal conditions, long-term tracking of physiological data and sleep habits of the old person, better arrangement of nursing human resources, and improvement of nursing quality and nursing work efficiency.

[0052] The specific installation process is as follows,

[0053] 1. Confirm whether the bed frame and the mattress are suitable for the bed frame The bed plate must be a hard plane, if placed on a rib frame, the width of the rib frame must be greater than the width (7 cm) of the monitoring mat;

[0054] 2. Install the SIM card in the off state, insert the Internet of Things card (metal side down, corner outward) into the SIM card slot, and then use your nails to push it in. When you hear a "click" and the edge of the SIM card is inside the slot, it means the card is in place.

[0055] Method to remove the SIM card:

[0056] Push the SIM card with your nails, and after hearing a "click", release your finger. The SIM card will then pop out partially, and you can then pinch the SIM card and pull it out.

[0057] 3. Place the monitoring pad

[0058] Lift the mattress and place the monitoring pad in the middle of the bed frame, about 50-60 cm from the head of the bed (about 1.5 times the length of the monitoring pad).

[0059] Two monitoring pad connection methods:

[0060] Connect the connection line of one monitoring pad to the USB port in the middle of the other monitoring pad, and then place it horizontally on the bed board (this method can effectively deal with the situation where the bed is too large).

[0061] 4. Connect the control box and monitoring pad, and turn on the power

[0062] Insert the monitoring pad connection line and adapter power line into the control box, then plug the adapter into the socket. If the networking indicator light is on, it means the power is on and the machine has started successfully. If the monitoring pad indicator light is always green, it means the monitoring pad is inserted tightly. If it does not light up, it means the monitoring pad is not inserted tightly.

[0063] 5. Cover the bottom hatch

[0064] Align the bottom hatch with the monitoring pad connection line and power line, then push the bottom hatch inwards. When you hear a "click", it means the bottom hatch is installed in place.

[0065] Method to remove the bottom hatch:

[0066] Press the buckle, then the bottom hatch will be pushed open. Then remove the bottom hatch according to the arrow direction.

[0067] 6. Secure the monitoring pad and control box

[0068] If installed on a lifting bed, please attach the back of the monitoring pad to the back of the bed board.

[0069] 7. Place the mattress above the device.

[0070] As a preferred embodiment of the present application, specifically, the relevant information includes real-time vital sign parameters, i.e. heart rate, respiratory rate, body movement data.

[0071] As a preferred embodiment of the present application, specifically, the relevant information is refined to obtain screening data, including,

[0072] For any type of data in the relevant information,

[0073] Step 210, assuming that the uploaded data set elements are , wherein T∈[1, N], N represents the maximum sampling number in the uploaded data;

[0074] Step 220, defining for the uploaded data to be judged for the pre-th sampling, the variable c, the value range of c is [1, pre], i.e. for the uploaded data of the c-th sampling, the dynamic threshold value is set as M, the independent variable j, the initial value of j and c are both 1;

[0075] Step 230, judging the size relationship between pre and M, if pre is less than M, then the mean value of is taken as the discrimination standard standard, if pre is not less than M, then the value of is taken as the discrimination standard standard;

[0076] Step 240, performing refinement operation according to the preset rule;

[0077] Step 250, when j≤M, j is increased by 1, and go to step 220; when j>M, j=1 and go to step 260;

[0078] Step 260, when c≤pre, the value of c is increased by M, and go to step 220; when c>pre, go to step 270;

[0079] Step 270, outputting the processed data as the screening data of the type of data;

[0080] The above steps 210 to 270 are performed for each type of data in the relevant information, to refine the relevant information to obtain screening data.

[0081] As a preferred embodiment of the present application, specifically, the refinement operation in step 240 according to the preset rule includes,

[0082] calculating the upper limit value of change and the lower limit value of change , wherein, ​

[0083] ;

[0084] ;

[0085] In the formula, indicates the median value of the calculation to , max[] indicates the calculation of the maximum value in the data set, and min[] indicates the calculation of the minimum value in the data set;

[0086] The difference between standard and is recorded as the first value, and the difference between and standard is recorded as the second value. If there is only one negative value in the first value and the second value, respectively, calculate , and The numerical difference is the difference between C, C1 and C2, and only the data corresponding to the middle value of C, C1 and C2 is retained.

[0087] If there are two negative values or no negative values in the first value and the second value, respectively, calculate , and The numerical difference is the difference between C', C1' and C2', and only the data corresponding to the maximum value of C', C1' and C2' is retained.

[0088] In the preferred embodiment, the uploaded data is processed by the above method, which can eliminate the less representative data. On the one hand, it reduces the amount of redundant data in the distributed system and saves the computing power of the cloud server. On the other hand, since the remaining data is more representative, the final analysis result can be more accurate to a certain extent.

[0089] As a preferred embodiment of the present application, specifically, the screening data is health monitored according to the preset judgment condition to obtain a monitoring result,

[0090] The screening data is judged according to the preset discrimination threshold. When the proportion of the data quantity of a certain type of data in the total quantity of the type of data that exceeds the preset discrimination threshold of the type of data reaches the preset proportion value, it is judged that the type of data is abnormal. The above judgment is sequentially performed on all types of data to obtain the monitoring result.

[0091] As a preferred embodiment of the present application, it further comprises,

[0092] When any type of data is abnormal, the screening data and the data before its refinement are jointly formed into log data and sent to the administrator for subsequent user inquiry and analysis.

[0093] In the preferred embodiment, both forms of data are sent to the administrator for analysis to quickly find the problem, considering that the subsequent user may have questions about the current monitoring data.

[0094] As a preferred embodiment of the present application, the system further comprises,

[0095] A coping scheme pushing module, for the abnormal data of the current user, calculates the similarity with the historical data uploaded by any distributed monitoring node in the monitoring network, finds the highest similarity historical data, and pushes the coping scheme corresponding to the highest similarity historical data to the current user

[0096] In the preferred embodiment, the historical optimal solution is found by using the similarity algorithm to inform the user to perform corresponding processing, and follow-up follow-up is performed, which can greatly improve the use experience of the application, considering that it is impossible to timely answer questions for all monitoring abnormal users due to limited manpower in the distributed monitoring system.

[0097] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software functional module.

[0098] The integrated module, if realized in the form of a software functional module and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. When the processor executes the computer program, the steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer-readable medium can include any entity or system that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0099] Although the description of the application has been quite detailed and particularly with respect to several described embodiments, it is not intended to limit the application to any of these details or embodiments or any particular embodiment, but rather it is intended to cover the intended scope of the application as provided by the appended claims, which should be interpreted as broadly as the prior art will permit, effectively encompassing the intended range of the application. Furthermore, the above description of the application is made by way of example with the embodiments that the inventors can foresee, and the purpose is to provide a useful description, and those non-essential changes to the application that have not yet been foreseen can still represent equivalent changes to the application.

[0100] The above description is only the preferred embodiments of the present application, and the present application is not limited to the above-described embodiments, but any technical solutions and / or embodiments within the protection scope of the present application, as long as they achieve the same purpose by the same means, should belong to the protection scope of the present application.

Claims

1. A distributed home user health monitoring system based on the Internet of Things and cloud interaction, characterized by: These include: 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 mounted on a hard bed board and electrically connected to the control box, the control box being connected to mains electricity, the monitoring mat being used to collect user-related information via a multi-sensor, and the control box being used to upload the collected user-related information; The cloud server is configured 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; Visually display the monitoring results for the IP devices associated with the current distributed monitoring node; 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 The uploaded data of the pre-th sampling to be judged, the value range of variables c, c is [1, pre], that is, For the uploaded data of the cth sampling, set the dynamic threshold to M and the independent variable to j; Step 230: Determine the size relationship between pre and M. If pre is less than M, calculate 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 refinement 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; when c>pre, go to step 270; Step 270, outputting the processed data as screening data for data of this type; Execute steps 210 to 270 for each type of data in the relevant information to refine the relevant information and obtain filtered data; Specifically, the refinement operation performed according to the preset rules in step 240 includes: Calculate the upper limit of change and the lower limit of change ,in, ； ； Where, Represents 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 value 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 and second values, calculate , and The numerical difference is the difference between the two, that is, the absolute value is obtained to obtain C', C1' and C2'. At this time, only the data corresponding to the maximum value among C', C1' and C2' is retained.

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 1 is characterized in that: Specifically, the screening data is subjected to health monitoring according to the preset judgment conditions to obtain monitoring results. The filtered data is judged according to a preset discrimination threshold. When the number of data of a certain type in the filtered data exceeds the preset discrimination threshold of the data of that type and the ratio of the data to the total number of data of that type reaches a preset ratio value, it is judged that there is an abnormality in the data of that type. The above judgment is performed on all types of data in turn to obtain the monitoring results.

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: 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 inquiries and analysis.

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: The system further includes, The response plan push module is used for abnormal data of the current user, calculates the similarity with 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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