A cloud-based home-based elderly care monitoring and dispatching system and method

Abstract

The application is suitable for the technical field of home care monitoring, and provides a cloud-based home care monitoring and scheduling system and method. The application performs data direct uploading monitoring; when data direct uploading is abnormal, neighbor scheduling is performed, and neighbor network is selected for sequential connection; when neighbor connection is successful, indirect transmission data is acquired, and whether there is data abnormality is judged. Analysis and scheduling can be performed based on the cloud platform, when data direct uploading is abnormal, three-level abnormality alarm is performed, and neighbor network is selected for sequential connection; when neighbor connection is unsuccessful, two-level abnormality alarm is performed; when there is data abnormality, one-level abnormality alarm is performed, abnormality caused by the device itself or network problems can be effectively handled, real health risks are avoided as much as possible, the best treatment opportunity is not missed, unnecessary panic and waste of medical resources are reduced, and the elderly users and their families are avoided from being disturbed and burdened.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of home care monitoring, and particularly relates to a cloud-based home care monitoring and dispatching system and method. BACKGROUND

[0002] Home care monitoring is the collection, transmission, analysis and management of information such as health status, living conditions and safety risks of the elderly living at home through the use of modern information technology, the Internet of Things, big data analysis and intelligent sensing devices, aiming to timely detect potential risks and provide personalized service support to ensure the safety, health and quality of life of the elderly.

[0003] Currently, home care monitoring mainly collects, transmits and analyzes the health data of the elderly users through various intelligent devices to determine whether the elderly users have health abnormalities. However, most elderly users are not familiar with the use of intelligent devices, and many abnormal situations may be caused by the devices themselves or network problems. The existing technology cannot effectively handle this, which not only may lead to the failure to monitor the real health risks and miss the best treatment opportunity, but also may cause unnecessary panic and waste of medical resources, causing great distress and burden to the elderly users and their families. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a cloud-based home care monitoring and dispatching system and method to solve the technical problems existing in the prior art mentioned in the background.

[0005] The embodiments of the present application are implemented as follows:

[0006] A cloud-based home care monitoring and dispatching method, which specifically comprises the following steps:

[0007] Based on the cloud platform, the data of the elderly users is directly uploaded for monitoring and judgment;

[0008] When the data is directly uploaded abnormally, a three-level abnormal alarm is performed, and an external neighbor dispatching is performed to select an external neighbor network for sequential connection, and whether the external neighbor connection is successful is determined;

[0009] When the external neighbor connection is unsuccessful, a two-level abnormal alarm is performed;

[0010] When the external neighbor connection is successful, indirect transmission data is obtained, and the cloud platform is used to analyze the indirect transmission data to determine whether there is data abnormality;

[0011] When there is data abnormality, a one-level abnormal alarm is performed.

[0012] As a further limitation of the technical scheme of the embodiment of the present application, the monitoring and judging of the direct data uploading of the elderly user based on the cloud platform specifically comprises the following steps:

[0013] Obtaining the historical uploading data of the elderly user;

[0014] Analyzing the historical uploading data and planning a monitoring uploading period;

[0015] Based on the cloud platform, monitoring records of the direct data uploading of the elderly user are generated to generate direct uploading monitoring data;

[0016] According to the monitoring uploading period, the direct uploading monitoring data is analyzed to determine whether the direct data uploading is abnormal.

[0017] As a further limitation of the technical scheme of the embodiment of the present application, when the direct data uploading is abnormal, a three-level abnormality alarm is performed, and an external neighbor scheduling is performed to select an external neighbor network for sequential connection, and whether the external neighbor connection is successful is determined, specifically comprising the following steps:

[0018] When the direct data uploading is abnormal, a three-level alarm information is generated;

[0019] The three-level alarm information is sent to a plurality of preset elderly care monitoring addresses for a three-level abnormality alarm;

[0020] An external neighbor scheduling is performed to determine a plurality of authorized external neighbor networks;

[0021] The external neighbor network states of the plurality of authorized external neighbor networks are obtained;

[0022] The plurality of external neighbor network states are compared and arranged to determine a plurality of corresponding external neighbor connection sequences;

[0023] According to the plurality of external neighbor connection sequences, the plurality of corresponding authorized external neighbor networks are sequentially connected, and whether the external neighbor connection is successful is determined.

[0024] As a further limitation of the technical scheme of the embodiment of the present application, when the external neighbor connection is unsuccessful, a two-level abnormality alarm is performed, specifically comprising the following steps:

[0025] When the external neighbor connection is unsuccessful, a two-level alarm information and an auxiliary processing request are generated;

[0026] The two-level alarm information is sent to a plurality of preset elderly care monitoring addresses for a two-level abnormality alarm;

[0027] A plurality of external neighbor communication addresses are obtained;

[0028] The auxiliary processing request is sent to the plurality of external neighbor communication addresses, and an auxiliary processing feedback is received;

[0029] According to the auxiliary processing feedback, after completing the auxiliary processing, the secondary exception alarm is canceled.

[0030] As a further limitation of the technical scheme of the embodiment of the application, when the outer neighbor connection is successful, the indirect transmission data is acquired, and the cloud platform is used to analyze the indirect transmission data to determine whether there is data anomaly.

[0031] When the outer neighbor connection is successful, the indirect transmission data is acquired.

[0032] The cloud platform is used to identify the indirect transmission data and extract effective health data.

[0033] Standard health data is acquired.

[0034] The cloud platform is used to compare the effective health data with the standard health data to determine whether there is data anomaly.

[0035] As a further limitation of the technical scheme of the embodiment of the application, when there is data anomaly, a first exception alarm is performed, which specifically includes the following steps.

[0036] When there is data anomaly, a first alarm information is generated.

[0037] The first alarm information is sent to a plurality of preset elderly care monitoring addresses to perform a first exception alarm.

[0038] A cloud-based home-based elderly care monitoring and dispatching system, the system comprises a direct uploading monitoring module, a third-level alarm processing module, a second-level alarm processing module, a data anomaly judgment module and a first-level alarm processing module, wherein:

[0039] The direct uploading monitoring module is used to monitor and judge the data direct uploading of the elderly user based on the cloud platform.

[0040] The third-level alarm processing module is used to perform a third exception alarm when the data direct uploading is abnormal, and to perform outer neighbor dispatching, select an outer neighbor network for sequential connection, and determine whether the outer neighbor connection is successful.

[0041] The second-level alarm processing module is used to perform a second exception alarm when the outer neighbor connection is unsuccessful.

[0042] The data anomaly judgment module is used to acquire indirect transmission data when the outer neighbor connection is successful, and analyze the indirect transmission data through the cloud platform to determine whether there is data anomaly.

[0043] The first-level alarm processing module is used to perform a first exception alarm when there is data anomaly.

[0044] As a further limitation of the technical scheme of the embodiment of the present application, the direct uploading monitoring module specifically comprises:

[0045] A historical data acquisition unit is configured to acquire historical uploading data of the elderly user.

[0046] A monitoring cycle planning unit is configured to analyze the historical uploading data and plan a monitoring uploading cycle.

[0047] An uploading monitoring record unit is configured to monitor the direct uploading of data of the elderly user based on a cloud platform, and generate direct uploading monitoring data.

[0048] An uploading abnormality judgment unit is configured to analyze the direct uploading monitoring data according to the monitoring uploading cycle, and judge whether the direct uploading of data is abnormal.

[0049] As a further limitation of the technical scheme of the embodiment of the present application, the three-level alarm processing module specifically comprises:

[0050] A three-level alarm information generation unit is configured to generate three-level alarm information when the direct uploading of data is abnormal.

[0051] A three-level abnormality alarm unit is configured to send the three-level alarm information to a plurality of preset elderly care monitoring addresses, and perform three-level abnormality alarm.

[0052] An external neighbor scheduling unit is configured to perform external neighbor scheduling, and determine a plurality of authorized external neighbor networks.

[0053] A network state acquisition unit is configured to acquire external neighbor network states of the plurality of authorized external neighbor networks.

[0054] A connection order planning unit is configured to compare and arrange the plurality of external neighbor network states, and determine a plurality of corresponding external neighbor connection orders.

[0055] An order connection judgment unit is configured to perform order connection through the plurality of corresponding authorized external neighbor networks according to the plurality of external neighbor connection orders, and judge whether the external neighbor connection is successful.

[0056] As a further limitation of the technical scheme of the embodiment of the present application, the two-level alarm processing module specifically comprises:

[0057] An information request generation unit is configured to generate two-level alarm information and auxiliary processing requests when the external neighbor connection is unsuccessful.

[0058] A two-level abnormality alarm unit is configured to send the two-level alarm information to a plurality of preset elderly care monitoring addresses, and perform two-level abnormality alarm.

[0059] An external neighbor communication address acquisition unit is configured to acquire a plurality of external neighbor communication addresses.

[0060] An auxiliary processing feedback receiving unit is configured to send an auxiliary processing request to the plurality of external neighbor communication addresses and receive auxiliary processing feedback.

[0061] A secondary alarm canceling unit is configured to cancel a secondary abnormality alarm after completing auxiliary processing according to the auxiliary processing feedback.

[0062] Compared with the prior art, the present application has the following advantages:

[0063] The embodiment of the present application can analyze and schedule based on a cloud platform, perform a three-level abnormality alarm when data direct uploading is abnormal, and sequentially connect to an external neighbor network; perform a secondary abnormality alarm when the external neighbor connection is unsuccessful; and perform a primary abnormality alarm when there is data abnormality, so as to effectively handle the abnormality caused by the device itself or network problems, avoid missing the best treatment opportunity due to the fact that the real health risk is not monitored, reduce unnecessary panic and waste of medical resources, and avoid disturbing and burdening the elderly users and their families. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 A flowchart of a cloud-based home-based elderly care monitoring and scheduling method provided by the embodiment of the present application is shown;

[0065] Figure 2 A flowchart of monitoring and judging of data direct uploading in the method provided by the embodiment of the present application is shown;

[0066] Figure 3 A flowchart of judging of successful external neighbor connection in the method provided by the embodiment of the present application is shown;

[0067] Figure 4 A flowchart of performing a secondary abnormality alarm in the method provided by the embodiment of the present application is shown;

[0068] Figure 5 A flowchart of data abnormality analysis and judgment in the method provided by the embodiment of the present application is shown;

[0069] Figure 6 A flowchart of performing a primary abnormality alarm in the method provided by the embodiment of the present application is shown;

[0070] Figure 7 An application architecture diagram of a cloud-based home-based elderly care monitoring and scheduling system provided by the embodiment of the present application is shown;

[0071] Figure 8A structural block diagram of a direct uploading monitoring module in the system provided by the embodiment of the application is shown;

[0072] Figure 9 A structural block diagram of a three-level alarm processing module in the system provided by the embodiment of the application is shown;

[0073] Figure 10 A structural block diagram of a two-level alarm processing module in the system provided by the embodiment of the application is shown. DETAILED DESCRIPTION

[0074] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0075] It can be understood that at present, home-based care monitoring mainly collects, transmits and analyzes health data of the elderly users through various intelligent devices to determine whether the elderly users have health abnormalities. However, the elderly users are not familiar with the use of intelligent devices, and many cases of abnormalities may be caused by the devices themselves or network problems. The existing technology cannot effectively handle this, which not only may lead to the fact that the real health risks are not monitored and the best treatment opportunity is missed, but also may cause unnecessary panic and waste of medical resources, causing great distress and burden to the elderly users and their families.

[0076] To solve the above problems, the home-based care monitoring and scheduling system and method based on the cloud disclosed by the embodiment of the application can monitor and determine the data direct uploading of the elderly users based on the cloud platform. When the data direct uploading is abnormal, three-level abnormal alarms are performed, and the neighborhood scheduling is performed to select the neighborhood network for sequential connection and determine whether the neighborhood connection is successful. When the neighborhood connection is unsuccessful, two-level abnormal alarms are performed. When the neighborhood connection is successful, indirect transmission data is obtained, and the indirect transmission data is analyzed by the cloud platform to determine whether there is data abnormality. When there is data abnormality, one-level abnormal alarms are performed. The cloud platform can be analyzed and scheduled, three-level abnormal alarms are performed when the data direct uploading is abnormal, and the neighborhood network is selected for sequential connection. When the neighborhood connection is unsuccessful, two-level abnormal alarms are performed. When there is data abnormality, one-level abnormal alarms are performed, which can effectively handle the abnormalities caused by the devices themselves or network problems, avoid the fact that the real health risks are not monitored and the best treatment opportunity is missed as much as possible, and reduce unnecessary panic and waste of medical resources, avoiding causing distress and burden to the elderly users and their families.

[0077] Specifically, Figure 1A flow chart of the cloud-based home care monitoring and scheduling method provided by the embodiment of the present application is shown.

[0078] In a preferred embodiment provided by the present application, a cloud-based home care monitoring and scheduling method specifically comprises the following steps:

[0079] In step S101, based on the cloud platform, the data direct uploading of the elderly user is monitored and judged.

[0080] In the embodiment of the present application, by acquiring the historical uploading data of the elderly user, the frequency of data uploading of the historical uploading data is identified and compared, the monitoring uploading period is planned according to the slowest frequency of historical uploading, based on the cloud platform, the time of data direct uploading of the elderly user is monitored and recorded, the direct uploading monitoring data is generated, the direct uploading monitoring data is analyzed according to the monitoring uploading period, whether the data direct uploading is abnormal is judged, specifically, according to the time in the uploading monitoring data, when the time of not receiving uploading data exceeds the monitoring uploading period, it is determined that the data direct uploading is abnormal.

[0081] It can be understood that the slowest frequency of historical uploading is the slowest data uploading frequency under the condition that the data uploading state is determined to be normal.

[0082] It can be understood that the data direct uploading abnormality may be that the intelligent monitoring device of the elderly user exists abnormality (for example, power consumption, manual shutdown, etc.), or that the wireless network in the room of the elderly user exists abnormality (for example, network arrears, network plug loosening, power plug loosening, etc.).

[0083] Specifically, Figure 2 A flow chart of the monitoring and judgment of data direct uploading in the method provided by the embodiment of the present application is shown.

[0084] In another preferred embodiment provided by the present application, the monitoring and judgment of data direct uploading of the elderly user based on the cloud platform specifically comprises the following steps:

[0085] In step S1011, the historical uploading data of the elderly user is acquired.

[0086] In step S1012, the historical uploading data is analyzed, and the monitoring uploading period is planned.

[0087] In step S1013, based on the cloud platform, the monitoring and recording of data direct uploading of the elderly user are performed, and the direct uploading monitoring data is generated.

[0088] In step S1014, the direct uploading monitoring data is analyzed according to the monitoring uploading period, and whether the data direct uploading is abnormal is judged.

[0089] Further, the cloud-based home-based elderly care monitoring and scheduling method further comprises the following steps:

[0090] In step S102, when the data direct uploading is abnormal, a three-level abnormality alarm is performed, and an outside neighbor scheduling is performed, an outside neighbor network is selected for sequential connection, and whether the outside neighbor connection is successful is judged.

[0091] In the embodiment of the present application, in the case of data direct uploading abnormality, three-level alarm information is generated, and the three-level alarm information is sent to a plurality of preset elderly care monitoring addresses, a three-level abnormality alarm is performed, and through the cloud platform, outside neighbor scheduling analysis is performed to determine a plurality of authorized outside neighbor networks, and then the signal strength, bandwidth, delay, security and other outside neighbor network states of the plurality of authorized outside neighbor networks are acquired, the plurality of authorized outside neighbor networks are compared and arranged through the plurality of outside neighbor network states, a plurality of corresponding outside neighbor connection sequences are determined, and then connection attempts are made through the plurality of corresponding authorized outside neighbor networks and the smart monitoring device of the elderly user according to the plurality of outside neighbor connection sequences, and whether the outside neighbor connection is successful is judged. Specifically, in the case that the connection attempt of the authorized outside neighbor network and the smart monitoring device of the elderly user is successful, it is determined that the outside neighbor connection is successful; in the case that the connection attempt of all authorized outside neighbor networks and the smart monitoring device of the elderly user fails, it is determined that the outside neighbor connection is unsuccessful.

[0092] It can be understood that the plurality of preset elderly care monitoring addresses include the communication addresses of community elderly care monitoring personnel, the communication addresses of the family members of the elderly user, etc.

[0093] It can be understood that the authorized outside neighbor network is not the room of the elderly user, but the adjacent room of the elderly user, and is a wireless network that has been authorized to connect in advance.

[0094] It can be understood that the successful outside neighbor connection can determine that the smart monitoring device of the elderly user is not abnormal, and the wireless network in the room of the elderly user is abnormal; and the unsuccessful outside neighbor connection can determine that the smart monitoring device of the elderly user is abnormal.

[0095] Specifically, Figure 3 A flow chart of the outside neighbor connection success judgment in the method provided by the embodiment of the present application is shown.

[0096] In another preferred embodiment provided by the present application, the three-level abnormality alarm is performed when the data direct uploading is abnormal, and the outside neighbor scheduling is performed, the outside neighbor network is selected for sequential connection, and whether the outside neighbor connection is successful is judged, which specifically comprises the following steps:

[0097] In step S1021, when the data direct uploading is abnormal, three-level alarm information is generated.

[0098] Step S1022, sending the three-level alarm information to a plurality of preset elderly care monitoring addresses to perform three-level abnormal alarm.

[0099] Step S1023, performing outer neighbor dispatching to determine a plurality of authorized outer neighbor networks.

[0100] Step S1024, acquiring outer neighbor network states of the plurality of authorized outer neighbor networks.

[0101] Step S1025, comparing and arranging the plurality of outer neighbor network states to determine a plurality of corresponding outer neighbor connection sequences.

[0102] Step S1026, sequentially connecting through the corresponding authorized outer neighbor networks according to the plurality of outer neighbor connection sequences, and determining whether the outer neighbor connection is successful.

[0103] Further, the cloud-based home-based elderly care monitoring and dispatching method further includes the following steps:

[0104] Step S103, performing secondary abnormal alarm when the outer neighbor connection is unsuccessful.

[0105] In the embodiment of the application, when the outer neighbor connection is unsuccessful, secondary alarm information and an auxiliary processing request are generated, and the secondary alarm information is sent to a plurality of preset elderly care monitoring addresses to perform secondary abnormal alarm to community elderly care monitoring personnel and family members of the elderly user, so that the community elderly care monitoring personnel and the family members of the elderly user can timely learn that the intelligent monitoring device is abnormal, and at the same time, outer neighbor communication addresses of a plurality of adjacent room occupants are acquired, and the auxiliary processing request is sent to the plurality of outer neighbor communication addresses, after the adjacent room occupants repair the abnormality of the intelligent monitoring device of the elderly user, the auxiliary processing feedback is received, and the secondary abnormal alarm is cancelled.

[0106] It can be understood that the plurality of outer neighbor communication addresses are communication addresses (for example, mobile phone communication addresses) of the plurality of adjacent room occupants acquired under the condition that the plurality of adjacent room occupants are authorized in advance.

[0107] Specifically, Figure 4 A flowchart of performing secondary abnormal alarm in the method provided by the embodiment of the application is shown.

[0108] In another preferred embodiment of the application, when the outer neighbor connection is unsuccessful, the secondary abnormal alarm specifically includes the following steps:

[0109] Step S1031, when the outer neighbor connection is unsuccessful, generating secondary alarm information and an auxiliary processing request.

[0110] Step S1032, sending the secondary alarm information to a plurality of preset elderly care monitoring addresses to perform secondary abnormality alarm.

[0111] Step S1033, acquiring a plurality of outer neighbor communication addresses.

[0112] Step S1034, sending an auxiliary processing request to the plurality of outer neighbor communication addresses and receiving auxiliary processing feedback.

[0113] Step S1035, according to the auxiliary processing feedback, canceling the secondary abnormality alarm after completing the auxiliary processing.

[0114] Further, the cloud-based home-based elderly care monitoring and dispatching method further comprises the following steps:

[0115] Step S104, when the outer neighbor connection is successful, acquiring indirect transmission data, and analyzing the indirect transmission data through a cloud platform to determine whether there is data abnormality.

[0116] In the embodiment of the application, in the case that the outer neighbor connection is successful, the indirect cloud uploading of data of the intelligent monitoring equipment of the elderly user is performed through the corresponding authorized outer neighbor network, the indirect transmission data of the cloud uploading is acquired, the indirect transmission data is identified through the cloud platform, the effective health data including blood pressure, heart rate, blood glucose, blood oxygen and the like are extracted from the indirect transmission data, the standard health data are acquired, and then the effective health data and the standard health data are compared through the cloud platform to determine whether there is data abnormality. Specifically, when the effective health data satisfies the data interval corresponding to the standard health data, it is determined that there is no data abnormality; and when the effective health data does not satisfy the data interval corresponding to the standard health data, it is determined that there is data abnormality.

[0117] Specifically, Figure 5 A flow chart of data abnormality analysis and determination in the method provided by the embodiment of the application is shown.

[0118] In another preferred embodiment of the application, the step of acquiring the indirect transmission data when the outer neighbor connection is successful and analyzing the indirect transmission data through the cloud platform to determine whether there is data abnormality specifically comprises the following steps:

[0119] Step S1041, acquiring the indirect transmission data when the outer neighbor connection is successful.

[0120] Step S1042, identifying the indirect transmission data through the cloud platform to extract the effective health data.

[0121] Step S1043, acquiring the standard health data.

[0122] Step S1044, comparing the effective health data with the standard health data through the cloud platform to determine whether there is data anomaly.

[0123] Further, the cloud-based home-based elderly monitoring and scheduling method further includes the following steps:

[0124] Step S105, when there is data anomaly, performing first-level abnormality alarm.

[0125] In the embodiment of the present application, when there is data anomaly, it indicates that the elderly user has health risks, at this time, first-level alarm information is generated, and the first-level alarm information is sent to a plurality of preset elderly care addresses for first-level abnormality alarm, reminding the community elderly care personnel and the family members of the elderly user to perform emergency treatment.

[0126] It can be understood that the first-level abnormality alarm is the most urgent alarm compared with the second-level abnormality alarm and the third-level abnormality alarm.

[0127] Specifically, Figure 6 A flow chart for performing first-level abnormality alarm in the method provided by the embodiment of the present application is shown.

[0128] In another preferred embodiment provided by the present application, when there is data anomaly, performing first-level abnormality alarm specifically includes the following steps:

[0129] Step S1051, when there is data anomaly, generating first-level alarm information.

[0130] Step S1052, sending the first-level alarm information to a plurality of preset elderly care addresses for first-level abnormality alarm.

[0131] Further, Figure 7 An application architecture diagram of the cloud-based home-based elderly monitoring and scheduling system provided by the embodiment of the present application is shown.

[0132] Specifically, in another preferred embodiment provided by the present application, a cloud-based home-based elderly monitoring and scheduling system includes:

[0133] The direct uploading monitoring module 101 is configured to perform data direct uploading monitoring and judgment of the elderly user based on the cloud platform.

[0134] In the embodiment of the present application, the direct uploading monitoring module 101 identifies and compares the data uploading frequency of the historical uploading data of the elderly user by acquiring the historical uploading data, plans the monitoring uploading period according to the slowest frequency of the historical uploading, monitors and records the data direct uploading time of the elderly user based on the cloud platform, generates direct uploading monitoring data, analyzes the direct uploading monitoring data according to the monitoring uploading period, and judges whether the data direct uploading is abnormal. Specifically, according to the time in the uploading monitoring data, when the time of not receiving the uploading data exceeds the monitoring uploading period, it is determined that the data direct uploading is abnormal.

[0135] Specifically, Figure 8 The structure block diagram of the direct uploading monitoring module 101 in the system provided by the embodiment of the present application is shown.

[0136] Among them, in another preferred embodiment provided by the present application, the direct uploading monitoring module 101 specifically includes:

[0137] The historical data acquisition unit 1011 is configured to acquire the historical uploading data of the elderly user.

[0138] The monitoring period planning unit 1012 is configured to analyze the historical uploading data and plan the monitoring uploading period.

[0139] The uploading monitoring recording unit 1013 is configured to monitor and record the data direct uploading of the elderly user based on the cloud platform, and generate direct uploading monitoring data.

[0140] The uploading abnormality judging unit 1014 is configured to analyze the direct uploading monitoring data according to the monitoring uploading period, and judge whether the data direct uploading is abnormal.

[0141] Further, the cloud-based home-based elderly care monitoring and scheduling system further includes:

[0142] The three-level alarm processing module 102 is configured to perform three-level abnormal alarm when the data direct uploading is abnormal, and perform neighbor scheduling, select neighbor network for sequence connection, and judge whether the neighbor connection is successful.

[0143] In the embodiment of the present application, in the case of data direct uploading exception, the third-level alarm processing module 102 generates third-level alarm information, and sends the third-level alarm information to a plurality of preset elderly care monitoring addresses to perform third-level abnormal alarm, and through the cloud platform, performs neighbor dispatch analysis to determine a plurality of authorized neighbor networks, and then acquires signal strength, bandwidth, delay, security and other neighbor network states of the plurality of authorized neighbor networks, compares and arranges the plurality of authorized neighbor networks through the plurality of neighbor network states, determines a plurality of corresponding neighbor connection sequences, and then attempts to connect the plurality of corresponding authorized neighbor networks and the smart monitoring device of the elderly user according to the plurality of neighbor connection sequences, and judges whether the neighbor connection is successful. Specifically, in the case that the connection attempt of one authorized neighbor network and the smart monitoring device of the elderly user is successful, it is determined that the neighbor connection is successful; in the case that the connection attempt of all authorized neighbor networks and the smart monitoring device of the elderly user fails, it is determined that the neighbor connection is unsuccessful.

[0144] Specifically, Figure 9 The structure block diagram of the third-level alarm processing module 102 in the system provided by the embodiment of the present application is shown.

[0145] In another preferred embodiment of the present application, the third-level alarm processing module 102 specifically includes:

[0146] The third-level alarm information generation unit 1021 is configured to generate third-level alarm information when data direct uploading is abnormal.

[0147] The third-level abnormal alarm unit 1022 is configured to send the third-level alarm information to a plurality of preset elderly care monitoring addresses to perform third-level abnormal alarm.

[0148] The neighbor dispatch unit 1023 is configured to perform neighbor dispatch to determine a plurality of authorized neighbor networks.

[0149] The network state acquisition unit 1024 is configured to acquire neighbor network states of the plurality of authorized neighbor networks.

[0150] The connection sequence planning unit 1025 is configured to compare and arrange the plurality of neighbor network states to determine a plurality of corresponding neighbor connection sequences.

[0151] The sequence connection judgment unit 1026 is configured to connect in sequence through the plurality of corresponding authorized neighbor networks according to the plurality of neighbor connection sequences, and judge whether the neighbor connection is successful.

[0152] Further, the cloud-based home-based elderly care monitoring and dispatch system further includes:

[0153] The second-level alarm processing module 103 is configured to perform second-level abnormal alarm when the neighbor connection is unsuccessful.

[0154] In the embodiment of the present application, in the case that the outer neighbor connection is unsuccessful, the secondary alarm processing module 103 generates secondary alarm information and an auxiliary processing request, and then sends the secondary alarm information to a plurality of preset elderly care monitoring addresses, to make a secondary abnormal alarm to the community elderly care monitoring personnel and the family members of the elderly user, so that the community elderly care monitoring personnel and the family members of the elderly user can know the abnormal condition of the intelligent monitoring device in time, and at the same time, the outer neighbor communication addresses of a plurality of adjacent room occupants are obtained, and the auxiliary processing request is sent to the plurality of outer neighbor communication addresses, after the adjacent room occupants repair the abnormality of the intelligent monitoring device of the elderly user, the auxiliary processing feedback is received, and the secondary abnormal alarm is cancelled.

[0155] Specifically, Figure 10 The structure block diagram of the secondary alarm processing module 103 in the system provided by the embodiment of the present application is shown.

[0156] In another preferred embodiment provided by the present application, the secondary alarm processing module 103 specifically includes:

[0157] The information request generation unit 1031 is configured to generate secondary alarm information and an auxiliary processing request when the outer neighbor connection is unsuccessful.

[0158] The secondary abnormal alarm unit 1032 is configured to send the secondary alarm information to a plurality of preset elderly care monitoring addresses, and make a secondary abnormal alarm.

[0159] The outer neighbor communication address acquisition unit 1033 is configured to obtain a plurality of outer neighbor communication addresses.

[0160] The auxiliary processing feedback receiving unit 1034 is configured to send the auxiliary processing request to a plurality of outer neighbor communication addresses, and receive auxiliary processing feedback.

[0161] The secondary alarm cancellation unit 1035 is configured to cancel the secondary abnormal alarm after completing the auxiliary processing according to the auxiliary processing feedback.

[0162] Further, the cloud-based home-based elderly care monitoring and dispatching system further includes:

[0163] The data abnormality judgment module 104 is configured to obtain indirect transmission data when the outer neighbor connection is successful, and analyze the indirect transmission data through a cloud platform to determine whether there is data abnormality.

[0164] In the embodiment of the present application, in the case of successful external neighbor connection, the data anomaly judgment module 104 performs indirect cloud uploading of data of the intelligent monitoring device of the elderly user through the corresponding authorized external neighbor network, obtains indirect transmission data of the cloud uploading, identifies the indirect transmission data through the cloud platform, extracts effective health data including blood pressure, heart rate, blood glucose, blood oxygen, etc. from the indirect transmission data, and obtains standard health data. Then, through the cloud platform, the effective health data is compared with the standard health data to determine whether there is data anomaly. Specifically, when the effective health data meets the data interval corresponding to the standard health data, it is determined that there is no data anomaly; and when the effective health data does not meet the data interval corresponding to the standard health data, it is determined that there is data anomaly.

[0165] The first alarm processing module 105 is configured to perform first abnormal alarm when there is data anomaly.

[0166] In the embodiment of the present application, when there is data anomaly, it indicates that the elderly user has health risk. At this time, the first alarm processing module 105 generates first alarm information, and sends the first alarm information to a plurality of preset elderly care monitoring addresses to perform first abnormal alarm, reminding the community elderly care monitoring personnel and the family members of the elderly user to perform emergency treatment.

[0167] It should be understood that, although each step in the flowchart of each embodiment of the present application is displayed in sequence according to the arrow direction, these steps are not necessarily executed in sequence according to the arrow direction. Unless otherwise specified herein, the execution of these steps has no strict sequence limitation, and these steps can be executed in other orders. Moreover, at least part of the steps in each embodiment can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps.

[0168] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0169] The above-mentioned embodiments only express several embodiments of the present application, which are described in detail and specifically, but cannot be understood as a limitation on the scope of the patent of the present application. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of protection of the present application. Therefore, the scope of protection of the patent of the present application should be subject to the appended claims.

Claims

1. A cloud-based method for monitoring and scheduling home-based elderly care, characterized in that, The method specifically includes the following steps: Based on the cloud platform, data from elderly users is directly uploaded for monitoring and assessment; The frequency of data uploads is identified and compared. Based on the slowest historical upload frequency, a monitoring upload cycle is planned. Then, based on the cloud platform, the time when elderly users directly upload data is monitored and recorded to generate direct upload monitoring data. According to the monitoring upload cycle, the direct upload monitoring data is analyzed to determine whether the direct upload is abnormal. According to the time in the upload monitoring data, if the time without receiving uploaded data exceeds the monitoring upload cycle, the direct upload is determined to be abnormal. When data upload fails, a level 3 alarm is triggered, and neighbor scheduling is performed to select neighboring networks for sequential connection and determine whether the neighboring connection is successful. In the event of an anomaly in direct data upload, a Level 3 alarm is generated and sent to multiple preset elderly care monitoring addresses to trigger a Level 3 anomaly alarm. Furthermore, through a cloud platform, external neighbor scheduling analysis is performed to identify multiple authorized external neighbor networks. The status of these authorized networks is then obtained, and they are compared and ranked to determine the corresponding connection order. Following this order, connection attempts are made with the elderly user's smart monitoring device through these authorized external neighbor networks. The success of the connection is assessed: if at least one authorized external neighbor network successfully connects to the elderly user's smart monitoring device, the connection is considered successful; if all attempts fail, the connection is considered unsuccessful. When the connection to an external neighbor fails, a level 2 anomaly alarm will be triggered; If the connection with the neighboring unit fails, a secondary alarm message and an auxiliary processing request are generated. The secondary alarm message is then sent to multiple preset elderly care monitoring addresses to alert community elderly care monitoring personnel and the family members of the elderly residents. This allows community elderly care monitoring personnel and the family members of the elderly residents to be aware of any abnormalities in the smart monitoring equipment in a timely manner. At the same time, the communication addresses of the neighboring residents in multiple adjacent rooms are obtained, and auxiliary processing requests are sent to these addresses. After the neighboring residents have repaired the abnormality of the smart monitoring equipment for the elderly residents, the auxiliary processing feedback is received, and the secondary alarm is canceled. When the external connection is successful, the indirect transmission data is obtained, and the indirect transmission data is analyzed through the cloud platform to determine whether there is any data anomaly. When data anomalies are detected, a Level 1 anomaly alarm will be triggered.

2. The cloud-based home-based elderly care monitoring and scheduling method according to claim 1, characterized in that, Upon successful connection to an external neighbor, the process of acquiring indirect transmission data and analyzing it through a cloud platform to determine whether there are any data anomalies specifically includes the following steps: When the external neighbor connection is successful, obtain the indirectly transmitted data; The indirectly transmitted data is identified and valid health data is extracted through a cloud platform. Obtain standard health data; The valid health data is compared with the standard health data through a cloud platform to determine whether there are any data anomalies.

3. The cloud-based home-based elderly care monitoring and scheduling method according to claim 1, characterized in that, The specific steps for triggering a Level 1 anomaly alarm when data anomalies occur include: When data anomalies are detected, a Level 1 alarm message is generated; The system sends Level 1 alarm information to multiple preset elderly care monitoring addresses to trigger Level 1 abnormality alarms.

4. A cloud-based home-based elderly care monitoring and scheduling system for executing the cloud-based home-based elderly care monitoring and scheduling method as described in any one of claims 1-3, characterized in that, The system includes a direct upload monitoring module, a three-level alarm processing module, a two-level alarm processing module, a data anomaly detection module, and a first-level alarm processing module, wherein: The direct upload monitoring module is used to monitor and assess data uploaded directly from elderly users via a cloud platform. The three-level alarm processing module is used to trigger a three-level alarm when there is an anomaly in the direct data upload, and to perform external neighbor scheduling, select external neighbor networks for sequential connection, and determine whether the external neighbor connection is successful. The secondary alarm processing module is used to generate a secondary abnormal alarm when the external neighbor connection fails. The data anomaly detection module is used to obtain indirect transmission data when the external neighbor connection is successful, and to analyze the indirect transmission data through the cloud platform to determine whether there is data anomaly. The Level 1 alarm processing module is used to trigger a Level 1 anomaly alarm when there is data abnormality.

5. The cloud-based home-based elderly care monitoring and dispatching system according to claim 4, characterized in that, The direct upload monitoring module specifically includes: The historical data acquisition unit is used to acquire the historical uploaded data of elderly users; The monitoring cycle planning unit is used to analyze the historical uploaded data and plan the monitoring upload cycle. The upload monitoring record unit is used to directly upload monitoring records of data from elderly users based on the cloud platform, and generate directly uploaded monitoring data; The upload anomaly judgment unit is used to analyze the directly uploaded monitoring data according to the monitoring upload period and determine whether the direct upload of data is abnormal.

6. The cloud-based home-based elderly care monitoring and dispatching system according to claim 4, characterized in that, The three-level alarm processing module specifically includes: The Level 3 Alarm Information Generation Unit is used to generate Level 3 alarm information when there is an anomaly in the direct data upload. The Level 3 Anomaly Alarm Unit is used to send Level 3 alarm information to multiple preset elderly care monitoring addresses to trigger Level 3 anomaly alarms. The outer neighbor scheduling unit is used to perform outer neighbor scheduling and determine multiple authorized outer neighbor networks; A network status acquisition unit is used to acquire the status of the neighboring networks of the multiple authorized neighboring networks; A connection order planning unit is used to compare and arrange the states of multiple external neighbor networks to determine multiple corresponding external neighbor connection orders. The sequence connection determination unit is used to perform sequential connection through multiple corresponding authorized external neighbor networks according to the sequence of multiple external neighbor connections, and to determine whether the external neighbor connection is successful.

7. The cloud-based home-based elderly care monitoring and dispatching system according to claim 4, characterized in that, The secondary alarm processing module specifically includes: The information request generation unit is used to generate secondary alarm information and auxiliary processing requests when the external neighbor connection fails. The Level 2 Anomaly Alarm Unit is used to send Level 2 alarm information to multiple preset elderly care monitoring addresses to trigger Level 2 anomaly alarms. The neighbor communication address acquisition unit is used to acquire multiple neighbor communication addresses; An auxiliary processing feedback receiving unit is used to send auxiliary processing requests to multiple external neighbor communication addresses and receive auxiliary processing feedback. The secondary alarm cancellation unit is used to cancel the secondary abnormal alarm after completing the auxiliary processing, based on the feedback from the auxiliary processing.

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