Old people monitoring system based on Internet of Things

Abstract

The invention discloses an old people monitoring system based on the Internet of Things, and relates to the technical field of medical monitoring. Comprising a body information acquisition unit which adopts a wearable monitoring device which is worn on the body of an old person and is responsible for acquiring vital sign data of the old person; the environment information acquisition unit is arranged in the activity range of the old people and is used for acquiring environment information of the activity area of the old people; the cloud server is used for receiving and processing the information from the body information acquisition unit and the environment information acquisition unit, and judging the real condition of the old people through a preset algorithm; an execution module which is in communication connection with the smart home in the old people activity area and is used for controlling the on-off and state of the smart home; and the monitoring terminal is in communication connection with the cloud server. Comprehensive monitoring can be achieved, the system can comprehensively monitor vital sign data and indoor environment information of the old people, and life quality and safety of the old people are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of medical monitoring, and in particular to an elderly monitoring system based on the Internet of Things. Background Art

[0002] As the quality of life of modern families improves, people's requirements for health services are gradually increasing. With the rapid expansion of the elderly population and the continuous development of socialization and marketization, the consumption demand of the elderly is growing. 20% of the elderly need help in their daily lives, and 5% of the elderly need care from others. Therefore, the issue of medical and health care for the elderly is also receiving more and more attention.

[0003] As the global aging process accelerates, the elderly have an increasing demand for medical care, mobility assistance, security and personal hygiene care, but traditional guardianship methods rely on on-site care by nurses or assistants, which is costly and inefficient. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides an elderly care system based on the Internet of Things, comprising:

[0005] The body information collection unit uses a wearable monitoring device that is worn on the body of the elderly and is responsible for collecting the elderly's vital signs data;

[0006] An environmental information collection unit is arranged within the activity range of the elderly and is used to collect environmental information of the elderly's activity area;

[0007] A cloud server receives and processes information from the body information collection unit and the environmental information collection unit, and determines the actual situation of the elderly through a preset algorithm;

[0008] An execution module is connected to the smart home in the elderly activity area to control the opening and closing and status of the smart home;

[0009] The monitoring terminal is connected to the cloud server and is owned by the elderly's family and / or guardian. The monitoring terminal can remotely view the elderly's actual situation and issue an alarm when it is determined that the elderly have safety risks.

[0010] Preferably, the monitoring logic of the elderly monitoring system includes the following steps:

[0011] S1: The body information collection unit and the environment information collection unit collect the vital sign data of the elderly and the environment information data of the elderly's activity area in real time, and transmit the data to the cloud server;

[0012] S2: A safe data threshold and a fluctuation coefficient are preset for each set of data in the cloud server;

[0013] S3: After receiving the data, the cloud server processes the data and then determines whether the elderly are at risk based on the comparison of the data threshold and the fluctuation coefficient;

[0014] S4: When there is a security risk, the cloud server transmits alarm information to the monitoring terminal on the one hand, and performs mitigation execution through the execution module on the other hand.

[0015] Preferably: in step S1, the data collected by the body information collection unit includes heart rate, body temperature, blood sugar, blood pressure, and position;

[0016] In the step S1, the data collected by the environmental information collection unit includes temperature, humidity, smoke concentration, and surveillance video.

[0017] Preferably: in step S2, the method for setting the threshold value comprises the following steps:

[0018] S21: Determine the normal range of each data through big data, medical experience and the physical condition of the elderly themselves It represents the normal range of the i-th data as (σ 1 , σ 2 );

[0019] S22: For a data, assign an importance coefficient according to its importance to the body. It represents the importance of the i-th data as σ k ;

[0020] S23: Then according to the formula Calculate the coefficient of fluctuation Δ of the data in the i-th i ;

[0021] S24: Then, according to the normal range threshold and the fluctuation coefficient, the final threshold range is determined as

[0022] Preferably: in the step S3, data preprocessing includes data normalization processing and data outlier processing.

[0023] Preferably: the method for normalizing the bracket numbers adopts:

[0024] Maximum-minimum normalization, the formula is: where min is the minimum value in the data, max is the maximum value in the data, x is the data before normalization, and y is the data after normalization;

[0025] Standardization, the formula is, where μ is the mean of the data, is the variance of the data, x is the data before normalization, and y is the data after normalization;

[0026] Absolute value normalization, its formula is, is the maximum absolute value in the data, x is the data before normalization, y is the data after normalization;

[0027] Any one of .

[0028] Preferably, the processing of data outliers includes blank value processing and duplicate value processing, which includes the following steps:

[0029] S31: Acquire data of each node according to the data collection cycle and the data collection node;

[0030] S32: If a data collection node has multiple data, all data are deleted to form a blank value; if a data collection node has no data, a blank value is directly formed;

[0031] S33: Then, a filling algorithm based on the relationship between expiring data is used to fill in blank values.

[0032] Preferably: in the step S33, the filling algorithm based on the expiring data relationship includes the following steps:

[0033] S331: Starting from the missing data node, obtain data of n nodes forward and data of m nodes backward;

[0034] S332: Then according to the formula Calculate the fill value M, where M i The i-th data value of the distance filling node in the set of n data nodes obtained forward, k i M i The weight, M o The oth data value of the distance filling node in the m data node set obtained backward, k o M o The weight of .

[0035] Preferably: in the step S332, k i >k i+1 , k o >k o+1 , k i , k o All are positive numbers.

[0036] Preferably: in the step S332, or k i -k i+1 >c,c>0、k o -k o+1 >d, d>0.

[0037] The beneficial effects of the present invention are embodied in:

[0038] 1. The present invention can realize comprehensive monitoring: the system can comprehensively monitor the vital signs data and indoor environment information of the elderly to ensure the quality of life and safety of the elderly.

[0039] 2. The present invention can achieve timely response: when an emergency situation such as an elderly person falling is detected, the system can immediately trigger an alarm device and notify family members or caregivers to achieve timely response and rescue.

[0040] 3. The present invention can reduce costs: remote monitoring is achieved through the Internet of Things technology, which reduces the cost of traditional monitoring methods and improves monitoring efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0042] Figure 1 This is a framework diagram of an Internet of Things-based elderly care system proposed by the present invention;

[0043] Figure 2 This is a flow chart of an Internet of Things-based elderly monitoring system proposed by the present invention. DETAILED DESCRIPTION

[0044] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0045] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0046] Embodiment 1:

[0047] An elderly care system based on the Internet of Things, comprising:

[0048] The body information collection unit uses a wearable monitoring device that is worn on the body of the elderly and is responsible for collecting the elderly's vital signs data;

[0049] An environmental information collection unit is arranged within the activity range of the elderly and is used to collect environmental information of the elderly's activity area;

[0050] A cloud server receives and processes information from the body information collection unit and the environmental information collection unit, and determines the actual situation of the elderly through a preset algorithm;

[0051] An execution module is connected to the smart home in the elderly activity area to control the opening and closing and status of the smart home;

[0052] The monitoring terminal is connected to the cloud server and is owned by the elderly's family and / or guardian. The monitoring terminal can remotely view the elderly's actual situation and issue an alarm when it is determined that the elderly have safety risks.

[0053] The monitoring logic of the elderly monitoring system includes the following steps:

[0054] S1: The body information collection unit and the environment information collection unit collect the vital sign data of the elderly and the environment information data of the elderly's activity area in real time, and transmit the data to the cloud server;

[0055] S2: A safe data threshold and a fluctuation coefficient are preset for each set of data in the cloud server;

[0056] S3: After receiving the data, the cloud server processes the data and then determines whether the elderly are at risk based on the comparison of the data threshold and the fluctuation coefficient;

[0057] S4: When there is a security risk, the cloud server transmits alarm information to the monitoring terminal on the one hand, and performs mitigation execution through the execution module on the other hand.

[0058] In step S1, the data collected by the body information collection unit includes heart rate, body temperature, blood sugar, blood pressure, and position.

[0059] In the step S1, the data collected by the environmental information collection unit includes temperature, humidity, smoke concentration, and surveillance video.

[0060] Embodiment 2:

[0061] An elderly care system based on the Internet of Things, comprising:

[0062] The body information collection unit uses a wearable monitoring device that is worn on the body of the elderly and is responsible for collecting the elderly's vital signs data;

[0063] An environmental information collection unit is arranged within the activity range of the elderly and is used to collect environmental information of the elderly's activity area;

[0064] A cloud server receives and processes information from the body information collection unit and the environmental information collection unit, and determines the actual situation of the elderly through a preset algorithm;

[0065] An execution module is connected to the smart home in the elderly activity area to control the opening and closing and status of the smart home;

[0066] The monitoring terminal is connected to the cloud server and is owned by the elderly's family and / or guardian. The monitoring terminal can remotely view the elderly's actual situation and issue an alarm when it is determined that the elderly have safety risks.

[0067] The monitoring logic of the elderly monitoring system includes the following steps:

[0068] S1: The body information collection unit and the environment information collection unit collect the vital sign data of the elderly and the environment information data of the elderly's activity area in real time, and transmit the data to the cloud server;

[0069] S2: A safe data threshold and a fluctuation coefficient are preset for each set of data in the cloud server;

[0070] S3: After receiving the data, the cloud server processes the data and then determines whether the elderly are at risk based on the comparison of the data threshold and the fluctuation coefficient;

[0071] S4: When there is a security risk, the cloud server transmits alarm information to the monitoring terminal on the one hand, and performs mitigation execution through the execution module on the other hand.

[0072] In step S1, the data collected by the body information collection unit includes heart rate, body temperature, blood sugar, blood pressure, and position.

[0073] In the step S1, the data collected by the environmental information collection unit includes temperature, humidity, smoke concentration, and surveillance video.

[0074] In step S2, the method for setting the threshold value comprises the following steps:

[0075] S21: Determine the normal range of each data through big data, medical experience and the physical condition of the elderly themselves It represents the normal range of the i-th data as (σ 1 , σ 2 );

[0076] S22: For a data, assign an importance coefficient according to its importance to the body. It represents the importance of the i-th data as σ k ;

[0077] S23: Then according to the formula Calculate the coefficient of fluctuation Δ of the data in the i-th i ;

[0078] S24: Then, according to the normal range threshold and the fluctuation coefficient, the final threshold range is determined as

[0079] Embodiment 3:

[0080] An elderly care system based on the Internet of Things, comprising:

[0081] The body information collection unit uses a wearable monitoring device that is worn on the body of the elderly and is responsible for collecting the elderly's vital signs data;

[0082] An environmental information collection unit is arranged within the activity range of the elderly and is used to collect environmental information of the elderly's activity area;

[0083] A cloud server receives and processes information from the body information collection unit and the environmental information collection unit, and determines the actual situation of the elderly through a preset algorithm;

[0084] An execution module is connected to the smart home in the elderly activity area to control the opening and closing and status of the smart home;

[0085] The monitoring terminal is connected to the cloud server and is owned by the elderly's family and / or guardian. The monitoring terminal can remotely view the elderly's actual situation and issue an alarm when it is determined that the elderly have safety risks.

[0086] The monitoring logic of the elderly monitoring system includes the following steps:

[0087] S1: The body information collection unit and the environment information collection unit collect the vital sign data of the elderly and the environment information data of the elderly's activity area in real time, and transmit the data to the cloud server;

[0088] S2: A safe data threshold and a fluctuation coefficient are preset for each set of data in the cloud server;

[0089] S3: After receiving the data, the cloud server processes the data and then determines whether the elderly are at risk based on the comparison of the data threshold and the fluctuation coefficient;

[0090] S4: When there is a security risk, the cloud server transmits alarm information to the monitoring terminal on the one hand, and performs mitigation execution through the execution module on the other hand.

[0091] In step S1, the data collected by the body information collection unit includes heart rate, body temperature, blood sugar, blood pressure, and position.

[0092] In the step S1, the data collected by the environmental information collection unit includes temperature, humidity, smoke concentration, and surveillance video.

[0093] In step S2, the method for setting the threshold value comprises the following steps:

[0094] S21: Determine the normal range of each data through big data, medical experience and the physical condition of the elderly themselves It represents the normal range of the i-th data as (σ 1 , σ 2 );

[0095] S22: For a data, assign an importance coefficient according to its importance to the body. It represents the importance of the i-th data as σ k ;

[0096] S23: Then according to the formula Calculate the coefficient of fluctuation Δ of the data in the i-th i ;

[0097] S24: Then, according to the normal range threshold and the fluctuation coefficient, the final threshold range is determined as

[0098] In the step S3, data preprocessing includes data normalization processing and data outlier processing.

[0099] The method for normalizing the bracket numbers is as follows:

[0100] Maximum-minimum normalization, the formula is: where min is the minimum value in the data, max is the maximum value in the data, x is the data before normalization, and y is the data after normalization.

[0101] Embodiment 4:

[0102] An elderly care system based on the Internet of Things, comprising:

[0103] The body information collection unit uses a wearable monitoring device that is worn on the body of the elderly and is responsible for collecting the elderly's vital signs data;

[0104] An environmental information collection unit is arranged within the activity range of the elderly and is used to collect environmental information of the elderly's activity area;

[0105] A cloud server receives and processes information from the body information collection unit and the environmental information collection unit, and determines the actual situation of the elderly through a preset algorithm;

[0106] An execution module is connected to the smart home in the elderly activity area to control the opening and closing and status of the smart home;

[0107] The monitoring terminal is connected to the cloud server and is owned by the elderly's family and / or guardian. The monitoring terminal can remotely view the elderly's actual situation and issue an alarm when it is determined that the elderly have safety risks.

[0108] The monitoring logic of the elderly monitoring system includes the following steps:

[0109] S1: The body information collection unit and the environment information collection unit collect the vital sign data of the elderly and the environment information data of the elderly's activity area in real time, and transmit the data to the cloud server;

[0110] S2: A safe data threshold and a fluctuation coefficient are preset for each set of data in the cloud server;

[0111] S3: After receiving the data, the cloud server processes the data and then determines whether the elderly are at risk based on the comparison of the data threshold and the fluctuation coefficient;

[0112] S4: When there is a security risk, the cloud server transmits alarm information to the monitoring terminal on the one hand, and performs mitigation execution through the execution module on the other hand.

[0113] In step S1, the data collected by the body information collection unit includes heart rate, body temperature, blood sugar, blood pressure, and position.

[0114] In the step S1, the data collected by the environmental information collection unit includes temperature, humidity, smoke concentration, and surveillance video.

[0115] In step S2, the method for setting the threshold value comprises the following steps:

[0116] S21: Determine the normal range of each data through big data, medical experience and the physical condition of the elderly themselves It represents the normal range of the i-th data as (σ 1 , σ 2 );

[0117] S22: For a data, assign an importance coefficient according to its importance to the body. It represents the importance of the i-th data as σ k ;

[0118] S23: Then according to the formula Calculate the coefficient of fluctuation Δ of the data in the i-th i ;

[0119] S24: Then, according to the normal range threshold and the fluctuation coefficient, the final threshold range is determined as

[0120] In the step S3, data preprocessing includes data normalization processing and data outlier processing.

[0121] The method for normalizing the bracket numbers is as follows:

[0122] Standardization, the formula is, where μ is the mean of the data, is the variance of the data, x is the data before normalization, and y is the data after normalization.

[0123] Embodiment 5:

[0124] An elderly care system based on the Internet of Things, comprising:

[0125] The body information collection unit uses a wearable monitoring device that is worn on the body of the elderly and is responsible for collecting the elderly's vital signs data;

[0126] An environmental information collection unit is arranged within the activity range of the elderly and is used to collect environmental information of the elderly's activity area;

[0127] A cloud server receives and processes information from the body information collection unit and the environmental information collection unit, and determines the actual situation of the elderly through a preset algorithm;

[0128] An execution module is connected to the smart home in the elderly activity area to control the opening and closing and status of the smart home;

[0129] The monitoring terminal is connected to the cloud server and is owned by the elderly's family and / or guardian. The monitoring terminal can remotely view the elderly's actual situation and issue an alarm when it is determined that the elderly have safety risks.

[0130] The monitoring logic of the elderly monitoring system includes the following steps:

[0131] S1: The body information collection unit and the environment information collection unit collect the vital sign data of the elderly and the environment information data of the elderly's activity area in real time, and transmit the data to the cloud server;

[0132] S2: A safe data threshold and a fluctuation coefficient are preset for each set of data in the cloud server;

[0133] S3: After receiving the data, the cloud server processes the data and then determines whether the elderly are at risk based on the comparison of the data threshold and the fluctuation coefficient;

[0134] S4: When there is a security risk, the cloud server transmits alarm information to the monitoring terminal on the one hand, and performs mitigation execution through the execution module on the other hand.

[0135] In step S1, the data collected by the body information collection unit includes heart rate, body temperature, blood sugar, blood pressure, and position.

[0136] In the step S1, the data collected by the environmental information collection unit includes temperature, humidity, smoke concentration, and surveillance video.

[0137] In step S2, the method for setting the threshold value comprises the following steps:

[0138] S21: Determine the normal range of each data through big data, medical experience and the physical condition of the elderly themselves It represents the normal range of the i-th data as (σ 1 , σ 2 );

[0139] S22: For a data, assign an importance coefficient according to its importance to the body. It represents the importance of the i-th data as σ k ;

[0140] S23: Then according to the formula Calculate the coefficient of fluctuation Δ of the data in the i-th i ;

[0141] S24: Then, according to the normal range threshold and the fluctuation coefficient, the final threshold range is determined as

[0142] In the step S3, data preprocessing includes data normalization processing and data outlier processing.

[0143] The method for normalizing the bracket numbers is as follows:

[0144] Absolute value normalization, its formula is, is the maximum absolute value in the data, x is the data before normalization, and y is the data after normalization.

[0145] Embodiment 6:

[0146] An elderly care system based on the Internet of Things, comprising:

[0147] The body information collection unit uses a wearable monitoring device that is worn on the body of the elderly and is responsible for collecting the elderly's vital signs data;

[0148] An environmental information collection unit is arranged within the activity range of the elderly and is used to collect environmental information of the elderly's activity area;

[0149] A cloud server receives and processes information from the body information collection unit and the environmental information collection unit, and determines the actual situation of the elderly through a preset algorithm;

[0150] An execution module is connected to the smart home in the elderly activity area to control the opening and closing and status of the smart home;

[0151] The monitoring terminal is connected to the cloud server and is owned by the elderly's family and / or guardian. The monitoring terminal can remotely view the elderly's actual situation and issue an alarm when it is determined that the elderly have safety risks.

[0152] The monitoring logic of the elderly monitoring system includes the following steps:

[0153] S1: The body information collection unit and the environment information collection unit collect the vital sign data of the elderly and the environment information data of the elderly's activity area in real time, and transmit the data to the cloud server;

[0154] S2: A safe data threshold and a fluctuation coefficient are preset for each set of data in the cloud server;

[0155] S3: After receiving the data, the cloud server processes the data and then determines whether the elderly are at risk based on the comparison of the data threshold and the fluctuation coefficient;

[0156] S4: When there is a security risk, the cloud server transmits alarm information to the monitoring terminal on the one hand, and performs mitigation execution through the execution module on the other hand.

[0157] In step S1, the data collected by the body information collection unit includes heart rate, body temperature, blood sugar, blood pressure, and position.

[0158] In the step S1, the data collected by the environmental information collection unit includes temperature, humidity, smoke concentration, and surveillance video.

[0159] In step S2, the method for setting the threshold value comprises the following steps:

[0160] S21: Determine the normal range of each data through big data, medical experience and the physical condition of the elderly themselves It represents the normal range of the i-th data as (σ 1 , σ 2 );

[0161] S22: For a data, assign an importance coefficient according to its importance to the body. It represents the importance of the i-th data as σ k ;

[0162] S23: Then according to the formula Calculate the coefficient of fluctuation Δ of the data in the i-th i ;

[0163] S24: Then, according to the normal range threshold and the fluctuation coefficient, the final threshold range is determined as

[0164] In the step S3, data preprocessing includes data normalization processing and data outlier processing.

[0165] The method for normalizing the bracket numbers is as follows:

[0166] Maximum-minimum normalization, the formula is: where min is the minimum value in the data, max is the maximum value in the data, x is the data before normalization, and y is the data after normalization;

[0167] Standardization, the formula is, where μ is the mean of the data, is the variance of the data, x is the data before normalization, and y is the data after normalization;

[0168] Absolute value normalization, its formula is, is the maximum absolute value in the data, x is the data before normalization, y is the data after normalization;

[0169] Any one of .

[0170] The processing of the data outliers includes blank value processing and duplicate value processing, which includes the following steps:

[0171] S31: Acquire data of each node according to the data collection cycle and the data collection node;

[0172] S32: If a data collection node has multiple data, all data are deleted to form a blank value; if a data collection node has no data, a blank value is directly formed;

[0173] S33: Then, a filling algorithm based on the relationship between expiring data is used to fill in blank values.

[0174] In the step S33, the filling algorithm based on the expiring data relationship includes the following steps:

[0175] S331: Starting from the missing data node, obtain data of n nodes forward and data of m nodes backward;

[0176] S332: Then according to the formula Calculate the fill value M, where M i The i-th data value of the distance filling node in the set of n data nodes obtained forward, k i M i The weight, M o The oth data value of the distance filling node in the m data node set obtained backward, k o M o The weight of .

[0177] Embodiment 7:

[0178] An elderly care system based on the Internet of Things, comprising:

[0179] The body information collection unit uses a wearable monitoring device that is worn on the body of the elderly and is responsible for collecting the elderly's vital signs data;

[0180] An environmental information collection unit is arranged within the activity range of the elderly and is used to collect environmental information of the elderly's activity area;

[0181] A cloud server receives and processes information from the body information collection unit and the environmental information collection unit, and determines the actual situation of the elderly through a preset algorithm;

[0182] An execution module is connected to the smart home in the elderly activity area to control the opening and closing and status of the smart home;

[0183] The monitoring terminal is connected to the cloud server and is owned by the elderly's family and / or guardian. The monitoring terminal can remotely view the elderly's actual situation and issue an alarm when it is determined that the elderly have safety risks.

[0184] The monitoring logic of the elderly monitoring system includes the following steps:

[0185] S1: The body information collection unit and the environment information collection unit collect the vital sign data of the elderly and the environment information data of the elderly's activity area in real time, and transmit the data to the cloud server;

[0186] S2: A safe data threshold and a fluctuation coefficient are preset for each set of data in the cloud server;

[0187] S3: After receiving the data, the cloud server processes the data and then determines whether the elderly are at risk based on the comparison of the data threshold and the fluctuation coefficient;

[0188] S4: When there is a security risk, the cloud server transmits alarm information to the monitoring terminal on the one hand, and performs mitigation execution through the execution module on the other hand.

[0189] In step S1, the data collected by the body information collection unit includes heart rate, body temperature, blood sugar, blood pressure, and position.

[0190] In the step S1, the data collected by the environmental information collection unit includes temperature, humidity, smoke concentration, and surveillance video.

[0191] In step S2, the method for setting the threshold value comprises the following steps:

[0192] S21: Determine the normal range of each data through big data, medical experience and the physical condition of the elderly themselves It represents the normal range of the i-th data as (σ 1 , σ 2 );

[0193] S22: For a data, assign an importance coefficient according to its importance to the body. It represents the importance of the i-th data as σ k ;

[0194] S23: Then according to the formula Calculate the coefficient of fluctuation Δ of the data in the i-th i ;

[0195] S24: Then, according to the normal range threshold and the fluctuation coefficient, the final threshold range is determined as

[0196] In the step S3, data preprocessing includes data normalization processing and data outlier processing.

[0197] The method for normalizing the bracket numbers is as follows:

[0198] Maximum-minimum normalization, the formula is: where min is the minimum value in the data, max is the maximum value in the data, x is the data before normalization, and y is the data after normalization;

[0199] Standardization, the formula is, where μ is the mean of the data, is the variance of the data, x is the data before normalization, and y is the data after normalization;

[0200] Absolute value normalization, its formula is, is the maximum absolute value in the data, x is the data before normalization, y is the data after normalization;

[0201] Any one of .

[0202] The processing of the data outliers includes blank value processing and duplicate value processing, which includes the following steps:

[0203] S31: Acquire data of each node according to the data collection cycle and the data collection node;

[0204] S32: If a data collection node has multiple data, all data are deleted to form a blank value; if a data collection node has no data, a blank value is directly formed;

[0205] S33: Then, a filling algorithm based on the relationship between expiring data is used to fill in blank values.

[0206] In the step S33, the filling algorithm based on the expiring data relationship includes the following steps:

[0207] S331: Starting from the missing data node, obtain data of n nodes forward and data of m nodes backward;

[0208] S332: Then according to the formula Calculate the fill value M, where M i The i-th data value of the distance filling node in the set of n data nodes obtained forward, k i M i The weight, M o The oth data value of the distance filling node in the m data node set obtained backward, k o M o The weight of .

[0209] In the step S332, k i >k i+1 , k o >k o+1 , k i , k o All are positive numbers.

[0210] Embodiment 8:

[0211] An elderly care system based on the Internet of Things, comprising:

[0212] The body information collection unit uses a wearable monitoring device that is worn on the body of the elderly and is responsible for collecting the elderly's vital signs data;

[0213] An environmental information collection unit is arranged within the activity range of the elderly and is used to collect environmental information of the elderly's activity area;

[0214] A cloud server receives and processes information from the body information collection unit and the environmental information collection unit, and determines the actual situation of the elderly through a preset algorithm;

[0215] An execution module is connected to the smart home in the elderly activity area to control the opening and closing and status of the smart home;

[0216] The monitoring terminal is connected to the cloud server and is owned by the elderly's family and / or guardian. The monitoring terminal can remotely view the elderly's actual situation and issue an alarm when it is determined that the elderly have safety risks.

[0217] The monitoring logic of the elderly monitoring system includes the following steps:

[0218] S1: The body information collection unit and the environment information collection unit collect the vital sign data of the elderly and the environment information data of the elderly's activity area in real time, and transmit the data to the cloud server;

[0219] S2: A safe data threshold and a fluctuation coefficient are preset for each set of data in the cloud server;

[0220] S3: After receiving the data, the cloud server processes the data and then determines whether the elderly are at risk based on the comparison of the data threshold and the fluctuation coefficient;

[0221] S4: When there is a security risk, the cloud server transmits alarm information to the monitoring terminal on the one hand, and performs mitigation execution through the execution module on the other hand.

[0222] In step S1, the data collected by the body information collection unit includes heart rate, body temperature, blood sugar, blood pressure, and position.

[0223] In the step S1, the data collected by the environmental information collection unit includes temperature, humidity, smoke concentration, and surveillance video.

[0224] In step S2, the method for setting the threshold value comprises the following steps:

[0225] S21: Determine the normal range of each data through big data, medical experience and the physical condition of the elderly themselves It represents the normal range of the i-th data as (σ 1 , σ 2 );

[0226] S22: For a data, assign an importance coefficient according to its importance to the body. It represents the importance of the i-th data as σ k ;

[0227] S23: Then according to the formula Calculate the coefficient of fluctuation Δ of the data in the i-th i ;

[0228] S24: Then, according to the normal range threshold and the fluctuation coefficient, the final threshold range is determined as

[0229] In the step S3, data preprocessing includes data normalization processing and data outlier processing.

[0230] The method for normalizing the bracket numbers is as follows:

[0231] Maximum-minimum normalization, the formula is: where min is the minimum value in the data, max is the maximum value in the data, x is the data before normalization, and y is the data after normalization;

[0232] Standardization, the formula is, where μ is the mean of the data, is the variance of the data, x is the data before normalization, and y is the data after normalization;

[0233] Absolute value normalization, its formula is, is the maximum absolute value in the data, x is the data before normalization, y is the data after normalization;

[0234] Any one of .

[0235] The processing of the data outliers includes blank value processing and duplicate value processing, which includes the following steps:

[0236] S31: Acquire data of each node according to the data collection cycle and the data collection node;

[0237] S32: If a data collection node has multiple data, all data are deleted to form a blank value; if a data collection node has no data, a blank value is directly formed;

[0238] S33: Then, a filling algorithm based on the relationship between expiring data is used to fill in blank values.

[0239] In the step S33, the filling algorithm based on the expiring data relationship includes the following steps:

[0240] S331: Starting from the missing data node, obtain data of n nodes forward and data of m nodes backward;

[0241] S332: Then according to the formula Calculate the fill value M, where M i The i-th data value of the distance filling node in the set of n data nodes obtained forward, k i M i The weight, M o The oth data value of the distance filling node in the m data node set obtained backward, k o M o The weight of .

[0242] In the step S332,

[0243] Embodiment 9:

[0244] An elderly care system based on the Internet of Things, comprising:

[0245] The body information collection unit uses a wearable monitoring device that is worn on the body of the elderly and is responsible for collecting the elderly's vital signs data;

[0246] An environmental information collection unit is arranged within the activity range of the elderly and is used to collect environmental information of the elderly's activity area;

[0247] A cloud server receives and processes information from the body information collection unit and the environmental information collection unit, and determines the actual situation of the elderly through a preset algorithm;

[0248] An execution module is connected to the smart home in the elderly activity area to control the opening and closing and status of the smart home;

[0249] The monitoring terminal is connected to the cloud server and is owned by the elderly's family and / or guardian. The monitoring terminal can remotely view the elderly's actual situation and issue an alarm when it is determined that the elderly have safety risks.

[0250] The monitoring logic of the elderly monitoring system includes the following steps:

[0251] S1: The body information collection unit and the environment information collection unit collect the vital sign data of the elderly and the environment information data of the elderly's activity area in real time, and transmit the data to the cloud server;

[0252] S2: A safe data threshold and a fluctuation coefficient are preset for each set of data in the cloud server;

[0253] S3: After receiving the data, the cloud server processes the data and then determines whether the elderly are at risk based on the comparison of the data threshold and the fluctuation coefficient;

[0254] S4: When there is a security risk, the cloud server transmits alarm information to the monitoring terminal on the one hand, and performs mitigation execution through the execution module on the other hand.

[0255] In step S1, the data collected by the body information collection unit includes heart rate, body temperature, blood sugar, blood pressure, and position.

[0256] In the step S1, the data collected by the environmental information collection unit includes temperature, humidity, smoke concentration, and surveillance video.

[0257] In step S2, the method for setting the threshold value comprises the following steps:

[0258] S21: Determine the normal range of each data through big data, medical experience and the physical condition of the elderly themselves It represents the normal range of the i-th data as (σ 1 , σ2 );

[0259] S22: For a data, assign an importance coefficient according to its importance to the body. It represents the importance of the i-th data as σ k ;

[0260] S23: Then according to the formula Calculate the coefficient of fluctuation Δ of the data in the i-th i ;

[0261] S24: Then, according to the normal range threshold and the fluctuation coefficient, the final threshold range is determined as

[0262] In the step S3, data preprocessing includes data normalization processing and data outlier processing.

[0263] The method for normalizing the bracket numbers is as follows:

[0264] Maximum-minimum normalization, the formula is: where min is the minimum value in the data, max is the maximum value in the data, x is the data before normalization, and y is the data after normalization;

[0265] Standardization, the formula is, where μ is the mean of the data, is the variance of the data, x is the data before normalization, and y is the data after normalization;

[0266] Absolute value normalization, its formula is, is the maximum absolute value in the data, x is the data before normalization, y is the data after normalization;

[0267] Any one of .

[0268] The processing of the data outliers includes blank value processing and duplicate value processing, which includes the following steps:

[0269] S31: Acquire data of each node according to the data collection cycle and the data collection node;

[0270] S32: If a data collection node has multiple data, all data are deleted to form a blank value; if a data collection node has no data, a blank value is directly formed;

[0271] S33: Then, a filling algorithm based on the relationship between expiring data is used to fill in blank values.

[0272] In the step S33, the filling algorithm based on the expiring data relationship includes the following steps:

[0273] S331: Starting from the missing data node, obtain data of n nodes forward and data of m nodes backward;

[0274] S332: Then according to the formula Calculate the fill value M, where M i The i-th data value of the distance filling node in the set of n data nodes obtained forward, k i M i The weight, M o The oth data value of the distance filling node in the m data node set obtained backward, k o M o The weight of .

[0275] In the step S332, k i -k i+1 >c,c>0、k o -k o+1 >d, d>0.

[0276] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An elderly care system based on the Internet of Things, characterized in that: include: The body information collection unit uses a wearable monitoring device that is worn on the body of the elderly and is responsible for collecting the elderly's vital signs data; An environmental information collection unit is arranged within the activity range of the elderly and is used to collect environmental information of the elderly's activity area; A cloud server receives and processes information from the body information collection unit and the environmental information collection unit, and determines the actual situation of the elderly through a preset algorithm; An execution module is connected to the smart home in the elderly activity area to control the opening and closing and status of the smart home; The monitoring terminal is connected to the cloud server and is owned by the elderly's family and / or guardian. The monitoring terminal can remotely view the elderly's actual situation and issue an alarm when it is determined that the elderly have safety risks.

2. The IoT-based elderly care system according to claim 1 is characterized in that: The monitoring logic of the elderly monitoring system includes the following steps: S1: The body information collection unit and the environment information collection unit collect the vital sign data of the elderly and the environment information data of the elderly's activity area in real time, and transmit the data to the cloud server; S2: A safe data threshold and a fluctuation coefficient are preset for each set of data in the cloud server; S3: After receiving the data, the cloud server processes the data and then determines whether the elderly are at risk based on the comparison of the data threshold and the fluctuation coefficient; S4: When there is a security risk, the cloud server transmits alarm information to the monitoring terminal on the one hand, and performs mitigation execution through the execution module on the other hand.

3. The IoT-based elderly care system according to claim 2 is characterized in that: In step S1, the data collected by the body information collection unit includes heart rate, body temperature, blood sugar, blood pressure, and position; In the step S1, the data collected by the environmental information collection unit includes temperature, humidity, smoke concentration, and surveillance video.

4. The IoT-based elderly care system according to claim 3 is characterized in that: In step S2, the method for setting the threshold value comprises the following steps: S21: Determine the normal range of each data through big data, medical experience and the physical condition of the elderly themselves It represents the normal range of the i-th data as (σ1, σ2); S22: For a data, assign an importance coefficient according to its importance to the body. It represents the importance of the i-th data as σ k ; S23: Then according to the formula Calculate the coefficient of fluctuation Δ of the data in the i-th i ; S24: Then, according to the normal range threshold and the fluctuation coefficient, the final threshold range is determined as 5. The IoT-based elderly care system according to claim 4 is characterized in that: In the step S3, data preprocessing includes data normalization processing and data outlier processing.

6. The IoT-based elderly care system according to claim 5, characterized in that: The method for normalizing the bracket numbers is as follows: Maximum-minimum normalization, the formula is: where min is the minimum value in the data, max is the maximum value in the data, x is the data before normalization, and y is the data after normalization; Standardization, the formula is, where μ is the mean of the data, is the variance of the data, x is the data before normalization, and y is the data after normalization; Absolute value normalization, its formula is, is the maximum absolute value in the data, x is the data before normalization, y is the data after normalization; Any one of .

7. The IoT-based elderly care system according to claim 6, characterized in that: The processing of the data outliers includes blank value processing and duplicate value processing, which includes the following steps: S31: Acquire data of each node according to the data collection cycle and the data collection node; S32: If a data collection node has multiple data, all data are deleted to form a blank value; if a data collection node has no data, a blank value is directly formed; S33: Then, a filling algorithm based on the relationship between expiring data is used to fill in blank values.

8. The IoT-based elderly care system according to claim 7, characterized in that: In the step S33, the filling algorithm based on the expiring data relationship includes the following steps: S331: Starting from the missing data node, obtain data of n nodes forward and data of m nodes backward; S332: Then according to the formula Calculate the fill value M, where M i The i-th data value of the distance filling node in the set of n data nodes obtained forward, k i M i The weight, M o The oth data value of the distance filling node in the m data node set obtained backward, k o M o The weight of .

9. The IoT-based elderly care system according to claim 8, characterized in that: In the step S332, k i >k i+1 , k o >k o+1 , k i , k o All are positive numbers.

10. The IoT-based elderly care system according to claim 9, characterized in that: In the step S332, or k i -k i+1 >c,c>0、k o -k o+1 >d, d>0.

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