Old people home risk early warning method, system and equipment based on multi-source data and medium

Abstract

The invention discloses an old people home risk early warning method, system and device based on multi-source data and a medium, belongs to the technical field of multi-source data processing and risk early warning, and aims to solve the technical problem of how to construct timely and effective early warning monitoring from the perspective of daily life and personal vital signs of old people. According to the technical scheme, the method comprises the following steps: constructing an elderly home early warning characteristic parameter data set; for each feature parameter in the elderly leaving early warning feature data set, analyzing a feature parameter curve corresponding to the elderly in a normal condition, and extracting an average value, a lowest threshold and a highest threshold from each monitoring feature data curve by observing the normal survival state of the elderly within 1-2 weeks; various monitoring data abnormity rules are formulated; formulating an early warning level system rule; and constructing a risk grade division rule according to the abnormal condition of each monitoring data.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-source data processing and risk warning, and in particular to a method, system, device and medium for warning risks at home of the elderly based on multi-source data. Background Art

[0002] In the scenario of elderly people at home, there are many characteristic factors related to their living conditions, including daily life characteristic factors, such as water and electricity consumption, and physical health characteristic factors, such as heart rate and sleep quality. Abnormal electricity and water consumption of the elderly over a period of time may lead to death, and abnormal heart rate and sleep may also lead to the risk of death. Therefore, how to build timely and effective early warning monitoring from the perspective of the elderly's daily life and personal vital signs is a technical problem that needs to be solved urgently. Summary of the invention

[0003] The technical task of the present invention is to provide a method, system, equipment and medium for early warning of risks at home for the elderly based on multi-source data, so as to solve the problem of how to build timely and effective early warning monitoring from the perspective of the elderly's daily life and personal vital signs.

[0004] The technical task of the present invention is achieved in the following way: a method for early warning of risks at home for the elderly based on multi-source data, the method is as follows:

[0005] Construct a data set of characteristic parameters for early warning of the elderly at home; wherein the parameters of the data set of characteristic parameters for early warning of the elderly at home include water consumption data α, electricity consumption data β, and heart rate data θ monitored by a sleep monitor;

[0006] For each characteristic parameter in the elderly death warning characteristic data set, analyze the characteristic parameter curve corresponding to the elderly under normal conditions, and extract the average value, minimum threshold and maximum threshold from each monitoring characteristic data curve by observing the normal survival status of the elderly within 1-2 weeks;

[0007] Formulate various monitoring data anomaly rules;

[0008] Establish rules for the warning level system;

[0009] Construct risk level classification rules based on abnormal situations of various monitoring data.

[0010] As a preferred option, the specific rules for abnormal monitoring data are as follows:

[0011] The monitoring data is based on daily units, and the situation is as follows:

[0012] If the water consumption data α i Between 0:00 and 24:00 Beijing time, it is continuously 0 or continuously in α lBelow, it is considered abnormal; among them, α l Indicates the minimum threshold of daily water consumption data;

[0013] If the electricity consumption data β i Between 0:00 and 24:00 Beijing time, it is continuously 0 or continuously at the minimum threshold β l Below, it is considered abnormal; l Indicates the minimum threshold of daily average electricity consumption data;

[0014] If the heart rate data θ i is 0 or fluctuates greatly, below the minimum threshold θ l or above the high threshold θ h , or the heart rate value compared to the average value θ a If the float is too large, it is considered abnormal.

[0015] Preferably, the rules for establishing the warning level system are: setting high-risk and low-risk warning levels based on abnormal conditions of data indicators.

[0016] Preferably, according to the abnormal conditions of each monitoring data, the risk level classification rules are constructed as follows:

[0017] The high risk levels are as follows:

[0018] The elderly's vital signs are monitored at night using a sleep monitor. If the heart rate is abnormal, there is a high probability of death, that is, if the elderly's heart rate is abnormal at night, it is defined as a high-risk level;

[0019] If the nighttime heart rate monitoring data is normal, but the water usage data and electricity usage data are abnormal, then the elderly person’s daily activities are abnormal or the probability of death is high, which is defined as a high-risk level;

[0020] The low risk level is as follows:

[0021] If the elderly person’s heart rate is normal at night, water usage data is normal, and electricity usage data is abnormal, then it is defined as a low risk level;

[0022] If the elderly person's heart rate is normal at night, electricity usage data is normal, and water usage data is abnormal, it is defined as a low risk level.

[0023] A risk early warning system for elderly people at home based on multi-source data, the system comprising:

[0024] A data set construction module is used to construct a data set of characteristic parameters for warning of the elderly at home; wherein the parameters of the data set of characteristic parameters for warning of the elderly at home include water consumption data α, electricity consumption data β, and heart rate data θ monitored by a sleep monitor;

[0025] The data analysis and extraction module is used to analyze the characteristic parameter curves corresponding to the elderly under normal conditions for each characteristic parameter in the elderly death warning characteristic data set, and extract the average value, minimum threshold and maximum threshold from each monitoring characteristic data curve by observing the normal survival status of the elderly within 1-2 weeks;

[0026] Abnormal rule formulation module, used to formulate abnormal rules for various monitoring data;

[0027] Warning level rule formulation module, used to formulate warning level system rules;

[0028] The level classification module is used to build risk level classification rules based on the abnormal conditions of various monitoring data.

[0029] Preferably, the abnormal rule formulation module monitors data on a daily basis, as follows:

[0030] If the water consumption data α i Between 0:00 and 24:00 Beijing time, it is continuously 0 or continuously in α l Below, it is considered abnormal; among them, α l Indicates the minimum threshold of daily water consumption data;

[0031] If the electricity consumption data β i Between 0:00 and 24:00 Beijing time, it is continuously 0 or continuously at the minimum threshold β l Below, it is considered abnormal; l Indicates the minimum threshold of daily average electricity consumption data;

[0032] If the heart rate data θ i is 0 or fluctuates greatly, below the minimum threshold θ l or above the high threshold θ h , or the heart rate value compared to the average value θ a If the float is too large, it is considered abnormal.

[0033] Preferably, the warning level rule formulation module sets high-risk and low-risk warning levels according to abnormal conditions of data indicators.

[0034] More preferably, the grading module categorizes the risk levels into high risk levels and low risk levels;

[0035] Among them, the high-risk levels are as follows:

[0036] The elderly's vital signs are monitored at night using a sleep monitor. If the heart rate is abnormal, there is a high probability of death, that is, if the elderly's heart rate is abnormal at night, it is defined as a high-risk level;

[0037] If the nighttime heart rate monitoring data is normal, but the water usage data and electricity usage data are abnormal, then the elderly person’s daily activities are abnormal or the probability of death is high, which is defined as a high-risk level;

[0038] The low risk level is as follows:

[0039] If the elderly person’s heart rate is normal at night, water usage data is normal, and electricity usage data is abnormal, then it is defined as a low risk level;

[0040] If the elderly person's heart rate is normal at night, electricity usage data is normal, and water usage data is abnormal, it is defined as a low risk level.

[0041] An electronic device comprising: a memory and at least one processor;

[0042] Wherein, the memory stores a computer program;

[0043] The at least one processor executes the computer program stored in the memory, so that the at least one processor executes the above-mentioned method for early warning of risks at home for the elderly based on multi-source data.

[0044] A computer-readable storage medium having a computer program stored therein, wherein the computer program can be executed by a processor to implement the above-mentioned method for early warning of risks at home for the elderly based on multi-source data.

[0045] The method, system, device and medium for early warning of risks at home for the elderly based on multi-source data of the present invention have the following advantages:

[0046] (1) The present invention proposes a non-contact, more private method for monitoring the living status of the elderly, which has important practical guiding significance for the elderly's children and their guardians to timely and effectively monitor the living status of the elderly;

[0047] (ii) The present invention starts from the perspective of the elderly's home life scenes, and establishes a remote and friendly risk monitoring method for the early warning of death of elderly people living alone or in empty nests by using water and electricity consumption data and heart rate data of vital signs parameters of nighttime sleep. It not only provides a means for children and their guardians to remotely pay attention to the living status of the elderly, but also has practical guiding significance for streets and communities to timely grasp the living and passing status of the elderly. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The present invention is further described below in conjunction with the accompanying drawings.

[0049] Attached Figure 1 This is a flowchart of the risk warning method for the elderly at home based on multi-source data. DETAILED DESCRIPTION

[0050] The following detailed description is made of the method, system, device and medium for early warning of risks at home for the elderly based on multi-source data with reference to the accompanying drawings and specific embodiments of the specification.

[0051] Embodiment 1:

[0052] As attached Figure 1 As shown, this embodiment provides a method for early warning of risks at home for the elderly based on multi-source data, and the method is specifically as follows:

[0053] S1. Construct a data set of characteristic parameters for early warning of the elderly at home; wherein the parameters of the data set of characteristic parameters for early warning of the elderly at home include water consumption data α, electricity consumption data β, and heart rate data θ monitored by a sleep monitor;

[0054] S2. Analyze the characteristic parameter curves of the elderly under normal conditions for each characteristic parameter in the elderly death warning characteristic data set. By observing the normal survival status of the elderly within 1-2 weeks, extract the average value, minimum threshold and maximum threshold from each monitoring characteristic data curve;

[0055] S3. Formulate various monitoring data anomaly rules;

[0056] S4. Formulate rules for the warning level system;

[0057] S5. Construct risk level classification rules based on abnormal conditions of each monitoring data.

[0058] The specific details of formulating various monitoring data anomaly rules in step S3 of this embodiment are as follows:

[0059] The monitoring data is based on daily units, and the situation is as follows:

[0060] If the water consumption data α i Between 0:00 and 24:00 Beijing time, it is continuously 0 or continuously in α l Below, it is considered abnormal; among them, α l Indicates the minimum threshold of daily water consumption data;

[0061] If the electricity consumption data β i Between 0:00 and 24:00 Beijing time, it is continuously 0 or continuously at the minimum threshold β l Below, it is considered abnormal; l Indicates the minimum threshold of daily average electricity consumption data;

[0062] If the heart rate data θ i is 0 or fluctuates greatly, below the minimum threshold θ l or above the high threshold θ h , or the heart rate value compared to the average value θ a If the float is too large, it is considered abnormal.

[0063] The rules for establishing the warning level system in step S4 of this embodiment are: setting high-risk and low-risk warning levels according to the abnormal conditions of the data indicators.

[0064] In step S5 of this embodiment, the risk level classification rules are constructed according to the abnormal conditions of each monitoring data as follows:

[0065] The high risk levels are as follows:

[0066] The elderly's vital signs are monitored at night using a sleep monitor. If the heart rate is abnormal, there is a high probability of death, that is, if the elderly's heart rate is abnormal at night, it is defined as a high-risk level;

[0067] If the nighttime heart rate monitoring data is normal, but the water usage data and electricity usage data are abnormal, then the elderly person’s daily activities are abnormal or the probability of death is high, which is defined as a high-risk level;

[0068] The low risk level is as follows:

[0069] If the elderly person’s heart rate is normal at night, water usage data is normal, and electricity usage data is abnormal, then it is defined as a low risk level;

[0070] If the elderly person's heart rate is normal at night, electricity usage data is normal, and water usage data is abnormal, it is defined as a low risk level.

[0071] Embodiment 2:

[0072] This embodiment provides a home risk early warning system for the elderly based on multi-source data, the system comprising:

[0073] A data set construction module is used to construct a data set of characteristic parameters for warning of the elderly at home; wherein the parameters of the data set of characteristic parameters for warning of the elderly at home include water consumption data α, electricity consumption data β, and heart rate data θ monitored by a sleep monitor;

[0074] The data analysis and extraction module is used to analyze the characteristic parameter curves corresponding to the elderly under normal conditions for each characteristic parameter in the elderly death warning characteristic data set, and extract the average value, minimum threshold and maximum threshold from each monitoring characteristic data curve by observing the normal survival status of the elderly within 1-2 weeks;

[0075] Abnormal rule formulation module, used to formulate abnormal rules for various monitoring data;

[0076] Warning level rule formulation module, used to formulate warning level system rules;

[0077] The level classification module is used to build risk level classification rules based on the abnormal conditions of various monitoring data.

[0078] The abnormal rule formulation module in this embodiment monitors data on a daily basis, as follows:

[0079] If the water consumption data α i Between 0:00 and 24:00 Beijing time, it is continuously 0 or continuously in α l Below, it is considered abnormal; among them, α l Indicates the minimum threshold of daily water consumption data;

[0080] If the electricity consumption data β i Between 0:00 and 24:00 Beijing time, it is continuously 0 or continuously at the minimum threshold β l Below, it is considered abnormal; l Indicates the minimum threshold of daily average electricity consumption data;

[0081] If the heart rate data θ i is 0 or fluctuates greatly, below the minimum threshold θ l or above the high threshold θ h , or the heart rate value compared to the average value θ a If the float is too large, it is considered abnormal.

[0082] The warning level rule formulation module in this embodiment sets high-risk and low-risk warning levels according to abnormal conditions of data indicators.

[0083] The level classification module in this embodiment divides the risk level into a high risk level and a low risk level;

[0084] Among them, the high-risk levels are as follows:

[0085] The elderly's vital signs are monitored at night using a sleep monitor. If the heart rate is abnormal, there is a high probability of death, that is, if the elderly's heart rate is abnormal at night, it is defined as a high-risk level;

[0086] If the nighttime heart rate monitoring data is normal, but the water usage data and electricity usage data are abnormal, then the elderly person’s daily activities are abnormal or the probability of death is high, which is defined as a high-risk level;

[0087] The low risk level is as follows:

[0088] If the elderly person’s heart rate is normal at night, water usage data is normal, and electricity usage data is abnormal, then it is defined as a low risk level;

[0089] If the elderly person's heart rate is normal at night, electricity usage data is normal, and water usage data is abnormal, it is defined as a low risk level.

[0090] Embodiment 3:

[0091] This embodiment also provides an electronic device, including: a memory and a processor;

[0092] Wherein, the memory stores computer-executable instructions;

[0093] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the home risk warning method for the elderly based on multi-source data in any embodiment of the present invention.

[0094] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor may be a microprocessor or any conventional processor, etc.

[0095] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory can also include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, at least one disk storage period, a flash memory device, or other volatile solid-state storage devices.

[0096] Embodiment 4:

[0097] This embodiment also provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions are loaded by a processor, so that the processor executes the home risk warning method for the elderly based on multi-source data in any embodiment of the present invention. Specifically, a system or device equipped with a storage medium can be provided, on which a software program code that implements the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage medium.

[0098] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0099] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RYM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer via a communication network.

[0100] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0101] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or written to a memory provided in an expansion unit connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or the expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0102] 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 with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A risk warning method for the elderly at home based on multi-source data, characterized in that: The method is as follows: Construct a data set of characteristic parameters for early warning of the elderly at home; wherein the parameters of the data set of characteristic parameters for early warning of the elderly at home include water consumption data α, electricity consumption data β, and heart rate data θ monitored by a sleep monitor; For each characteristic parameter in the elderly death warning characteristic data set, analyze the characteristic parameter curve corresponding to the elderly under normal conditions, and extract the average value, minimum threshold and maximum threshold from each monitoring characteristic data curve by observing the normal survival status of the elderly within 1-2 weeks; Formulate various monitoring data anomaly rules; Establish rules for the warning level system; Construct risk level classification rules based on abnormal situations of various monitoring data.

2. The method for early warning of risks at home for the elderly based on multi-source data according to claim 1 is characterized in that: The specific rules for abnormal monitoring data are as follows: The monitoring data is based on daily units, and the situation is as follows: If the water consumption data α i Between 0:00 and 24:00 Beijing time, it is continuously 0 or continuously in α l Below, it is considered abnormal; among them, α l Indicates the minimum threshold of daily water consumption data; If the electricity consumption data β i Between 0:00 and 24:00 Beijing time, it is continuously 0 or continuously at the minimum threshold β l Below, it is considered abnormal; l Indicates the minimum threshold of daily average electricity consumption data; If the heart rate data θ i is 0 or fluctuates greatly, below the minimum threshold θ l or above the high threshold θ h , or the heart rate value compared to the average value θ a If the float is too large, it is considered abnormal.

3. The method for early warning of risks at home for the elderly based on multi-source data according to claim 1 or 2, characterized in that: The rules for establishing the warning level system are: high-risk and low-risk warning levels are set according to the abnormal conditions of data indicators.

4. The method for early warning of risks at home for the elderly based on multi-source data according to claim 3 is characterized in that: According to the abnormal conditions of each monitoring data, the risk level classification rules are constructed as follows: The high risk levels are as follows: The elderly's vital signs are monitored at night using a sleep monitor. If the heart rate is abnormal, there is a high probability of death, that is, if the elderly's heart rate is abnormal at night, it is defined as a high-risk level; If the nighttime heart rate monitoring data is normal, but the water usage data and electricity usage data are abnormal, then the elderly person’s daily activities are abnormal or the probability of death is high, which is defined as a high-risk level; The low risk level is as follows: If the elderly person’s heart rate is normal at night, water usage data is normal, and electricity usage data is abnormal, then it is defined as a low risk level; If the elderly person's heart rate is normal at night, electricity usage data is normal, and water usage data is abnormal, it is defined as a low risk level.

5. A risk warning system for elderly people at home based on multi-source data, characterized in that: The system includes: A data set construction module is used to construct a data set of characteristic parameters for warning of the elderly at home; wherein the parameters of the data set of characteristic parameters for warning of the elderly at home include water consumption data α, electricity consumption data β, and heart rate data θ monitored by a sleep monitor; The data analysis and extraction module is used to analyze the characteristic parameter curves corresponding to the elderly under normal conditions for each characteristic parameter in the elderly death warning characteristic data set, and extract the average value, minimum threshold and maximum threshold from each monitoring characteristic data curve by observing the normal survival status of the elderly within 1-2 weeks; Abnormal rule formulation module, used to formulate abnormal rules for various monitoring data; Warning level rule formulation module, used to formulate warning level system rules; The level classification module is used to build risk level classification rules based on the abnormal conditions of various monitoring data.

6. The elderly home risk early warning system based on multi-source data according to claim 5 is characterized in that: The abnormal rule formulation module monitors data on a daily basis, as follows: If the water consumption data α i Between 0:00 and 24:00 Beijing time, it is continuously 0 or continuously in α l Below, it is considered abnormal; among them, α l Indicates the minimum threshold of daily water consumption data; If the electricity consumption data β i Between 0:00 and 24:00 Beijing time, it is continuously 0 or continuously at the minimum threshold β l Below, it is considered abnormal; l Indicates the minimum threshold of daily average electricity consumption data; If the heart rate data θ i is 0 or fluctuates greatly, below the minimum threshold θ l or above the high threshold θ h , or the heart rate value compared to the average value θ a If the float is too large, it is considered abnormal.

7. The elderly home risk early warning system based on multi-source data according to claim 5 or 6, characterized in that: The warning level rule formulation module sets high-risk and low-risk warning levels based on abnormal conditions of data indicators.

8. The elderly home risk early warning system based on multi-source data according to claim 7 is characterized in that: The level classification module divides the risk level into high risk level and low risk level; Among them, the high-risk levels are as follows: The elderly's vital signs are monitored at night using a sleep monitor. If the heart rate is abnormal, there is a high probability of death, that is, if the elderly's heart rate is abnormal at night, it is defined as a high-risk level; If the nighttime heart rate monitoring data is normal, but the water usage data and electricity usage data are abnormal, then the elderly person’s daily activities are abnormal or the probability of death is high, which is defined as a high-risk level; The low risk level is as follows: If the elderly person’s heart rate is normal at night, water usage data is normal, and electricity usage data is abnormal, then it is defined as a low risk level; If the elderly person's heart rate is normal at night, electricity usage data is normal, and water usage data is abnormal, it is defined as a low risk level.

9. An electronic device, characterized in that: include: memory and at least one processor; Wherein, the memory stores a computer program; The at least one processor executes the computer program stored in the memory, so that the at least one processor executes the home risk warning method for the elderly based on multi-source data as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which can be executed by a processor to implement the home risk warning method for the elderly based on multi-source data as described in any one of claims 1 to 4.

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