Work management system for intelligent old-age care service

Abstract

The invention relates to the technical field of pension monitoring systems, and provides a work management system for smart pension services, which comprises a sensing data acquisition terminal, a data analysis terminal, a task scheduling terminal and a service quality evaluation terminal, the sensing data acquisition terminal is used for acquiring physiological index data, environmental data and behavior data of old people in real time; the data analysis terminal is used for performing anomaly detection and health assessment according to the physiological index data, the environment data and the behavior data to generate data analysis information; the task scheduling terminal is used for generating task scheduling information according to the data analysis information and service requirements input by an administrator; and the service quality evaluation terminal is used for generating service quality evaluation information according to the data analysis information and the task scheduling information. The method has the effect of improving the old-age care monitoring quality.

Description

Technical Field

[0001] The present invention relates to the technical field of elderly care monitoring systems, and in particular to a work management system for smart elderly care services. Background Art

[0002] Smart elderly care services use modern scientific and technological means, especially information technology, the Internet of Things, artificial intelligence, big data and other technologies, to enhance and optimize traditional elderly care services. The core of smart elderly care is to help the elderly improve their quality of life, protect their health and safety, and provide convenient life care and rescue services through intelligent equipment and systems. Smart elderly care aims to enable the elderly to enjoy a higher level of care services while reducing the burden on families, society and medical institutions.

[0003] Now many elderly care monitoring systems have been developed. After a lot of searching and reference, we found that the elderly care monitoring systems in the prior art include the elderly care monitoring systems disclosed in CN111816289A, CN113077883A, CN105249945A, EP2947634A1, and US20030058111A1. These elderly care monitoring systems generally include: elderly care data acquisition terminal, monitoring data analysis terminal, and monitoring decision terminal; the elderly care data acquisition terminal is used to obtain elderly care data from intelligent monitoring equipment; the monitoring data analysis terminal is used to analyze the elderly care data; the monitoring decision terminal is used to generate monitoring decision information according to the data analysis results. Because the monitoring process of the above-mentioned elderly care monitoring system is relatively simple and the monitoring mode is less, the monitoring accuracy is reduced, resulting in the defect of reduced elderly care quality of the system. Summary of the invention

[0004] The purpose of the present invention is to propose a work management system for smart elderly care services in view of the shortcomings of the above-mentioned elderly care monitoring system.

[0005] The present invention adopts the following technical solution:

[0006] A work management system for smart elderly care services, comprising a sensor data acquisition terminal, a data analysis terminal, a task scheduling terminal and a service quality evaluation terminal; the sensor data acquisition terminal is used to collect physiological indicator data, environmental data and behavioral data of the elderly in real time; the data analysis terminal is used to perform abnormality detection and health evaluation based on the physiological indicator data, environmental data and behavioral data, and generate data analysis information; the task scheduling terminal is used to generate task scheduling information based on the data analysis information and service requirements input by an administrator; the service quality evaluation terminal is used to generate service quality evaluation information based on the data analysis information and the task scheduling information;

[0007] The sensor data acquisition terminal includes a physiological indicator data acquisition module, an environmental data acquisition module and a behavioral data acquisition module; the physiological indicator data acquisition module is used to collect physiological indicator data from the sensor equipment worn by the elderly; the physiological indicator data include heart rate, blood pressure and body temperature; the environmental data acquisition module is used to collect environmental data of the room where the elderly are located; the environmental data include room temperature, humidity and air quality; the behavioral data acquisition module is used to collect behavioral data of the elderly; the behavioral data include activity trajectories and work and rest time.

[0008] Optionally, the data analysis terminal includes an anomaly detection module and a health assessment module; the anomaly detection module is used to perform anomaly detection on the elderly based on physiological indicator data and generate anomaly detection information; the health assessment module is used to perform health assessment on the elderly based on physiological indicator data, environmental data and behavioral data and generate health assessment information.

[0009] Optionally, the task scheduling terminal includes a basic task scheduling module and an emergency task scheduling module; the basic task scheduling module is used to perform task scheduling according to data analysis information and service requirements input by an administrator, and generate basic task scheduling information; the emergency task scheduling module is used to perform task scheduling according to the urgency of the emergency task when an emergency task occurs, and generate emergency task scheduling information.

[0010] Optionally, the service quality assessment terminal includes an anomaly detection reading module, a health assessment reading module, a task scheduling reading module, a user rating reading module and a service quality assessment module; the anomaly detection reading module is used to read anomaly detection information; the health assessment reading module is used to read health assessment information; the task scheduling reading module is used to read task scheduling information; the user rating reading module is used to read user ratings from users after being served; the service quality assessment module is used to perform service quality assessment based on the contents read by the anomaly detection reading module, the health assessment reading module, the task scheduling reading module and the user rating reading module, and generate service quality assessment information.

[0011] Optionally, the health assessment module includes a personal health risk dynamic assessment submodule, a lifestyle health risk assessment submodule and a health assessment information generation submodule; the personal health risk dynamic assessment submodule is used to perform personal health risk assessment on the elderly based on physiological indicator data, environmental data and behavioral data, and generate personal health risk assessment data; the lifestyle health risk assessment submodule is used to perform lifestyle health risk assessment on the elderly based on their monitoring data and electrical equipment usage data, and generate lifestyle health risk assessment data; the health assessment information generation submodule is used to generate health assessment information based on the personal health risk assessment data and lifestyle health risk assessment data.

[0012] Optionally, the emergency task scheduling module includes an emergency event identification submodule, a time sensitivity calculation submodule and an emergency task scheduling information generation submodule; the emergency event identification submodule is used to identify the occurring emergency events in real time based on physiological indicator data and monitoring data; the time sensitivity calculation submodule is used to calculate the time sensitivity based on the emergency event level of the emergency event, service personnel information, health assessment information of the elderly and network delay information; the emergency task scheduling information generation submodule is used to generate emergency task scheduling information based on time sensitivity.

[0013] A work management method for smart elderly care services is applied to the above-mentioned work management system for smart elderly care services, and the work management method for smart elderly care services includes:

[0014] S1, real-time collection of physiological index data, environmental data and behavioral data of the elderly;

[0015] S2, performs abnormality detection and health assessment based on physiological indicator data, environmental data, and behavioral data to generate data analysis information;

[0016] S3, generating task scheduling information based on data analysis information and service requirements input by the administrator;

[0017] S4, generating service quality evaluation information according to the data analysis information and the task scheduling information.

[0018] The beneficial effects achieved by the present invention are:

[0019] 1. Through the setting of sensor data acquisition terminal, data analysis terminal, task scheduling terminal and service quality evaluation terminal, and the setting of sensor data acquisition terminal including physiological index data acquisition module, environmental data acquisition module and behavior data acquisition module, the monitoring process is first enriched from the data dimension, so that the accuracy of data analysis is improved, which is conducive to improving the accuracy and efficiency of the system's monitoring services for the elderly, thereby improving the quality of the system's elderly care monitoring;

[0020] 2. By setting up the anomaly detection module and the health assessment module, the accuracy and efficiency of task scheduling are improved, which is conducive to improving the efficiency and accuracy of generating basic task scheduling information and emergency task scheduling information, thereby improving the quality of elderly care in the system;

[0021] 3. The setting of the anomaly detection reading module, health assessment reading module, task scheduling reading module, user rating reading module and service quality assessment module is conducive to accurate quality assessment and improving the accuracy of service quality assessment information, which is conducive to the system to continuously optimize the elderly care service through self-feedback, thereby improving the elderly care quality of the system;

[0022] 4. Through the setting of the personal health risk dynamic assessment submodule, the lifestyle health risk assessment submodule and the health assessment information generation submodule in conjunction with the personal health risk index algorithm, it is beneficial to improve the accuracy of personal health risk assessment data, and then improve the accuracy and calculation efficiency of health assessment information, which is beneficial to improve the quality of elderly care monitoring in the system.

[0023] 5. The setting of the emergency event identification submodule, the time sensitivity calculation submodule and the emergency task scheduling information generation submodule in conjunction with the time sensitivity algorithm is conducive to improving the accuracy of time sensitivity calculation, thereby improving the accuracy and generation efficiency of emergency task scheduling information, enhancing the system's ability to respond to emergency events and reaction speed, and thus helping to further improve the system's elderly care quality;

[0024] 6. By setting up the lifestyle health risk assessment sub-module and coordinating the lifestyle health risk assessment index algorithm, it is beneficial to improve the calculation accuracy and timeliness of the lifestyle health risk assessment index, thereby further improving the accuracy of the lifestyle health risk assessment data, which is beneficial to improving the quality of the system's elderly care monitoring.

[0025] To further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are only for reference and description and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the overall structure of the present invention;

[0027] Figure 2 It is a structural schematic diagram of the health assessment module in the present invention;

[0028] Figure 3 It is a structural diagram of the emergency task scheduling module in the present invention;

[0029] Figure 4 The present invention is a method flow chart of a work management method for smart elderly care services. DETAILED DESCRIPTION

[0030] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. In addition, the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual dimensions. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention.

[0031] Embodiment 1: This embodiment provides a work management system for smart elderly care services. Figure 1 As shown, a work management system for smart elderly care services includes a sensor data acquisition terminal, a data analysis terminal, a task scheduling terminal and a service quality evaluation terminal; the sensor data acquisition terminal is used to collect physiological indicator data, environmental data and behavioral data of the elderly in real time; the data analysis terminal is used to perform abnormality detection and health evaluation based on the physiological indicator data, environmental data and behavioral data, and generate data analysis information; the task scheduling terminal is used to generate task scheduling information based on the data analysis information and the service requirements input by the administrator; the service quality evaluation terminal is used to generate service quality evaluation information based on the data analysis information and the task scheduling information;

[0032] The sensor data acquisition terminal includes a physiological indicator data acquisition module, an environmental data acquisition module and a behavioral data acquisition module; the physiological indicator data acquisition module is used to collect physiological indicator data from the sensor equipment worn by the elderly; the physiological indicator data include heart rate, blood pressure and body temperature; the environmental data acquisition module is used to collect environmental data of the room where the elderly are located; the environmental data include room temperature, humidity and air quality; the behavioral data acquisition module is used to collect behavioral data of the elderly; the behavioral data include activity trajectories and work and rest time.

[0033] Optionally, the data analysis terminal includes an anomaly detection module and a health assessment module; the anomaly detection module is used to perform anomaly detection on the elderly based on physiological indicator data and generate anomaly detection information; the health assessment module is used to perform health assessment on the elderly based on physiological indicator data, environmental data and behavioral data and generate health assessment information.

[0034] Optionally, the task scheduling terminal includes a basic task scheduling module and an emergency task scheduling module; the basic task scheduling module is used to perform task scheduling according to data analysis information and service requirements input by an administrator, and generate basic task scheduling information; the emergency task scheduling module is used to perform task scheduling according to the urgency of the emergency task when an emergency task occurs, and generate emergency task scheduling information.

[0035] Optionally, the service quality assessment terminal includes an anomaly detection reading module, a health assessment reading module, a task scheduling reading module, a user rating reading module and a service quality assessment module; the anomaly detection reading module is used to read anomaly detection information; the health assessment reading module is used to read health assessment information; the task scheduling reading module is used to read task scheduling information; the user rating reading module is used to read user ratings from users after being served; the service quality assessment module is used to perform service quality assessment based on the contents read by the anomaly detection reading module, the health assessment reading module, the task scheduling reading module and the user rating reading module, and generate service quality assessment information.

[0036] Optional, combined Figure 2 As shown, the health assessment module includes a personal health risk dynamic assessment submodule, a lifestyle health risk assessment submodule and a health assessment information generation submodule; the personal health risk dynamic assessment submodule is used to perform personal health risk assessment on the elderly based on physiological indicator data, environmental data and behavioral data, and generate personal health risk assessment data; the lifestyle health risk assessment submodule is used to perform lifestyle health risk assessment on the elderly based on the elderly's monitoring data and electrical equipment usage data, and generate lifestyle health risk assessment data; the health assessment information generation submodule is used to generate health assessment information based on the personal health risk assessment data and lifestyle health risk assessment data.

[0037] Specifically, when the personal health risk dynamic assessment submodule is working, the following formula is satisfied:

[0038] R1=k1·H(t)+k2·E(t)+k3·B(t)+k4·M(t);

[0039] Among them, R1 represents the personal health risk index; the evaluation period of the personal health risk index can be but not limited to once an hour, once six hours, once twelve hours, once twenty-four hours, once every two days, once every ten days and once a month, etc.; H(t) represents the physiological index data value of the evaluation period t; E(t) represents the environmental index data value of the evaluation period t; B(t) represents the behavioral index data value of the evaluation period t; M(t) represents the emotional index data value of the evaluation period t; k1 to k4 represent different indicator weights respectively, and k1 to k4 are all positive numbers. Since physiological indicators are data that directly reflect the physical health of the elderly, k1 is set to the highest. A good environment helps to maintain the physical and mental health of the elderly. Behavioral habits are closely related to the occurrence and development of various chronic diseases. Long-term emotional problems not only affect the quality of life, but may also aggravate or induce physical diseases. Different elderly people have different sensitivities to the environment, behavior, and emotions. Therefore, the specific values ​​of k2 to k4 are set by the administrator based on the elderly's daily status. For example, when the elderly have higher requirements for the environment, k2 is set higher than k3 to k4 and lower than k1; when the elderly rely more on behavioral indicator data (for example, the elderly usually exercise more, and sudden inactivity will induce physical illness), k3 is set higher than k2 and k4, and lower than k1; when the elderly are sensitive to emotional indicators (for example, the elderly are usually emotionally stable, and sudden emotional abnormalities will induce physical illness), k4 is set higher than k3 and k2, and lower than k1; and for those who are equally sensitive to two or more indicators, the corresponding indicator weights can be set to the same. When R1≥r 1ref When the personal health risk dynamic assessment submodule generates personal health risk assessment data for indicating that the personal health risk of the elderly exceeds the standard;

[0040] H(t)=0.25×(h HR +h BP +h Temp +h SpO2 );

[0041]

[0042] Among them, h HR represents the heart rate item value; HR' represents the actual heart rate value of the elderly during the evaluation period t; HR min Indicates the lower limit of the heart rate reference range; HR max Indicates the upper limit of the heart rate reference range; h BP Represents the blood pressure value; BP' represents the actual diastolic blood pressure value of the elderly during the evaluation period t; BP min Indicates the lower limit of the diastolic blood pressure reference range; BP max Indicates the upper limit of the reference range of diastolic blood pressure; h Temprepresents the value of body temperature; Temp' represents the actual body temperature value of the elderly during the evaluation period t; Temp min Indicates the lower limit of the temperature reference range; Temp max Indicates the upper limit of the body temperature reference range; h SpO2 Indicates the value of blood oxygen saturation; SpO2' indicates the actual blood oxygen saturation value of the elderly during the evaluation period t; SpO2 min Indicates the lower limit of the reference range of blood oxygen saturation; SpO2 max Indicates the upper limit of the reference range of blood oxygen saturation;

[0043] E(t)=0.25×(e Temp +e H +e AQI +e Noise );

[0044] Among them, e Temp Indicates the temperature item value; e H Indicates the humidity item value; e AQI Indicates the value of air quality item, the value of air quality index in air forecast information; e Noise Indicates the noise item value;

[0045]

[0046] Among them, t1 represents the actual temperature value of the elderly’s environment during calculation; t ref It represents the reference ambient temperature, which is set by the administrator based on experience; H1 represents the actual humidity value of the elderly's environment during calculation; H ref Indicates the reference environmental humidity, which is set by the administrator based on experience; Noise1 indicates the actual noise value of the elderly's environment during calculation; Noise ref Represents the reference environmental noise, which is set by the administrator based on experience;

[0047]

[0048] Among them, X i (t) represents the current actual data value of the i-th behavior data; μ i represents the historical average value of the i-th behavior data; σ irepresents the historical standard deviation of the i-th behavior data; in this embodiment, X1(t) represents the first behavior data, specifically walking behavior data, that is, the number of steps from the motion sensor device worn by the elderly; X2(t) represents the second behavior data, specifically sleeping behavior data, that is, the sleep duration from the sleep monitoring sensor device or smart mattress of the elderly; X3(t) represents the third behavior data, specifically the door-in and door-out behavior data, that is, the number of times the elderly enter and leave the door from the monitoring device at the elderly's location; μ1 represents the historical average value of walking behavior data, that is, the average number of steps in each historical evaluation period; μ2 represents represents the historical average of the sleep behavior data, i.e., the average sleep duration of each historical evaluation cycle; μ3 represents the historical average of the door-in and door-out behavior data, i.e., the average number of door-in and door-outs in each historical evaluation cycle; σ1 represents the historical standard deviation of the walking behavior data, i.e., the historical standard deviation calculated based on the average number of steps in each historical evaluation cycle; σ2 represents the historical standard deviation of the sleep behavior data, i.e., the historical standard deviation calculated based on the average sleep duration of each historical evaluation cycle; σ3 represents the historical standard deviation of the door-in and door-out behavior data, i.e., the historical standard deviation calculated based on the average number of door-in and door-outs in each historical evaluation cycle;

[0049]

[0050] n represents the total number of sentiment scores performed within the evaluation period t; m i It represents the emotional score of the system's emotional behavior of the elderly for the i-th time within the evaluation period t; each time the emotional score is scored, the monitoring image information of the elderly within the hour before the score is retrieved, and the facial expressions of the monitoring image information are recognized and scored. During the scoring, the system recognizes the elderly's emotional expressions including joy, annoyance, sadness and anger. Each time the emotional score is scored, the number of occurrences of four types of expressions is counted according to the monitoring image information within the previous hour, and the risk score corresponding to the type of expression with the largest number of occurrences is used as the emotional score of the emotional score; the risk score corresponding to the joy type expression is 3, the risk score corresponding to the annoyance type expression is 4, the risk score corresponding to the sadness type expression is 5, and the risk score corresponding to the anger type expression is 6; Represents the average sentiment score of all sentiment scores within the evaluation period t.

[0051] Optional, combined Figure 3As shown, the emergency task scheduling module includes an emergency event identification submodule, a time sensitivity calculation submodule and an emergency task scheduling information generation submodule; the emergency event identification submodule is used to identify the emergency events that occur in real time based on physiological indicator data and monitoring data; the time sensitivity calculation submodule is used to calculate the time sensitivity based on the emergency event level of the emergency event, service personnel information, health assessment information of the elderly and network delay information; the emergency task scheduling information generation submodule is used to generate emergency task scheduling information based on time sensitivity.

[0052] Specifically, when the time sensitivity calculation submodule is working, the following formula is satisfied:

[0053]

[0054] Among them, T s represents the time sensitivity corresponding to the occurrence of an emergency event; d(t) represents the distance between the service personnel closest to the elderly when the emergency event occurs; v(t) represents the average speed of the transportation vehicle used by the service personnel; C e Indicates the emergency event level of the emergency event; the emergency event identification submodule is used to identify the emergency events that occur in real time according to the physiological indicator data and monitoring data of various emergency events, and the time sensitivity calculation submodule obtains the emergency event level according to the emergency event identified by the emergency time identification submodule. The emergency event level is pre-set by the administrator in the time sensitivity calculation submodule. The larger the level, the higher the level. For example, the emergency event level of a collision event is 1, and the emergency event level of a fall event is 2, etc.; R1 represents the personal health risk index, and the personal health risk index of the elderly person that was most recently updated when the emergency event occurred is selected; N d (t) represents the system network delay when an emergency event occurs; N b Indicates the baseline network delay. Network delay refers to the network transmission delay from the current computer to the target server. The target server corresponding to the network delay can be a server built by the nursing home or a server of ordinary broadband after telecommunications. It is set according to the actual application requirements. s ≥t ref When the emergency task dispatch information generating submodule generates emergency task dispatch information indicating that special service personnel are required to go to the elderly's environment for rescue. ref Indicates the time sensitivity threshold, which is set by the administrator based on experience.

[0055] An example of the time sensitivity calculation process: After an emergency event occurs and is received by the system, the system needs to optimize the response process and service personnel allocation. The known parameters are as follows: d(t) = 10 km, v(t) = 40 km / h, the emergency level of the emergency event is 2, R1 = 6, Nd (t) = 200ms, N b =50ms, t ref =10. The calculation steps are as follows:

[0056] Distance and speed section:

[0057]

[0058] Impact of emergency events:

[0059] 1+C e ×R1=1+2×6≈13;

[0060] Network delay affects the following parts:

[0061]

[0062] T s =0.25×13×5=16.25;

[0063] Since 16.25>10, the emergency task dispatch information generating submodule generates emergency task dispatch information indicating that special service personnel are needed to go to the elderly's environment for rescue. The system immediately dispatches the service personnel closest to the elderly to the elderly's location for rescue according to the emergency task dispatch information.

[0064] In summary, through the setting of sensor data acquisition terminals, data analysis terminals, task scheduling terminals and service quality evaluation terminals, and the setting of sensor data acquisition terminals including physiological indicator data acquisition modules, environmental data acquisition modules and behavioral data acquisition modules, the monitoring process is first enriched from the data dimension, so that the accuracy of data analysis is improved, which is conducive to improving the accuracy and efficiency of the system's monitoring services for the elderly; through the setting of anomaly detection modules and health assessment modules, the accuracy and efficiency of task scheduling are improved, which is conducive to improving the generation efficiency and accuracy of basic task scheduling information and emergency task scheduling information; through the setting of anomaly detection reading modules, health assessment reading modules, task scheduling reading modules, user rating reading modules and service quality evaluation modules, it is conducive to accurate quality evaluation and improve The accuracy of service quality assessment information is improved, which is conducive to the system to continuously optimize the elderly care services through self-feedback; through the setting of personal health risk dynamic assessment submodule, living habit health risk assessment submodule and health assessment information generation submodule in conjunction with the personal health risk index algorithm, it is conducive to improving the accuracy of personal health risk assessment data, thereby improving the accuracy and calculation efficiency of health assessment information; through the setting of emergency event identification submodule, time sensitivity calculation submodule and emergency task scheduling information generation submodule in conjunction with the time sensitivity algorithm, it is conducive to improving the calculation accuracy of time sensitivity, thereby improving the accuracy and generation efficiency of emergency task scheduling information, enhancing the system's ability to respond to emergencies and reaction speed, which is conducive to further improving the system's elderly care quality.

[0065] A work management method for smart elderly care services, applied to the above-mentioned work management system for smart elderly care services, combined with Figure 4 As shown, the work management method for smart elderly care services includes:

[0066] S1, real-time collection of physiological index data, environmental data and behavioral data of the elderly;

[0067] S2, performs abnormality detection and health assessment based on physiological indicator data, environmental data, and behavioral data to generate data analysis information;

[0068] S3, generating task scheduling information based on data analysis information and service requirements input by the administrator;

[0069] S4, generating service quality evaluation information according to the data analysis information and the task scheduling information.

[0070] Embodiment 2: This embodiment includes all the contents of Embodiment 1, and provides a work management system for smart elderly care services. Specifically, when the lifestyle health risk assessment submodule is working, the following formula is satisfied:

[0071]

[0072] Among them, L S represents the health risk assessment index of lifestyle habits; f j represents the total number of times the jth household device is used within the evaluation period t; time j represents the total working time of the jth household appliance in the evaluation period t; f ref represents the total reference usage times of the jth household device in the evaluation period t; time ref represents the reference total working time of the jth household device in the evaluation period t; J represents the total number of monitored household devices; A d (t) represents the total number of abnormal states of the jth home device in the evaluation period t; S(t) represents the device security risk level of the jth home device in the evaluation period t; the device security risk level is pre-set by the administrator, and the higher the security risk of the device, the lower the device security risk level number. S ≥L ref When t, the lifestyle health risk assessment submodule generates prompt information indicating that the lifestyle health risk of the elderly exceeds the standard within the assessment period t; ref Represents the threshold of health risk of living habits, which is set by the administrator based on experience. J is the total number of household devices of the monitoring target. In this embodiment, J=3, and the three types of household devices are: smart night light, smart door lock and smart TV. Among them, the abnormal state of the smart night light refers to abnormal extinguishing during the prescribed working time period; the abnormal state of the smart door lock refers to automatic unlocking under the prescribed locked state; the abnormal state of the smart TV refers to abnormal shutdown during the working time period.

[0073] An example of the calculation process of the lifestyle health risk assessment index: The known parameters are as follows: the assessment period t is one week, the lifestyle health risk threshold L ref =300, f1 of the smart night light in the evaluation period t = 10 times, time1 of the smart night light in the evaluation period t = 1 hour, f of the smart night light in the evaluation period t ref = 7 times, time of the smart night light in the evaluation period t ref = 0.5 hours, smart night light A d (t) = 3, S(t) of the smart night light = 3; f2 of the smart TV in the evaluation period t = 30 times, time2 of the smart TV in the evaluation period t = 50 hours, f of the smart TV in the evaluation period t ref = 15 times, time of smart TV in evaluation period t ref = 35 hours, smart TV A d(t) = 1, S(t) of smart TV = 2; f2 of smart door lock = 20 times in evaluation period t, time2 of smart door lock = 0.2 hours in evaluation period t, f of smart door lock = 20 times in evaluation period t ref = 20 times, time of smart door lock within evaluation period t ref = 0.2 hours, smart door lock A d (t)=2, and the smart door lock's S(t)=1. The calculation process is as follows:

[0074]

[0075] Since 329.3>300, the lifestyle health risk assessment submodule generates a prompt message indicating that the elderly person's lifestyle health risk exceeds the standard within the assessment period t, so as to prompt the administrator to send service personnel to the elderly person's residence for verification and confirmation.

[0076] To sum up, the setting of the lifestyle health risk assessment sub-module and the lifestyle health risk assessment index algorithm are conducive to improving the calculation accuracy and timeliness of the lifestyle health risk assessment index, thereby further improving the accuracy of the lifestyle health risk assessment data, which is conducive to improving the quality of elderly care monitoring in the system.

[0077] The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention description and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A work management system for smart elderly care services, characterized in that: It includes a sensor data acquisition terminal, a data analysis terminal, a task scheduling terminal and a service quality assessment terminal; the sensor data acquisition terminal is used to collect physiological index data, environmental data and behavioral data of the elderly in real time; the data analysis terminal is used to perform abnormality detection and health assessment based on the physiological index data, environmental data and behavioral data, and generate data analysis information; The task scheduling terminal is used to generate task scheduling information according to the data analysis information and the service requirements input by the administrator; The service quality evaluation terminal is used to generate service quality evaluation information according to the data analysis information and the task scheduling information; The sensor data acquisition terminal includes a physiological indicator data acquisition module, an environmental data acquisition module and a behavioral data acquisition module; the physiological indicator data acquisition module is used to collect physiological indicator data from the sensor equipment worn by the elderly; the physiological indicator data include heart rate, blood pressure and body temperature; the environmental data acquisition module is used to collect environmental data of the room where the elderly are located; the environmental data include room temperature, humidity and air quality; the behavioral data acquisition module is used to collect behavioral data of the elderly; the behavioral data include activity trajectories and work and rest time.

2. A work management system for smart elderly care services as claimed in claim 1, characterized in that: The data analysis terminal includes an anomaly detection module and a health assessment module; the anomaly detection module is used to perform anomaly detection on the elderly based on physiological indicator data and generate anomaly detection information; the health assessment module is used to perform health assessment on the elderly based on physiological indicator data, environmental data and behavioral data and generate health assessment information.

3. A work management system for smart elderly care services as claimed in claim 2, characterized in that: The task scheduling terminal includes a basic task scheduling module and an emergency task scheduling module; the basic task scheduling module is used to perform task scheduling according to data analysis information and service requirements input by an administrator, and generate basic task scheduling information; the emergency task scheduling module is used to perform task scheduling according to the urgency of the emergency task when an emergency task occurs, and generate emergency task scheduling information.

4. A work management system for smart elderly care services as claimed in claim 3, characterized in that: The service quality assessment terminal includes an anomaly detection reading module, a health assessment reading module, a task scheduling reading module, a user score reading module and a service quality assessment module; the anomaly detection reading module is used to read anomaly detection information; the health assessment reading module is used to read health assessment information; the task scheduling reading module is used to read task scheduling information; The user rating reading module is used to read the user rating from the user after being served; the service quality assessment module is used to perform service quality assessment based on the contents read by the anomaly detection reading module, the health assessment reading module, the task scheduling reading module and the user rating reading module, and generate service quality assessment information.

5. A work management system for smart elderly care services as claimed in claim 4, characterized in that: The health assessment module includes a personal health risk dynamic assessment submodule, a lifestyle health risk assessment submodule and a health assessment information generation submodule; The personal health risk dynamic assessment submodule is used to conduct personal health risk assessment on the elderly based on physiological indicator data, environmental data and behavioral data, and generate personal health risk assessment data; The lifestyle health risk assessment submodule is used to conduct lifestyle health risk assessment on the elderly based on the elderly's monitoring data and electrical equipment usage data, and generate lifestyle health risk assessment data; The health assessment information generation submodule is used to generate health assessment information based on personal health risk assessment data and lifestyle health risk assessment data.

6. A work management system for smart elderly care services as claimed in claim 5, characterized in that: The emergency task scheduling module includes an emergency event identification submodule, a time sensitivity calculation submodule and an emergency task scheduling information generation submodule; the emergency event identification submodule is used to identify the emergency events that occur in real time based on physiological indicator data and monitoring data; the time sensitivity calculation submodule is used to calculate the time sensitivity based on the emergency event level of the emergency event, service personnel information, health assessment information of the elderly and network delay information; the emergency task scheduling information generation submodule is used to generate emergency task scheduling information based on time sensitivity.

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