Intelligent old-age care management system based on AI

Abstract

The invention relates to the technical field of customer personalized services, in particular to an AI-based smart old-age care management system, which comprises an AI customer service customer obtaining module for obtaining customer information through guided voice interaction, recording and storing customer basic information, obtaining a home environment image, drawing a customer portrait after analysis and generating customer portrait data, meanwhile, different labels are marked according to the customer portrait data, and customer behavior labels are generated. According to the method, physiological data of the elderly are collected and monitored, time sequence analysis is carried out by adopting a long-short-term memory network, the trend of the physiological data is accurately predicted, accurate health condition prediction is provided, physiological data abnormity is detected by adopting a first-class support vector machine, and a normal mode of the physiological data is learned, so that the health condition of the elderly is accurately predicted. The health risk can be quickly recognized, an alarm can be automatically given, when an abnormal condition is detected, an alarm notice can be immediately sent out, a medical institution and family members are contacted, and health suggestions and emergency responses are provided in time.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an AI-based smart elderly care management system. Background Art

[0002] The field of customer personalized service technology focuses on using artificial intelligence technology to provide customized service solutions to meet the specific needs of customers. In elderly care services, the importance of personalized services is particularly prominent, because the health status, preferences and life needs of the elderly may vary greatly. Using AI technology, a large amount of user data can be analyzed and processed, thereby providing more accurate health management, life support and entertainment activities and other services.

[0003] Traditional systems have shortcomings in personalized services and real-time monitoring. It is difficult to accurately predict the health trends of the elderly and cannot identify health risks in a timely manner, resulting in delayed and inaccurate health management. When dealing with sudden health events, the response speed is slow and there is a lack of automatic alarm and emergency response mechanisms, which can easily cause delays and affect the safety of the elderly. It lacks scientificity and systematicity, making it difficult to fully identify and assess potential risks in the living environment, affecting the effectiveness of personalized renovation design. In terms of service deployment and resource management, traditional systems lack real-time data support and automated adjustment capabilities, have low resource utilization, and find it difficult to dynamically adjust services according to demand, resulting in inefficient services and high costs, which restricts the overall quality and efficiency of elderly care services. Summary of the invention

[0004] The purpose of this invention is to solve the shortcomings of the prior art and propose an AI-based smart elderly care management system. The AI-based smart elderly care management system aims to improve the efficiency and quality of elderly care services, meet the diverse needs of the elderly through highly customized services, thereby improving their quality of life, and through data analysis and machine learning technology, achieve comprehensive recognition of elderly care customers and accurate matching of services, so as to achieve the effect of improving quality of life, enhancing sense of security and promoting social interaction.

[0005] In order to achieve the above objectives, the present invention adopts the following technical solutions: An AI-based smart elderly care management system includes: AI customer service acquisition module: obtains customer information through guided voice interaction, records and stores basic customer information, obtains home environment images, draws customer portraits after analysis and generates customer portrait data, and adds different labels based on customer portrait data to generate customer behavior labels; Intelligent health monitoring module: Using AI's long-short term memory network, the system monitors and analyzes the physiological data of the elderly. AI monitoring and diagnosis technology continuously tracks changes in physiological data, generates detailed records of physiological indicators, and analyzes data change trends to generate physiological monitoring results. Automatic alarm and response module: Based on the physiological data monitoring results, a type of support vector machine is used to detect whether the physiological data exceeds the preset threshold, and the difference between the key indicators and the normal range is compared through AI analysis and evaluation technology to determine whether an abnormal situation occurs. When an abnormality is detected, the AI ​​system automatically issues an alarm notification message, automatically contacts medical institutions and family members, and establishes an emergency response record; Risk assessment and reporting module: Based on physiological data monitoring results and home environment images, Bayesian network is used to analyze risk points in the living environment, calculate the number of risk points in the environment and their impact, and generate a comprehensive risk assessment report based on the customer's health status; Assisted design and recommendation module: Based on the comprehensive risk assessment report, the AI-assisted design system calls the preset renovation design plan in the background, matches the customer's health status and living environment characteristics, recommends a matching design plan, and estimates the renovation project volume, construction period and quotation, and generates a renovation project plan; Real-time service deployment module: Based on customer behavior tags and renovation project plans, the AI ​​system adjusts the operation and management of various application services within the elderly care facilities in real time, monitors the resource usage of the services, and automatically adjusts the number of service instances and resource allocation based on real-time data and user needs to generate resource allocation optimization results.

[0006] As a further solution of the present invention, the AI ​​customer service acquisition module includes a voice interaction submodule, a customer information storage submodule and a customer portrait generation submodule, wherein: Voice interaction submodule: Based on guided voice interaction, obtain customer information, ask questions to customers, record customer names, contact information and demand information, and generate basic customer information records; Customer information storage submodule: based on the customer basic information record, store customer information, including home environment images obtained through customer authorization, and classify and organize the recorded customer information, enter it into the database, perform data backup, and establish a customer information database; Customer portrait generation submodule: Based on the customer information database, data analysis is performed to generate customer portraits through feature extraction, aggregation of customer behaviors and preferences, and labeling based on the portrait data to establish customer behavior labels.

[0007] As a further solution of the present invention, the intelligent health monitoring module includes a physiological data collection submodule, a physiological data monitoring submodule and a physiological data trend analysis submodule, wherein: Physiological data collection submodule: collects customer physiological data, uses long short-term memory networks, measures and monitors daily physiological indicators through equipment, including heart rate, blood pressure, and body temperature, and records them to generate daily physiological data records; Physiological data monitoring submodule: Based on the daily physiological data records, the AI ​​monitoring and diagnosis technology is used to monitor data changes, calculate the range of changes in daily physiological indicators, and compare the daily data to obtain the range of changes in physiological data; Physiological data trend analysis submodule: Based on the range of changes in the physiological data, the AI ​​system is used to analyze the data trend, evaluate the physiological health status of the elderly, and generate physiological data monitoring results.

[0008] As a further solution of the present invention, the automatic alarm and response module includes a threshold detection submodule, an abnormal notification submodule and an emergency response recording submodule, wherein: Threshold detection submodule: Based on the physiological data monitoring results, a support vector machine is used to detect key indicators. The AI ​​system compares the physiological data with the preset threshold value to determine whether each physiological data exceeds the normal range and generate a threshold detection result. Abnormal notification submodule: Based on the threshold detection results, the AI ​​system determines the abnormal situation. The AI ​​system issues an alarm notification message based on the detected abnormal indicators, automatically contacts the medical institution and family members, and establishes the alarm notification message. Emergency response record submodule: based on the alarm notification information, record the emergency response, record the response process and processing results, and generate an emergency response record.

[0009] As a further solution of the present invention, the type of support vector machine is according to the formula:

[0010] in: is the input physiological data, is the number of support vectors, is the Lagrange multiplier, The class labels of the support vectors, is the kernel function, is the bias term, is the weight coefficient of environmental factors, is the kernel function parameter, is the preset threshold, is the time factor weight coefficient, is the angular frequency, which represents the frequency of the periodic factor, For time.

[0011] As a further solution of the present invention, the risk assessment and reporting module includes an environmental risk detection submodule, a risk point calculation submodule and a comprehensive risk generation submodule, wherein: Environmental risk detection submodule: based on the physiological data monitoring results and home environment images, detect risk points in the living environment, identify potential dangers through image analysis, detect the location and type of each risk point, and generate environmental risk point records; Risk point calculation submodule: based on the environmental risk point records, calculate the number and impact of each risk point, evaluate the degree of harm by counting the number of each risk point, calculate the overall impact by analyzing the distribution and frequency of risk points, and generate a risk point impact assessment; Comprehensive risk generation submodule: Based on the risk point impact assessment, the Bayesian network is used to conduct a comprehensive risk assessment in combination with the customer's health status. By associating physiological data with environmental risks, various risk information is comprehensively analyzed to generate a comprehensive risk assessment result.

[0012] As a further solution of the present invention, the Bayesian network is according to the formula:

[0013] in: For environmental risks , physiological data , social behavior and genetic information Health status under condition The posterior probability of For in a healthy state Environmental risks under The conditional probability of For in a healthy state Physiological data The conditional probability of For in a healthy state Social behavior under The conditional probability For in a healthy state Genetic information The conditional probability of For health status The prior probability Environmental risks The marginal probability For physiological data The marginal probability For social behavior The marginal probability of Genetic information The marginal probability of .

[0014] As a further solution of the present invention, the auxiliary design and recommendation module includes a design scheme matching submodule, a renovation project estimation submodule, and a renovation plan generation submodule, wherein: Design scheme matching submodule: Based on the comprehensive risk assessment results, the AI ​​system calls the background preset transformation design scheme, analyzes the customer's health status and living environment characteristics, matches the appropriate design scheme, and generates the design scheme matching results; Renovation project estimation submodule: Based on the design scheme matching results, the renovation project quantity is estimated, the required materials and labor are evaluated through the AI ​​system, the project quantity is calculated, the construction time is estimated through the estimation of the construction period, the total quotation is estimated through the cost calculation, and the renovation project estimation result is generated; Renovation plan generation submodule: Based on the estimated results of the renovation project, a detailed renovation project plan is formulated, and by integrating the design plan and the estimated data, the specific steps and time schedule of the renovation are determined to generate a renovation project plan.

[0015] As a further solution of the present invention, the real-time service deployment module includes a service operation adjustment submodule, a resource usage monitoring submodule and a resource allocation optimization submodule, wherein: Service operation adjustment submodule: Based on the customer behavior tags and renovation project plans, the AI ​​system is used to adjust various application services within the elderly care facility. At the same time, the AI ​​system is used to analyze customer behavior data, adjust the number of service instances in real time, and generate service operation adjustment results.

[0016] Resource usage monitoring submodule: Based on the service operation adjustment results, monitor resource usage. The AI ​​system collects real-time data, analyzes the resource consumption of each service instance, and generates a resource usage report.

[0017] Resource allocation optimization submodule: based on the resource usage report, adjust the number of service instances and resource allocation, optimize the resource allocation strategy, and generate resource allocation optimization results.

[0018] Compared with the prior art, the advantages and positive effects of the present invention are: 1. In the present invention, by collecting and monitoring the physiological data of the elderly, the long short-term memory network is used to perform time series analysis to accurately predict the trend of physiological data, thereby providing a more accurate prediction of health status and improving the accuracy of real-time monitoring of health status; 2. In the present invention, a type of support vector machine is used to detect abnormal physiological data. By learning the normal pattern of physiological data, it can quickly identify health risks and automatically alarm. When an abnormal situation is detected, it can immediately issue an alarm notification and contact medical institutions and family members to provide timely health advice and emergency response; 3. In this invention, Bayesian network is used to analyze the risk points of the residential environment and calculate their impact, providing an accurate risk assessment report, providing a scientific basis for personalized renovation design, and recommending the most suitable design plan based on the risk assessment report and preset plan to ensure the efficient implementation of the renovation project, improve service efficiency and system response speed, and improve the accuracy, timeliness and safety of elderly care services. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A system flow chart of an embodiment of the present invention; Figure 2 Schematic diagram of the system framework described in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this disclosure.

[0021] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0022] In addition, in the description of the present invention, the terms used are only for illustrative purposes and are not intended to limit the scope of the present disclosure. The terms "include" and / or "comprise" are used to specify the existence of elements, steps, operations and / or components, but do not exclude the existence or addition of one or more other elements, steps, operations and / or components. The terms "first", "second", etc. may be used to describe various elements, do not represent order, and do not limit these elements. In addition, in the description of the present invention, unless otherwise specified, the meaning of "multiple" is two and more than two. These terms are only used to distinguish one element from another element. In conjunction with the following drawings, these and / or other aspects become apparent, and it is easier for those of ordinary skill in the art to understand the description of the embodiments of the present invention. The accompanying drawings are used to describe the embodiments of the present disclosure for illustrative purposes only. Those skilled in the art will easily recognize from the following description that, without departing from the principles of the present disclosure, alternative embodiments of the structures and methods shown in the present invention can be adopted.

[0023] like Figure 1 As shown, an embodiment of the present invention provides an AI-based smart elderly care management system, which includes: AI customer service acquisition module: obtains customer information through guided voice interaction, records and stores basic customer information, obtains home environment images, draws customer portraits after analysis and generates customer portrait data, and adds different labels based on customer portrait data to generate customer behavior labels; Intelligent health monitoring module: Using AI's long-short term memory network, the system monitors and analyzes the physiological data of the elderly. AI monitoring and diagnosis technology continuously tracks changes in physiological data, generates detailed records of physiological indicators, and analyzes data change trends to generate physiological monitoring results. Automatic alarm and response module: Based on the physiological data monitoring results, a class of support vector machines is used to detect whether the physiological data exceeds the preset threshold, and the difference between key indicators and the normal range is compared through AI analysis and evaluation technology to determine whether an abnormal situation occurs. When an abnormality is detected, the AI ​​system automatically issues an alarm notification message, automatically contacts medical institutions and family members, and establishes an emergency response record; Risk assessment and reporting module: Based on physiological data monitoring results and home environment images, Bayesian network is used to analyze risk points in the living environment, calculate the number of risk points in the environment and their impact, and generate a comprehensive risk assessment report based on the customer's health status; Assisted design and recommendation module: Based on the comprehensive risk assessment report, the AI-assisted design system calls the preset renovation design plan in the background, matches the customer's health status and living environment characteristics, recommends a matching design plan, and estimates the renovation project volume, construction period and quotation, and generates a renovation project plan; Real-time service deployment module: Based on customer behavior tags and renovation project plans, the AI ​​system adjusts the operation and management of various application services within the elderly care facilities in real time, monitors the resource usage of the services, and automatically adjusts the number of service instances and resource allocation based on real-time data and user needs to generate resource allocation optimization results.

[0024] An optional implementation method, such as Figure 2 As shown in the figure, the AI ​​customer service acquisition module includes a voice interaction submodule, a customer information storage submodule, and a customer portrait generation submodule, among which: Voice interaction submodule: Based on guided voice interaction, obtain customer information, ask questions to customers, record customer names, contact information and demand information, and generate basic customer information records; Customer information storage submodule: based on the basic customer information records, store customer information, including home environment images obtained through customer authorization, and classify and organize the recorded customer information, enter it into the database, perform data backup, and establish a customer information database; Customer portrait generation submodule: Based on the customer information database, data analysis is performed to generate customer portraits by extracting features, aggregating customer behaviors and preferences, and labeling the portrait data to establish customer behavior labels. The voice interaction submodule is based on guided voice interaction and adopts a hidden Markov model. Its parameters include the initial probability vector π, the state transition probability matrix A, and the observation probability matrix B. It processes the customer's voice signal, extracts voice features through the Mel-frequency cepstral coefficients, converts the voice signal, generates text data, performs semantic analysis, and uses the TF-IDF algorithm to extract keywords, record the customer's name, contact information, and demand information, and generate a record of the customer's basic information. The customer information storage submodule uses a relational database to store customer information based on basic customer information records, classifies and organizes customer information, enters the database, performs data backup, exports the database using the mysqldump command, establishes a customer information database, and regularly maintains the database, checks table consistency, and performs data repair; The customer portrait generation submodule performs data analysis based on the customer information database and adopts the K-means clustering algorithm to perform cluster analysis on customer information. It divides clusters by iteratively calculating cluster centers, aggregates customer behaviors and preferences, generates customer portraits, and uses One-hot encoding to label customer portrait data, label customer behaviors, and establish a customer behavior label database.

[0025] The AI ​​customer service acquisition module can efficiently and quickly collect customer information, generate customer portraits, establish a customer information database, and serve as a data source for other data analysis.

[0026] An optional implementation method, such as Figure 2 As shown, the intelligent health monitoring module includes a physiological data collection submodule, a physiological data monitoring submodule and a physiological data trend analysis submodule, wherein: Physiological data collection submodule: collects customer physiological data, uses long short-term memory networks, measures and monitors daily physiological indicators through equipment, including heart rate, blood pressure, and body temperature, and records them to generate daily physiological data records; Physiological data monitoring submodule: Based on daily physiological data records, AI monitoring and diagnosis technology is used to monitor data changes, calculate the range of changes in daily physiological indicators, and compare daily data to obtain the range of changes in physiological data; Physiological data trend analysis submodule: Based on the range of physiological data changes, the AI ​​system analyzes data trends, evaluates the physiological health status of the elderly, and generates physiological data monitoring results; Physiological data collection submodule: collects customer physiological data, uses long short-term memory network to process the daily physiological indicators measured and monitored by the equipment, collects data through heart rate sensor, blood pressure sensor and body temperature sensor, records heart rate, blood pressure and body temperature, and pre-processes the data. It uses standardization method to normalize each physiological indicator value to between 0 and 1, and generates daily physiological data records; Physiological data monitoring submodule: Based on daily physiological data records, monitor data changes, use a sliding window algorithm with a window size of 7 days and a step size of 1 day, calculate the range of changes in daily physiological indicators, and obtain the range of changes in heart rate, blood pressure and body temperature by comparing the maximum and minimum values ​​in the current window. Record them in the database, perform daily data comparisons, and obtain the range of changes in physiological data; Physiological data trend analysis submodule: Based on the range of physiological data changes, data trends are analyzed, and time series analysis algorithms are used to predict the trend of the range of changes in each physiological indicator. By fitting the historical data of physiological indicators, trend forecasts for a period of time in the future are generated, and based on the prediction results, physiological data monitoring results are generated.

[0027] By collecting and monitoring the physiological data of the elderly, using long short-term memory networks for time series analysis, and accurately predicting the trends of physiological data, more accurate predictions of health conditions can be provided, improving the accuracy of real-time monitoring of health conditions.

[0028] An optional implementation method, such as Figure 2 As shown, the automatic alarm and response module includes a threshold detection submodule, an abnormal notification submodule and an emergency response record submodule, wherein: Threshold detection submodule: Based on the physiological data monitoring results, a class of support vector machines is used to detect key indicators. The AI ​​system compares the physiological data with the preset thresholds to determine whether the physiological data exceeds the normal range and generate threshold detection results. Abnormal notification submodule: Based on the threshold detection results, the AI ​​system judges abnormal situations. It sends out alarm notification information based on the detected abnormal indicators, automatically contacts medical institutions and family members, and establishes alarm notification information at the same time; Emergency response record submodule: based on the alarm notification information, record the emergency response, record the response process and processing results, and generate emergency response records; Threshold detection submodule: Based on the physiological data monitoring results, a first-class support vector machine is used to detect key indicators. A first-class support vector machine algorithm is used, and the regularization parameter ν is set to 0.1. The physiological data is trained and classified. The AI ​​system compares the physiological data with the preset threshold to determine whether each physiological data exceeds the normal range and generate the threshold detection result. Abnormal notification submodule: Based on the threshold detection results, abnormal situations are judged, if-else conditional judgment statements are used to analyze the detected abnormal indicators, set the abnormal threshold range, detect physiological data that exceeds the range through abnormal detection functions, issue alarm notification information, call the email sending API, automatically contact medical institutions and family members, establish alarm notification information, and record the notification time, notification object and notification content; Emergency response record submodule: based on the alarm notification information, emergency response is recorded, the log recording module is used to record the response process and processing results, the log recording API is called to record the response start time, responding personnel, processing measures and processing results, emergency response records are generated, and the records are stored in the database.

[0029] An optional implementation uses a type of support vector machine, according to the following formula:

[0030] in: is the input physiological data, is the number of support vectors, is the Lagrange multiplier, The class labels of the support vectors, is the kernel function, is the bias term, is the weight coefficient of environmental factors, is the kernel function parameter, is the preset threshold, is the time factor weight coefficient, is the angular frequency, which represents the frequency of the periodic factor, For time; Execution process: First, obtain the input physiological data , and perform standardization and denoising to ensure the accuracy and consistency of the data. Then, the Lagrange multiplier in the support vector machine model is used and the class labels of the support vectors , combined with the kernel function Calculate the similarity between the input data and the support vector, obtain the influence of the support vector on the input data by summing, and then calculate the bias term , to adjust the intercept of the decision function, and then add the impact of environmental factors, the environmental factor weight coefficient The training is obtained through environmental sensor data, which includes the elderly’s living environment data including temperature, humidity and air pressure. The gradient descent method is used for optimization to determine the correction effect of environmental factors on physiological data. Subsequently, the kernel function parameters are calculated. and preset thresholds Correction effect on input data, kernel function parameters Determined by cross-validation during model training, the preset threshold By statistically analyzing the physiological data of a large number of healthy elderly people and combining it with expert experience, we finally calculated the correction term of the time factor and the weight coefficient of the time factor. , angular frequency ω and phase It is obtained by fitting through time series analysis of the physiological data of the elderly, optimized through the least squares method, and its correction effect in the decision function is determined. All items are integrated to calculate the threshold detection result.

[0031] The embodiment of the present invention adopts a type of support vector machine to detect physiological data anomalies. By learning the normal patterns of physiological data, it can quickly identify health risks and automatically alarm. When an abnormal situation is detected, it can immediately issue an alarm notification and contact medical institutions and family members to provide timely health advice and emergency response.

[0032] An optional implementation method, such as Figure 2 As shown, the risk assessment and reporting module includes an environmental risk detection submodule, a risk point calculation submodule and a comprehensive risk generation submodule, among which: Environmental risk detection submodule: Based on physiological data monitoring results and home environment images, it detects risk points in the living environment, identifies potential dangers through image analysis, detects the location and type of each risk point, and generates environmental risk point records; Risk point calculation submodule: Based on the environmental risk point records, the number and impact of each risk point are calculated. By counting the number of each risk point, the degree of harm is evaluated. By analyzing the distribution and frequency of risk points, the overall impact is calculated to generate a risk point impact assessment. Comprehensive risk generation submodule: Based on the risk point impact assessment, the Bayesian network is used to combine the customer's health status to conduct a comprehensive risk assessment. By associating physiological data with environmental risks, various risk information is comprehensively analyzed to generate a comprehensive risk assessment result. The environmental risk detection submodule detects risk points in the living environment based on physiological data monitoring results and home environment images, identifies potential dangers, and detects the location and type of each risk point. Customers upload pictures or videos of their home environment, and use pre-trained convolutional neural network models for image recognition and segmentation, marking risk points, including steps, whether the floor is non-slip, handle locations, obstacles, etc., and generates detailed environmental risk point records in combination with the customer's physiological data; The risk point calculation submodule calculates the number and impact of each risk point based on the environmental risk point records. It uses statistical analysis methods and fault tree analysis to count the number of various risk points identified and evaluate their degree of harm. It uses frequency analysis algorithms to analyze the spatial distribution of risk points and calculate their overall impact to generate a risk point impact assessment. The comprehensive risk generation submodule is based on the risk point impact assessment, using the Bayesian network, combined with the AI-assisted system and the customer's physiological data, including age, medical history, and mobility, and correlation analysis with environmental risk point data to conduct a comprehensive risk assessment. By correlating physiological data with environmental risks, the various risk information is comprehensively analyzed to generate a comprehensive risk assessment result. Specifically: using the Bayesian network model, the customer's physiological data is used to evaluate the comprehensive impact of each risk point through Bayesian inference, and combined with the AI-assisted system to generate a comprehensive risk assessment result.

[0033] By utilizing image and video automatic recognition technology, analyzing residential environment risk sources through AI, and combining customer portrait information, we can efficiently and accurately generate residential risk assessment reports to meet customers' personalized needs.

[0034] An optional implementation method uses a Bayesian network according to the following formula:

[0035] in: For environmental risks , physiological data , social behavior and genetic information Health status under condition The posterior probability of For in a healthy state Environmental risks under The conditional probability of For in a healthy state Physiological data The conditional probability of For in a healthy state Social behavior under The conditional probability For in a healthy state Genetic information The conditional probability of For health status The prior probability Environmental risks The marginal probability For physiological data The marginal probability For social behavior The marginal probability of Genetic information The marginal probability of Implementation process: First, collect and organize environmental risk data , including air quality, temperature and humidity, and health status Association, calculation and , collect physiological data of customers , including heart rate, blood pressure and body temperature, calculate and , while collecting social behavior data from customers , including eating habits, exercise frequency and work pressure, calculate and , and collect genetic information from customers , including family medical history and genetic test results, calculate and , and finally substitute it into the formula to calculate By comprehensively analyzing various risk information, a comprehensive risk assessment result is generated.

[0036] Bayesian networks are used to analyze risk points in the living environment and calculate their impacts, providing accurate risk assessment reports and a scientific basis for personalized renovation design. At the same time, based on risk assessment reports and preset plans, the most suitable design plan is recommended to ensure the efficient implementation of the renovation project, improve service efficiency and system response speed, and enhance the accuracy, timeliness and safety of elderly care services.

[0037] An optional implementation method, such as Figure 2 As shown, the auxiliary design and recommendation module includes a design scheme matching submodule, a renovation project estimation submodule, and a renovation plan generation submodule, among which: Design scheme matching submodule: Based on the comprehensive risk assessment results, the AI ​​system calls the background preset renovation design scheme, analyzes the customer's health status and living environment characteristics, matches the appropriate design scheme, and generates the design scheme matching results; Renovation project estimation submodule: Based on the design scheme matching results, the renovation project quantity is estimated. The required materials and labor are evaluated through the AI ​​system, the project quantity is calculated, the construction time is estimated through the construction period estimation, the total quotation is estimated through the cost calculation, and the renovation project estimation result is generated; Renovation plan generation submodule: Based on the estimated results of the renovation project, a detailed renovation project plan is formulated. By integrating the design plan and estimated data, the specific steps and time schedule of the renovation are determined, and the renovation project plan is generated; The design scheme matching submodule calls the background preset renovation design scheme based on the comprehensive risk assessment results. It analyzes the customer's health status and living environment characteristics by adopting fuzzy logic and decision tree algorithms, matches the appropriate design scheme, generates design scheme matching results, extracts the customer's health data and living environment characteristics, and uses the preset design scheme templates in the database for matching analysis. Through the customer dimension and space dimension, different spaces give design schemes suitable for the customer, push design points, and ensure the comprehensiveness and standardization of the design.

[0038] The renovation project estimation submodule estimates the renovation project quantity based on the design scheme matching results. It uses a linear regression model to evaluate the required materials and labor, calculates the project quantity, estimates the construction time through time series analysis, and estimates the total quotation through cost calculation to generate the renovation project estimation results. It decomposes the materials of the matched design scheme, lists the types and quantities of required materials, calculates the material costs using historical data and market prices, and estimates the labor costs in combination with the labor rate. The construction period is estimated through the Bayesian estimation method, and the total quotation is generated by combining the above data. The AI-assisted design estimates a project quantity and construction period, including the quotation range, to ensure the lower limit of design quality.

[0039] The renovation plan generation submodule formulates a detailed renovation project plan based on the estimated results of the renovation project. By adopting the Gantt chart and critical path method, it integrates the design plan and estimated data, determines the specific steps and time schedule of the renovation, generates a renovation project plan, breaks down the construction process in detail, lists the start and end time of each step, uses project management software to generate a Gantt chart, and analyzes and optimizes the critical path to ensure that the design quality is not lower than the standard level and improve work efficiency. Through AI-assisted design, it integrates standardized and modular renovation concepts and estimated data to further optimize the renovation plan, improve design and business work efficiency, and raise the lower limit of results quality.

[0040] An optional implementation method, such as Figure 2 As shown, the real-time service deployment module includes a service operation adjustment submodule, a resource usage monitoring submodule and a resource allocation optimization submodule, wherein: Service operation adjustment submodule: Based on customer behavior tags and renovation project plans, the AI ​​system is used to adjust various application services within the elderly care facility. At the same time, the AI ​​system analyzes customer behavior data, adjusts the number of service instances in real time, and generates service operation adjustment results; Resource usage monitoring submodule: monitors resource usage based on the service operation adjustment results. The AI ​​system collects real-time data, analyzes the resource consumption of each service instance, and generates a resource usage report. Resource allocation optimization submodule: Based on the resource usage report, adjust the number of service instances and resource allocation, optimize the resource allocation strategy, and generate resource allocation optimization results; Service operation adjustment submodule: Based on customer behavior tags and renovation project plans, various application services within the elderly care facility are adjusted. Dynamic resource scheduling algorithms and Docker container technology are used to make real-time adjustments to each service instance. By analyzing customer behavior data, collecting data, and performing data visualization, the operation status of service instances is monitored in real time, the number of service instances is adjusted, and the service operation adjustment results are generated; Resource usage monitoring submodule: monitors resource usage based on the service operation adjustment results, collects real-time data, analyzes resource consumption of each service instance, records CPU usage, memory consumption, and network bandwidth usage by setting monitoring items and triggers, and generates resource usage reports; Resource allocation optimization submodule: Based on the resource usage report, the number of service instances and resource allocation are adjusted. The genetic algorithm is used to optimize the resource allocation strategy. The parameters include population size 50, crossover rate 0.8, and mutation rate 0.05. The Python DEAP library is used to iteratively optimize the resource allocation scheme. The resource utilization of each scheme is evaluated through the fitness function to generate the resource allocation optimization result.

[0041] The real-time service deployment module can be used to adjust and optimize service instances and resource usage in real time, thereby improving service efficiency and system response speed, enhancing the accuracy, timeliness and safety of elderly care services, and maximizing resource utilization.

[0042] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0043] In addition, it will be understood by those skilled in the art that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0044] It will be appreciated by those skilled in the art that, although the present invention has been described with reference to exemplary embodiments, various changes may be made and equivalents may be substituted for its elements without departing from the scope of the present invention. In addition, many modifications may be made to adapt specific circumstances or materials to the teachings of the present invention without departing from the essential scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed, but the present invention will include all embodiments falling within the scope of the appended claims.

Claims

1. An AI-based smart elderly care management system, characterized by: The system comprises: AI customer service acquisition module: obtains customer information through guided voice interaction, records and stores basic customer information, obtains home environment images, draws customer portraits after analysis and generates customer portrait data, and adds different labels based on customer portrait data to generate customer behavior labels; Intelligent health monitoring module: Using AI's long-short term memory network, the system monitors and analyzes the physiological data of the elderly. AI monitoring and diagnosis technology continuously tracks changes in physiological data, generates detailed records of physiological indicators, and analyzes data change trends to generate physiological monitoring results. Automatic alarm and response module: Based on the physiological data monitoring results, a type of support vector machine is used to detect whether the physiological data exceeds the preset threshold, and the difference between the key indicators and the normal range is compared through AI analysis and evaluation technology to determine whether an abnormal situation occurs. When an abnormality is detected, the AI ​​system automatically issues an alarm notification message, automatically contacts medical institutions and family members, and establishes an emergency response record; Risk assessment and reporting module: Based on the physiological data monitoring results and home environment images, a Bayesian network is used to analyze the risk points in the living environment, calculate the number of risk points in the environment and their impact, and generate a comprehensive risk assessment report in combination with the customer's health status; Assisted design and recommendation module: Based on the comprehensive risk assessment report, the AI-assisted design system calls the preset renovation design plan in the background, matches the customer's health status and living environment characteristics, recommends a matching design plan, and estimates the renovation project volume, construction period and quotation, and generates a renovation project plan; Real-time service deployment module: Based on customer behavior tags and renovation project plans, the AI ​​system adjusts the operation and management of various application services within the elderly care facilities in real time, monitors the resource usage of the services, and automatically adjusts the number of service instances and resource allocation based on real-time data and user needs to generate resource allocation optimization results.

2. The AI-based smart elderly care management system according to claim 1 is characterized in that: The AI ​​customer service acquisition module includes a voice interaction submodule, a customer information storage submodule and a customer portrait generation submodule, wherein: Voice interaction submodule: Based on guided voice interaction, obtain customer information, ask questions to customers, record customer names, contact information and demand information, and generate basic customer information records; Customer information storage submodule: based on the customer basic information record, store customer information, including home environment images obtained through customer authorization, and classify and organize the recorded customer information, enter it into the database, perform data backup, and establish a customer information database; Customer portrait generation submodule: Based on the customer information database, data analysis is performed to generate customer portraits through feature extraction, aggregation of customer behaviors and preferences, and labeling based on the portrait data to establish customer behavior labels.

3. The AI-based smart elderly care management system according to claim 1 is characterized in that: The intelligent health monitoring module includes a physiological data collection submodule, a physiological data monitoring submodule and a physiological data trend analysis submodule, wherein: Physiological data collection submodule: collects customer physiological data, uses long short-term memory networks, measures and monitors daily physiological indicators through equipment, including heart rate, blood pressure, and body temperature, and records them to generate daily physiological data records; Physiological data monitoring submodule: Based on the daily physiological data records, the AI ​​monitoring and diagnosis technology is used to monitor data changes, calculate the range of changes in daily physiological indicators, and compare the daily data to obtain the range of changes in physiological data; Physiological data trend analysis submodule: Based on the range of changes in the physiological data, the AI ​​system is used to analyze the data trend, evaluate the physiological health status of the elderly, and generate physiological data monitoring results.

4. The AI-based smart elderly care management system according to claim 1 is characterized in that: The automatic alarm and response module includes a threshold detection submodule, an abnormal notification submodule and an emergency response recording submodule, wherein: Threshold detection submodule: Based on the physiological data monitoring results, a support vector machine is used to detect key indicators. The AI ​​system compares the physiological data with the preset threshold value to determine whether each physiological data exceeds the normal range and generate a threshold detection result. Abnormal notification submodule: Based on the threshold detection results, the AI ​​system determines the abnormal situation. The AI ​​system issues an alarm notification message based on the detected abnormal indicators, automatically contacts the medical institution and family members, and establishes the alarm notification message. Emergency response record submodule: based on the alarm notification information, record the emergency response, record the response process and processing results, and generate an emergency response record.

5. The AI-based smart elderly care management system according to claim 1 is characterized in that: The first type of support vector machine is according to the formula: in: is the input physiological data, is the number of support vectors, is the Lagrange multiplier, The class labels of the support vectors, is the kernel function, is the bias term, is the weight coefficient of environmental factors, is the kernel function parameter, is the preset threshold, is the time factor weight coefficient, is the angular frequency, which represents the frequency of the periodic factor, For time.

6. The AI-based smart elderly care management system according to claim 1 is characterized in that: The risk assessment and reporting module includes an environmental risk detection submodule, a risk point calculation submodule and a comprehensive risk generation submodule, wherein: Environmental risk detection submodule: based on the physiological data monitoring results and home environment images, detect risk points in the living environment, identify potential dangers through image analysis, detect the location and type of each risk point, and generate environmental risk point records; Risk point calculation submodule: based on the environmental risk point records, calculate the number and impact of each risk point, evaluate the degree of harm by counting the number of each risk point, calculate the overall impact by analyzing the distribution and frequency of risk points, and generate a risk point impact assessment; Comprehensive risk generation submodule: Based on the risk point impact assessment, the Bayesian network is used to conduct a comprehensive risk assessment in combination with the customer's health status. By associating physiological data with environmental risks, various risk information is comprehensively analyzed to generate a comprehensive risk assessment result.

7. The AI-based smart elderly care management system according to claim 1 is characterized in that: The Bayesian network, according to the formula: in: For environmental risks , physiological data , social behavior and genetic information Health status under conditions The posterior probability of For in a healthy state Environmental risks under The conditional probability of For in a healthy state Physiological data The conditional probability of For in a healthy state Social behavior under The conditional probability For in a healthy state Genetic information The conditional probability of For health status The prior probability Environmental risks The marginal probability For physiological data The marginal probability For social behavior The marginal probability of Genetic information The marginal probability of .

8. The AI-based smart elderly care management system according to claim 1 is characterized in that: The auxiliary design and recommendation module includes a design scheme matching submodule, a renovation project estimation submodule, and a renovation plan generation submodule, wherein: Design scheme matching submodule: Based on the comprehensive risk assessment results, the AI ​​system calls the background preset transformation design scheme, analyzes the customer's health status and living environment characteristics, matches the appropriate design scheme, and generates the design scheme matching results; Renovation project estimation submodule: Based on the design scheme matching results, the renovation project quantity is estimated, the required materials and labor are evaluated through the AI ​​system, the project quantity is calculated, the construction time is estimated through the estimation of the construction period, the total quotation is estimated through the cost calculation, and the renovation project estimation result is generated; Renovation plan generation submodule: Based on the estimated results of the renovation project, a detailed renovation project plan is formulated, and by integrating the design plan and the estimated data, the specific steps and time schedule of the renovation are determined to generate a renovation project plan.

9. The AI-based smart elderly care management system according to claim 1 is characterized in that: The real-time service deployment module includes a service operation adjustment submodule, a resource usage monitoring submodule and a resource allocation optimization submodule, wherein: Service operation adjustment submodule: Based on the customer behavior tags and renovation project plan, the AI ​​system is used to adjust various application services within the elderly care facility. At the same time, the AI ​​system is used to analyze customer behavior data, adjust the number of service instances in real time, and generate service operation adjustment results; Resource usage monitoring submodule: monitors resource usage based on the service operation adjustment results. The AI ​​system collects real-time data, analyzes the resource consumption of each service instance, and generates a resource usage report. Resource allocation optimization submodule: based on the resource usage report, adjust the number of service instances and resource allocation, optimize the resource allocation strategy, and generate resource allocation optimization results.

Images