HIS-based nursing home nursing worker service intelligent scheduling and task optimization system

Abstract

The invention discloses an HIS-based nursing home nursing worker service intelligent scheduling and task optimization system, and relates to the technical field of intelligent scheduling, and the system comprises an HIS-based nursing home nursing worker service intelligent scheduling system which comprises a state perception collection module for obtaining the real-time data of the old and nursing workers; the positioning health fusion module is used for accurately positioning and integrating health data; the intelligent scheduling decision module calculates a qualification matching degree and a comprehensive priority; the emergency collaborative linkage module starts emergency equipment and evaluates the efficiency; the rescue closed-loop management module tracks rescue and optimizes algorithm parameters; according to the invention, through multi-module cooperation, a full-process intelligent old-age care service system is constructed, comprehensive and accurate data acquisition, efficient integration and correction, scientific matching, dynamic evaluation and scheduling, rapid equipment linkage in emergency and efficiency evaluation are carried out, and continuous optimization is carried out based on historical data, so that human errors are reduced, the service standardization level is improved, and efficient utilization of resources is realized; and a reliable and efficient intelligent service scheme is provided for the nursing home.

Description

Technical Field

[0001] This invention relates to the field of intelligent scheduling technology, specifically to an intelligent scheduling and task optimization system for nursing home caregiver services based on HIS. Background Technology

[0002] As the population ages, the demand for nursing home services continues to grow. Elderly safety and health care have become a focus of social attention. The elderly care service industry is gradually transforming towards intelligent and refined operations. Improving service efficiency and quality through information technology has become an industry trend. The mature application of HIS systems in the healthcare field provides fundamental support for the digital upgrade of nursing home services. The rapid development of positioning technology, data fusion technology, and intelligent decision-making technology is driving the transformation of elderly care services from traditional manual management to intelligent scheduling. There is an urgent need to build a service system that can integrate multi-dimensional data and achieve precise scheduling and efficient collaboration to meet the core needs of nursing homes for optimized allocation of caregiver resources, real-time safety assurance for the elderly, and rapid response to emergencies.

[0003] Traditional nursing home caregiver service management relies heavily on manual dispatch, which has significant limitations. Manual dispatch makes it difficult to fully grasp the real-time location, workload, and skill suitability of caregivers, leading to unreasonable resource allocation. Some areas are overcrowded with caregivers while others are underserved. In emergency response, human judgment is easily influenced by subjective factors, making it difficult to quickly match caregivers with the appropriate skills, resulting in low response efficiency and potential delays in optimal treatment. Furthermore, the traditional model lacks systematic integration and analysis of service data, making it impossible to optimize dispatch strategies based on historical service data, hindering continuous improvement in service quality. In addition, data from different service links is fragmented, and functions such as health monitoring, location tracking, and equipment linkage lack effective coordination, making it difficult to form a complete service loop and provide comprehensive and integrated care for the elderly. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent scheduling and task optimization system for nursing home caregiver services based on HIS (Hospital Information System). This invention proposes an intelligent scheduling and task optimization system for nursing home caregiver services based on HIS. It acquires real-time status information of the elderly and caregivers through a status perception and acquisition module, accurately locates and integrates health data using a location and health fusion module, generates scheduling instructions based on a caregiver qualification and risk-skill matching algorithm, activates emergency equipment and evaluates the effectiveness of the linkage, and tracks the rescue progress and optimizes algorithm parameters and rules through a rescue closed-loop management module. The system achieves efficient scheduling and task optimization of caregiver services, improving the emergency response and rescue capabilities of nursing homes.

[0005] To solve the above-mentioned technical problems, this invention provides the following technical solution: an intelligent scheduling and task optimization system for nursing home caregiver services based on HIS, the system comprising:

[0006] The status awareness and data acquisition module is used to collect data on the elderly’s safety status, caregiver’s location signal, initial information on the elderly’s health risk dimensions, emergency event types, and caregiver’s on-duty and busy time. All data is accompanied by millisecond-level timestamps.

[0007] The positioning and health fusion module receives raw data from the status perception acquisition module, activates the environment adaptive UWB positioning correction algorithm to obtain accurate positioning coordinates, retrieves and desensitizes elderly health data, and integrates it into a structured dataset.

[0008] The intelligent scheduling and decision-making module calls the nurse qualification database, starts the risk-skill two-way matching algorithm to calculate the qualification matching degree, and then analyzes the comprehensive priority through multi-factor dynamic weighted evaluation technology to generate scheduling instructions and equipment linkage instructions;

[0009] The emergency coordination and linkage module receives linkage instructions from the intelligent dispatch and decision-making module, activates various emergency equipment and executes preset operations, and uses multi-dimensional dynamic performance evaluation technology to evaluate linkage effectiveness.

[0010] The rescue closed-loop management module tracks the rescue progress in real time, collects all rescue data and archives it to the HIS system, and optimizes relevant algorithm parameters and rules based on historical data.

[0011] Furthermore, the specific methods for collecting data on the elderly's safety status, caregiver location signals, initial information on the elderly's health risk dimensions, emergency event types, and caregiver's shift duration and busy time in the aforementioned status perception and acquisition module are as follows: Human activity status is continuously captured by human body sensing devices and infrared detection devices deployed in the elderly's residence area, combined with the opening and closing status information fed back by door and window status monitoring devices to comprehensively determine the elderly's safety status; the caregiver's positioning terminal receives satellite positioning signals and base station assisted positioning signals in real time, and obtains the caregiver's precise location signal through signal analysis and processing; the elderly's physiological parameters are collected by connecting to health monitoring devices, combined with data such as daily activity level, sleep quality, and diet, to extract initial information on the elderly's health risk dimensions; the characteristic data of an emergency event is captured by manually triggering the emergency call button and automatically identifying the signal by the intelligent monitoring device, thus clarifying the emergency event type; the entire process of the caregiver from check-in to check-out is recorded through the system's timing function, and based on the start and stop records of the caregiver's tasks and status feedback information, the actual busy time of the caregiver's service is calculated. All collected data is simultaneously accompanied by a millisecond-level timestamp to ensure the timeliness and uniqueness of data collection.

[0012] Furthermore, in the positioning and health fusion module, the mathematical expression of the environment-adaptive UWB positioning correction algorithm is: ,in The final three-dimensional positioning coordinates for the elderly and their caregivers. The coordinates of the original location signal received by the UWB positioning base station. The environmental degradation coefficient is calibrated according to the type of nursing home area. The cumulative thickness of the obstacle between the positioning terminal and the nearest base station. For the material attenuation weight of the corresponding obstacle, This is a floor height correction factor based on the actual floor height of the nursing home. The target floor and the reference floor are the difference between the target floor and the reference floor. After receiving the raw data, the positioning and health fusion module substitutes the above parameters to calculate the accurate positioning coordinates.

[0013] Furthermore, in the aforementioned intelligent scheduling and decision-making module, the caregiver qualification database stores basic information, skill certification information, work history information, service rating information, and special qualification information of caregivers. The basic information includes caregiver identification, age, and years of service. The skill certification information includes emergency care skill level, chronic disease care qualification, and rehabilitation nursing certification. The work history information includes the types of elderly people served in the past, cumulative service hours, and number of emergency incidents handled. The service rating information includes the satisfaction rating of the elderly and their families, and monthly service assessment results. The special qualification information includes qualifications for caring for elderly people with dementia and language communication ability certification. When the intelligent scheduling and decision-making module calls this database, it can extract the corresponding fields as needed for qualification matching calculation.

[0014] Furthermore, in the intelligent scheduling decision module, the mathematical expression for the risk-skill bidirectional matching algorithm is: M represents the degree of matching between the caregiver's qualifications and the current needs, with a value ranging from 0 to 1. The closer the value is to 1, the higher the degree of matching. The weight coefficient representing the i-th skill is preset based on the urgency of the service scenario and the health risk level of the elderly, and the total weight coefficient is 1; This represents the actual pass rate of the i-th skill of the caregiver, with a value ranging from 0 to 1. It is calculated based on the skill certification level and practical assessment results in the caregiver qualification database. This represents the minimum requirement for the i-th skill in the current demand, with a value ranging from 0 to 1. (If the caregiver...) but Calculated as 1, if but Using a value of 0, this expression enables a two-way quantitative matching of caregiver skills and demand risk.

[0015] Furthermore, in the intelligent scheduling decision-making module, the specific steps for analyzing the comprehensive priority using multi-factor dynamic weighted evaluation technology are as follows: After obtaining the qualification matching result, the comprehensive priority of caregiver scheduling is derived through multi-factor dynamic weighted evaluation technology. This technology uses qualification matching as the basic factor, combined with the caregiver's current workload, the distance radius of the service recipient, historical service response time, the service recipient's family's specified preferences, and the resource distribution in the service area to determine influencing factors. Each factor is quantitatively integrated and calculated based on the weight coefficient dynamically adjusted according to the actual service scenario, forming a comprehensive priority ranking result for caregiver scheduling. Based on this ranking result, the system automatically generates a scheduling instruction that includes caregiver identification, service recipient information, service task content, and execution time limit requirements. At the same time, it links with intelligent terminal devices in the service area to generate device activation instructions, information push instructions, and status synchronization instructions, ensuring that the scheduling instructions are accurately transmitted to the corresponding caregivers, and that the relevant devices provide real-time feedback on the execution status, achieving coordinated and efficient operation of scheduling instructions and device linkage.

[0016] Furthermore, the preset operations in the emergency coordination module are as follows: activating the emergency call device in the service recipient's residence to issue a continuous audio-visual prompt signal; turning on the video monitoring equipment to collect real-time images of the service site and transmit them synchronously to the back-end management platform; starting the environmental monitoring equipment to collect real-time data on indoor temperature, humidity, and gas concentration; triggering the access control system to automatically unlock the entrance and exit channels of the service recipient's residence; linking the lighting system to adjust the lighting equipment in the service area to maximum brightness; starting the voice broadcasting equipment to loop emergency guidance information; establishing a two-way voice communication link with the back-end management platform and the caregiver's terminal; synchronously turning on the positioning and tracking equipment to update the caregiver's location information in real time and feeding it back to the dispatch center; activating the emergency medical auxiliary equipment to enter standby mode and automatically calibrating operating parameters; and triggering the fire alarm equipment to enter alert mode to monitor fire hazards in real time.

[0017] Furthermore, in the emergency coordination module, the specific steps for evaluating coordination effectiveness using multi-dimensional dynamic performance evaluation technology are as follows: Multi-dimensional dynamic performance evaluation technology uses equipment response timeliness, operational accuracy, functional synergy matching, data transmission stability, and on-site handling support as evaluation dimensions. It collects operational parameters, status feedback information, and on-site environmental change data from the start-up to completion of each emergency device. Using a combination of quantitative analysis and qualitative judgment, it assigns scores to each dimension's indicators, comprehensively calculates the overall coordination effectiveness evaluation result, identifies performance shortcomings and optimization directions in the equipment coordination process, generates an evaluation report, and synchronously feeds it back to the intelligent scheduling decision module, providing data support for subsequent coordination strategy optimization and equipment coordination mechanism improvement.

[0018] Furthermore, in the aforementioned closed-loop rescue management module, the specific steps for optimizing relevant algorithm parameters and rules based on historical data are as follows: All rescue data archived to the HIS system is categorized and sorted according to rescue scenario type, event urgency, and handling results; data dimensions such as rescue response time, rescue handling process node time, rescue resource allocation ratio, equipment linkage response efficiency, and on-site problem resolution rate are extracted; duplicate records are removed and abnormal deviation data is eliminated through data cleaning to ensure data authenticity and validity; statistical analysis methods are used to mine the correlation patterns and trends between various data dimensions to identify factors affecting rescue efficiency and handling effectiveness; and the original data is optimized based on the actual needs and handling standards of different rescue scenarios. The algorithm dynamically adjusts parameter thresholds to correct deviations between parameters and actual scenarios; it streamlines and improves rule execution logic, supplements rules based on historically high-frequency issues, incorporates practical rules based on successful handling cases, and removes redundant rules that do not match actual application scenarios; the optimized parameters and rules are applied to system trial operation, continuously collecting data feedback during the trial operation, and comparing and analyzing rescue efficiency indicators before and after optimization; for efficiency indicators that fail to meet the standards in the comparison results, the associated parameters and rule clauses are traced, the reasons for the deviation are analyzed, and secondary adjustments are made, repeating the trial operation and adjustment process until all rescue efficiency indicators stably meet the standards, ensuring that the algorithm parameters and rules are adapted to the needs of various rescue scenarios.

[0019] Furthermore, the specific steps for analyzing the causes of deviations and making secondary adjustments are as follows: Identify the core difference indicators before and after optimization, including the reduction in rescue response time, the improvement in compliance rate of handling process nodes, changes in resource coordination rationality scores, the decrease in equipment linkage failure rate, and the percentage increase in problem resolution rate; define reasonable fluctuation ranges and compliance standards for each indicator; for indicators that do not meet the compliance standards, trace the directly related algorithm parameters and rule clauses, and analyze the reasons for substandard performance due to parameter threshold setting deviations, rule logic omissions, and insufficient scenario adaptation; for parameter threshold deviations, combine the degree of difference with the distribution characteristics of the corresponding data, and gradually correct the parameter values ​​using a gradient-based step-by-step adjustment method, while also referencing historical similar rescue scenarios. The parameter configuration of the scenario is cross-referenced and calibrated; for omissions in the rule logic, missing constraints and related logic are supplemented, the judgment criteria for the applicable scenarios of the rules are clarified, and the precise boundaries and operational basis for rule execution are determined; for cases of insufficient scenario adaptation, based on the characteristics of different rescue scenarios, exclusive parameter combinations and independent rule systems are formulated for specific rescue scenarios to ensure that parameters, rules and scenario characteristics directly correspond; after the adjustment is completed, the parameters and rules are put into the system for trial operation again, and the real-time changes of the difference indicators are continuously tracked. The complete process of difference analysis, cause tracing and adjustment optimization is repeated until all indicators are stable within a reasonable fluctuation range and meet the standards, so as to achieve precise adaptation of algorithm parameters and rules to actual rescue needs.

[0020] Compared with existing technologies, this HIS-based intelligent scheduling and task optimization system for nursing home caregiver services has the following advantages:

[0021] I. This invention constructs a fully intelligent service system through multi-module collaborative operation and precise data processing. The status perception and acquisition module comprehensively captures data related to the safety, health, and work of the elderly and caregivers, and achieves accurate positioning by combining environmental adaptive positioning correction technology. The positioning and health fusion module integrates multi-dimensional data into structured information, providing solid data support for scheduling decisions. The intelligent scheduling decision module relies on the caregiver qualification database to generate the optimal scheduling plan through two-way matching of skills and needs and multi-factor dynamic weighted evaluation, ensuring that caregiver resources are rationally allocated. The emergency coordination and linkage module activates various devices to execute preset operations, evaluates linkage efficiency from multiple dimensions, and ensures efficient and orderly emergency response. The rescue closed-loop management module integrates historical data to optimize algorithms and rules, continuously improves system adaptability, significantly improves the response speed and accuracy of nursing home services, and reduces safety risks.

[0022] Second, this invention achieves intelligent optimization and closed-loop management of the entire service process through systematic design. From comprehensive coverage and accurate capture of data in the data collection stage, to efficient integration and correction in the data processing stage, and scientific matching and dynamic evaluation in the scheduling and decision-making stage, a complete service chain is formed. During emergency response, equipment linkage and efficiency evaluation are promoted simultaneously to ensure rapid response and coordinated cooperation of various resources. Based on the continuous optimization mechanism of historical data, the system can continuously adapt to different service scenarios and actual needs, reduce human decision-making bias, and improve the standardization and normalization of services. At the same time, the efficient collaboration and data interoperability between modules realize the dynamic allocation and efficient utilization of service resources, effectively reduce the workload of caregivers, improve the satisfaction of the elderly and their families, and provide nursing homes with a more reliable and efficient intelligent service solution.

[0023] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0025] Figure 1This is a framework diagram of an HIS-based intelligent scheduling and task optimization system for nursing home caregiver services.

[0026] Figure 2 A flowchart of a HIS-based intelligent scheduling and task optimization system for nursing home caregiver services;

[0027] Figure 3 A simplified transmission diagram of the core process of the intelligent scheduling decision module. Detailed Implementation

[0028] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0029] Example 1:

[0030] Implementation of daily care and safety monitoring scenarios for elderly people with dementia

[0031] Many elderly people with dementia live in large senior living communities. These elderly people are prone to getting lost, accidental bumps and falls, and require professionally qualified caregivers to provide daily care and emotional support services. This system fully intervenes in the care and monitoring process for such elderly people. The status awareness and data acquisition module operates continuously, using human body sensors and infrared detectors deployed in the elderly's residences and public activity areas to capture their activity trajectories and behavioral states in real time. Combined with feedback from door and window status monitoring devices, it promptly identifies potential safety hazards such as unauthorized departures. By connecting to health monitoring devices, it collects physiological parameters such as the elderly's heart rate and body temperature, and combines this with data on daily food intake, sleep patterns, and emotional fluctuations to comprehensively extract initial information on health risk dimensions, providing a basis for health status assessment. Utilizing positioning terminals carried by caregivers, it continuously receives and analyzes satellite and base station-assisted positioning signals to accurately obtain the caregiver's real-time location, facilitating optimal route planning during subsequent scheduling. The system's timekeeping function records caregiver shift duration, accurately calculating actual busy time based on task start / stop records and status feedback. All collected data is accompanied by millisecond-level timestamps to ensure information timeliness and uniqueness, laying the foundation for subsequent data processing and scheduling decisions.

[0032] After receiving the above raw data, the positioning and health fusion module immediately starts the environment-adaptive UWB positioning correction algorithm, the mathematical expression of which is: ,in The final three-dimensional positioning coordinates for the elderly and their caregivers. The coordinates of the original location signal received by the UWB positioning base station. The environmental degradation coefficient is calibrated according to the type of nursing home area. The cumulative thickness of the obstacle between the positioning terminal and the nearest base station. For the material attenuation weight of the corresponding obstacle, This is a floor height correction factor based on the actual floor height of the nursing home. The positioning and health fusion module receives the raw data and calculates the precise positioning coordinates by substituting the parameters mentioned above, using the difference between the target floor and the reference floor. This effectively corrects for interference from obstacles such as building walls and furniture on the positioning signal, obtaining the precise positioning coordinates of the elderly and caregivers, ensuring accurate and reliable location information. Subsequently, the module retrieves the elderly's health records and performs anonymization processing to avoid privacy leaks. At the same time, it integrates and correlates positioning data, health data, and safety status data to form a structured dataset, allowing the scattered data to form an effective connection and providing complete and coherent data support for subsequent scheduling decisions.

[0033] The intelligent scheduling and decision-making module calls upon the caregiver qualification database to extract special qualification information such as caregiver qualifications for elderly people with dementia, language communication ability certification, as well as emergency skill levels, service scores, and past work experience in caring for elderly people with dementia. It then initiates a risk-skill bidirectional matching algorithm, the mathematical expression of which is: Where M represents the degree of matching between the caregiver's qualifications and the current needs; The weight coefficient representing the i-th skill; This represents the actual achievement value of the caregiver's i-th skill, ranging from 0 to 1; This represents the minimum requirement value for the i-th skill in the current needs, ranging from 0 to 1. It accurately calculates the qualification matching degree between each caregiver and the care needs of each elderly person with dementia, ensuring the matching results meet actual service requirements. Based on this, a multi-factor dynamic weighted evaluation technology is used, with qualification matching degree as the core, combined with influencing factors such as the caregiver's current workload, the distance radius between the caregiver and the elderly person's location, historical service response time, and the distribution of caregiver resources in the service area. The weights of each factor are dynamically adjusted and quantitatively integrated to form a comprehensive priority ranking for caregiver scheduling, making scheduling decisions more scientific and reasonable. Based on the ranking results, the system generates scheduling instructions that include caregiver identification, service recipient information, daily care and safety monitoring tasks, and execution time limits. Simultaneously, it links with smart terminal devices in the elderly person's residence and public areas to generate device activation instructions, information push instructions, and status synchronization instructions, ensuring that scheduling information is quickly and accurately transmitted to the corresponding caregiver, improving the efficiency and accuracy of scheduling instruction transmission. Figure 3 As shown.

[0034] The emergency coordination module remains on standby, monitoring the operational status of various equipment and caregiver service feedback in real time. Upon detecting abnormal activity or safety risks in the elderly, it immediately initiates pre-set procedures to ensure their safety. The rescue closed-loop management module tracks the entire service process, collecting all service data and archiving it to the HIS system, accumulating data for future system optimization. Based on this historical data, the relevant parameters and rules of the risk-skill bidirectional matching algorithm and multi-factor dynamic weighted assessment technology will be optimized to continuously improve the accuracy and safety of care services for dementia-affected elderly, allowing the system to continuously adapt to actual service needs, such as… Figure 1 As shown.

[0035] In this embodiment, the system addresses the specific needs of dementia care by comprehensively capturing multi-dimensional data through a state-aware acquisition module, providing timely and accurate information support for subsequent processes. The positioning and health fusion module obtains precise coordinates using an environment-adaptive UWB positioning correction algorithm, integrating anonymized health data to form a structured dataset, ensuring data availability and privacy. The intelligent scheduling and decision-making module, relying on a caregiver qualification database, generates the optimal scheduling plan through a risk-skill bidirectional matching algorithm and multi-factor dynamic weighted evaluation technology, ensuring precise matching of caregiver resources to needs. An emergency coordination and linkage module is on standby to ensure safety, while a rescue closed-loop management module archives data and optimizes algorithm rules. The entire process achieves intelligent and precise care for dementia care, effectively preventing safety risks, improving service quality and efficiency, and providing reliable care for dementia caregivers.

[0036] Example 2:

[0037] Emergency rescue scenario for elderly people experiencing sudden health abnormalities

[0038] While elderly residents in nursing homes rest at night, health monitoring devices can detect abnormal fluctuations in their physiological parameters, prompting the system to quickly initiate emergency rescue procedures for sudden health anomalies. The status perception and data acquisition module rapidly acquires abnormal physiological parameters such as heart rate, blood pressure, and blood oxygen levels from the health monitoring devices. Combined with abnormal physical activity signals captured by human body sensors, it identifies the emergency as a sudden health anomaly, providing clear guidance for subsequent rescue efforts. Simultaneously, it quickly collects data on the status of doors and windows and relevant indoor environmental data from the residents' living quarters, providing a comprehensive understanding of the on-site environment. Furthermore, it obtains the real-time location signals of all on-duty caregivers through caregiver positioning terminals, calculating each caregiver's shift duration and current busy time. All data includes millisecond-level timestamps and is simultaneously uploaded to the system backend, ensuring timely, comprehensive, and accurate data for rescue decisions.

[0039] After receiving the raw data, the location and health fusion module activates an environment-adaptive UWB positioning correction algorithm to eliminate interference from nighttime environments and building structures on the positioning signal, accurately locating the elderly person's residence and the real-time coordinates of each caregiver, providing precise location support for caregivers to quickly arrive at the scene. Subsequently, it retrieves and anonymizes the elderly person's past medical history, medication records, and other health data to protect their privacy, and integrates the location information, abnormal health data, and emergency event information to form a structured emergency rescue dataset, clearly presenting rescue-related data and providing key data support for rapid dispatch.

[0040] The intelligent dispatch decision-making module urgently accesses the caregiver qualification database, focusing on extracting information such as caregivers' emergency skills levels, number of emergency incidents handled, and chronic disease care qualifications. It then activates a risk-skill bidirectional matching algorithm to quickly calculate the qualification match between each caregiver and the specific health emergency rescue needs, ensuring the selection of caregivers with the appropriate capabilities. Next, using multi-factor dynamic weighted evaluation technology, based on the qualification match, it dynamically adjusts weights and quantifies influencing factors such as the distance radius between the caregiver and the elderly person's residence, current workload, and historical emergency response time, determining the comprehensive priority for caregiver dispatch and ensuring the efficiency and rationality of dispatch decisions. Based on the priority ranking, the system generates dispatch instructions that include the rescue caregiver's identification, the location of the rescued person, the emergency response task, and execution requirements. Simultaneously, it issues linkage instructions to the nursing home's emergency equipment to ensure the orderly conduct of the rescue operation.

[0041] Upon receiving the linkage command, the emergency coordination module immediately initiates preset operations: activating the emergency call device within the elderly person's residence, emitting continuous audio and visual alerts to facilitate rapid detection by those nearby; turning on video surveillance equipment to capture real-time footage of the rescue scene and transmit it to the back-end management platform, allowing back-end personnel to monitor the situation in real time; activating environmental monitoring equipment to collect indoor temperature, humidity, and gas concentration data to ensure a safe rescue environment; triggering the access control system to automatically unlock the entrance and exit channels of the elderly person's residence, facilitating rapid entry for caregivers; linking with the lighting system to adjust the lighting equipment in the service area to maximum brightness, improving on-site lighting conditions; activating the voice broadcasting equipment to continuously play emergency guidance information, providing guidance for the elderly and rescue personnel; establishing a two-way voice communication link with the back-end management platform and caregiver terminals to ensure real-time communication; activating the location tracking equipment to update the caregiver's location in real time and report it to the dispatch center, allowing the dispatch center to monitor the caregiver's movement; activating emergency medical auxiliary equipment, putting it into standby mode and automatically calibrating its operating parameters to ensure the equipment is ready for immediate use; and triggering the fire alarm equipment to enter alert mode to monitor potential fire hazards and comprehensively prevent secondary risks. Meanwhile, by employing multi-dimensional dynamic performance evaluation technology, the system collects equipment operating parameters, status feedback information, and on-site environmental change data from dimensions such as equipment response timeliness, operational accuracy, functional synergy, data transmission stability, and on-site handling support. Through a combination of quantitative analysis and qualitative judgment, the system assigns scores to each dimension's indicators, generates a comprehensive evaluation report on linkage performance, and feeds it back to the intelligent scheduling decision module, providing a basis for subsequent equipment linkage optimization.

[0042] Rescue workers quickly arrive at the scene according to dispatch instructions to carry out emergency response. The rescue closed-loop management module tracks the rescue progress in real time, collecting full data such as rescue response time, handling process nodes, equipment linkage efficiency, and problem resolution rate, and archiving it to the HIS system to accumulate complete data for subsequent system optimization. Based on this historical data, relevant algorithm parameters and rules are subsequently optimized and adjusted, including dynamically adjusting parameter thresholds, improving rule execution logic, and supplementing high-frequency problem prediction rules. After system trial operation and secondary adjustments, it is ensured that the algorithm parameters and rules can accurately adapt to emergency health abnormality rescue scenarios, improve rescue response speed and handling effectiveness, and provide stronger protection for the life and health of the elderly. Figure 2 As shown.

[0043] This embodiment focuses on the emergency rescue needs of elderly people experiencing sudden health abnormalities, with all modules of the system operating efficiently and collaboratively. The status perception and acquisition module quickly captures abnormal data and related information, providing timely support for rescue decisions. The positioning and health fusion module accurately locates the elderly through an environment-adaptive UWB positioning correction algorithm, integrating multiple types of data to form an emergency rescue dataset. The intelligent dispatch and decision-making module quickly matches qualified caregivers, generates scientific dispatch instructions, and activates equipment. The emergency coordination and linkage module activates various devices to execute preset operations, evaluates linkage efficiency from multiple dimensions, and ensures efficient rescue operations. The rescue closed-loop management module tracks the entire rescue process, archives data, and optimizes algorithm rules. Through intelligent operation throughout the entire process, the system significantly improves the speed and accuracy of emergency rescue response, maximizing the protection of the elderly's life and health, and providing strong technical support for emergency rescue in nursing homes.

[0044] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A smart scheduling and task optimization system for nursing home caregiver services based on HIS, characterized in that, The system includes: The status awareness and data acquisition module is used to collect data on the elderly’s safety status, caregiver’s location signal, initial information on the elderly’s health risk dimensions, emergency event types, and caregiver’s on-duty and busy time. All data is accompanied by millisecond-level timestamps. The positioning and health fusion module receives raw data from the status perception acquisition module, activates the environment adaptive UWB positioning correction algorithm to obtain accurate positioning coordinates, retrieves and desensitizes elderly health data, and integrates it into a structured dataset. The intelligent scheduling and decision-making module calls the nurse qualification database, starts the risk-skill two-way matching algorithm to calculate the qualification matching degree, and then analyzes the comprehensive priority through multi-factor dynamic weighted evaluation technology to generate scheduling instructions and equipment linkage instructions; The emergency coordination and linkage module receives linkage instructions from the intelligent dispatch and decision-making module, activates various emergency equipment and executes preset operations, and uses multi-dimensional dynamic performance evaluation technology to evaluate linkage effectiveness. The rescue closed-loop management module tracks the rescue progress in real time, collects all rescue data and archives it to the HIS system, and optimizes relevant algorithm parameters and rules based on historical data.

2. The intelligent scheduling and task optimization system for nursing home caregiver services based on HIS as described in claim 1, characterized in that, The specific methods for collecting data on the elderly's safety status, caregiver location signals, initial information on the elderly's health risk dimensions, emergency event types, and caregiver's shift duration and busy time in the aforementioned status perception and acquisition module are as follows: Human activity status is continuously captured by human body sensing devices and infrared detection devices deployed in the elderly's living area, combined with the opening and closing status information fed back by door and window status monitoring devices, to comprehensively determine the elderly's safety status; the caregiver's positioning terminal receives satellite positioning signals and base station assisted positioning signals in real time, and obtains the caregiver's precise location signal through signal analysis and processing; the elderly's physiological parameters are collected by connecting to health monitoring devices, combined with data on daily activity levels, sleep quality, and diet, to extract initial information on the elderly's health risk dimensions; and the characteristic data of an emergency event is captured by manually triggering the emergency call button and automatically identifying the signal through intelligent monitoring devices, thus clarifying the emergency event type. The system uses a timer function to record the entire process of caregivers from check-in to check-out. It also uses the start and stop records of caregivers' tasks and status feedback information to calculate the actual busy time that caregivers devote to services. All collected data is accompanied by millisecond-level timestamps.

3. The intelligent scheduling and task optimization system for nursing home caregiver services based on HIS as described in claim 1, characterized in that, In the positioning and health fusion module, the mathematical expression of the environment adaptive UWB positioning correction algorithm is: ,in The final three-dimensional positioning coordinates for the elderly and caregivers. The coordinates of the original location signal received by the UWB positioning base station. The environmental degradation coefficient is calibrated according to the type of nursing home area. The cumulative thickness of the obstacle between the positioning terminal and the nearest base station. For the material attenuation weight of the corresponding obstacle, This is a floor height correction factor based on the actual floor height of the nursing home. The target floor and the reference floor are the difference between the target floor and the reference floor. After receiving the raw data, the positioning and health fusion module substitutes the above parameters to calculate the accurate positioning coordinates.

4. The intelligent scheduling and task optimization system for nursing home caregiver services based on HIS as described in claim 1, characterized in that, In the intelligent dispatch and decision-making module, the caregiver qualification database stores basic information, skill certification information, work history information, service rating information, and special qualification information of caregivers. The basic information includes caregiver identification, age, and years of service. The skill certification information includes emergency care skill level, chronic disease care qualification, and rehabilitation nursing certification. The work history information includes the types of elderly people served in the past, the cumulative service time, and the number of emergencies handled. The service rating information includes the satisfaction rating of the elderly and their families and the monthly service assessment results. The special qualification information includes the qualification for caring for elderly people with dementia and the certification for language communication ability.

5. The intelligent scheduling and task optimization system for nursing home caregiver services based on HIS as described in claim 1, characterized in that, In the intelligent scheduling decision module, the mathematical expression of the risk-skill bidirectional matching algorithm is: Where M represents the degree of matching between the caregiver's qualifications and the current needs; The weight coefficient representing the i-th skill; This represents the actual achievement value of the caregiver's i-th skill, ranging from 0 to 1; This represents the minimum requirement for the i-th skill in the current demand, and its value ranges from 0 to 1.

6. The intelligent scheduling and task optimization system for nursing home caregiver services based on HIS as described in claim 1, characterized in that, The intelligent scheduling decision-making module uses a multi-factor dynamic weighted evaluation technique to analyze the comprehensive priority. The specific steps are as follows: After obtaining the qualification matching result, the comprehensive priority of caregiver scheduling is derived using this technique. This technique uses qualification matching as the basic factor, combined with the caregiver's current workload, the service recipient's distance radius, historical service response time, the service recipient's family's specified preferences, and the resource distribution in the service area to determine influencing factors. Each factor is quantified and integrated based on dynamically adjusted weight coefficients according to the actual service scenario, forming a comprehensive priority ranking result for caregiver scheduling. Based on this ranking result, the system automatically generates scheduling instructions containing caregiver identification, service recipient information, service task content, and execution time limits. Simultaneously, it links with intelligent terminal devices within the service area to generate device activation instructions, information push instructions, and status synchronization instructions.

7. The intelligent scheduling and task optimization system for nursing home caregiver services based on HIS as described in claim 1, characterized in that, The preset operations in the emergency coordination module are as follows: activating the emergency call device in the service recipient's residence to issue a continuous audio-visual alert signal; turning on the video monitoring equipment to collect real-time images of the service site and transmit them synchronously to the back-end management platform; starting the environmental monitoring equipment to collect real-time data on indoor temperature, humidity, and gas concentration; triggering the access control system to automatically unlock the entrance and exit channels of the service recipient's residence; linking the lighting system to adjust the lighting equipment in the service area to maximum brightness; starting the voice broadcasting equipment to loop emergency guidance information; establishing a two-way voice communication link with the back-end management platform and the caregiver's terminal; synchronously turning on the positioning and tracking equipment to update the caregiver's location information in real time and feeding it back to the dispatch center; activating the emergency medical auxiliary equipment to enter standby mode and automatically calibrating operating parameters; and triggering the fire early warning equipment to enter alarm mode to monitor fire hazards in real time.

8. The intelligent scheduling and task optimization system for nursing home caregiver services based on HIS as described in claim 1, characterized in that, In the emergency coordination module, the specific steps for evaluating coordination effectiveness using multi-dimensional dynamic performance evaluation technology are as follows: The multi-dimensional dynamic performance evaluation technology uses equipment response timeliness, operational accuracy, functional coordination and matching, data transmission stability, and on-site handling support as evaluation dimensions. By collecting the operating parameters, status feedback information, and on-site environmental change data of each emergency device from startup to completion, a combination of quantitative analysis and qualitative judgment is used to classify and score the indicators of each dimension, and a comprehensive evaluation result of coordination effectiveness is obtained. At the same time, the performance shortcomings and optimization directions in the equipment coordination process are identified, an evaluation report is generated, and the results are simultaneously fed back to the intelligent dispatch decision module.

9. The intelligent scheduling and task optimization system for nursing home caregiver services based on HIS as described in claim 1, characterized in that, In the aforementioned closed-loop rescue management module, the specific steps for optimizing relevant algorithm parameters and rules based on historical data are as follows: All rescue data archived to the HIS system is categorized and sorted according to rescue scenario type, event urgency, and handling results; data dimensions such as rescue response time, rescue handling process node time, rescue resource allocation ratio, equipment linkage response efficiency, and on-site problem resolution rate are extracted; duplicate records are removed and abnormal deviations are eliminated through data cleaning to ensure data authenticity and validity; statistical analysis methods are used to mine the correlation patterns and trends between various data dimensions, clarifying the factors affecting rescue efficiency and handling effectiveness. Based on the needs and handling standards of different rescue scenarios, the threshold parameters of the original algorithm were dynamically adjusted to correct the adaptation deviation between the parameters and the actual scenarios. The rule execution logic was sorted out and improved, supplemented with rules based on historical high-frequency problems, incorporated practical rules based on successful handling cases, and deleted redundant rules that did not match the actual application scenarios. The optimized parameters and rules were applied to the system trial operation, and data feedback was continuously collected during the trial operation to compare and analyze the rescue efficiency indicators before and after optimization. For the efficiency indicators that did not meet the standards in the comparison results, the related parameters and rule clauses were traced, the reasons for the deviation were analyzed and secondary adjustments were made, and the trial operation and adjustment process was repeated until all rescue efficiency indicators stably met the standards.

10. The intelligent scheduling and task optimization system for nursing home caregiver services based on HIS as described in claim 9, characterized in that, The specific steps for analyzing the causes of deviations and making secondary adjustments are as follows: First, identify the core difference indicators before and after optimization, including the percentage reduction in rescue response time, the improvement in compliance rate of handling process nodes, changes in resource coordination rationality scores, the decrease in equipment linkage failure rate, and the percentage increase in problem resolution rate. Then, define the reasonable fluctuation range and compliance standards for each indicator. For indicators that do not meet the compliance standards, trace the directly related algorithm parameters and rule clauses, and analyze the reasons for substandard performance due to parameter threshold setting deviations, rule logic omissions, and insufficient scenario adaptation. Finally, for parameter threshold deviations, combine the degree of difference with the distribution characteristics of the corresponding data, and gradually adjust using a gradient-based step-by-step approach. Positive parameter values ​​are selected, and parameter configurations from similar historical rescue scenarios are cross-referenced for calibration. For rule logic omissions, missing constraints and related logic are supplemented, the criteria for determining the applicable scenarios of the rules are clarified, and the precise boundaries and operational basis for rule execution are determined. For scenarios with insufficient adaptation, specific parameter combinations and independent rule systems are developed for specific rescue scenarios based on their characteristic differences. After adjustments are completed, the parameters and rules are put back into the system for trial operation, continuously tracking the real-time changes of the difference indicators, repeating the complete process of difference analysis, cause tracing, and adjustment optimization until all indicators are stable within a reasonable fluctuation range and meet the compliance standards.

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