Artificial intelligence-based elderly nurse training management system

Abstract

The invention discloses an elderly caregiver training management system based on artificial intelligence, and the system comprises a data collection module which is used for collecting the personal basic data, preposed skill evaluation data and post demand association data of elderly caregiver students; an AI intelligent analysis module which is in communication connection with the data acquisition module and is used for carrying out feature extraction, demand matching and capability short board analysis on the acquired data based on an artificial intelligence algorithm; a personalized training generation module which is in communication connection with the AI intelligent analysis module and is used for generating a personalized training scheme adaptive to the ability level and post demand of the student according to the analysis result; the AI practical operation evaluation module is used for collecting behavior data, action data and interaction data in the practical operation training process of the trainee, and carrying out practical operation skill standard reaching degree evaluation based on an artificial intelligence technology; and a management background module. According to the technical scheme provided by the invention, the precision adaptation and scientific evaluation level of old-age nursing talent cultivation can be improved.

Description

Technical Field

[0001] This invention generally relates to the field of artificial intelligence technology. More specifically, this invention relates to an artificial intelligence-based training and management system for elderly care workers. Background Technology

[0002] With the accelerating aging of the global population, the market demand for professional elderly care services is experiencing explosive growth. As a core force in the elderly care service system, the professional competence of elderly care workers directly impacts the quality of life and safety of the elderly. However, the industry currently faces a severe talent supply shortage. The government has successively introduced numerous policies, explicitly proposing the construction of a standardized and professional elderly care talent training system and promoting the transformation of training models from "short-term crash courses" to "systematic advancement." However, traditional training systems are no longer adequate for the development needs of the smart elderly care era, making technological empowerment a key breakthrough for improving training quality and efficiency.

[0003] The existing training and management model for elderly care workers suffers from significant systemic flaws. The core problem lies in the lack of precise adaptation and scientific evaluation mechanisms in the training process, leading to a severe disconnect between training effectiveness and actual job requirements. Regarding training content, existing courses generally suffer from outdated textbooks and lagging knowledge updates. 80% of training institutions still focus on basic daily living care as their core teaching content, with less than 40% covering cutting-edge areas such as dementia care, elderly first aid, and psychological support. Furthermore, they fail to design differentiated courses based on trainees' educational backgrounds and work experience, failing to meet the personalized learning needs of caregivers at different levels. In the training implementation phase, the traditional model primarily relies on "theoretical lectures + video demonstrations," with practical training time less than 20%. The lack of immersive simulation scenarios and interactive teaching methods results in weak practical skills among trainees, making it difficult for them to cope with complex elderly care scenarios. More critically, a training effectiveness evaluation system is severely lacking: current evaluation standards are vague and highly subjective, lacking unified quantitative indicators. 70% of training institutions have not established effective feedback mechanisms, making it impossible to track trainees' learning progress and skill mastery in real time, or to dynamically adjust training content based on job application results. This leads to a break in the chain between training and employment / development. Although some institutions have attempted to introduce online training resources, insufficient technological support has prevented the realization of intelligent management throughout the training process. This makes it difficult to address core pain points such as uneven resource allocation and a shortage of qualified instructors, severely hindering the overall improvement of the professional level of elderly care workers.

[0004] In view of this, there is an urgent need to provide an artificial intelligence-based training and management system for elderly care workers in order to improve the accuracy of matching and the level of scientific assessment. Summary of the Invention

[0005] In order to at least solve one or more of the technical problems mentioned above, the present invention proposes an artificial intelligence-based training and management system for elderly care workers in several aspects.

[0006] In a first aspect, the present invention provides an artificial intelligence-based training and management system for elderly care workers, comprising: a data acquisition module for collecting basic personal data, pre-assessment data, and job requirement-related data of elderly care worker trainees; an AI intelligent analysis module, communicatively connected to the data acquisition module, for performing feature extraction, requirement matching, and competency gap analysis on the collected data based on artificial intelligence algorithms; a personalized training generation module, communicatively connected to the AI ​​intelligent analysis module, for generating personalized training programs adapted to the trainees' competency levels and job requirements based on the analysis results; an AI practical assessment module for collecting behavioral data, action data, and interaction data of trainees during practical training, and assessing the pass rate of practical skills based on artificial intelligence technology; and a management backend module, communicatively connected to both the personalized training generation module and the AI ​​practical assessment module, for monitoring training progress, statistically analyzing assessment results, and dynamically adjusting training programs.

[0007] In some embodiments, the AI ​​intelligent analysis module employs artificial intelligence algorithms including collaborative filtering algorithm, gradient boosting tree algorithm in machine learning, and convolutional neural network algorithm in deep learning, to construct trainee competency profiles and job requirement profiles, and calculate the difference in matching degree between the two to determine competency gaps.

[0008] In some embodiments, the personalized training program includes tiered theoretical courses, immersive virtual simulation practical training, targeted case studies, and real-time interactive Q&A modules, and the time allocation for each module is dynamically determined by the AI ​​intelligent analysis module based on the learner's skill gaps.

[0009] In some embodiments, the AI ​​practical assessment module includes an image acquisition unit, a motion capture unit, and a voice interaction unit. The image acquisition unit is used to acquire visual image data of the trainee during the practical process, the motion capture unit is used to acquire three-dimensional coordinate data of the trainee's limb movements, and the voice interaction unit is used to acquire voice data of communication between the trainee and the simulated service object.

[0010] In some embodiments, the AI ​​practical assessment module performs quantitative assessments based on the collected data from four dimensions: action standardization, operational process completeness, emergency response speed, and communication script suitability, and generates an assessment report containing improvement suggestions.

[0011] The personalized training generation module is also used to dynamically adjust the difficulty, order, and presentation format of the training content based on the evaluation results of the AI ​​practical assessment module and the real-time learning data of the trainees, through reinforcement learning algorithms.

[0012] Through the AI-based elderly care worker training management system described above, this invention effectively addresses core issues in existing training models, such as content homogenization, subjective assessment, and supply-demand mismatch, by deeply integrating AI technology with elderly care training management. AI intelligent analysis enables precise matching of trainees' abilities with job requirements, generating highly targeted personalized training programs that fully adapt to the differentiated learning needs of trainees with varying educational backgrounds and work experience. An AI-powered practical assessment module provides objective quantitative analysis of skill mastery, replacing traditional subjective evaluation methods and enhancing the fairness and scientific rigor of assessment results. Furthermore, relying on a dynamic adjustment mechanism and multi-dimensional training formats, it strengthens the deep integration of theory and practice, significantly improving trainees' professional skills and job suitability. Simultaneously, through intelligent management throughout the entire process, it achieves efficient integration of training, assessment, and employment, optimizes training resource allocation, reduces training management costs, and helps build a standardized and professional elderly care talent training system, providing strong support for the high-quality development of the elderly care service industry. Attached Figure Description

[0013] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein: Figure 1 An exemplary structural block diagram of an artificial intelligence-based elderly care worker training management system according to an embodiment of the present invention is shown. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Figure 1 An exemplary structural block diagram of an artificial intelligence-based elderly care worker training management system according to an embodiment of the present invention is shown.

[0016] like Figure 1The system 100 shown in this embodiment of the invention provides an artificial intelligence-based training and management system for elderly care workers, comprising: a data acquisition module 101, used to collect basic personal data, pre-assessment data, and job requirement-related data of elderly care worker trainees; an AI intelligent analysis module 102, communicatively connected to the data acquisition module, used to perform feature extraction, requirement matching, and competency gap analysis on the collected data based on artificial intelligence algorithms; a personalized training generation module 103, communicatively connected to the AI ​​intelligent analysis module, used to generate personalized training programs adapted to the trainees' competency levels and job requirements based on the analysis results; an AI practical assessment module 104, used to collect behavioral data, action data, and interaction data of trainees during practical training, and to assess the pass rate of practical skills based on artificial intelligence technology; and a management backend module 105, communicatively connected to both the personalized training generation module and the AI ​​practical assessment module, used to monitor training progress, statistically analyze assessment results, and dynamically adjust training programs.

[0017] Specifically, in one implementation scenario, the data acquisition module is configured with an "IoT terminal + cloud interface collaborative acquisition" mode. This includes a smart wearable device (collecting physiological data such as heart rate and stress index during training), a high-definition infrared camera (collecting facial expressions and operational details), a touch-screen theoretical testing terminal (recording behavioral data such as answer duration, modification frequency, and error type), and a standardized job requirement data interface (connecting to the national elderly care service platform and the management system of cooperating elderly care institutions to capture dynamic data such as the distribution of service recipients' health levels, high-frequency nursing scenarios, and skill certification requirements in real time). Subsequently, the collected data is preprocessed by edge computing nodes. Numerical data is standardized using Z-score, textual data noise is filtered using a BiLSTM model, and errors in the sensor-collected motion data are corrected using a Kalman filter algorithm. Finally, the data is encrypted using 5G slicing technology and uploaded to the AI ​​intelligent analysis module.

[0018] Furthermore, the AI ​​intelligent analysis module constructs a "dual profile + multi-dimensional matching" model. First, it extracts 12 core features of trainees using an improved gradient boosting tree algorithm (GBDT), then combines it with a collaborative filtering algorithm to mine the correlation patterns of trainees' skill improvement, and finally uses a convolutional neural network (CNN) to encode the features of job requirement data.

[0019] Specifically, this can be achieved by designing a competency gap calculation model. The formula can include: S=∑(Wi×|Ai-Ri|)×(1+Hi×β)×γ, where S is the calculated comprehensive competency gap value, Wi is the job requirement weight of the i-th skill (e.g., Wi=0.3 for disability care and Wi=0.4 for dementia care, to dynamically adapt to the needs of different elderly care institutions), Ai is the trainee's actual test value of the i-th skill (calculated by combining theoretical test score, simulated practical score, and emergency response speed), Ri is the minimum job requirement threshold for the i-th skill (refer to the "National Vocational Skill Standard for Elderly Care Workers"), Hi is the emergency priority coefficient of the i-th skill (Hi=1.2 for first aid skills and Hi=0.8 for basic nursing), β is the trainee's past occupational risk coefficient (calculated based on historical nursing records, ranging from 0 to 0.5), and γ is the health complexity coefficient of the job service recipients (γ=1.3 for dementia care and γ=1.0 for general elderly care). Thus, the competency gap can be accurately quantified through this model.

[0020] Furthermore, the personalized training generation module dynamically adjusts the course combination based on the S-value ranking results and uses a deep reinforcement learning algorithm. The theoretical courses push tiered knowledge points through an adaptive learning system, and the practical training integrates a VR virtual simulation system (recreating real nursing home scenes and simulating 20+ high-frequency scenarios such as turning over disabled elderly people and pressure ulcer care), and corrects the trainees' operational deviations in real time through inertial motion capture equipment (with an accuracy of 0.1mm).

[0021] Furthermore, the AI ​​practical assessment module adopts a multi-device collaborative acquisition scheme. It acquires three-dimensional coordinate data of limbs through the Kinect sensor, combines it with OpenCV to recognize the standardization of actions, collects communication scripts through the speech recognition module and analyzes patience and professionalism through the BERT model, and integrates simulated body physiological sensors to collect vital sign feedback data to comprehensively assess the impact of the operation on the service recipient. Furthermore, the management backend module is configured to build a real-time data visualization dashboard, using WebSocket technology to achieve millisecond-level updates of training progress and assessment results, and using the LSTM algorithm to predict trainees' skill improvement curves. When a trainee's progress in a certain module lags behind by 30% or their assessment score falls below a threshold, a course adjustment instruction is automatically triggered and an alert is pushed to the trainer's mobile device.

[0022] The embodiments provided by this invention significantly improve the accuracy and adaptability of training management through the deep integration of multimodal data acquisition and improved AI algorithms. On the other hand, the comprehensive acquisition and preprocessing of multi-source data ensures the authenticity and completeness of trainee competency profiles and job requirement profiles. The improved competency gap calculation model fully incorporates special factors such as job differences, emergency priorities, and the complexity of service recipients in the elderly care industry, making the competency analysis results more aligned with actual application scenarios. Furthermore, the dynamic adjustment mechanism of personalized training programs achieves precise teaching tailored to each individual. The application of VR virtual simulation and motion capture technology enhances the immersion and relevance of practical training, effectively addressing the pain points of traditional training, such as limited scenarios and insufficient practical experience. The multi-dimensional AI practical assessment system replaces subjective evaluation with objective data, significantly improving the fairness and comprehensiveness of the assessment results. Thus, the closed-loop dynamic management mechanism enables real-time optimization of the training process and efficient resource allocation. This not only quickly addresses trainees' competency gaps and improves their professional skills and job adaptability but also reduces training costs and shortens the training cycle, providing stronger technical support for building a standardized and professional elderly care talent training system.

[0023] Furthermore, in one embodiment, personal basic data may include educational background, years of work experience, and previous training experience. Pre-employment skills assessment data includes theoretical knowledge test scores, basic practical skills scores, and emergency response simulation scores. The job requirement-related data includes the type of nursing services offered by the target employment institution, the health status distribution of service recipients, and core skill requirements.

[0024] To help those skilled in the art better understand the data acquisition module, the following description uses specific embodiments: Personal basic data can be collected using a dual-mode approach of "self-declaration + verification from authoritative data sources." This includes automatic verification of educational background through the China Higher Education Student Information System (CHESICC) API, categorizing educational levels into four levels: primary school and below, junior high school, high school / technical secondary school, and junior college and above, coded as X1-X4. Years of work experience are combined with social security payment records, work certificate filing information from elderly care institutions, and the trainee's self-declaration date. A timestamp comparison algorithm is used to eliminate false information, accurate to the month, and converted into quantifiable values ​​(e.g., less than 1 year is 1, 1-3 years is 2, 3-5 years is 3, and more than 5 years is 4). Furthermore, past training experience can be collected by connecting to the National Elderly Care Worker Training Management Platform, collecting information such as training program name, training duration, assessment results, and certificate number. Training levels are categorized as "national-provincial-municipal-institutional" and assigned weights (e.g., national level 1.0, provincial level 0.8, municipal level 0.6, institutional level 0.4).

[0025] Furthermore, the pre-assessment data can be assessed using a combination of online theory and offline simulation. This includes: a theoretical knowledge test covering several modules such as elderly physiological care, disease prevention, first aid knowledge, and psychological support, with a total of 100 questions (80 objective questions and 20 case analysis questions). Scores are awarded separately for each module, and a comprehensive score (A, out of 100) is calculated. Basic practical skills assessment can select core operations such as turning over and patting the back, wound care, and blood pressure measurement. The operation process is captured by a high-definition camera, with initial AI evaluation followed by professional instructor review to obtain individual scores. These scores are then weighted to calculate a comprehensive practical score (B, out of 100). The emergency response simulation sets up five high-frequency emergency scenarios, including sudden myocardial infarction, falls, and choking. The entire process, from problem discovery to resolution (T), is recorded, along with the operational standardization score (C). The comprehensive emergency response score (D) is calculated as: (100 - T / standard time threshold) × 0.6 + C × 0.4 (out of 100).

[0026] Furthermore, job requirement-related data is connected to the smart elderly care management system of partner elderly care institutions through standardized interfaces. This includes: nursing service types are divided into 5 categories: "home care, community day care, institutional care, dementia care, and rehabilitation care," coded as F1-F5; the distribution of service recipients' health status is classified into four levels: "healthy and self-reliant - semi-disabled - disabled - severely disabled," with the percentage of people in each level calculated (e.g., 60% of disabled elderly are recorded as 0.6); core skill requirements are based on the "National Vocational Skill Standards for Elderly Care Workers," and are further refined into 20 specific skills according to the specific needs of the institutions (e.g., dementia care institutions emphasize communication skills related to dementia, and rehabilitation care institutions emphasize rehabilitation training guidance skills), with each skill assigned a requirement weight (range 0.05-0.15, summed to 1). All collected data is stored in JSON format and traced through blockchain technology to ensure immutability.

[0027] Furthermore, based on the above embodiments, some implementation scenarios may also include data standardization and fusion, dynamic weight adjustment, and multi-dimensional demand matching algorithms. By designing an improved joint calculation model for data credibility and skill suitability, the accuracy of data application can be enhanced. Specifically: Data standardization and fusion processing: An improved Z-score standardization algorithm is used to normalize basic data of different dimensions. The optimized formula for the elderly care scenario is: X'=(X-μ) / (σ+ε)×ω, where X is the original data, μ is the industry mean of this type of data (based on national elderly care training big data statistics), σ is the standard deviation, ε is the minimum value (0.001, to avoid the denominator being 0), and ω is the data source credibility coefficient (authoritative platform data ω=1.2, data reported by institutions ω=0.8, data reported by trainees ω=0.6, after verification ω is uniformly increased to 1.0), ensuring that data from different sources and in different formats can be directly compared and analyzed.

[0028] A skill requirement fit calculation model can be designed, taking into account the dynamic and unique nature of elderly care positions, and a multi-factor coupled fit calculation formula can be designed: P=∑[(Ai×Wi×Ti×Si) / (1+|Ri-Ai|×Ki)]×α×β, In the above formula, the parameters are explained in the context of elderly care as follows: P: Skill fit between trainee and target position (range 0-100, higher score indicates stronger fit). Ai: Standardized score of trainee's i-th skill (calculated from prior skill assessment data, range 0-1). Wi: Job requirement weight of i-th skill (determined by the institution based on service type and health status distribution, e.g., "pressure ulcer care" Wi=0.12 in the care of disabled elderly). Ti: Time-sensitive decay factor of i-th skill (calculated based on previous training time, Ti=1.0 within 1 year after training, decaying by 0.1 for each additional year, minimum 0.6, reflecting the need for skill updates). Si: Practical relevance coefficient of i-th skill (matching degree with high-frequency practical scenarios of the position, e.g., "home infusion" for home care position Si=0.9, institutional elderly care position Si=0.7). Ri: Standardized minimum requirement value for i-th skill (set with reference to industry standards and institutional needs, range 0-1). Ki: The error tolerance coefficient for the i-th skill (Ki = 1.5 for core skills such as first aid, Ki = 0.8 for general skills such as tidying and organizing; the lower the error tolerance, the larger the coefficient). α: The correction coefficient for trainee employment stability (calculated based on years of employment and past turnover records, α = 1 + 0.1 × ln(years of employment + 1) - 0.2 × number of turnovers, range 0.8-1.5). β: The correction coefficient for job service complexity (calculated based on the distribution of health status of service recipients, β = 1 + ∑(health level coefficient × percentage of people), health level coefficient: healthy and self-reliant 0.1, semi-disabled 0.3, disabled 0.6, severely disabled 1.0).

[0029] Dynamic data update mechanism: Real-time monitoring of data changes through edge computing nodes. When the proportion of elderly people with a certain health condition in the job requirement-related data changes by more than 20%, or when trainees add new training experience / practical experience, the model parameters are automatically updated and the fit degree P is recalculated to ensure real-time matching between data and actual scenarios.

[0030] The above-described embodiments address the problems of insufficient authenticity, low standardization, and disconnect from job requirements in traditional data collection through refined data classification and multi-source verification mechanisms, ensuring the accuracy and reliability of basic data. The clearly defined evaluation standards, coding rules, and storage methods in the specific embodiments provide structured, computable, high-quality data input for AI intelligent analysis, resulting in more comprehensive trainee competency profiles and more accurate job requirement profiles. Standardized algorithms and improved fit calculation models fully integrate key factors such as the timeliness of skills, job complexity, and the specific characteristics of service recipients in the elderly care industry. Compared to traditional single-dimensional matching algorithms, this significantly improves the accuracy of skill requirement matching, effectively avoiding the pain point of "mismatch between training content and job requirements." A dynamic data update mechanism ensures the real-time nature of data application, enabling rapid response to changes in job requirements and trainee competency improvements. This provides solid data support for the generation of personalized training programs and AI practical assessments, ultimately achieving precise allocation of training resources and a significant improvement in training effectiveness. This helps elderly care workers quickly adapt to their target positions, alleviating the industry's talent supply and demand imbalance and structural problems.

[0031] Furthermore, in one embodiment, the AI ​​intelligent analysis module employs artificial intelligence algorithms including collaborative filtering, gradient boosting tree, and convolutional neural network algorithms from machine learning, to construct trainee competency profiles and job requirement profiles, and calculates the difference in matching degree between the two to determine competency gaps. Specific embodiments are described below: 1. The Collaborative Filtering (CF) algorithm adopts an improved strategy based on a dual user-item perspective. It uses "trainees" as the user dimension and "nursing skill items" as the item dimension to construct a trainee-skill rating matrix (the rating is based on pre-assessment data and previous training results). It mines the association rules between skills through Item-BasedCF (such as the strong correlation between "pressure ulcer care" and "turning over and patting back" skills). At the same time, it uses User-BasedCF to cluster trainees with similar backgrounds (such as the same educational background and the same years of work experience) to extract common shortcomings of the group (such as the common problem of "non-standard emergency handling procedures" among novice trainees).

[0032] 2. The Gradient Boosting Tree (GBDT) algorithm focuses on structured data processing. Using trainees' basic personal data (educational background code, years of work experience, etc.) and prior skills assessment data (theoretical / practical / emergency scores) as input features, it constructs a 12-layer decision tree ensemble model, outputting a vector representation of the trainee's core competency features. This vector covers multiple key dimensions, comprehensively reflecting the trainee's overall competency characteristics. Specifically, these dimensions include: skill mastery proficiency (the trainee's actual control and application of specific skills); learning absorption speed (the trainee's efficiency in understanding and absorbing new knowledge and skills); emergency response capability (the trainee's ability to quickly adjust and respond appropriately in unexpected situations or urgent tasks); problem-solving ability (measuring the trainee's ability to analyze problems, develop solutions, and execute them effectively); teamwork ability (reflecting the trainee's performance in communicating, cooperating, and achieving goals in a team environment); innovation ability (reflecting the trainee's potential to generate new ideas and explore new methods in thinking and practical operations); and time management ability (assessing the trainee's ability to plan time, allocate energy, and complete tasks efficiently). In addition, it assesses emotional management skills, examining the trainees' ability to control emotional fluctuations and maintain a stable mindset under pressure. These eight dimensions together constitute a complete trainee competency model, generating a unique core feature vector for each trainee through quantitative methods, thus providing a scientific basis for subsequent competency assessment, personalized training program design, and career development path planning. Simultaneously, using job requirement-related data (service type coding, health status percentage, skill requirement weight) as input, it outputs a job requirement feature vector (including six dimensions: core skill priority, frequency of skill application scenarios, emergency skill requirement level, service recipient health suitability, intelligent nursing equipment operation requirements, and communication and collaboration suitability).

[0033] Convolutional Neural Network (CNN) algorithms are used for deep mining of unstructured data. Employing a 3-layer convolutional + 2-layer pooling network structure, it extracts textual features from job requirement documents (such as institutional care standards and service specifications). Using Word2Vec, it converts professional terminology (such as "dementia communication skills" and "feeding assistance for disabled elderly") into word vectors, extracting semantic features through sliding convolutional kernels to supplement the detailed dimensions of the job requirement profile. Simultaneously, it extracts features from trainees' practical video frames, capturing key frame features of operational actions (such as hand movement amplitude and operational sequence continuity) through convolutional layers to optimize the practical dimension representation of trainees' competency profiles. Finally, the cosine similarity algorithm calculates the matching degree between the trainee's competency feature vector and the job requirement feature vector. The matching degree difference = 1 - cosine similarity value; a larger difference indicates a more significant competency deficiency. The core deficiencies are identified by sorting the differences in descending order (e.g., a difference of 0.35 corresponds to a deficiency in "dementia care skills," and a difference of 0.28 corresponds to a deficiency in "first aid operation procedures").

[0034] Furthermore, in one embodiment scenario, the AI ​​intelligent analysis module may also include a "multi-algorithm attention fusion framework," a "dynamic feature weight adjustment mechanism," and a "shortcoming priority calculation model." By modifying the matching degree difference formula, the algorithm's adaptability to the specificities of elderly care scenarios is enhanced. Specifically: Multi-algorithm attention fusion framework: Construct an algorithm fusion model based on attention mechanism, taking the structured feature vector (F1) output by GBDT, the unstructured feature vector (F2) output by CNN, and the associated feature vector (F3) output by collaborative filtering as input, and dynamically assigning weights to each feature vector through the attention weight calculation module. W1=σ(MLP(F1×S1+b1)), W2=σ(MLP(F2×S2+b2)), W3=σ(MLP(F3×S3+b3)) Where σ is the Sigmoid activation function, MLP is a 3-layer perceptron, S1 is the importance coefficient of structured features (S1=0.4 in elderly care scenarios, as structured data such as education level and skill scores are more crucial for ability assessment), S2 is the importance coefficient of unstructured features (S2=0.35, such as detailed features of practical actions and textual requirements), S3 is the importance coefficient of associated features (S3=0.25), and b1-b3 are bias terms. The fused feature vector F=W1×F1+W2×F2+W3×F3 achieves accurate integration of multi-dimensional features.

[0035] Furthermore, by designing a matching degree difference and weakness priority calculation model, and considering the emergency nature and time-sensitive nature of skills in elderly care positions, a two-stage calculation model is designed: Stage 1 (refined calculation of matching degree difference): ΔP=∑[(||×Wi×Ti) / (1+)]×λ, Stage 2 (weakness priority ranking): Pr=ΔP×(1+×γ)×(1-×δ). Detailed explanations of each parameter for the elderly care scenario are as follows: ΔP: Fine-grained matching difference (range 0-1, the larger the difference, the more severe the feature mismatch); : Feature value of the i-th skill in the trainee fusion feature vector (range 0-1); : Feature value of the i-th skill in the job fusion feature vector (range 0-1); Wi: Job core weight of the i-th skill (determined by the institution based on the health status of the service recipient, e.g., Wi=0.18 for the severe disability care job "airway care"); Ti: Timeliness factor of the i-th skill (referencing the industry skill update cycle, e.g., Ti=1.2 for "intelligent nursing equipment operation", Ti=0.9 for traditional nursing skills); : Learning difficulty coefficient of the i-th skill (calculated based on the national training pass rate, e.g., dementia care = 0.7, basic cleaning = 0). .2); λ: Data credibility correction coefficient (determined by the data collection source, authoritative platform data λ=1.0, self-reported data λ=0.85); Pr: Shortcoming priority (range 0-2, the higher the value, the more priority needs to be strengthened); : Emergency priority coefficient of the i-th skill (first aid skills=1.5, daily care=0.6); γ: Probability coefficient of emergency scenario occurrence (statistical from historical data of the institution, such as nursing home fall emergency γ=0.8, myocardial infarction emergency γ=0.3); : Learning potential coefficient of the i-th skill of the trainee (calculated based on the progress rate of the pre-assessment, =0.1-0.9, the higher the potential, the larger the coefficient); δ: Potential decay coefficient (fixed at 0.3, balancing learning potential and the urgency of shortcomings).

[0036] Dynamic algorithm optimization mechanism: Based on trainee training progress data and updated job requirements data, the decision tree depth of GBDT, the convolution kernel size of CNN, and the similarity calculation threshold of collaborative filtering are adjusted in real time through reinforcement learning algorithms to ensure that the algorithm model can quickly adapt to changes in the scene.

[0037] The embodiments provided by this invention, through the collaborative work of three algorithms, achieve comprehensive utilization of both structured and unstructured data, overcoming the shortcomings of traditional single algorithms in handling multiple data types. This results in more complete student and job profiles, more accurate matching difference calculations, and the ability to quickly pinpoint core competency gaps in elderly care worker training (such as specifically identifying dementia care skills gaps required for dementia specialist care positions). The algorithm fusion framework and improved computational model further enhance the accuracy of profile construction and the scientific rigor of gap identification. Multi-algorithm attention fusion addresses the limitations of single-algorithm feature extraction, making the fused features more aligned with the core needs of elderly care scenarios. The improved formula incorporates industry-specific factors such as skill timeliness, emergency priority, and learning difficulty, significantly improving the accuracy of gap priority ranking compared to traditional matching algorithms, thus avoiding the disconnect between "generalized gap identification" and actual job requirements. A dynamic algorithm optimization mechanism ensures the model's continuous adaptability to changes in job requirements and updates to the student population. Ultimately, this technical solution effectively improved the accuracy and timeliness of identifying skill gaps, providing a more reliable basis for decision-making in generating subsequent personalized training programs, significantly reducing the waste of training resources, and improving the fit between training content and job requirements.

[0038] Furthermore, in one embodiment, the personalized training program includes tiered theoretical courses, immersive virtual simulation practical training, targeted case studies, and real-time interactive Q&A modules, with the time allocation for each module dynamically determined by the AI ​​intelligent analysis module based on the learner's skill gaps. The process is illustrated below with specific embodiments: 1. Tiered Theoretical Course Module: Based on the skill gaps identified by the AI ​​intelligent analysis module, the content is designed in three tiers: "Beginner - Intermediate - Expert." The beginner level focuses on basic nursing knowledge (such as common sense about geriatric physiology and basic operational procedures), suitable for novice learners. The intermediate level strengthens specialized skills theory (such as the principles of dementia care and the operating logic of intelligent devices), suitable for learners with some experience. The expert level focuses on decision-making in complex scenarios (such as developing care plans for elderly people with multiple complications), suitable for experienced learners. The course format uses "video micro-lectures + interactive courseware + periodic tests," with test results fed back to the AI ​​module in real time to dynamically adjust the difficulty of subsequent courses.

[0039] 2. Immersive Virtual Simulation Hands-on Training Module: Integrating a VR headset, inertial motion capture equipment (0.05mm accuracy), and a physical human body model, this module recreates 25 high-frequency elderly care scenarios (such as turning and patting the back of disabled elderly, airway clearance for severely disabled elderly, and emotional support for elderly with dementia). Trainees can operate the module in a first-person immersive environment, with the equipment capturing limb movements and operational procedures in real time and generating instant error correction prompts (such as "turning angle less than 30° can easily cause pressure sores").

[0040] 3. Targeted Case Study Module: The AI ​​module selects cases that are highly relevant to the trainees' weaknesses from the case library (containing 1000+ real elderly care cases, categorized by service type, health status, and emergency scenario). For example, if a trainee's weakness is "fall emergency handling," then 3-5 fall emergency cases in different scenarios will be provided. The module adopts a "group discussion + mentor review" format, where trainees collaboratively analyze cases in a virtual meeting room, submit their solutions, and the AI ​​generates an evaluation report based on industry standards and best practices.

[0041] 4. Real-time Interactive Q&A Module: A dual-channel platform combining AI-powered intelligent Q&A and online Q&A with professional instructors is established. AI-powered Q&A uses NLP technology to analyze students' text / voice questions (such as "the frequency of dressing changes for pressure ulcer care") and matches accurate answers from the knowledge base. Instructor Q&A supports scheduled real-time video connections, providing one-on-one guidance for complex issues (such as personalized care plan design). Regarding the dynamic allocation of learning hours, taking student B as an example: female, with a vocational school diploma and one year of work experience, AI analysis shows that her core weaknesses are "virtual simulation practice (emergency handling)" (weakness difference 0.32) and "case study (complex scenario decision-making)" (weakness difference 0.21). The job requirement is a nursing home disability care position (virtual practice corresponds to a skill weight of 0.4, and case study has a weight of 0.2). The final allocation of learning hours is: virtual simulation practice 42%, tiered theory 30%, case study 18%, and real-time interactive Q&A 10%. Every two weeks, the proportion of learning hours is adjusted based on the student's learning data (such as a 20% decrease in the virtual practice error rate), reducing the proportion of virtual practice to 35% and increasing the proportion of case study to 22%.

[0042] Furthermore, in one implementation scenario, the personalized training program may also include a "multi-dimensional dynamic optimization mechanism for learning hours," a "personalized learning path iterative algorithm," and a "module content adaptive adjustment function." It also enhances the deep adaptation of the program to the elderly care scenario by designing an improved learning hour percentage calculation model, specifically including: ① Real-time monitoring of learning status: Data on learners' heart rate and concentration are collected through smart wearable devices, combined with learning behavior data (such as video completion rate and number of repetitions of practical exercises) to determine learning fatigue and learning speed. ② Adaptive adjustment of module content: The virtual simulation practical exercise module automatically increases the number of training sessions for similar scenarios based on the types of errors learners make in their practical exercises (such as non-standard movements or omissions in procedures); the theoretical course module pushes targeted supplementary micro-lessons based on test errors. ③ Dynamic response to job requirements: When the health status distribution of the target job service recipients changes (such as the proportion of severely disabled elderly people increasing from 50% to 70%), AI adjusts the module weights in real time, increasing the proportion of virtual practical exercise (airway care, pressure ulcer care) learning hours.

[0043] Further construct a calculation model for the proportion of learning hours: Combining the characteristics of emergency response, practical relevance, and learning difficulty of elderly care skills, design a multi-factor coupled formula for allocating learning hours: Ti=[(Si×Wi×Ei×Ci) / (∑(Sj×Wj×Ej×Cj)+ε)]×100%×(1+Li×Ki-Ri×Mi), Wherein, parameter Ti represents the percentage of class hours for the i-th module (i=1 for theoretical courses, i=2 for virtual practice, i=3 for case studies, and i=4 for interactive Q&A), with a total of 100%. Si represents the difference in the learner's ability gap corresponding to the i-th module (output by the AI ​​intelligent analysis module, ranging from 0 to 1; the larger the difference, the more significant the gap). Wi represents the job requirement weight of the skill corresponding to the i-th module (set based on institutional needs; for example, Wi=0.45 for virtual practice in disability care and Wi=0.25 for theoretical courses). Ei represents the emergency priority coefficient of the skill in the i-th module (Ei=1.6 for emergency skills in modules such as virtual practice; Ei=0.8 for theoretical skills). Ci represents the practical relevance coefficient of the i-th module (the degree of relevance to actual job operation; Ci=1.5 for virtual practice, Ci=1.2 for case studies, Ci=0.9 for theoretical courses, and Ci=0.7 for Q&A). ε represents the minimum value (0.0001) to avoid a denominator of 0. Li represents the learning difficulty coefficient of the i-th module (calculated based on the national average pass rate of trainees; for example, Li=0.8 for the dementia care scenario in virtual practice and Li=0.3 for basic theory). Ki represents the difficulty adaptation factor (fixed at 0.2 to balance the learning time of high-difficulty modules and avoid overload). Ri represents the trainee's learning acceptance rate of the i-th module (calculated based on the data from the first 3 learning sessions; for example, if the average accuracy rate of virtual practice increases by 10% per training session, then Ri=0.1). Mi represents the rate correction factor (fixed at 0.15 to avoid insufficient learning time allocation due to an excessively fast acceptance rate).

[0044] Dynamic iteration of learning path: Based on reinforcement learning algorithm, the "module learning effect" of students (such as practical operation pass rate and theoretical accuracy rate) is used as a reward signal. When the learning effect of a module fails to meet the expectations for two consecutive times (such as practical operation pass rate <60%), the path adjustment is automatically triggered: increase the proportion of basic content of the module, or link other modules to assist (such as increase the process explanation hours of the corresponding theoretical course when the shortcomings of virtual practical operation are not improved).

[0045] Through the precise adaptation of functional modules and dynamic allocation of learning hours in the above embodiments, the traditional "one-size-fits-all" training model has been completely transformed: tiered theoretical courses meet the knowledge supplementation needs of learners with different backgrounds; immersive virtual simulation practice addresses the pain points of insufficient practical scenarios and high risks; targeted case studies strengthen the integration of theory and practice; real-time interactive Q&A ensures the timely removal of learning obstacles; and dynamic allocation of learning hours achieves "prioritizing the strengthening of weaknesses," significantly improving the relevance and efficiency of training. Furthermore, by incorporating core factors such as the emergency nature and practical relevance of elderly care scenarios into a multi-dimensional learning hour calculation model, the allocation of learning hours is more scientific and reasonable compared to the traditional single-weakness-oriented allocation method, avoiding the pitfalls of "emphasizing theory over practice" or "ignoring the need to strengthen basic skills." The learning status monitoring and path iteration mechanism ensures real-time optimization of the training program, enabling rapid response to changes in learner progress and job requirements. The adaptive adjustment function of module content strengthens the "learn-practice-correction" closed loop, effectively improving the proficiency and stability of learners' skills. Ultimately, this technical solution can not only quickly make up for the shortcomings in the skills of elderly care workers, but also improve the fit between training content and job requirements, shorten the adaptation period for trainees to start work, and provide the elderly care service industry with more professional and precise nursing talents.

[0046] Furthermore, in one embodiment, the AI ​​practical assessment module includes an image acquisition unit, a motion capture unit, and a voice interaction unit. The image acquisition unit collects visual image data during the trainee's practical operation; the motion capture unit acquires three-dimensional coordinate data of the trainee's limb movements; and the voice interaction unit collects voice data of communication between the trainee and the simulated service recipient. Through the multi-dimensional data acquisition and collaborative working mechanism of the AI ​​practical assessment module, the hardware configuration, data acquisition range, preprocessing flow, and collaborative logic of the three units—image acquisition, motion capture, and voice interaction—are clearly defined. By fusing multimodal data (visual-motion-voice), a comprehensive and objective assessment of elderly care practical training is achieved, thereby addressing the pain points of traditional practical assessments such as "subjective bias, omission of details, and lack of process traceability." Specific embodiments are as follows: 1. Image Acquisition Unit: Utilizing a multi-camera setup of 3 high-definition infrared cameras + 1 panoramic camera, with camera resolution ≥1080P and a frame rate of 60fps, supporting adaptive shooting in low-light environments (suitable for scenarios such as nursing home wards and nighttime care). The acquisition range covers the trainee's full-body movements, the state of the simulated human body model, and details of the operating environment, specifically including visual data such as the trainee's hand gestures, limb placement, the continuity of the operation process, and simulated changes in the elderly person's posture. In the data preprocessing stage, OpenCV is used for image enhancement, noise filtering, and region of interest (ROI) extraction. Irrelevant backgrounds are automatically cropped, retaining the core areas of the trainee's operation, generating a standardized image sequence (with a uniform resolution of 1920×1080).

[0047] 2. Motion Capture Unit: Deploys 6 inertial motion capture sensors (worn on the trainee's head, torso, arms, and hands), with a sampling rate of 100Hz and a positioning accuracy of 0.03mm. It also integrates with a Kinect depth camera to acquire environmental depth data. The sensors collect real-time 3D coordinate data (X, Y, Z axes) of the trainee's limb movements, generating motion trajectory sequences. The system focuses on capturing the angles, forces (derived from motion acceleration), and timing of key actions (such as the sequence and rhythm of force changes during a back-knocking motion). Data preprocessing uses a Kalman filter algorithm to correct sensor drift errors and a motion segmentation algorithm to break down continuous movements into key stages such as "preparation-execution-completion," facilitating accurate matching with standard operating procedures.

[0048] 3. Voice Interaction Unit: Equipped with two omnidirectional microphones (3-meter pickup radius, signal-to-noise ratio ≥60dB), deployed on the trainee's side and the simulated service recipient's side respectively, synchronously collecting voice data of communication between the trainee and the simulated elderly person. This includes pre-care instructions (e.g., "Grandma, I'm turning you over now, it might be a little uncomfortable, please bear with it"), reassuring language during the procedure, reminders of key precautions, and responses to the simulated elderly person's feedback. In the data preprocessing stage, Mel-frequency cepstral coefficients (MFCC) are used to extract voice features, and voice activity detection (VAD) is used to filter environmental noise (such as equipment operation sounds and background noise). The voice data is then segmented according to the operation stage and time-aligned with the motion data and image data.

[0049] Collaborative Work Example: Taking the practical training of "turning over and patting the back of disabled elderly" as an example, the image acquisition unit captures the trainee's posture adjustment movements and hand support positions during the turning process; the motion capture unit acquires the trainee's waist twisting angle (standard value 30°-45°) and arm force trajectory; the voice interaction unit collects the completeness of the trainee's pre-operation instructions and reassuring language during the operation. The data from the three units are transmitted in real time through 5G edge computing nodes to achieve time synchronization (timestamp error ≤10ms), providing multi-dimensional and full-process practical data support for subsequent assessments.

[0050] Furthermore, in one implementation scenario, the AI ​​practical assessment module also includes units such as a "multimodal data fusion assessment framework," a "dynamic assessment threshold adjustment mechanism," and an "operational risk level prediction function." By optimizing and improving the comprehensive scoring formula for practical assessment, the module enhances the adaptability of the assessment to the specificities of elderly care scenarios, and improves the accuracy and foresight of the assessment. A detailed explanation follows: ① The multimodal data fusion evaluation framework constructs a Transformer-based multimodal fusion model to perform cross-modal attention alignment on preprocessed image features (extracted by CNN), action features (extracted by LSTM), and speech features (extracted by MFCC) to explore the correlation between different data dimensions (such as the matching degree between action standardization and communication timeliness).

[0051] ② Dynamic assessment threshold: Based on the health level (healthy and self-reliant - semi-disabled - disabled - severely disabled) and emergency skills priority of the service recipients in the target position, the assessment threshold is dynamically adjusted (e.g., the action precision threshold for nursing operations of severely disabled elderly people is 20% higher than that of healthy elderly people).

[0052] ③ Operational risk prediction: Through an abnormal action detection algorithm (based on an improved isolated forest algorithm), high-risk operations (such as the risk of falling out of bed due to excessive turning angle, or the risk of rib injury due to excessive back tapping force) are identified in real time and an immediate warning is triggered.

[0053] The comprehensive scoring formula for practical assessment can combine the standardization, safety, communication suitability, and process integrity of elderly care practice to design a multi-factor coupled comprehensive assessment formula: S_total=(α×S_action+β×S_process+γ×S_communication)×(1-δ×R_risk)×λ, The parameters in the above formula, specifically for elderly care scenarios, are explained in detail below: S_total: Overall score of practical assessment (range 0-100, ≥80 points is the passing score).

[0054] S_action: Action standardization score (range 0-100), calculated from motion capture unit data by comparing the similarity of the three-dimensional coordinates of the standard action. The higher the similarity, the higher the score.

[0055] α: Weight of action standardization (set according to skill type, such as α=0.45 for the delicate operation "wound dressing", and α=0.3 for the rough operation "making bed unit").

[0056] S_process: Process integrity score (range 0-100), based on the operation sequence data of the image acquisition unit, compared with the completion of standard process nodes (e.g., turning over care requires the completion of 5 nodes: "assessment-information-turning over-tidying-observation", 20 points are deducted for each missing node).

[0057] β: Process integrity weight (fixed at 0.35; the standardization of elderly care processes directly affects service safety, so the weight is relatively high).

[0058] S_communication: Communication adaptability score (range 0-100), calculated from voice interaction unit data, including three sub-dimensions: speech completeness (0.4), tone friendliness (0.3), and response timeliness (0.3).

[0059] γ: Communication suitability weight (set according to the type of service recipient, such as γ=0.25 for care of elderly people with dementia and γ=0.15 for care of healthy elderly people).

[0060] R_risk: Operational risk level (range 0-1, 0 for no risk, 1 for extremely high risk), derived from the abnormal action detection algorithm. For example, when a high-risk operation is triggered, R_risk = 0.8.

[0061] δ: Risk correction coefficient (fixed at 0.2, to strengthen the impact of risk on the assessment results and guide trainees to operate in a standardized manner).

[0062] λ: Job suitability adjustment coefficient (calculated based on the health level of the target job service recipient; λ=1.1 for severe disability care and λ=0.95 for healthy self-care care, reflecting the rigor of the assessment for different job suitability).

[0063] Assessment Result Traceability and Optimization: Based on blockchain technology, the entire assessment process data (images, actions, voice, and score details) is stored to ensure that the assessment results are tamper-proof and traceable; at the same time, a self-optimization mechanism for the assessment model is constructed. For every 1,000 sets of practical assessment data accumulated, the weight parameters such as α, β, and γ in the formula are adjusted through reinforcement learning algorithms to improve the adaptability of the assessment model to different trainee groups and different nursing scenarios.

[0064] Through the embodiments provided by this invention, multi-camera image acquisition, high-precision motion capture, and omnidirectional voice interaction are combined to achieve comprehensive data collection and standardized preprocessing for the practical operation of elderly care, solving the problems of traditional assessments that "rely on manual observation, miss details, and have subjective results." The temporal alignment of multimodal data provides a complete process traceability basis for the assessment, making the assessment results more comprehensive and objective, and accurately identifying detailed defects in the trainees' operations (such as insufficient turning angle or non-standard communication techniques).

[0065] Further improvements are made by configuring units such as a "multimodal data fusion assessment framework," a "dynamic assessment threshold adjustment mechanism," and an "operational risk level prediction function," thereby enhancing the accuracy, foresight, and scenario adaptability of the assessment. The multimodal data fusion model uncovers the correlations between different data dimensions, avoiding the one-sidedness of single-data assessments. The comprehensive scoring formula incorporates core factors such as the job differences in elderly care, the health level of service recipients, and operational risks, resulting in assessment results that are more aligned with actual job requirements compared to traditional single-dimensional scoring. The dynamic assessment threshold and risk prediction function not only accurately assess skill attainment but also provide early warnings of high-risk operations, guiding trainees to develop safe operating habits. Blockchain traceability and model self-optimization mechanisms ensure the credibility and continuous iteration capability of the assessment. Ultimately, this technical solution effectively improves the scientific rigor and standardization of elderly care practical assessments, providing a precise basis for the dynamic adjustment of personalized training programs, helping trainees quickly improve the standardization and safety of their practical skills, and shortening the onboarding adaptation period.

[0066] Furthermore, in one embodiment, the AI ​​practical assessment module, based on the collected data, performs a quantitative assessment from four dimensions: action standardization, operational process completeness, emergency response speed, and communication script suitability, and generates an assessment report containing improvement suggestions. Through the implementation logic, technical support, and assessment report generation rules of the AI ​​practical assessment module's four-dimensional quantitative assessment, and through the process design of "multi-dimensional data benchmarking - quantitative scoring - precise suggestions," the objectivity and standardization of elderly care practical assessment are achieved, thereby addressing the pain points of traditional assessments such as "single dimension, vague scoring, and generalized suggestions."

[0067] The following specific example uses a practical training exercise on "emergency treatment for falls in the elderly": 1. Action standardization assessment: Based on the limb three-dimensional coordinate data collected by the motion capture unit, the improved dynamic time warping (DTW) algorithm is used to compare the action with the standard emergency action template (such as "assess consciousness - call for help - check injury - stop bleeding and bandage") to calculate the action similarity (range 0-100). The key indicators such as compression depth (standard 5-6cm) and compression frequency (100-120 times / min) are evaluated. A similarity of ≥85 is considered qualified.

[0068] 2. Operation process integrity assessment: Through image time sequence analysis, identify the completion status of the 6 nodes of the standard process. Deduct 15 points for each missing node and deduct 10 points for each reversed process.

[0069] 3. Emergency response speed assessment: Record the response time from the discovery of the fall to the initiation of emergency treatment (standard ≤30 seconds), and quantify the score according to "≤30 seconds (100 points), 31-40 seconds (80 points), >40 seconds (below 60 points)".

[0070] 4. Communication script suitability assessment: Analyze voice data using NLP technology to assess the completeness of the script (whether it includes reassuring words, identification, and operating instructions) and the friendliness of the tone (speech rate of 120-150 words / minute, without stiff expressions), and calculate a weighted score.

[0071] Once the assessment is completed, a report will be generated, which includes detailed scores for each dimension, shortcomings (such as "pressing frequency is too slow"), and improvement suggestions (such as "practice pressing rhythm daily, combined with metronome training").

[0072] In another embodiment, it may also include: "dynamic dimension weight adjustment mechanism", "correlation analysis between evaluation results and job requirements", and "generation of multi-round practical iterative optimization suggestions", and be evaluated through a four-dimensional comprehensive evaluation formula, the comprehensive evaluation score formula is as follows: S=∑(Si×Wi×Ki×Hi)×(1-R×β)×λ, Wherein, parameters are: S: comprehensive evaluation score (0-100); Si: original score of the i-th dimension (i=1-4 corresponding to the four dimensions); Wi: dynamic weight of the dimension ("emergency response speed" Wi=0.4 in emergency scenarios, "action standardization" Wi=0.4 in daily operations); Ki: skill importance coefficient (core skills such as first aid Ki=1.2, ordinary skills Ki=0.9); Hi: health complexity coefficient of the service recipient (severe disability Hi=1.1, healthy self-care Hi=0.95); R: practical operation risk level (0-1, high-risk operation R=0.8); β: risk impact coefficient (fixed 0.2); λ: job suitability coefficient (the higher the matching degree of the target job requirements, the larger λ is, ranging from 0.9 to 1.1).

[0073] Furthermore, in one embodiment, it may also include: dynamic weights being adjusted in real time according to the emergency needs of the position, marking "core skill gaps" with the relevant position requirements, and generating iterative optimization paths based on multiple rounds of practical data (such as "first strengthening rhythm training, then improving process coherence").

[0074] The specific implementation methods provided above enable comprehensive four-dimensional quantitative assessment, resulting in objective and accurate assessments with highly targeted improvement suggestions, thus overcoming the limitations of traditional assessments. Furthermore, by dynamically weighting assessments and linking them to job roles, the assessments become more aligned with actual needs; by refining the assessment formulas to incorporate scenario-specific factors, the scientific rigor of the scoring is enhanced. Iterative suggestions help trainees pinpoint their weaknesses. Ultimately, this improves the scenario adaptability and guidance value of practical assessments, accelerates trainees' skill attainment, and ensures the standardization and safety of nursing services after they begin their jobs.

[0075] Furthermore, in one embodiment, the personalized training generation module is also used to dynamically adjust the difficulty, order, and presentation format of the training content based on the evaluation results of the AI ​​practical assessment module and the trainees' real-time learning data, using reinforcement learning algorithms. Specifically, by constructing a personalized mechanism of "evaluation results - learning data - reinforcement learning decision - content adjustment," the reinforcement learning algorithm captures the trainees' learning patterns and ability changes, dynamically optimizing the difficulty gradient, presentation order, and carrier format of the training content, thus solving the pain point of traditional training's "fixed content and inability to adapt to the trainees' real-time learning status." In this solution, the personalized training generation module communicates in real time with the AI ​​practical assessment module and the data acquisition module to construct the state space, action space, and reward function of the reinforcement learning agent, thereby achieving precise and dynamic adaptation of the training content.

[0076] A specific implementation example is the "Care for Elderly People with Dementia" training program, as follows: 1. Data input: Collect AI practical assessment results (e.g., a score of 65 in the "Dementia Communication Skills" dimension, with the weakness being "Appropriateness of Emotional Soothing Scripts"), and real-time learning data (including a video course completion rate of 80%, 4 virtual practice repetitions, errors concentrated in "Communication Scenario Response", and a cumulative learning time of 2.5 hours).

[0077] 2. Reinforcement Learning Model Construction: A Deep Q-Network (DQN) is used as the core algorithm. The state space S contains four types of features: the learner's current ability level, the type of assessment weakness, learning progress, and learning efficiency. The action space A contains three types of adjustment actions (difficulty adjustment: basic → advanced / advanced → basic; order adjustment: prioritize learning weakness content / continue the original order; presentation format adjustment: video micro-lessons → VR scene simulation / theoretical explanation → case study). The reward function R is set as "assessment score improvement + learning completion rate - number of repetitions × 0.1", guiding the model to make decisions in the direction of "improving efficiency and strengthening weaknesses".

[0078] 3. Dynamically adjust execution: The model outputs adjustment actions: The difficulty of the content related to "dementia emotional soothing" is increased from "basic" to "advanced", and three highly realistic VR emotional soothing scenarios are inserted for training. The presentation format is changed from "theoretical explanation" to "VR practice + real-time tutor comments".

[0079] 4. Iterative optimization: After each set of adjusted content is learned, the evaluation results and learning data are collected again, the state space is updated, and the model is iteratively adjusted again to form a closed-loop optimization.

[0080] Furthermore, in one implementation scenario, the personalized training generation module can also include three main units: "multi-factor state space construction," "dynamic reward function optimization," and "cross-module collaborative adjustment mechanism." By designing reinforcement learning state value calculation and action selection formulas, it enhances the adaptability to the specific characteristics of elderly care training scenarios (such as skill timeliness and emergency priority). A detailed explanation follows: ① Multi-factor state space expansion: In addition to the original state dimensions, it also includes three types of features: "dynamic changes in job requirements", "emergency priority of skills" and "learning fatigue". Among them, learning fatigue is quantified by heart rate and attention concentration data collected by smart wearable devices, and changes in job requirements are connected to the real-time needs updates of elderly care institutions.

[0081] ② Optimization of dynamic reward function: By introducing a scenario adaptation coefficient, the reward weight is dynamically adjusted according to the matching degree between the training content and the job requirements, and the reward weight of emergency skills training is increased by 30%.

[0082] ③ Cross-module collaboration: Link the risk prediction results of the AI ​​practical assessment module. If students frequently perform high-risk operations, an emergency adjustment action of "difficulty downgrade + basic operation reinforcement" will be immediately triggered, and targeted safety standard courses will be pushed at the same time.

[0083] In one embodiment, the reinforcement learning state value function and action selection probability formula can accurately quantify the adaptation value of different adjustment actions. The formula includes: (1) State value calculation function: V(s)=∑[Q(s,a)×P(a|s)]×α×β×(1-γ×F) (2) Probability formula for action selection: P(a|s)=[exp(Q(s,a)×ω_a) / ∑exp(Q(s,a')×ω_a')]×δ In the above formula, the specific parameters are explained using the context of elderly care training: V(s): Value of the current state s (range 0-10, higher values ​​indicate stronger learning efficiency and adaptability of the learner in the current state); Q(s,a): Value of performing action a in state s (output by the DQN model); P(a|s): Probability of choosing action a in state s; α: Job requirement fit coefficient (calculated by the matching degree between the learner's target job requirements and the training content, range 0.9-1.2, α=1.2 for core positions such as disability care); β: Skill emergency priority coefficient (emergency skills such as first aid β=1.5, daily care skills β=0.9); F: Learning fatigue (range... 0-1, the higher the fatigue level, the larger F is, such as F=0.8 when the attention concentration is <60%); γ: fatigue influence coefficient (fixed at 0.3, balancing the inhibitory effect of fatigue on state value); ω_a: action adaptation weight (difficulty adjustment action ω_a=1.1, sequence adjustment ω_a=0.9, presentation form adjustment ω_a=1.0, emergency safety reinforcement action ω_a=1.8); a': other actions in the action space besides a; δ: exploration-utilization balance coefficient (initially 0.9, gradually decreasing to 0.7 with the increase of learning rounds, encouraging exploration of adaptation actions in the early stage, and strengthening the optimal action in the later stage).

[0084] The above-described implementation scheme achieves precise matching of training content with trainees' real-time learning status and skill gaps, effectively addressing the core pain points of traditional elderly care training—namely, "rigid content and a one-size-fits-all approach"—and improving the relevance and efficiency of trainees' learning. This helps trainees quickly solidify their weak skills and deepen their understanding of professional knowledge and practical procedures. Specifically, the training content is dynamically optimized for trainees with different backgrounds and weaknesses, preventing trainees with weak foundations from feeling frustrated due to overly difficult content, and also preventing experienced trainees from wasting learning resources due to overly superficial content.

[0085] Furthermore, by expanding the multi-factor state space, the model can comprehensively capture the specificities of elderly care training scenarios, fully considering key factors such as dynamic changes in job requirements and emergency skill priorities, thus avoiding adjustment biases caused by information gaps. By designing an improved reinforcement learning formula with multi-dimensional coefficient coupling, action selection is made more aligned with the core needs of elderly care training, improving the accuracy of dynamic adjustments. A cross-module collaboration mechanism enables timely linkage of risk prediction results, proactively mitigating practical safety hazards and strengthening trainees' awareness of standardized operations. Optimization of the dynamic reward function guides trainees to prioritize mastering core job skills and emergency skills, shortening the adaptation period from training to on-the-job training. Ultimately, the entire technical solution achieves scenario adaptability and scientific decision-making in elderly care training, significantly improving training quality and job suitability.

[0086] Furthermore, in one embodiment, the management backend module also includes a job matching submodule, used to intelligently match trainees' training outcome data with the job demand data of partner elderly care institutions, and generate a job recommendation list. This constructs a precise mapping system between "trainee training outcomes and job demands." The job matching submodule achieves efficient integration of training and employment through multi-source data integration, feature alignment, and intelligent matching algorithms. The module employs a four-step process of "data standardization - feature extraction - multi-dimensional matching - priority ranking," communicating in real-time with the personalized training generation module, the AI ​​practical assessment module, and the partner elderly care institution management system to ensure data timeliness and matching accuracy.

[0087] The core data consists of: 1. Trainee training outcome data (input end): includes AI practical assessment comprehensive score, the level of achievement of each core skill (such as pressure ulcer care, dementia care and 20 other sub-skills), emergency response capability rating, training attendance rate, theoretical examination scores, list of virtual simulation practical achievement scenarios and training qualification certificate level.

[0088] 2. Job Requirements Data (Input): Through standardized API integration with partner elderly care institution systems, data is collected on job types (home care / institutional care / dementia care, etc.), the distribution of service recipients' health status (percentage of severely disabled / disabled / semi-disabled / healthy self-care), core skill requirement thresholds, emergency response level requirements, working hours, salary range, and regional preferences; 3. Intermediate Processing Data: Trainee skill profiles and job requirements profiles are constructed, and feature dimensions (such as skill item coding and health level coding) are aligned using unified coding rules.

[0089] To help those skilled in the art better understand the technical solutions of the embodiments of the present invention, a specific embodiment is described below: Trainee Zhang's training results are: a comprehensive practical score of 92 points, with "turning over and caring for disabled elderly" and "airway clearing" achieving an A level in core skills, an emergency response rating of level two, and holding an intermediate elderly care certificate. The cooperative institutions have three target positions: Institution A, a disabled care position (core skill requirements: turning over and caring for disabled elderly A level, emergency response level two, 70% of the elderly being severely disabled), Institution B, a home care position (core skills: medication guidance A level, communication suitability ≥ 85 points), and Institution C, a dementia care position (core skill: dementia communication A level).

[0090] Job matching submodule workflow: ① Data standardization: Convert Zhang's skill level and assessment score into 0-1 quantitative values, and simultaneously quantify the job requirement threshold of Institution A; ② Feature matching: Use cosine similarity to calculate the core skill suitability. Zhang's skill suitability for the disability care position at Institution A is 0.92, with Institution B it is 0.75, and with Institution C it is 0.68; ③ Priority ranking: Combine the urgency of job requirements (Institution A urgently needs 2 disability care workers, urgency coefficient 1.2) and the geographical matching degree (Zhang's residence is 3 kilometers away from Institution A, geographical coefficient 1.1) to generate a recommendation list: Institution A (matching score 1.01) > Institution B (0.75) > Institution C (0.68). The list marks the core requirement matching points and salary range for each position.

[0091] Furthermore, in one embodiment, to improve adaptability to dynamic changes in supply and demand and the specific characteristics of scenarios in the elderly care industry, it may also include units such as "dynamic demand perception and updating," "multi-factor weighted matching," and "matching result verification and feedback." Further, by designing an improved comprehensive calculation model for job matching degree, the adaptability to dynamic changes in supply and demand and the specific characteristics of scenarios in the elderly care industry is enhanced. Specific details are as follows: ① Dynamic Demand Awareness: Real-time monitoring of changes in job demand data from partner institutions via edge computing. When a shortage of more than 3 people for a certain type of position or a change in the health status distribution of service recipients exceeding 20%, automatic priority re-ranking of matching is triggered. ② Multi-Dimensional Weighted Matching: In addition to core skills, new dimensions such as "employment stability," "service fit preference," and "institutional culture fit" are added. Employment stability is quantified by the trainee's years of service and past attrition records. Service fit preference is derived from high-frequency practical scenarios during trainees' training (e.g., priority is given to matching corresponding positions if there is a preference for disability care scenarios). ③ Feedback Verification Mechanism: Connecting institutional recruitment results with trainee job fit data, "successful onboarding rate after matching" and "fitness score one month after onboarding" are used as feedback signals to optimize matching algorithm parameters.

[0092] Specifically, the job matching comprehensive calculation model accurately quantifies the suitability value between trainees and positions by designing a multi-factor coupled matching calculation model. The specific formula for comprehensive matching calculation is as follows: M = (α × M_skill + β × M_emergency + γ × M_stability) × δ × M_demand × ε × M_geo, where the parameters are explained as follows: M: Overall match between trainee and position (range 0-1.5, higher score indicates stronger fit, ≥1.0 indicates high fit); M_skill: Core skill fit (0-1), calculated by weighted summation of the similarity between the trainee's skill attainment level and the position's requirement threshold, with the weight being the proportion of each skill required by the position; α: Skill fit weight (core coefficient, fixed at 0.5, skill matching is a core prerequisite for elderly care positions); M_emergency: Emergency response capability fit (0-1), the match value between the trainee's emergency handling rating and the position's emergency requirements, such as 1.0 for level 2 emergency response and 0.6 for level 3; β: Emergency fit weight (0.25, elderly care emergency scenarios occur frequently, so it has a higher priority); M_stability: Job stability fit (0-1), calculated as 1-0. 0.3 × number of churns + 0.1 × ln(years of service + 1), range 0.7-1.0; γ: stability weight (0.15, to reduce the organization's recruitment and training costs); δ: job demand urgency coefficient (1.0-1.3, 1.3 when the shortage is ≥3 people, 1.1 when 1-2 people, 1.0 when there is no shortage); M_demand: job demand and student preference fit (0.9-1.1, 1.1 when student preference and job service type are consistent, 0.9 when inconsistent); ε: geographical fit coefficient (0.9-1.2, 1.2 when distance ≤5 km, 1.0 when 5-10 km, 0.9 when >10 km); M_geo: geographical convenience fit (0-1, calculated by combining student commuting mode and job location, 1.0 for direct public transportation, 0.7 for more than 2 transfers).

[0093] The embodiments provided by this invention effectively address the core pain points of subjective, inefficient, and supply-demand mismatched talent-job matching in the traditional elderly care industry through standardized data integration and intelligent matching algorithms. Compared to manual screening, matching efficiency is improved by over 60%, and the accuracy rate is increased by 50%. The generated recommendation list clearly indicates the core matching criteria, reducing the information gap between job seekers and recruitment agencies. In these embodiments, multi-dimensional feature matching ensures a high degree of fit between job requirements and job skills, reducing the adaptation period for job seekers and improving the stability of recruitment agencies.

[0094] Furthermore, a dynamic demand perception mechanism enables matching results to quickly respond to changes in institutional positions, avoiding the problem of "matching lag." By incorporating core factors specific to the elderly care scenario, such as emergency priorities and service recipient suitability, into the improved matching formula, the success rate of highly suitable positions is increased by 40% compared to traditional single-skill matching. A feedback verification mechanism enables continuous iterative optimization of the matching algorithm, forming a closed loop of "training-matching-employment-feedback-optimization." Geographical adaptation and preference matching dimensions improve trainee job satisfaction and reduce later attrition rates. Ultimately, this technical solution enhances the practicality and scenario adaptability of the solution, opens up key links in the training and employment of elderly care professionals, helps achieve the goal of "training equals matching, graduation equals employment," effectively alleviates the talent supply and demand imbalance in the industry, and promotes the standardization and high-quality development of the elderly care service system.

[0095] Furthermore, in one embodiment, the management backend module also includes a certificate management submodule, which automatically generates training qualification certificates based on the trainees' final assessment results and connects to the National Elderly Care Skills Level Certification System for certificate verification and filing. As a key hub connecting training outcomes and industry certification, the certificate management submodule addresses the pain points of traditional certificate management—"cumbersome processes, difficult verification, and data disconnect"—through its design of "automatic judgment - compliant generation - authoritative verification - full lifecycle management," ensuring seamless alignment between trainees' training outcomes and national vocational skills standards.

[0096] Specifically, certificate generation includes real-time linkage between the certificate management submodule, the AI ​​practical assessment module, and the personalized training generation module. This automatically collects trainees' final assessment data (including comprehensive practical scores, theoretical exam scores, core skills attainment rates, and training duration compliance), and generates corresponding training qualification certificates based on preset judgment rules. Specific judgment criteria can be categorized into basic, intermediate, and advanced training qualification certificates based on a comprehensive assessment of certificate level, comprehensive practical scores, theoretical exam scores, core skills attainment rates, and training duration compliance.

[0097] During the certificate generation process, the system automatically embeds key information such as the trainee's unique identifier (ID number + training number), certificate number (e.g., using an 18-digit encoding rule of "regional code-year-institutional code-serial number", such as "310101-2024-AICN-000001"), training period, and core skills attainment list, and uses PDF encryption technology to prevent tampering, while also generating an electronic watermark (including the system logo and generation time).

[0098] Furthermore, in the process of connecting and verifying with the national certification system, the sub-module achieves two-way data interaction through the standardized API interface opened by the National Elderly Care Skills Level Certification System. The specific process is as follows: 1. Data pre-verification: The system automatically extracts the core data of trainees' training results (name, ID number, training qualification certificate level, core skills score) and pre-matches it with the trainees' basic information in the national certification system (such as previous certification records and identity verification results). If there is any inconsistency in identity information or duplicate certification applications, an alert will be triggered immediately and the process will be suspended until manual verification is performed before it can be re-initiated.

[0099] 2. Certificate Filing Submission: After the pre-verification is passed, the system will package and encrypt the electronic file of the training certificate and the original assessment data (including the index of practical video clips and scanned copies of the theoretical examination answer sheets) according to the data format requirements of the national certification system, and upload them at the same time. A filing application will be submitted, and a unique filing application number will be generated for subsequent inquiries.

[0100] 3. Authoritative verification feedback: After the national certification system completes the verification, it will send the verification results (pass / fail / requires supplementary materials) and verification opinions back to this system in real time. If the verification is successful, the system will automatically link the national certification number to the trainee's training file and update the certificate status to "nationally registered and valid". If supplementary materials are required, the system will generate a list of materials and push it to the trainee's mobile device, reminding them to submit them within the specified time.

[0101] 4. Certificate Inquiry and Verification: Partnering elderly care institutions and regulatory authorities can use the publicly accessible inquiry portal provided by the system to enter the certificate number or the trainee's ID number to check the authenticity of the certificate, its registration status, and the achievement of core skills standards. The inquiry results will simultaneously display the verification mark of the national certification system to ensure its authority.

[0102] Furthermore, it also includes certificate lifecycle management functions as follows: 1. Expiration Warning and Renewal Reminder: For training certificates with expiration dates (such as some specialized skill certificates with a validity period of 2 years), the system uses the LSTM time series algorithm to automatically trigger a warning 3 months before the certificate expires, push a renewal notification to the student's mobile device, and simultaneously generate a personalized renewal plan (focusing on the skill update content corresponding to the expired certificate).

[0103] 2. Certificate Replacement and Change Management: When a student applies for a replacement certificate due to loss or information change (such as name correction), the system verifies the student's identity information and training file, generates a replacement application form, and after the administrator approves it, regenerates the electronic certificate and updates the national certification system filing information. For paper certificate replacement, a third-party printing agency is used to mail the replacement certificate.

[0104] 3. Certificate Data Statistics and Analysis: The submodule regularly generates certificate statistics reports, covering dimensions such as the number of certificates issued at each level, the national registration pass rate, regional distribution, and industry institution recognition. At the same time, it displays the certificate acquisition rate corresponding to each core skill through heat maps, providing data support for training content optimization and job demand prediction.

[0105] Furthermore, in one embodiment, the system also includes a mobile application module, which supports trainees in accessing personalized training programs, submitting practical training videos, and viewing evaluation reports via smartphones and tablets. The mobile application module synchronizes data with the management backend module in real time. Specifically, the mobile application module, as the core carrier for extending system services, is developed based on both iOS and Android platforms, adopting a design principle of "lightweight interaction + high-frequency function priority" to achieve mobile management of the entire trainee training process. This solves the problems of "limited scenarios and delayed data synchronization" in traditional training. The module's functions and the management backend module achieve millisecond-level data synchronization via WebSocket technology, ensuring real-time information delivery.

[0106] The specific explanation is as follows: 1. Personalized training program viewing and learning, including a visual display of the program: After logging in, students can intuitively view their personalized training programs on the homepage, categorized into four modules: "Theoretical Courses," "Virtual Practice," "Case Studies," and "Q&A Interaction." The remaining class hours, learning progress, and deadline for each module are indicated, and students can view the programs by priority and time. Fragmented learning support: The theoretical course module provides video micro-lessons (supporting accelerated playback and offline download) and interactive courseware (including in-class quizzes, with results synchronized to the management backend in real time). The virtual practice module provides videos of key points and standard action breakdown diagrams for VR training that cannot be completed on mobile devices, and also supports booking offline VR training venues and times. Learning progress tracking: The system automatically records student learning behavior data (such as video viewing time, courseware completion rate, and test accuracy), generating daily / weekly learning reports. Progress bars and percentages visually display the completion status of each module, with unmet learning tasks highlighted in red.

[0107] 2. Practical Training Video Submission and Feedback. This includes multi-scenario video capture: trainees can record practical training videos using their mobile cameras in actual nursing scenarios (such as home internships and institutional training) (supporting 1080P HD recording and picture-in-picture explanation mode). During recording, the system provides prompts on key operational points (such as "pay attention to the elderly person's breathing status when turning them over and patting their back") to avoid missing crucial steps. Intelligent Video Preprocessing: Before video submission, the system automatically performs format conversion (unified to MP4 format), duration trimming (single submission video duration limited to 5-15 minutes, focusing on core operational steps), and watermark addition (including trainee number and recording time). Simultaneously, edge computing is used to initially detect video clarity and image stability; videos that do not meet the requirements are prompted to be re-recorded. Real-time feedback and evaluation: After the video is submitted, the system pushes it to the AI ​​practical evaluation module in real time. After the evaluation is completed (average evaluation time ≤ 10 minutes), the evaluation result notification is pushed to the mobile device in real time. Trainees can view the scores of each dimension, operation defect annotations (such as the segment of "insufficient pressing depth" circled in the video) and improvement suggestions. They can also provide feedback on the evaluation results and have the trainer answer them online.

[0108] 3. Assessment Report and Training File Management, including Assessment Report Visualization: The system converts AI practical assessment reports and phased training assessment reports into mobile-friendly graphic formats. Key data (such as overall scores, weak skills, and rankings) are displayed intuitively through charts (bar charts, radar charts), supporting report downloads and sharing to WeChat / email. Complete Training File Records: The mobile app stores complete training files for trainees, including personal basic data, pre-assessment results, learning data at each stage, assessment reports, and electronic training certificates. Historical records can be queried by time, and file data is synchronized with the management backend in real time to ensure consistency. Certificate Inquiry and Display: Trainees can view their acquired training certificates and national registration status in the "My Certificates" section. The system supports generating electronic certificate display cards (including QR codes for verification by institutions), facilitating quick presentation during job applications.

[0109] 4. Interactive Communication and Message Notifications, including: Multi-channel interactive functions: Integrating three major interactive modules: "AI Intelligent Q&A," "Online Instructor Consultation," and "Student Community." AI Intelligent Q&A supports text and voice questions, returning accurate answers within 10 seconds; Instructor Consultation supports scheduled time slots and real-time video connections; The Student Community allows users to publish learning experiences and practical insights, supporting likes, comments, and favorites. Intelligent Message Push: The system pushes personalized messages based on student learning status, training progress, and changes in job requirements, including learning task reminders (such as "2 hours of virtual practical training required today"), evaluation result notifications, continued training warnings, and job recommendation information. Messages can be categorized by "importance" to avoid information overload.

[0110] Furthermore, the module prioritizes data security and privacy protection in terms of technical safeguards and user experience optimization: end-to-end encryption technology (TLS 1.3 protocol for transmission, AES-256 encryption for storage) is employed to protect sensitive information such as trainees' personal data and practical videos; it strictly adheres to the Personal Information Protection Law, clearly defining the scope and purpose of data collection, and trainees can independently manage data authorization permissions (such as whether to allow institutions to view training files). It also supports low-network environment adaptation: addressing the issue of weak network signals in some elderly care institutions and home settings, the module supports offline mode, allowing trainees to download course videos and materials for offline learning, with automatic synchronization of learning data once the network is restored; video submission supports breakpoint resumption to avoid duplicate uploads due to network interruptions. Finally, it features an "elderly-friendly design": considering that some trainees are older and less familiar with smartphone operation, the module provides a "senior mode" that supports enlarged fonts (up to 24-point font), voice navigation (full voice prompts for operation steps), and a simplified interface (reducing redundant function entry points, with one-click access to core functions), lowering the barrier to entry.

[0111] Furthermore, the system integrates seamlessly with other modules, including the management backend module: Real-time data synchronization is achieved via a RESTful API interface. Practical videos and learning data submitted from the mobile app are instantly uploaded to the management backend. Adjustments to training plans and updates to evaluation results from the management backend are also pushed to the mobile app, ensuring information symmetry between trainees and administrators. The job matching submodule generates a new list of recommended jobs, which is pushed to the trainee's mobile app in real time. Trainees can view job details (including institution introduction, salary, and skill requirements) and submit job applications directly through the mobile app. Application status (viewed, applied, interview notification) is updated synchronously in the management backend. Finally, the certificate management submodule generates training qualification certificates and national registration results, which are instantly synchronized to the "My Certificates" section on the mobile app. Certificate expiration warnings and renewal reminders are also pushed via mobile app notifications, ensuring trainees receive timely certificate-related information.

[0112] Based on the embodiments provided by the present invention described above, the specific implementation process of the technical solution will be further illustrated below through a complete exemplary application scheme, "Practical Application of the System in Specialized Training for the Care of Disabled Elderly": Application Background: A provincial-level elderly care service group plans to conduct "Specialized Training for the Care of Disabled Elderly," targeting 150 existing caregivers (including 30% new trainees, 50% with 1-3 years of experience, and 20% with more than 3 years of experience). The needs of the cooperating elderly care institutions are concentrated on three core skills: "turning over and caring for disabled elderly," "pressure ulcer prevention and care," and "airway clearing." The training requires that the trainees achieve a practical proficiency rate of ≥90% and a job suitability rate of ≥85% after the training.

[0113] 1. Data Collection and Initial Analysis (1 week) 1) Data Collection Implementation: Through the data collection module, a combination of "online filling + offline assessment" is adopted to collect trainees' basic personal data (education level: junior high school and below 40%, high school / technical secondary school 50%, junior college and above 10%; years of work experience: less than 1 year 30%, 1-3 years 50%, more than 3 years 20%), pre-test skills assessment data (average theoretical test score of 58, average practical test score of 62, average emergency response simulation score of 55), and job requirement-related data (the average proportion of disabled elderly in partner institutions is 75%, and the core skills requirement weights are: turning over care 0.35, pressure ulcer care 0.3, airway clearing 0.25, and others 0.1).

[0114] 2) AI Intelligent Analysis: The AI ​​intelligent analysis module uses a collaborative filtering algorithm to cluster similar trainees (e.g., novice trainees generally have the weakness of "unfamiliarity with emergency procedures"). It constructs a trainee competency profile through the GBDT algorithm, and extracts job requirement features by combining the CNN algorithm. It calculates the matching degree difference and determines the core weaknesses: novice trainees mainly have "non-standard basic practical actions" (difference 0.32), while experienced trainees mainly have "emergency handling in complex scenarios" (difference 0.28).

[0115] 2. Implementation of personalized training program (8 weeks) 1) Program Generation and Allocation: Based on the analysis results, the personalized training generation module allocates differentiated programs to different trainees: For novice trainees, theoretical courses account for 35% (focusing on learning the physiological characteristics of disabled elderly and basic operation procedures), virtual simulation practice accounts for 45% (focusing on turning over and basic pressure ulcer care movements), case studies account for 15%, and Q&A accounts for 5%; For experienced trainees, theoretical courses account for 25% (focusing on learning the management of complex complications), virtual simulation practice accounts for 40% (adding airway clearance and emergency scenario training), case studies account for 25%, and Q&A accounts for 10%.

[0116] 2) Mobile learning support: Students can view the solution through the mobile application module. Novice students can download basic operation videos offline and learn in their spare time. Experienced students can book offline VR training venues through the mobile app, complete high-difficulty emergency scenario simulations (such as airway clearing for a disabled elderly person who suddenly coughs), and submit the practical operation videos to the AI ​​practical operation assessment module.

[0117] 3. Practical assessment and dynamic adjustment (once every 2 weeks) 1) Multi-dimensional assessment implementation: The AI ​​practical assessment module uses an image acquisition unit (multi-camera shooting of the turning and nursing process), a motion capture unit (collecting limb 3D coordinates to determine whether the turning angle meets the standard of 30°-45°), and a voice interaction unit (collecting communication scripts) to assess from four dimensions: standardization of actions, completeness of process, emergency response speed, and communication adaptability. Novice trainees initially scored an average of 65 points in standardization of actions, while experienced trainees scored an average of 70 points in emergency response speed.

[0118] 2) Dynamic adjustment of the plan: The personalized training generation module adjusts the plan based on the evaluation results and through reinforcement learning algorithms: If the standardization of the movements of novice trainees is not up to standard, a "movement decomposition training" VR course is added to increase the proportion of virtual practice to 50%; If the emergency response speed of experienced trainees is insufficient, three high-frequency emergency case studies are inserted to increase the proportion of case studies to 30%.

[0119] 4. Training outcomes application and job matching (2 weeks) 1) Certificate generation and registration: After the training, the AI ​​practical assessment module gave the final assessment results. The overall pass rate of 150 trainees was 94% (141 people passed the standard). The certificate management sub-module automatically generated the corresponding level training qualification certificates, including 102 intermediate certificates and 39 advanced certificates. The certificates were also registered with the National Elderly Care Skills Level Certification System with a 100% registration pass rate.

[0120] 2) Job Matching and Recommendation: The job matching submodule matches trainees’ training results (such as advanced certificate trainees’ turning care and airway clearing skills reaching level A) with the job requirements of partner institutions and generates a recommendation list. Of the 141 qualified trainees, 121 received job recommendations through mobile devices, and 115 of them were successfully hired, with a job fit rate of 95%, far exceeding the expected target.

[0121] By applying the system to specialized training in the care of disabled elderly, three core objectives can be achieved: First, the accuracy of training is significantly improved, with the pass rate of trainees' core skills increasing from the initial 60% to 94%; second, training efficiency is greatly optimized, with the training cycle shortened by 30% compared to the traditional model, and time costs reduced through fragmented learning on mobile devices; and third, employment connection efficiency is improved, with a job matching rate of 95%, effectively alleviating the shortage of disabled care personnel in partner institutions, fully validating the practicality and value of the system in the training and management of elderly care workers.

[0122] In summary, the embodiments of the present invention have the following technical advantages: 1. Multimodal data fusion and deep AI empowerment: The system breaks through the limitations of traditional training's "single data collection and manual analysis". Through the data collection module, it realizes multimodal collection of personal basic data, practical behavior data and job requirement data. Combined with the AI ​​intelligent analysis module, it integrates multiple algorithms such as collaborative filtering, GBDT, and CNN to build accurate profiles of trainees' abilities and job requirements. The accuracy of matching degree calculation is more than 40% higher than that of traditional methods.

[0123] 2. Dynamic closed-loop management throughout the entire process: From data collection, solution generation, practical evaluation to job matching, the system forms a closed loop of "analysis-execution-evaluation-optimization". The personalized training generation module dynamically adjusts the content based on real-time evaluation results, and the management backend module monitors the progress in real time, avoiding the defects of traditional training of "one-time solution without dynamic optimization", and improving the efficiency of training effect iteration by 50%.

[0124] 3. Multi-terminal collaboration and scenario adaptation: The system integrates PC-based management backend, mobile application, and VR virtual practice equipment, covering the entire scenario of "online learning - offline training - mobile office". The offline learning and elderly mode design on the mobile terminal are adapted to the characteristics of the elderly care industry, such as the dispersed personnel and the weak operation ability of some trainees; VR virtual practice solves the pain points of "scarce scenarios and high risks" of traditional practice, and the practice training coverage scenarios are 3 times more than the traditional mode.

[0125] In summary, the AI-based elderly care worker training management system provided by this invention, through the collaborative work of the aforementioned core modules, achieves precise, intelligent, and standardized training for elderly care workers, effectively solving core problems in traditional training models such as content homogenization, subjective assessment, and supply-demand mismatch. Technically, the system integrates multimodal data acquisition, multi-algorithm AI analysis, and cross-terminal collaboration; in application, it focuses on the personalized needs of trainees and the actual requirements of the job, forming a complete value chain of "training-assessment-employment," providing a practical and replicable solution for the cultivation of elderly care nursing talent.

[0126] While numerous embodiments of the invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of the invention. The appended claims are intended to define the scope of protection of the invention and therefore cover equivalents or alternatives within the scope of these claims.

Claims

1. An artificial intelligence-based training and management system for elderly care workers, characterized in that, include: The data acquisition module is used to collect basic personal data, pre-test skills assessment data, and job requirement-related data from elderly care worker trainees. The AI ​​intelligent analysis module is connected in communication with the data acquisition module and is used to perform feature extraction, demand matching and capability deficiency analysis on the acquired data based on artificial intelligence algorithms. The personalized training generation module communicates with the AI ​​intelligent analysis module and is used to generate personalized training programs that are adapted to the trainees' ability level and job requirements based on the analysis results. The AI ​​practical assessment module is used to collect behavioral data, action data and interaction data of trainees during practical training, and to assess the degree of achievement of practical skills based on artificial intelligence technology. The management backend module communicates with the personalized training generation module and the AI ​​practical assessment module respectively, and is used to realize training progress monitoring, assessment result statistics and dynamic adjustment of training plan.

2. The system according to claim 1, characterized in that, The personal basic data includes educational background, years of work experience, and previous training experience. The pre-employment skills assessment data includes theoretical knowledge test scores, basic practical skills scores, and emergency response simulation scores. The job requirement-related data includes the nursing service type of the target employment institution, the health status distribution of service recipients, and core skills requirements.

3. The system according to claim 1, characterized in that, The AI ​​intelligent analysis module employs artificial intelligence algorithms including collaborative filtering, gradient boosting tree, and convolutional neural network algorithms from machine learning. These algorithms are used to construct trainee competency profiles and job requirement profiles, and to calculate the difference in matching degree between the two to identify competency gaps.

4. The system according to claim 3, characterized in that, The personalized training program includes tiered theoretical courses, immersive virtual simulation practical training, targeted case studies, and real-time interactive Q&A modules. The time allocation for each module is dynamically determined by the AI ​​intelligent analysis module based on the learner's skill gaps.

5. The system according to any one of claims 1, characterized in that, The AI ​​practical assessment module includes an image acquisition unit, a motion capture unit, and a voice interaction unit. The image acquisition unit is used to collect visual image data during the student's practical operation. The motion capture unit is used to obtain three-dimensional coordinate data of the student's limb movements. The voice interaction unit is used to collect voice data of communication between the student and the simulated service object.

6. The system according to any one of claims 5, characterized in that, The AI ​​practical assessment module uses the collected data to conduct a quantitative assessment from four dimensions: action standardization, operation process completeness, emergency response speed, and communication script suitability, and generates an assessment report containing improvement suggestions.

7. The system according to any one of claims 1, characterized in that, The personalized training generation module is also used to dynamically adjust the difficulty, order, and presentation format of the training content based on the evaluation results of the AI ​​practical assessment module and the real-time learning data of the trainees, using reinforcement learning algorithms.

8. The system according to any one of claims 1, characterized in that, The management backend module also includes a job matching submodule, which is used to intelligently match the training results data of trainees with the job demand data of cooperative elderly care institutions and generate a job recommendation list.

9. The system according to any one of claims 1, characterized in that, The management backend module also includes a certificate management sub-module, which is used to automatically generate training qualification certificates based on the trainees' final assessment results and connect to the National Elderly Care Skill Level Certification System for certificate verification and filing.

10. The system according to any one of claims 1, characterized in that, The system also includes a mobile application module, which allows trainees to access personalized training programs, submit practical training videos, and view evaluation reports via smartphones and tablets. The mobile application module synchronizes data with the management backend module in real time.

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