Household service auxiliary training system based on AI

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a housekeeping service auxiliary training system based on AI. According to the AI-based household service auxiliary training system, detailed user archives are established through the user registration management module, the training course management module provides diversified course selection and records registration conditions, the online learning module supports a flexible learning mode and tracks the progress of the user in real time, and the practical operation training module ensures that the user obtains practical experience. The intelligent evaluation module comprehensively evaluates learning and practical operation capabilities by using an AI technology, and finally generates a visual report and optimizes training content through the data feedback module, so that the system not only improves professional accomplishment and practical operation capabilities of service personnel, but also realizes personalized learning guidance, and improves the training efficiency. Therefore, the professionality and the service quality of the housekeeping service are improved, the requirements of consumers for high-quality housekeeping service are met, and the customer satisfaction is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence, and specifically to an AI-based auxiliary training system for domestic service. Background Art

[0002] Domestic service refers to various life services provided for families, including but not limited to cleaning, cooking, laundry, taking care of the elderly and children, etc. With the development of social economy and the acceleration of people's life rhythm, more families begin to rely on professional domestic services to improve the quality of life and save time. Domestic service not only meets the social demand for high-quality life, but also provides employment opportunities for many job seekers. A solid domestic service system can effectively improve the quality of family life, promote family harmony, and thus promote the overall development of society.

[0003] Although the importance of domestic service is becoming increasingly prominent, there are problems in the industry such as imperfect training systems, uneven quality of service personnel, and difficulty in quantifying training effects. This affects the professionalism and service quality of domestic service, and then leads to a decline in consumer satisfaction. Traditional training methods often rely on offline teaching, lack interaction and personalized guidance, and are difficult to meet the needs of different users. Therefore, how to build an efficient and systematic domestic service training platform to improve the professional quality and practical operation ability of service personnel has become a technical problem to be solved urgently. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] In view of the deficiencies of the prior art, the present invention provides an AI-based auxiliary training system for domestic service. By establishing a detailed user profile through the user registration and management module, the training course management module provides diverse course selections and records the registration situation, the online learning module supports flexible learning methods and tracks the user progress in real time, the practical training module ensures that users obtain practical experience, the intelligent evaluation module uses AI technology to comprehensively evaluate the learning and practical operation abilities, and finally generates a visual report and optimizes the training content through the data feedback module. This system not only improves the professional quality and practical operation ability of service personnel, but also realizes personalized learning guidance, thereby improving the professionalism and service quality of domestic service, meeting the needs of consumers for high-quality domestic service, significantly enhancing customer satisfaction, and solving the above problems.

[0006] (II) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solution: An AI-based auxiliary training system for domestic service, including a user registration and management module, a training course management module, an online learning module, a practical training module, an intelligent evaluation module, and a data feedback module;

[0008] The user registration management module is used to receive and store the registration information of domestic service personnel, including name, contact information, and work experience, and generate user profiles. A unique user ID is generated based on the registration information input by the user for data management and permission settings;

[0009] The training course management module is used to provide a list of training courses, including course content, duration, and assessment criteria, and record the registration status of users. After the user registers, the permission for the user to select suitable courses and register is opened, and the number of registered students for the courses is counted and the course completion rate is calculated to evaluate the popularity and effectiveness of the courses;

[0010] The online learning module is used to support users in learning the selected courses online, including videos, documents, and learning tests, and to record the learning progress, exam scores, and course completion status of users in real time. Based on the learning behavior data of users, learning reports are generated regularly, and the learning activity of users, the average value of user test scores, and the standard deviation of user test scores are calculated to evaluate the learning effect of users;

[0011] The practical training module is used to organize offline practical training, including actual operation demonstrations and assessments, record the participation status and assessment scores of users. After the user completes online learning, arrange practical training for the user, and calculate the passing rate and average score of the user's practical assessment to evaluate the practical ability of the user in domestic service;

[0012] The intelligent evaluation module is used to comprehensively evaluate the learning effect and practical ability of users using AI. After the user completes online learning and practical training, analyze their learning scores, practical assessment scores, and user feedback, calculate the comprehensive ability score of the user in domestic service, and put forward personalized development suggestions to guide the subsequent learning direction of users;

[0013] The data feedback module is used to summarize and analyze the data collected by each module, generate a visual report, and send the report results to users and managers via text messages. At the same time, the satisfaction index of all users is calculated to optimize the content of domestic service training.

[0014] Preferably, the formula for generating the unique user ID is as follows:

[0015] ADID = Hash(P1 + P2 + P3)

[0016] In the formula, ADID represents the unique ID of the user, Hash(*) represents the hash function, which is used to convert the input parameters into a fixed-length string, P1 represents the user's name, P2 represents the timestamp at the time of user registration, and P3 represents a random number automatically generated by the system.

[0017] Preferably, the calculation formula for the course completion rate is as follows:

[0018]

[0019] In the formula, Kcwc represents the course completion rate, N c represents the number of users who have completed the course study, N r represents the total number of users who have selected and signed up for this course.

[0020] Preferably, the calculation formula for the user learning activity is as follows:

[0021]

[0022] In the formula, Yhhy represents the user learning activity, T l represents the total actual learning duration of the user, T max represents the maximum learning duration.

[0023] Preferably, the calculation formula for the average value of the user test scores is as follows:

[0024]

[0025] In the formula, Pt avg represents the average value of the user test scores, S g represents the sum of all user test scores, N t represents the total number of users participating in the test.

[0026] Preferably, the calculation formula for the standard deviation of the user test scores is as follows:

[0027]

[0028] In the formula, Bz represents the standard deviation of the user test scores, S d represents the sum of the squares of the deviations of each score from the average score, N t represents the total number of users participating in the test.

[0029] Preferably, the calculation formula for the passing rate of the user practical assessment is as follows:

[0030]

[0031] In the formula, Sckh represents the passing rate of the user practical assessment, Tk represents the number of users who have reached the passing standard in the practical assessment, and Pn represents the total number of users participating in the practical assessment.

[0032] Preferably, the calculation formula for the average score of the user practical assessment is as follows:

[0033]

[0034] In the formula, Sckt represents the average score of the user's practical assessment, S s represents the total score of all users' practical assessment results, and M represents the total number of users participating in the practical assessment.

[0035] Preferably, the calculation formula for the comprehensive ability score of the user's household service is as follows:

[0036]

[0037] In the formula, Zhpf represents the comprehensive ability score of the user's household service, L s represents the average test score of the user's online learning, Sckt represents the average score of the user's practical assessment, F r represents the overall satisfaction score of the user with the courses and training, W l represents the importance weight of the learning achievement, W s represents the importance weight of the practical achievement, W f represents the importance weight of the user feedback score, and the weight value is automatically assigned by AI.

[0038] Preferably, the calculation formula for the satisfaction index of the overall users is as follows:

[0039]

[0040] In the formula, Mx represents the satisfaction index of the overall users, K f represents the total score of all users' scores for the courses and training, N u represents the number of users participating in the evaluation.

[0041] Compared with the prior art, the present invention provides an AI-based auxiliary training system for household services, which has the following beneficial effects:

[0042] The present invention establishes a detailed user profile through the user registration management module, the training course management module provides diverse course selections and records the registration situation, the online learning module supports flexible learning methods and real-time tracks the user's progress, the practical training module ensures that users obtain practical experience, the intelligent evaluation module uses AI technology to comprehensively evaluate the learning and practical abilities, and finally generates a visual report and optimizes the training content through the data feedback module. This system not only improves the professional quality and practical ability of service personnel, but also realizes personalized learning guidance, thereby improving the professionalism and service quality of household services, meeting the needs of consumers for high-quality household services, and significantly enhancing customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1This is a schematic diagram of the system process of the present invention. Specific embodiments

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] Regarding the problem of how to build an efficient and systematic domestic service training platform to improve the professional qualities and practical abilities of service personnel, a domestic service auxiliary training system based on AI is proposed. Please refer to Figure 1 , this system includes a user registration management module, a training course management module, an online learning module, a practical training module, an intelligent evaluation module, and a data feedback module;

[0046] The user registration management module adopts a modern technical architecture and is responsible for receiving and storing the registration information of domestic service personnel, specifically including core data such as name, contact information, and work experience. This module uses a front-end framework (such as React or Vue.js) to provide a friendly user interface to ensure a smooth experience for users during the registration process. After the user submits the registration information, the back-end service (such as using Node.js or Python Flask) will verify and clean the received data, including format checks and prompts for missing fields;

[0047] To ensure the uniqueness of user identities and the security of the system, the module will use an encryption algorithm to generate a unique user ID. This ID is obtained through a hash algorithm by combining the user's name, the current timestamp, and a generated random number, which not only protects the privacy of user data but also provides an effective identifier for subsequent data management and permission settings. At the same time, the module also uses a database (such as MySQL or MongoDB) for data storage to ensure the persistence and security of user information. To further enhance the functions of the system, this module can be integrated with subsequent training and assessment platforms to support recording learning progress and assessment results in user profiles, thereby achieving more comprehensive user management and service optimization;

[0048] Among them, the formula for generating a unique user ID is as follows:

[0049] ADID = Hash(P1 + P2 + P3)

[0050] Generating a unique user ID can ensure that each domestic service worker has a unique identity in the system, avoiding data confusion and duplication. This mechanism helps with subsequent data management, such as querying and updating personal information. In the formula, AdID represents the user's unique ID, Hash(*) represents the hash function used to convert the input parameters into a fixed-length string, P1 represents the user's name, P2 represents the timestamp at the time of user registration, and P3 represents a randomly generated number by the system. By using the hash algorithm to generate the user ID, the user's sensitive information (such as name, contact information, etc.) is not stored in plain text, thus improving data security and preventing information leakage. At the same time, the unique user ID provides the necessary support for permission settings within the system. Based on this ID, the system can precisely control user access permissions to ensure that sensitive data is not obtained by unauthorized users;

[0051] The training course management module is a comprehensive system designed to provide a detailed list of training courses, including important information such as course content, duration, and assessment criteria. This module uses modern front-end technology stacks (such as React or Angular) to build a user-friendly interface, enabling users to have a smooth experience when browsing and selecting courses. After the user completes registration, the system immediately opens the permission for the user to select suitable courses. Users can conveniently view course details, compare the features of different courses, and thus make informed choices;

[0052] To improve management efficiency, the training course management module is equipped with powerful back-end support (such as using the Node.js or Django framework), interacts with the front-end through RESTful APIs, and records the user's registration information in real-time into the database (such as MySQL or PostgreSQL). This module also implements an automated data statistics function that can track the number of course registrations in real-time and calculate the completion rate of each course based on the user's registration situation and course completion status. These data not only provide intuitive chart analysis for administrators (by using data visualization libraries such as D3.js or Chart.js), but also help evaluate the popularity and effectiveness of courses, generating reports regularly to optimize course content and teaching methods;

[0053] In addition, the built-in administrator background of the module can analyze the user's registration trends, guiding institutions to adjust training courses according to market demand, increasing or improving course settings to enhance the attractiveness of the courses to the target user group;

[0054] Among them, the calculation formula for the course completion rate is as follows:

[0055]

[0056] The course completion rate is an important indicator to measure the training effect of domestic service training. A high completion rate indicates the attractiveness and effectiveness of the course for users, helping training institutions evaluate the overall quality of the course. In the formula, Kcwc represents the course completion rate, and N c represents the number of users who have completed the course learning, and N r represents the total number of users who have selected and signed up for the course. By analyzing the course completion rate, courses with a low completion rate can be identified, and then these courses can be optimized or reorganized to rationally allocate resources to better meet the learning needs;

[0057] The online learning module is an integrated learning platform designed specifically to support users in learning the selected courses online. It covers various learning forms such as videos, documents, and learning tests to meet different learning needs. The module uses modern front-end technologies (such as React or Vue.js) to build an intuitive user interface. Users can easily browse the course content, select learning materials suitable for themselves. Through the embedded video player and document viewer, learners can conveniently access learning resources to ensure the coherence and efficiency of learning;

[0058] On the back end, the module uses technology stacks such as Node.js or Python Flask to handle the data logic of learning activities. At the same time, it uses databases (such as MongoDB or PostgreSQL) to record the learning progress, exam scores, and course completion status of users in real time. These data not only include the viewing time of each video, the reading situation of documents, but also cover the detailed scores of users participating in tests, which are used to reflect the learning performance of users in various aspects;

[0059] One of the core functions of the module is to regularly generate learning reports by analyzing the learning behavior data of users. The reports contain key indicators such as the learning activity level of users, the average value and standard deviation of test scores. The calculation of the learning activity level is evaluated based on the login frequency, learning duration, and participation of users, helping training institutions understand the learning habits and participation of users. At the same time, the analysis of the average value and standard deviation of exam scores can deeply reveal the performance differences of users in the learning process, providing a basis for personalized learning recommendations and tutoring plans;

[0060] Among them, the calculation formula for the user learning activity level is as follows:

[0061]

[0062] The calculation of the user learning activity level can help the system understand the learning habits and participation frequency of users, which is helpful for providing personalized learning plans, such as recommending suitable courses or learning times. In the formula, Yhhy represents the user learning activity level, and T l represents the total actual learning duration of users, and T maxRepresents the maximum learning duration. Through regular activity reports, it helps users become aware of their learning behaviors, thereby motivating them to develop good learning habits and enhance learning effects;

[0063] The formula for calculating the average value of users' test scores is as follows:

[0064]

[0065] The average value of test scores can reflect the overall performance of a group of users on specific knowledge points, helping training institutions evaluate teaching effects and identify areas for improvement in teaching. In the formula, Pt avg represents the average value of users' test scores, S g represents the sum of all users' test scores, N t represents the total number of users participating in the test. The performance of users around the average score can be used as feedback to help users identify their weaknesses, adjust learning strategies, and make the learning direction more explicit;

[0066] The formula for calculating the standard deviation of users' test scores is as follows:

[0067]

[0068] A large standard deviation indicates a large difference in users' scores. It may be necessary to analyze the differences between high-scoring and low-scoring users in order to make targeted improvements or provide additional support. In the formula, Bz represents the standard deviation of users' test scores, S d represents the sum of the squares of the deviations of each score from the average score, N t represents the total number of users participating in the test. By understanding the standard deviation of users' scores, the system can formulate personalized learning plans or tutoring strategies based on the learning abilities of different users, enabling each user to receive the most suitable teaching support;

[0069] The practical training module aims to improve users' practical abilities through actual operation demonstrations and assessments. The module adopts modern front-end technologies (such as React or Vue.js) and back-end architectures (such as Node.js or Django) to ensure that users have a good experience when browsing training information and viewing assessment results. After users complete the online learning courses, the system will automatically arrange the time and location of practical training according to their learning progress and performance, and send notifications and reminders to users to ensure that they participate in the training according to the plan;

[0070] During actual training, the module can record users' participation in real time, including information such as attendance, hands-on practice, and participation in interactions. The assessment results are evaluated through an automatic grading system, which combines the actual operation performance and the examiner's evaluation to generate accurate scores for each user. These data will be stored in a backend database (such as MySQL or MongoDB) to ensure the security and traceability of information;

[0071] To further analyze the training effect, the module also has the function of calculating the passing rate and average score of users' practical assessments. By statistically analyzing the assessment results of all participating users, the module can provide specific passing rate data to reflect the training effect and course quality. At the same time, calculating the average score of the assessment can help users understand their relative performance and conduct self-assessment and improvement on this basis. The practical assessment scores of users will be used as important indicators to evaluate their practical abilities in domestic services, which not only provides a basis for users' subsequent career development but also provides data support for the course design and optimization of training institutions;

[0072] In addition, the module will generate a detailed practical training report, including assessment results, participation status, and overall performance analysis, and display it on the user and administrator interfaces through visualization tools (such as Chart.js or D3.js) to help both parties intuitively understand the learning outcomes and training effects. This data-driven decision-making model helps improve users' practical abilities while continuously strengthening the pertinence and effectiveness of training courses;

[0073] The intelligent assessment module is built based on artificial intelligence technology and aims to comprehensively evaluate users' learning effects and practical abilities, helping users identify their strengths and weaknesses. After users complete online learning and practical training, the module automatically collects and analyzes users' learning scores, practical assessment scores, and user feedback. These data are deeply analyzed through intelligent algorithms (such as machine learning algorithms) to identify learning patterns and ability characteristics;

[0074] The core of the module lies in its powerful data processing capabilities. Python and data analysis libraries (such as Pandas and NumPy) are used in the backend to statistically analyze users' score data, and scoring models (such as weighted scoring and linear regression) are applied to calculate the comprehensive ability scores of users in domestic services. This score covers multiple dimensions, including knowledge mastery, practical proficiency, and user feedback satisfaction, providing a comprehensive ability assessment for users;

[0075] To enable users to understand how to improve and enhance themselves, the intelligent evaluation module will generate personalized development suggestions based on the comprehensive ability score. These suggestions are automatically generated using natural language processing (NLP) technology to ensure concise and clear expression, while providing users with executable learning direction guidance. The system can also recommend relevant courses, practical training, and other supplementary learning resources to help users improve in weak areas;

[0076] In addition, the module supports presenting the evaluation results to users in the form of visual charts (using libraries such as D3.js or Chart.js), which facilitates users to intuitively understand their learning progress and deficiencies. This design not only enhances users' learning awareness but also provides important feedback information for training institutions, enabling them to optimize course settings and training content, continuously improve the quality of education. Through such an intelligent evaluation mechanism, users can receive more personalized support throughout the learning process, ultimately enhancing their professional capabilities and market competitiveness in the domestic service industry;

[0077] Among them, the calculation formula for the comprehensive ability score of users' domestic service is as follows:

[0078]

[0079] The comprehensive ability score comprehensively evaluates users' abilities from multiple dimensions by combining learning scores, practical operation scores, and user feedback, making the evaluation more comprehensive and accurate. In the formula, Zhpf represents the comprehensive ability score of users' domestic service, L s represents the average test score of users' online learning, Sckt represents the average score of users' practical operation assessment, F r represents the overall satisfaction score of users with the courses and training, W l represents the importance weight of learning scores, W s represents the importance weight of practical operation scores, W f represents the importance weight of user feedback scores. The weight values are automatically assigned by AI. The comprehensive score can help better identify users' strengths and weaknesses, and then provide targeted improvement plans and courses to ensure that all users can effectively improve their professional skills;

[0080] The data feedback module is a key component, aiming to efficiently summarize and analyze the data collected by each module, generate visual reports, and help users and managers deeply understand the training effect and user experience. This module utilizes a powerful data processing backend, usually based on Python in combination with data analysis libraries (such as Pandas and Matplotlib), to clean, summarize, and analyze relevant data. The data sources include users' learning scores, practical operation assessment results, user feedback, and participation status, etc. By integrating this information, the module can quickly generate detailed analysis reports;

[0081] To ensure the comprehensibility and operability of information, the module uses data visualization tools (such as D3.js or Chart.js) to present the analysis results in the form of charts and dashboards, covering various statistical indicators, such as user satisfaction, course participation rate, and completion status, etc. These visual reports not only provide users with intuitive learning feedback but also offer key data support to managers, thus helping them formulate targeted improvement measures;

[0082] In the most important feedback session, the module designs an automated notification system that can real-time feedback the generated report results to users and managers via text messages. This function can rely on the integration of cloud communication services (such as Twilio or Aliyun) to ensure that feedback information is delivered in a timely manner, enhancing users' sense of participation and feedback awareness;

[0083] Meanwhile, the data feedback module also calculates the overall user satisfaction index, which is quantified based on users' ratings, feedback, and participation. This index provides a key basis for the content optimization of domestic service training. Through an efficient data feedback mechanism, the system can continuously track and optimize the training quality to ensure a close connection between the course content and users' needs.

[0084] Through the comprehensive application of the above system, not only the professional qualities and practical abilities of service personnel are improved, but also personalized learning guidance is realized, thus enhancing the professionalism and service quality of domestic services, meeting consumers' demands for high-quality domestic services, and significantly improving customer satisfaction.

[0085] Example 1:

[0086] The comprehensive ability score comprehensively evaluates users' abilities from multiple dimensions by combining learning achievements, practical operation achievements, and user feedback, making the evaluation more comprehensive and accurate. And the comprehensive score can help better identify users' strengths and weaknesses, and then provide targeted improvement plans and courses to ensure that all users can effectively improve their professional skills. Through this system, the average test score L of users' online learning is obtained s = 85, the average score of users' practical operation assessment Sckt = 90, and the overall satisfaction score F of users with the course and training r = 4, the importance weight W of learning achievements l = 0.5, the importance weight W of practical operation achievements s = 0.4, the importance weight W of user feedback score f = 0.1. According to the comprehensive ability score calculation formula:

[0087]

[0088] This calculation result shows that the comprehensive ability score of this user is 78.9, which is a relatively high score, indicating that the user performs well in online learning and practical training, and has a relatively high satisfaction with the course. This score can show that the user has strong domestic service capabilities and is suitable for engaging in related work;

[0089] Example Two:

[0090] The comprehensive ability score comprehensively evaluates the user's ability from multiple dimensions by combining learning achievements, practical achievements, and user feedback, making the evaluation more comprehensive and accurate. Moreover, the comprehensive score can help better identify the user's strengths and weaknesses, and then provide targeted improvement plans and courses to ensure that all users can effectively improve their professional skills. The average test score L of the user's online learning is obtained through this system s = 60, the average score of the user's practical assessment Sckt = 70, and the overall satisfaction score F of the user with the course and training r = 2, the importance weight W of the learning achievement l = 0.3, the importance weight W of the practical achievement s = 0.5, the importance weight W of the user feedback score f = 0.2. According to the comprehensive ability score calculation formula:

[0091]

[0092] This calculation result shows that the comprehensive ability score of this user is 53.4, which is a relatively low score, indicating that the user performs poorly in learning and practice, and also has a low satisfaction with the course. This may imply that the user lacks the ability in the field of domestic service and needs further training and support from the system.

[0093] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An AI-based housekeeping service auxiliary training system, characterized by: It includes user registration management module, training course management module, online learning module, practical training module, intelligent evaluation module and data feedback module; The user registration management module is used to receive and store the registration information of the domestic service personnel, including name, contact information and work experience, and generate a user profile, and generate a unique user ID based on the registration information input by the user for data management and permission setting; The training course management module is used to provide a list of training courses, including course content, duration and assessment standards, and record the user's registration status. After the user registers, the user is allowed to select a suitable course and register, and the number of course registrations and course completion rates are counted to evaluate the popularity and effectiveness of the course. The online learning module is used to support users to learn selected courses online, including videos, documents and learning tests, and to record users' learning progress, test scores and course completion in real time. Based on users' learning behavior data, learning reports are generated regularly, and user learning activity, the average value of user test scores and the standard deviation of user test scores are calculated to evaluate users' learning effects. The practical training module is used to organize offline practical training, including practical demonstration and assessment, record the user's participation and assessment results, arrange user practical training after the user completes online learning, and calculate the pass rate of the user's practical assessment and the average score of the user's practical assessment, so that the user can evaluate the user's housekeeping service practical ability; The intelligent evaluation module is used to use AI to comprehensively evaluate the user's learning effect and practical ability. After the user completes online learning and practical training, the module analyzes the user's learning performance, practical assessment results and user feedback, calculates the user's comprehensive housekeeping service ability score, and puts forward personalized development suggestions to guide the user's subsequent learning direction; The data feedback module is used to summarize and analyze the data collected by each module, generate a visual report, and send text message feedback of the report results to users and managers. It also calculates the overall user satisfaction index for optimizing the housekeeping service training content.

2. The AI-based housekeeping service auxiliary training system according to claim 1, characterized in that: The formula for generating a unique user ID is as follows: AdID=Hash(P1+P2+P3) In the formula, AdID represents the user's unique ID, Hash(*) represents a hash function used to convert the input parameters into a string of fixed length, P1 represents the user's name, P2 represents the timestamp when the user registered, and P3 represents a random number automatically generated by the system.

3. The AI-based housekeeping service auxiliary training system according to claim 2, characterized in that: The calculation formula for the course completion rate is as follows: In the formula, Kcwc represents the course completion rate, N c Indicates the number of users who have completed the course, N r Indicates the total number of users who have selected and registered for this course.

4. The AI-based housekeeping service auxiliary training system according to claim 3 is characterized in that: The calculation formula of the user learning activity is as follows: In the formula, Yhhy represents the user's learning activity, T l Indicates the total time that users actually participate in learning, T max Indicates the maximum learning time.

5. The AI-based housekeeping service auxiliary training system according to claim 4 is characterized in that: The calculation formula for the average value of the user test scores is as follows: In the formula, Pt avg represents the average value of user test scores, S g Represents the sum of all user test scores, N t Indicates the total number of users who participated in the test.

6. The AI-based housekeeping service auxiliary training system according to claim 5, characterized in that: The standard deviation calculation formula of the user test score is as follows: In the formula, Bz represents the standard deviation of user test scores, S d It represents the sum of the squares of the deviations of each grade from the average score, N t Indicates the total number of users who participated in the test.

7. The AI-based housekeeping service auxiliary training system according to claim 6, characterized in that: The calculation formula for the pass rate of the user practical examination is as follows: In the formula, Sckh represents the pass rate of the user practical assessment, Tk represents the number of users who reach the passing standard in the practical assessment, and Pn represents the number of all users who participate in the practical assessment.

8. The AI-based housekeeping service auxiliary training system according to claim 7 is characterized in that: The calculation formula for the average score of the user practical assessment is as follows: In the formula, Sckt represents the average score of the user's practical assessment, S s represents the sum of the practical assessment scores of all users, and M represents the total number of users who participated in the practical assessment.

9. The AI-based housekeeping service auxiliary training system according to claim 8, characterized in that: The calculation formula for the user's comprehensive housekeeping service ability score is as follows: In the formula, Zhpf represents the user's comprehensive housekeeping service ability score, L s represents the average test score of users in online learning, Sckt represents the average score of users in practical assessment, and F r represents the user's overall satisfaction rating for the course and training, W l represents the importance weight of learning performance, W s Indicates the importance weight of practical performance, W f Indicates the importance weight of user feedback ratings. The weight value is automatically assigned by AI.

10. The AI-based housekeeping service auxiliary training system according to claim 9, characterized in that: The calculation formula of the overall user satisfaction index is as follows: In the formula, Mx represents the overall user satisfaction index, K f Represents the sum of all users’ scores on courses and training, N u Indicates the number of users who participated in the evaluation.

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