Household service simulation training environment system and device based on AI

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a housekeeping service simulation training environment system and device based on AI. According to the system and the device, a housekeeping service basic data set is constructed by collecting user demands, service personnel skills, service scenes and historical feedback data in real time, the comprehensiveness and accuracy of data are ensured, in the data processing stage, key features are extracted, the user demand matching degree is calculated, a pure data set is generated, and the user experience is improved. The model training module utilizes an advanced machine learning algorithm to construct user demand prediction, service duration prediction and service efficiency index models, and the application of the models enables service personnel to more accurately meet diversified user demands, optimize service duration and improve service efficiency. The simulation environment module provides an interactive training platform for users and service personnel, the actual effect of skill training is enhanced, and the evaluation feedback module collects feedback information in real time to realize continuous monitoring and optimization of household service quality.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence, and particularly to an AI-based domestic service simulation training environment system and device. Background Art

[0002] In modern society, the domestic service industry is increasingly becoming an important part of meeting the needs of family life. With the acceleration of the pace of life and the increase in work pressure, more and more families choose to rely on professional domestic services to help handle daily chores, such as cleaning, cooking, taking care of the elderly and children, etc. This not only improves the quality of family life but also provides broad employment opportunities for domestic service personnel. The market's demand for efficient and high-quality domestic services continues to grow. Therefore, how to improve the matching efficiency and service quality of domestic services has become the key to the development of the industry.

[0003] However, problems such as uneven skill levels of domestic service personnel, diverse user needs, and unpredictable service duration and efficiency have led to the inability to guarantee service quality. In addition, the transmission and feedback of information are lagging, resulting in low matching efficiency between service providers and users and the inability to achieve precise services. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] Aiming at the deficiencies of the prior art, the present invention provides an AI-based domestic service simulation training environment system and device. By collecting user requirements, service personnel skills, service scenarios, and historical feedback data in real time, a detailed basic data set for domestic services is constructed, ensuring the comprehensiveness and accuracy of the data. In the data processing stage, the system can extract key features and calculate the matching degree of user requirements to generate a pure data set, thereby improving the matching efficiency between service personnel and users. The model training module uses advanced machine learning algorithms to construct models for predicting user requirements, service duration, and service efficiency indicators, providing scientific decision-making support for domestic services. The application of these models enables service personnel to more precisely meet diverse user needs, optimize service duration, and improve service efficiency. The simulation environment module provides an interactive training platform for users and service personnel, enhancing the actual effect of skill training. The evaluation and feedback module realizes continuous monitoring and optimization of service quality by collecting feedback information in real time, thereby improving the overall level of domestic services, enhancing the accuracy and efficiency of services, and effectively guaranteeing the quality of domestic services, thus solving the above problems.

[0006] (2) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solutions: A home service simulation training environment system based on AI, including a data collection module, a data processing module, a model training module, a simulation environment module, and an evaluation feedback module;

[0008] The data collection module is used to collect user demand data, service personnel skill data, service scenario data, user feedback data, and historical service record data in real time, and construct a home service basic data set;

[0009] The data processing module filters the home service basic data set and then extracts features, extracts user demand features, service personnel features, service scenario features, and historical service features, and calculates the user demand matching degree to generate a pure home service data set;

[0010] The model training module uses machine learning algorithms to train models based on the features extracted by the data processing module and the pure home service data set, and constructs a user demand prediction model, a service duration prediction model, and a service efficiency index model;

[0011] The simulation environment module creates a virtual home service simulation environment based on the user demand prediction model, the service duration prediction model, and the service efficiency index model for users and service personnel to conduct interactive training;

[0012] After the interactive training between users and service personnel is completed, the evaluation feedback module conducts real-time evaluation according to the training results, collects the feedback information of users and service personnel, and transmits the feedback information of users and service personnel back to the data processing module for continuous optimization and iteration of the system.

[0013] Preferably, the extraction formula for the user demand features is as follows:

[0014]

[0015] In the formula, U n represents the user demand feature vector, m represents the number of user demand feature dimensions, N i represents the i-th user demand feature, w i represents the weight of the i-th feature, automatically assigned by AI, and i represents the index subscript.

[0016] Preferably, the extraction formula for the service personnel features is as follows:

[0017]

[0018] In the formula, S p represents the service personnel feature vector, p represents the number of service personnel feature dimensions, P j represents the j-th service personnel feature, vj It represents the weight of the j-th service staff feature, which is automatically assigned by AI, and j represents the index subscript.

[0019] Preferably, the extraction formula of the service scenario feature is as follows:

[0020]

[0021] In the formula, C s represents the service scenario feature vector, q represents the service scenario feature vector, S k represents the k-th service scenario feature, m k represents the weight of the k-th feature, which is automatically assigned by AI, and k represents the counting subscript.

[0022] Preferably, the extraction formula of the historical service feature is as follows:

[0023]

[0024] In the formula, H j represents the historical service feature vector, r represents the number of historical service feature dimensions, H l represents the l-th historical service feature, t l represents the weight of the l-th feature, which is automatically assigned by AI, and l represents the index subscript.

[0025] Preferably, the calculation formula of the user demand matching degree is as follows:

[0026]

[0027] In the formula, D um represents the user demand matching degree, U n represents the user demand feature vector, S p represents the service staff feature vector, ||U n || represents the norm of the user demand feature vector, ||S p || represents the norm of the service staff feature vector.

[0028] Preferably, the user demand prediction model is constructed as follows:

[0029]

[0030] In the formula, N pred represents the predicted user demand, β 0 represents the intercept term, β i represents the regression coefficient of the i-th feature, X i represents the i-th user feature input, and i represents the index counting subscript.

[0031] Preferably, the service duration prediction model is constructed as follows:

[0032]

[0033] In the formula, T pred represents the predicted service duration, α 0 represents the intercept term, α j represents the regression coefficient of the j-th feature, Y j represents the input of the j-th service feature, and j represents the index.

[0034] Preferably, the calculation formula of the service efficiency index model is as follows:

[0035]

[0036] In the formula, E eff represents the service efficiency index, S complete represents the number of services completed, and T total represents the total service time.

[0037] Compared with the prior art, the present invention provides an AI-based home service simulation training environment system and device, which has the following beneficial effects:

[0038] The present invention constructs a detailed home service basic data set by collecting user requirements, service personnel skills, service scenarios and historical feedback data in real time, ensuring the comprehensiveness and accuracy of the data. In the data processing stage, the system can extract key features and calculate the user requirement matching degree to generate a pure data set, thereby improving the matching efficiency between service personnel and users. The model training module uses advanced machine learning algorithms to construct user requirement prediction, service duration prediction and service efficiency index models, providing scientific decision-making support for home services. The application of these models enables service personnel to more accurately meet diverse user needs, optimize service duration and improve service efficiency. The simulation environment module provides an interactive training platform for users and service personnel, enhancing the actual effect of skill training. The evaluation feedback module realizes continuous monitoring and optimization of service quality by collecting feedback information in real time, thereby improving the overall level of home services, improving service accuracy and efficiency, and effectively ensuring home service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0041] In view of the problems such as uneven skill levels of domestic service personnel, diverse user needs, and unpredictable service duration and efficiency, which lead to the inability to guarantee service quality, a simulation training environment system for domestic services based on AI is proposed. Please refer to Figure 1 This system includes a data collection module, a data processing module, a model training module, a simulation environment module, and an evaluation and feedback module;

[0042] The data collection module uses advanced sensor technology, online questionnaires, mobile applications, and API interfaces to collect user demand data, service personnel skill data, service scenario data, user feedback data, and historical service record data in real time, thereby constructing a comprehensive basic dataset for domestic services. Specifically, user demand data is collected through the intelligent customer service system and the interactive interface in the mobile application, allowing users to directly input service preferences and requirements; service personnel skill data is automatically obtained through the qualification certification platform and its professional training records to ensure the real-time update of service personnel information. Service scenario data integrates environmental perception information from Internet of Things (IoT) devices to monitor the specific conditions of the service location in real time, such as house structure, environmental characteristics, etc. In addition, user feedback data is collected through follow-up service surveys and online evaluation systems, and historical service records are extracted from the enterprise management system, including past service content, service personnel performance, and user satisfaction scores. Through data fusion, machine learning, and real-time analysis, the basic dataset for domestic services formed by this module will provide a solid foundation for subsequent data processing, model training, and service optimization;

[0043] During the processing of the basic household service dataset, the data processing module first applies data cleaning and filtering techniques, using methods such as Kappa filtering, mean filtering, and outlier detection algorithms to remove noisy data and invalid information to ensure data quality. Next, through feature engineering techniques, the data is deeply analyzed, and statistical analysis and machine learning methods are used to extract user demand features, service personnel features, service scenario features, and historical service features. Specifically, user demand features include cleaning frequency, preferred service types, etc., which are obtained through cluster analysis of historical data input by users. Service personnel features evaluate the degree of skill mastery and experience value, and are analyzed based on service records. Service scenario features integrate environmental data from IoT devices to build an understanding of the panoramic service conditions. Historical service feature analysis focuses on service effects and customer feedback, and analysis algorithms are used to identify key influencing factors. Finally, the module quantifies the matching effect by comprehensively calculating the matching degree between user demands and service personnel, and then generates a high-quality pure household service dataset, laying a solid foundation for subsequent user demand prediction, service duration estimation, and service efficiency analysis, ultimately promoting service personalization and precision;

[0044] Among them, the extraction formula for user demand features is as follows:

[0045]

[0046] The extraction of user demand features enables the system to deeply analyze users' personal preferences and service expectations, such as regular cleaning, demands during specific periods, etc., which helps to provide more personalized services. In the formula, U n represents the user demand feature vector, m represents the number of user demand feature dimensions, N i represents the i-th user demand feature, w i represents the weight of the i-th feature, automatically assigned by AI, and i represents the index subscript. By understanding users' features, service personnel can prepare the required tools and solutions in advance, reducing the preparation time during on-site services and improving the overall quality of services and user satisfaction;

[0047] The extraction formula for service personnel features is as follows:

[0048]

[0049] Extracting features such as the skills, experience, and qualifications of service personnel helps the system to more accurately match suitable service personnel with user demands when assigning tasks, thereby increasing the service success rate. In the formula, S p represents the service personnel feature vector, p represents the number of service personnel feature dimensions, P j represents the j-th service personnel feature, v jRepresents the weight of the j-th service staff characteristic, automatically assigned by AI. j represents the index subscript. Through regular analysis of service staff characteristics, managers can evaluate the performance of service staff, provide them with constructive feedback, and help them improve their professional skills and service quality;

[0050] The extraction formula for service scenario characteristics is as follows:

[0051]

[0052] The extraction of service scenario characteristics such as family structure, area, and the number of family members helps service staff adjust service plans in different environments, thereby improving the adaptability of services. In the formula, C s Represents the service scenario feature vector, q represents the service scenario feature vector, S k Represents the k-th service scenario characteristic, m k Represents the weight of the k-th characteristic, automatically assigned by AI. k represents the counting subscript. Analyzing service scenario characteristics can guide enterprises to optimize service processes, such as specifying work standards and efficiency requirements in different scenarios, making service provision more standardized and consistent;

[0053] The extraction formula for historical service characteristics is as follows:

[0054]

[0055] Historical service characteristics such as past service records, customer feedback, and service duration can help enterprises understand service effects, evaluate service quality, and identify potential improvement points. In the formula, H h Represents the historical service feature vector, r represents the number of historical service feature dimensions, H l Represents the l-th historical service characteristic, t l Represents the weight of the l-th characteristic, automatically assigned by AI. l represents the index subscript. Regularly reviewing and analyzing historical service characteristics can encourage the team to continuously pursue improvements and innovations in service quality to adapt to the rapid changes in the market;

[0056] The calculation formula for user demand matching degree is as follows:

[0057]

[0058] By calculating the matching degree of user demands, the system can achieve more intelligent service recommendations, more accurately lock in the services required by users, thereby improving the user experience. In the formula, D um Represents the user demand matching degree, U n Represents the user demand feature vector, S p Represents the service staff feature vector, ||U n|| represents the norm of the user demand feature vector, ||S p || represents the norm of the service staff feature vector. The calculation of the matching degree provides a real-time data feedback mechanism for the system. Enterprises can obtain information on service effects in a timely manner, which is convenient for adjusting and optimizing the system;

[0059] Based on the features extracted by the data processing module and the filtered pure domestic service dataset, the model training module uses a variety of machine learning algorithms for model training to construct a user demand prediction model, a service duration prediction model, and a service efficiency index model. Specifically, this module first uses feature engineering techniques to transform the user features, service staff features, service scenario features, and historical service features in the dataset into a format suitable for model input, and standardizes the data to ensure the training stability and efficiency of the model. For the user demand prediction model, the module can apply algorithms such as regression analysis, support vector machine (SVM), and random forest. By training historical user behavior data, it detects potential user demand patterns, thereby achieving high-precision demand prediction. The service duration prediction model can use time series analysis and regression algorithms, combined with historical service records and scenario data, to establish an efficient duration estimation model, making service arrangements more reasonable. The service efficiency index model uses algorithms such as decision trees, clustering analysis, and deep learning to identify key factors affecting service efficiency, helping managers formulate optimization strategies. The entire model training process uses cross-validation and hyperparameter tuning techniques to ensure the generalization ability and prediction accuracy of the model, and ultimately provides data-driven decision support for the operation and management of domestic services, improving the intelligence level and user experience of services;

[0060] The user demand prediction model is constructed as follows:

[0061]

[0062] The user demand prediction model can help enterprises identify changing market demands, plan services in advance, and ensure sufficient response capabilities when user demands break out. In the formula, N pred represents the predicted user demand, β 0 represents the intercept term, β i represents the regression coefficient of the i-th feature, X i represents the input of the i-th user feature, and i represents the index counting subscript;

[0063] The service duration prediction model is constructed as follows:

[0064]

[0065] By accurately predicting the service duration, enterprises can effectively arrange the working hours of service staff, reduce waiting and idle time, and improve overall service efficiency. In the formula, Tpred Represents the predicted service duration, α 0 Represents the intercept term, α j Represents the regression coefficient of the j-th feature, Y j Represents the input of the j-th service feature. j represents the index. The service duration prediction model enables users to reasonably arrange their time and be informed in advance of the estimated time required for the service, thereby enhancing the user experience;

[0066] The calculation formula of the service efficiency index model is as follows:

[0067]

[0068] Through the quantitative evaluation of service efficiency, enterprises can clearly understand the efficiency levels of various services, facilitating the formulation of subsequent improvement measures. In the formula, E eff Represents the service efficiency index, S complete Represents the number of services completed, T total Represents the total service time. Efficient services can not only improve the user's service experience but also enhance user satisfaction through faster response times and higher completion quality, forming a virtuous cycle;

[0069] The simulation environment module creates a virtual home service simulation environment by integrating the user demand prediction model, service duration prediction model, and service efficiency index model. Its purpose is to provide an interactive training platform for users and service personnel. This module first uses advanced simulation software and virtual reality (VR) technology to construct immersive service scenarios, enabling participants to experience different service scenarios as if they were on the scene, thereby enhancing the authenticity of the interaction and the effectiveness of the training. In this simulation environment, the roles of users and service personnel are dynamically assigned. Users can set specific service requests based on the demand parameters output by the prediction model, while service personnel formulate corresponding service plans and strategies to respond based on the duration prediction model and efficiency index model;

[0070] In addition, the module also integrates a real-time data feedback mechanism, allowing participants to obtain instant feedback information on service quality, participation time, and user popularity during the simulation. By analyzing this feedback, the system can dynamically adjust and optimize the training scenario to ensure the pertinence and effectiveness of the training. To enhance the training effect, the simulation environment module also uses machine learning technology to record the results of each simulation interaction for subsequent model iteration and capacity improvement. This interactive training can not only enhance the response ability and practical experience of service personnel but also help users better understand the service process and quality standards, ultimately achieving the goal of an efficient service experience and improved user satisfaction;

[0071] After the interaction training between the user and the service staff is completed, the evaluation feedback module is responsible for the real-time evaluation and analysis of the training results. This module first collects and analyzes the data generated by the participants during the simulation through an integrated real-time monitoring system, including key performance indicators such as the accuracy of service execution, the effect of time management, and user satisfaction. In addition, the module actively solicits opinions from the user and the service staff by setting up interactive questionnaires and feedback mechanisms to obtain their intuitive feedback on service quality, interaction experience, and training process. These feedbacks are processed by intelligent analysis tools (such as natural language processing technology and sentiment analysis algorithms) to identify potential improvement points and optimization suggestions;

[0072] The feedback information collected from the user and the service staff will be systematically sorted out and sent back to the data processing module. This process includes data classification, annotation, and storage to ensure the high availability and traceability of the data. The data processing module uses this feedback to further optimize the model and adjust the algorithm parameters to improve the accuracy of user demand prediction, service duration estimation, and efficiency indicator evaluation. At the same time, the evaluation feedback module also combines with historical service data to form a continuous learning mechanism, enabling the system to adapt to the changing market demands and user preferences. Through this closed-loop feedback mechanism, the domestic service system can achieve continuous optimization and iteration, continuously improving service quality and user experience.

[0073] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art 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 simulation training environment system, characterized by: It includes data acquisition module, data processing module, model training module, simulation environment module and evaluation feedback module; The data collection module is used to collect user demand data, service personnel skill data, service scenario data, user feedback data and historical service record data in real time to build a basic data set for housekeeping services; The data processing module performs feature extraction on the basic data set of housekeeping services after filtering, extracts user demand features, service personnel features, service scenario features and historical service features, and calculates the user demand matching degree to generate a pure data set of housekeeping services; The model training module uses a machine learning algorithm to perform model training based on the features extracted by the data processing module and the clean data set of housekeeping services, and constructs a user demand prediction model, a service duration prediction model, and a service efficiency index model; The simulation environment module creates a virtual housekeeping service simulation environment based on the user demand prediction model, the service duration prediction model and the service efficiency index model for interactive training of users and service personnel; After the interactive training between users and service personnel is completed, the evaluation feedback module performs real-time evaluation based on the training results, collects feedback information from users and service personnel, and transmits the feedback information from users and service personnel back to the data processing module for continuous optimization and iteration of the system.

2. The AI-based housekeeping service simulation training environment system according to claim 1, characterized in that: The formula for extracting the user demand feature is as follows: In the formula, U n represents the user demand feature vector, m represents the number of user demand feature dimensions, N i represents the i-th user demand feature, w i It represents the weight of the i-th feature, which is automatically assigned by AI, and i represents the index subscript.

3. The AI-based housekeeping service simulation training environment system according to claim 2, characterized in that: The extraction formula of the service personnel features is as follows: In the formula, S p represents the service personnel feature vector, p represents the number of service personnel feature dimensions, P j represents the jth service personnel feature, v j It represents the weight of the j-th service personnel feature, which is automatically assigned by AI. j represents the index subscript.

4. The AI-based housekeeping service simulation training environment system according to claim 3, characterized in that: The extraction formula of the service scenario feature is as follows: In the formula, C s represents the service scenario feature vector, q represents the service scenario feature vector, S k represents the kth service scenario feature, m k It represents the weight of the k-th feature, which is automatically assigned by AI. k represents the counting subscript.

5. The AI-based housekeeping service simulation training environment system according to claim 4, characterized in that: The extraction formula of the historical service feature is as follows: In the formula, H h represents the historical service feature vector, r represents the number of historical service feature dimensions, H l represents the lth historical service feature, t l It represents the weight of the lth feature, which is automatically assigned by AI, and l represents the index subscript.

6. The AI-based housekeeping service simulation training environment system according to claim 5, characterized in that: The user demand matching calculation formula is as follows: In the formula, D um Indicates the matching degree of user needs, U n represents the user demand feature vector, S p represents the service personnel feature vector, ||U n || represents the modulus of the user demand feature vector, ||S p || represents the modulus of the service personnel feature vector.

7. The AI-based housekeeping service simulation training environment system according to claim 6, characterized in that: The user demand prediction model is constructed as follows: In the formula, N pred represents the predicted user demand, β0 represents the intercept term, β i represents the regression coefficient of the i-th feature, X i Represents the i-th user feature input, and i represents the index count subscript.

8. The AI-based housekeeping service simulation training environment system according to claim 7, characterized in that: The service duration prediction model is constructed as follows: In the formula, T pred represents the predicted service time, α0 represents the intercept term, α j represents the regression coefficient of the jth feature, Y j represents the j-th service feature input, where j represents the index.

9. The AI-based housekeeping service simulation training environment system according to claim 8, characterized in that: The calculation formula of the service efficiency index model is as follows: In the formula, E eff represents the service efficiency index, S complete Indicates the number of completed services, T total Indicates the total service time.

10. An AI-based housekeeping service simulation training device, characterized in that: The device is configured with an AI-based housekeeping service simulation training environment system as described in any one of claims 1 to 9.

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