Intelligent hotel guide and convenient service system based on artificial intelligence

Abstract

The invention discloses a hotel intelligent guide and convenient service system based on artificial intelligence, which relates to the technical field of convenient service systems and comprises a guest information module, a service information module, a quality monitoring module, a dynamic analysis module, a dynamic adjustment module, a central guide control module and a visual control module. The guest information module collects and manages guest information including guest names, room numbers, health information and room service levels, and accurately grasps guest service quality and dynamic conditions of service personnel through quantitative processing of guest information, service personnel information and guest service quality. The work of the service personnel is dynamically adjusted in time, meanwhile, the guest service prediction model is adjusted, optimized and improved according to the feedback opinion model, and the scheduling information of the service personnel is dynamically adjusted in time according to the prediction result obtained by the guest service prediction model, so that the overload work of the service personnel is avoided in advance; and the work of service personnel can be optimized in time.

Description

Technical Field

[0001] The present invention relates to the technical field of convenient service systems, and in particular to an artificial intelligence-based hotel intelligent guide and convenient service system. Background Art

[0002] With the rapid development of science and technology, artificial intelligence (AI) technology has been widely used in many industries, among which the hotel service industry is gradually moving towards intelligent transformation. As an important part of intelligent transformation, the hotel intelligent guide and convenient service system uses technical means to improve guest experience and optimize hotel operation efficiency. However, in the actual application process, the system still faces some technical challenges, especially in handling urgent guest needs and service personnel task scheduling.

[0003] Existing hotel smart guide and convenient service systems optimize service processes through automation and intelligent means. However, these systems still have some shortcomings in the implementation process. For example, they cannot accurately grasp the guests' service quality requirements, resulting in a deviation between the service content and guests' expectations. At the same time, the system also lacks the ability to monitor and dynamically adjust the dynamic situation of service personnel in real time, making it easy for service personnel to be overloaded during peak hours, and resources may be wasted during off-peak hours.

[0004] In addition, the current scheduling management system shows significant limitations in terms of flexibility. It relies on preset and fixed scheduling templates to arrange the work of service personnel, and lacks the ability to make immediate and dynamic adjustments based on the guest flow forecast results and the real-time status of service personnel. More importantly, most modern hotel services are divided into multiple levels such as ordinary services, VIP services and SVIP services. This rigid scheduling model is not only likely to cause an imbalance between the workload and rest time of service personnel, but may also have an adverse impact on the overall service quality level and guest satisfaction. Summary of the invention

[0005] In view of the above situation, the present invention makes dynamic adjustments to the work of service personnel in a timely manner through quantitative processing of guest information, service personnel information, and guest service quality, and makes dynamic adjustments to the service personnel's scheduling information in a timely manner based on the prediction results obtained by the guest service prediction model, so as to avoid overload of service personnel in advance and optimize the work of service personnel in a timely manner.

[0006] The technical solution includes a guest information module, a service information module, a quality monitoring module, a dynamic analysis module and a dynamic adjustment module, a central control module, and a visual control module; the guest information module collects and manages guest information, including the guest's name, room number, health information, and room service level; the service information module collects and organizes service personnel information, shift information, and service personnel level;

[0007] The quality monitoring module collects and organizes guest service information in real time, including service records, service execution rates, guest feedback and evaluation, and service error events;

[0008] The dynamic analysis module first calculates the required service value M of each guest, M=α×0.4+β×0.6, where α represents the quantitative value corresponding to the guest room service level, and β represents the quantitative value corresponding to the guest health information level;

[0009] Then calculate the service value N of each service personnel, N = γ × 0.4 + δ × 0.6, where γ represents the quantitative value of the service personnel's scheduling information, and δ represents the quantitative value of the service personnel's level;

[0010] Finally, the quality monitoring value Y of each guest is calculated, Y = a × 0.1 + b × 0.3 + c × 0.3 + d × 0.3, where a represents the quantitative value of the service record completion, b represents the quantitative value of the service execution rate, c represents the quantitative value of the guest feedback evaluation, and d represents the quantitative value of the service error event;

[0011] The dynamic adjustment module first calculates the service evaluation value Q of each guest, Q=M-N+1-Y, Q is greater than 0.3, indicating that the guest service evaluation is unqualified, and then further compares the required service value M and the corresponding service value N of this guest. The difference MN is greater than 0.2 and less than 0.4, indicating that the service staff and the service required by the guest do not match. The central guide and control module controls the visualization control module to send information to replace the service staff. The difference MN is greater than 0.4, indicating that the guest's service situation is not good and the service level needs to be improved. The central guide and control module controls the visualization control module to send information to improve the corresponding service level of the guest. Otherwise, it means qualified. The central guide and control module controls the visualization control module to display that the guest service is normal.

[0012] Further, the dynamic analysis module quantifies the information sent by the guest information module, the service information module, and the quality monitoring module. First, the guest information collected by the guest information module is retrieved, and classified according to the room number and name of each guest. The guest name corresponding to a room service level is used as an information package, and the guest room service level and the guest health information level are quantified. The guest service information is quantified into three levels of 1-3, and the guest service level is quantified into three required service levels of SVIP, VIP, and ordinary, so as to facilitate the evaluation of the guest's need for service, and the required service value M of each guest is calculated, M=α×0.3+β×0.7, wherein α represents the quantified value corresponding to the guest service level, the first level corresponds to α of 1, the second level corresponds to α of 0.6, and the third level corresponds to α of 0.4, and β represents the quantified value corresponding to the guest service level, the SVIP service level corresponds to β of 1, the VIP service level corresponds to β of 0.6, and the ordinary service level corresponds to β of 0.4;

[0013] Then, the service personnel information is used as a label to quantify the scheduling information and service personnel level. The scheduling information is quantified into normal scheduling, normal overtime, and fatigue overtime. The service personnel level is quantified into three service qualifications: SVIP, VIP, and ordinary. This is convenient for evaluating the service capabilities of service personnel and calculating the service value N of each service personnel, N = γ × 0.4 + δ × 0.6, where γ represents the quantified value of the service personnel's scheduling information. Normal scheduling γ is 1, normal overtime γ is 0.6, and fatigue overtime γ is 0.1. δ represents the quantified value of the service personnel level. SVIP service qualification corresponds to δ of 1, VIP service qualification corresponds to δ of 0.6, and ordinary service qualification corresponds to δ of 0.4.

[0014] Finally, the guests are classified according to their room numbers and corresponding names, and the corresponding service record completion is quantified into qualified and unqualified. The service execution rate is divided into qualified and unqualified, the guest feedback evaluation is divided into satisfied, general and unsatisfied, the service error events are divided into 0 times, 1-2 times, and unqualified, and the quality monitoring value Y of each guest is calculated, Y=a×0.1+b×0.3+c×0.3+d×0.3, where a represents the quantitative value of the service record completion degree, the service record completion degree is qualified a is 1, and unqualified a is 0, b represents the quantitative value of the service execution rate, the service execution rate is qualified b is 1, and unqualified b is 0, c represents the quantitative value of the guest feedback evaluation, the guest feedback evaluation is satisfied general is 1, general general is 0.7, and unqualified general is 0.2, d represents the quantitative value of the service error event, the service error event 0 times d is 1, 1-2 times d is 0.4, and unqualified d is 0.

[0015] Furthermore, the dynamic adjustment module monitors the range of normal dynamic adjustment in real time by calculating the service load value on the basis of normal dynamic adjustment. According to the required service value M of each guest and the service value N of each service staff, when the number of guests is n and the number of service staff is h, the total service value required by the guests is MZ=M1+M2+M3+......+Mn, where M1, M2, M3...Mn are the service values ​​required by the 1st, 2nd, 3rd...nth guests;

[0016] Service load value NZ = N1 + N2 + N3 ... + Nn, where N1, N2, N3 ... Nn are the service values ​​of the 1st, 2nd, 3rd ... nth service personnel;

[0017] When MZ is greater than NZ, it means that the service personnel are overloaded and exceed the normal dynamic adjustment range. The dynamic adjustment module controls the visual control module through the central control module to add or second other temporary service personnel to ensure service quality;

[0018] When MZ is less than or equal to NZ, it indicates that the service load is normal, the dynamic adjustment module is dynamically adjusted normally, and the visualization control module is controlled by the central control module to display normal service information.

[0019] Furthermore, the service level classification of the guests first formulates a clear service level classification standard based on the guest's room number, age, health information, and special service needs;

[0020] The service staff will then check and evaluate the guest's actual situation against the service level standards, including the guest's room number, age, health information, and special service needs;

[0021] According to the evaluation results, guests are classified into corresponding service levels, SVIP service level, VIP service level, and ordinary service level;

[0022] Finally, the guests’ service information and service levels were substituted into the guest big data model and adjusted iteratively. It was found that the SVIP service level corresponds to β of 1, the VIP service level corresponds to β of 0.6, and the ordinary service level corresponds to β of 0.4.

[0023] Furthermore, the central control module collects and organizes the guest service information in real time by the quality monitoring module, including service records, service execution rate, guest feedback evaluation and service error events, and retrieves the guest's quality monitoring value Y, the quantitative value a of the service record completion, the quantitative value b of the service execution rate, the quantitative value c of the guest feedback evaluation, and the quantitative value d of the service error event, and then retrieves the corresponding guest service prediction model. According to the prediction results obtained by the guest service prediction model, the corresponding service personnel give feedback on the prediction results through the visual control module to understand the actual application effect of the prediction model. Finally, the guest service prediction model is optimized and upgraded according to the feedback model. At the same time, when the prediction results are accurate, the central control module makes dynamic adjustments to the service personnel's scheduling information in a timely manner according to the guest's prediction results, so as to avoid overload of service personnel in advance and optimize the service personnel's work in a timely manner.

[0024] Due to the adoption of the above technical scheme, the present invention has the following advantages compared with the prior art: 1. Through the quantitative processing of guest information, service personnel information, and guest service quality, the guest service quality and the dynamic situation of service personnel can be accurately grasped, and the work of service personnel can be dynamically adjusted in time. At the same time, the guest service prediction model is tuned and improved according to the feedback model to improve the accuracy and practicality of the guest service prediction model, and according to the prediction results obtained by the guest service prediction model, the service personnel's scheduling information is dynamically adjusted in time to avoid overload of service personnel in advance and to optimize the work of service personnel in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The present invention is a flowchart of the hotel intelligent navigation and convenient service system based on artificial intelligence. DETAILED DESCRIPTION

[0026] The above and other technical contents, features and effects of the present invention are described in detail below with reference to the attached Figure 1 The detailed description of the embodiments will clearly show that the structural contents mentioned in the following embodiments are all based on the drawings in the specification.

[0027] In order to solve the problem of unreasonable service task allocation and difficult to monitor service quality, dynamic adjustment and monitoring are realized, including guest information module, service information module, quality monitoring module, dynamic analysis module and dynamic adjustment module, central control module and visual control module; the guest information module collects and manages guest information, including guest name, room number, health information and room service level; the service information module collects and organizes service personnel information, shift information and service personnel level;

[0028] The quality monitoring module collects and organizes guest service information in real time, including service records, service execution rates, guest feedback and evaluation, and service error events;

[0029] The dynamic analysis module first calculates the required service value M of each guest, M=α×0.4+β×0.6, where α represents the quantitative value corresponding to the guest room service level, and β represents the quantitative value corresponding to the guest health information level;

[0030] Then calculate the service value N of each service personnel, N = γ × 0.4 + δ × 0.6, where γ represents the quantitative value of the service personnel's scheduling information, and δ represents the quantitative value of the service personnel's level;

[0031] Finally, the quality monitoring value Y of each guest is calculated, Y = a × 0.1 + b × 0.3 + c × 0.3 + d × 0.3, where a represents the quantitative value of the service record completion, b represents the quantitative value of the service execution rate, c represents the quantitative value of the guest feedback evaluation, and d represents the quantitative value of the service error event;

[0032] The dynamic adjustment module first calculates the service evaluation value Q of each guest, Q=M-N+1-Y, Q is greater than 0.3, indicating that the guest service evaluation is unqualified, and then further compares the required service value M and the corresponding service value N of this guest. The difference MN is greater than 0.2 and less than 0.4, indicating that the service staff and the service required by the guest do not match. The central guide and control module controls the visualization control module to send information to replace the service staff. The difference MN is greater than 0.4, indicating that the guest's service situation is not good and the service level needs to be improved. The central guide and control module controls the visualization control module to send information to improve the corresponding service level of the guest. Otherwise, it means qualified. The central guide and control module controls the visualization control module to display that the guest service is normal.

[0033] Further, the dynamic analysis module quantifies the information sent by the guest information module, the service information module, and the quality monitoring module. First, the guest information collected by the guest information module is retrieved, and classified according to the room number and name of each guest. The guest name corresponding to a room service level is used as an information package, and the guest room service level and the guest health information level are quantified. The guest service information is quantified into three levels of 1-3, and the guest service level is quantified into three required service levels of SVIP, VIP, and ordinary, so as to facilitate the evaluation of the guest's need for service, and the required service value M of each guest is calculated, M=α×0.3+β×0.7, wherein α represents the quantified value corresponding to the guest service level, the first level corresponds to α of 1, the second level corresponds to α of 0.6, and the third level corresponds to α of 0.4, and β represents the quantified value corresponding to the guest service level, the SVIP service level corresponds to β of 1, the VIP service level corresponds to β of 0.6, and the ordinary service level corresponds to β of 0.4;

[0034] Then, the service personnel information is used as a label to quantify the scheduling information and service personnel level. The scheduling information is quantified into normal scheduling, normal overtime, and fatigue overtime. The service personnel level is quantified into three service qualifications: SVIP, VIP, and ordinary. This is convenient for evaluating the service capabilities of service personnel and calculating the service value N of each service personnel, N = γ × 0.4 + δ × 0.6, where γ represents the quantified value of the service personnel's scheduling information. Normal scheduling γ is 1, normal overtime γ is 0.6, and fatigue overtime γ is 0.1. δ represents the quantified value of the service personnel level. SVIP service qualification corresponds to δ of 1, VIP service qualification corresponds to δ of 0.6, and ordinary service qualification corresponds to δ of 0.4.

[0035] Finally, the guests are classified according to their room numbers and corresponding names, and the corresponding service record completion is quantified into qualified and unqualified. The service execution rate is divided into qualified and unqualified, the guest feedback evaluation is divided into satisfied, general and unsatisfied, the service error events are divided into 0 times, 1-2 times, and unqualified, and the quality monitoring value Y of each guest is calculated, Y=a×0.1+b×0.3+c×0.3+d×0.3, where a represents the quantitative value of the service record completion degree, the service record completion degree is qualified a is 1, and unqualified a is 0, b represents the quantitative value of the service execution rate, the service execution rate is qualified b is 1, and unqualified b is 0, c represents the quantitative value of the guest feedback evaluation, the guest feedback evaluation is satisfied general is 1, general general is 0.7, and unqualified general is 0.2, d represents the quantitative value of the service error event, the service error event 0 times d is 1, 1-2 times d is 0.4, and unqualified d is 0.

[0036] Furthermore, the dynamic adjustment module monitors the range of normal dynamic adjustment in real time by calculating the service load value on the basis of normal dynamic adjustment. According to the required service value M of each guest and the service value N of each service staff, when the number of guests is n and the number of service staff is h, the total service value required by the guests is MZ=M1+M2+M3+......+Mn, where M1, M2, M3...Mn are the service values ​​required by the 1st, 2nd, 3rd...nth guests;

[0037] Service load value NZ = N1 + N2 + N3 ... + Nn, where N1, N2, N3 ... Nn are the service values ​​of the 1st, 2nd, 3rd ... nth service personnel;

[0038] When MZ is greater than NZ, it means that the service personnel are overloaded and exceed the normal dynamic adjustment range. The dynamic adjustment module controls the visual control module through the central control module to add or second other temporary service personnel to ensure service quality;

[0039] When MZ is less than or equal to NZ, it indicates that the service load is normal, the dynamic adjustment module is dynamically adjusted normally, and the visualization control module is controlled by the central control module to display normal service information.

[0040] Furthermore, the service level classification of the guests first formulates a clear service level classification standard based on the guest's room number, age, health information, and special service needs;

[0041] The service staff will then check and evaluate the guest's actual situation against the service level standards, including the guest's room number, age, health information, and special service needs;

[0042] According to the evaluation results, guests are classified into corresponding service levels, SVIP service level, VIP service level, and ordinary service level;

[0043] Finally, the guests’ service information and service levels were substituted into the guest big data model and adjusted iteratively. It was found that the SVIP service level corresponds to β of 1, the VIP service level corresponds to β of 0.6, and the ordinary service level corresponds to β of 0.4.

[0044] Furthermore, the central control module collects and organizes the guest service information in real time by the quality monitoring module, including service records, service execution rate, guest feedback evaluation and service error events, and retrieves the guest's quality monitoring value Y, the quantitative value a of the service record completion, the quantitative value b of the service execution rate, the quantitative value c of the guest feedback evaluation, and the quantitative value d of the service error event, and then retrieves the corresponding guest service prediction model. According to the prediction results obtained by the guest service prediction model, the corresponding service personnel give feedback on the prediction results through the visual control module to understand the actual application effect of the prediction model. Finally, the guest service prediction model is optimized and upgraded according to the feedback model. At the same time, when the prediction results are accurate, the central control module makes dynamic adjustments to the service personnel's scheduling information in a timely manner according to the guest's prediction results, so as to avoid overload of service personnel in advance and optimize the service personnel's work in a timely manner.

[0045] When the present invention is used specifically, on the basis of the prior art, the guest information module collects and manages the guest information, including the guest's name, room number, health information, and room service level, and the service information module collects and organizes the service personnel's personnel information, shift information, and service personnel level;

[0046] The quality monitoring module collects and organizes guest service information in real time, including service records, service execution rates, guest feedback and evaluation, and service error events;

[0047] The dynamic analysis module first calculates the required service value M of each guest, M=α×0.4+β×0.6, where α represents the quantitative value corresponding to the guest room service level, and β represents the quantitative value corresponding to the guest health information level;

[0048] Then calculate the service value N of each service personnel, N = γ × 0.4 + δ × 0.6, where γ represents the quantitative value of the service personnel's scheduling information, and δ represents the quantitative value of the service personnel's level;

[0049] Finally, the quality monitoring value Y of each guest is calculated, Y = a × 0.1 + b × 0.3 + c × 0.3 + d × 0.3, where a represents the quantitative value of the service record completion, b represents the quantitative value of the service execution rate, c represents the quantitative value of the guest feedback evaluation, and d represents the quantitative value of the service error event;

[0050] The dynamic adjustment module first calculates each guest service evaluation value Q, Q = M-N+1-Y, Q is greater than 0.3, indicating that the guest service evaluation is unqualified, and then further compares the required service value M and the corresponding service value N of this guest. The difference of MN is greater than 0.2 and less than 0.4, indicating that the service required by the service staff and the guest does not match. The central control module controls the visualization control module to send information on replacing service staff. The difference of MN is greater than 0.4, indicating that the guest's service situation is not good and the service level needs to be improved. The central control module controls the visualization control module to send information on improving the corresponding service level of the guest. Otherwise, it is qualified. The central control module controls the visualization control module to display that the guest service is normal. Through quantitative processing of guest information, service staff information, and guest service quality, the work of the service staff is dynamically adjusted in a timely manner. According to the prediction results obtained by the guest service prediction model, the scheduling information of the service staff is dynamically adjusted in a timely manner to avoid overload of service staff in advance and optimize the work of service staff in a timely manner.

[0051] The above is a further detailed description of the present invention in combination with a specific implementation method, and it cannot be determined that the specific implementation of the present invention is limited to this; for technical personnel in the technical field to which the present invention belongs and related technical fields, based on the technical solution of the present invention, the expansion and replacement of operating methods and data should all fall within the protection scope of the present invention.

Claims

1. The hotel intelligent guide and convenient service system based on artificial intelligence is characterized by: It includes a guest information module, a service information module, a quality monitoring module, a dynamic analysis module and a dynamic adjustment module, a central control module, and a visual control module; the guest information module collects and manages guest information, including the guest's name, room number, health information, and room service level; the service information module collects and organizes service personnel information, shift information, and service personnel level; The quality monitoring module collects and organizes guest service information in real time, including service records, service execution rates, guest feedback and evaluation, and service error events; The dynamic analysis module first calculates the required service value M of each guest, M=α×0.4+β×0.6, where α represents the quantitative value corresponding to the guest room service level, and β represents the quantitative value corresponding to the guest health information level; Then calculate the service value N of each service personnel, N = γ × 0.4 + δ × 0.6, where γ represents the quantitative value of the service personnel's scheduling information, and δ represents the quantitative value of the service personnel's level; Finally, the quality monitoring value Y of each guest is calculated, Y = a × 0.1 + b × 0.3 + c × 0.3 + d × 0.3, where a represents the quantitative value of the service record completion, b represents the quantitative value of the service execution rate, c represents the quantitative value of the guest feedback evaluation, and d represents the quantitative value of the service error event; The dynamic adjustment module first calculates the service evaluation value Q of each guest, Q=M-N+1-Y, Q is greater than 0.3, indicating that the guest service evaluation is unqualified, and then further compares the required service value M and the corresponding service value N of this guest. The difference MN is greater than 0.2 and less than 0.4, indicating that the service staff and the service required by the guest do not match. The central guide and control module controls the visualization control module to send information to replace the service staff. The difference MN is greater than 0.4, indicating that the guest's service situation is not good and the service level needs to be improved. The central guide and control module controls the visualization control module to send information to improve the corresponding service level of the guest. Otherwise, it means qualified. The central guide and control module controls the visualization control module to display that the guest service is normal.

2. The hotel intelligent guide and convenient service system based on artificial intelligence according to claim 1 is characterized in that: The dynamic analysis module quantifies the information sent by the guest information module, the service information module, and the quality monitoring module. First, the guest information collected by the guest information module is retrieved, and classified according to the room number and name of each guest. The guest name corresponding to a room service level is used as an information package, and the guest room service level and the guest health information level are quantified. The guest service information is quantified into three levels of 1-3, and the guest service level is quantified into three required service levels of SVIP, VIP, and ordinary, so as to facilitate the evaluation of the service needs of the guests, and the required service value M of each guest is calculated, M=α×0.3+β×0.7, wherein α represents the quantified value corresponding to the guest service level, the first level corresponds to α of 1, the second level corresponds to α of 0.6, and the third level corresponds to α of 0.

4. β represents the quantitative value corresponding to the guest service level. The SVIP service level corresponds to β of 1, the VIP service level corresponds to β of 0.6, and the ordinary service level corresponds to β of 0.4; Then, the service personnel information is used as a label to quantify the scheduling information and service personnel level. The scheduling information is quantified into normal scheduling, normal overtime, and fatigue overtime. The service personnel level is quantified into three service qualifications: SVIP, VIP, and ordinary. This is convenient for evaluating the service capabilities of service personnel and calculating the service value N of each service personnel, N = γ × 0.4 + δ × 0.6, where γ represents the quantified value of the service personnel's scheduling information. Normal scheduling γ is 1, normal overtime γ is 0.6, and fatigue overtime γ is 0.

1. δ represents the quantified value of the service personnel level. SVIP service qualification corresponds to δ of 1, VIP service qualification corresponds to δ of 0.6, and ordinary service qualification corresponds to δ of 0.

4. Finally, the guests are classified according to their room numbers and corresponding names, and the corresponding service record completion is quantified into qualified and unqualified. The service execution rate is divided into qualified and unqualified, the guest feedback evaluation is divided into satisfied, general and unsatisfied, the service error events are divided into 0 times, 1-2 times, and unqualified, and the quality monitoring value Y of each guest is calculated, Y=a×0.1+b×0.3+c×0.3+d×0.3, where a represents the quantitative value of the service record completion degree, the service record completion degree is qualified a is 1, and unqualified a is 0, b represents the quantitative value of the service execution rate, the service execution rate is qualified b is 1, and unqualified b is 0, c represents the quantitative value of the guest feedback evaluation, the guest feedback evaluation is satisfied general is 1, general general is 0.7, and unqualified general is 0.2, d represents the quantitative value of the service error event, the service error event 0 times d is 1, 1-2 times d is 0.4, and unqualified d is 0.

3. The hotel intelligent guide and convenient service system based on artificial intelligence according to claim 2 is characterized in that: The dynamic adjustment module monitors the range of normal dynamic adjustment in real time by calculating the service load value. According to the required service value M of each guest and the service value N of each service staff, when the number of guests is n and the number of service staff is h, the total service value required by the guests is MZ=M1+M2+M3+......+Mn. Among them, M1, M2, M3...Mn are the service values ​​required by the 1st, 2nd, 3rd...nth guests; Service load value NZ = N1 + N2 + N3 ... + Nn, where N1, N2, N3 ... Nn are the service values ​​of the 1st, 2nd, 3rd ... nth service personnel; When MZ is greater than NZ, it means that the service personnel are overloaded and exceed the normal dynamic adjustment range. The dynamic adjustment module controls the visual control module through the central control module to add or second other temporary service personnel to ensure service quality; When MZ is less than or equal to NZ, it indicates that the service load is normal, the dynamic adjustment module is dynamically adjusted normally, and the visualization control module is controlled by the central control module to display normal service information.

4. The hotel intelligent guide and convenient service system based on artificial intelligence as claimed in claim 2, characterized in that: The service level classification of the guests is firstly to formulate a clear service level classification standard based on the guest's room number, age, health information, and special service needs; The service staff will then check and evaluate the guest's actual situation against the service level standards, including the guest's room number, age, health information, and special service needs; According to the evaluation results, guests are classified into corresponding service levels, SVIP service level, VIP service level, and ordinary service level; Finally, the guests’ service information and service levels were substituted into the guest big data model and adjusted iteratively. It was found that the SVIP service level corresponds to β of 1, the VIP service level corresponds to β of 0.6, and the ordinary service level corresponds to β of 0.

4.

5. The hotel intelligent guide and convenient service system based on artificial intelligence as claimed in claim 1, characterized in that: The central control module collects and organizes the guest service information in real time according to the quality monitoring module, including service records, service execution rate, guest feedback evaluation and service error events, and retrieves the guest's quality monitoring value Y, the quantitative value a of the service record completion, the quantitative value b of the service execution rate, the quantitative value c of the guest feedback evaluation, and the quantitative value d of the service error event, and then retrieves the corresponding guest service prediction model. According to the prediction results obtained by the guest service prediction model, the corresponding service personnel give feedback on the prediction results through the visual control module to understand the actual application effect of the prediction model. Finally, the guest service prediction model is optimized and upgraded according to the feedback model. At the same time, when the prediction results are accurate, the central control module makes dynamic adjustments to the service personnel's scheduling information in a timely manner according to the guest's prediction results.

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