A dynamic optimization method for tourism service quality based on tourists' evaluation

Abstract

The present invention discloses a method for dynamically optimizing tourism service quality based on tourist evaluation. The method integrates and supervises tourist evaluation statistical information in tourist attraction areas, adopts a big data analysis model to encode and fuse the information position of tourist evaluation statistical information, and evaluates service categories at the same time. The service category complaint concentration points are analyzed through service category scoring statistical data. The dynamic optimization order of service defects of service categories is calculated through the service category complaint concentration points. According to the dynamic optimization order of service defects, the tourist attraction area service adjustment steps and the tourist attraction area service defect improvement plan for the service category are generated. The method combines comprehensive information processing and dynamic optimization, which helps to improve the service quality and the efficiency of tourist attraction area services and protect the interests of tourists.

Description

Technical Field

[0001] The present invention relates to the field of service quality optimization in tourist attractions, and in particular to a method for dynamically optimizing tourist service quality based on visitor evaluation. Background Art

[0002] With the improvement of residents' living standards, the tourism industry has developed rapidly. Faced with the good development prospects and market demand of the tourism industry, how to achieve standardized management of tourism and how to provide residents with more professional, systematic and comprehensive tourism services have become hot topics. In such an environment, various tourism service quality information management platforms have emerged.

[0003] In the related art, since each tourism service quality information management platform has developed a user review function, there is a massive amount of review data for tourist attractions on each tourism information platform. However, due to the different data collection / storage standards of different tourism information platforms, the massive review data is scattered and complicated, which requires users to spend more time browsing these scattered and complicated review data on various tourism information platforms to obtain the information they need, and the existing platform cannot improve the service quality based on the feedback results of tourists, resulting in the inability to improve tourism services in a timely manner. Summary of the invention

[0004] In order to solve at least one of the above technical problems, the present invention proposes a method for dynamically optimizing tourism service quality based on tourist evaluation.

[0005] The first aspect of the present invention provides a method for dynamically optimizing tourism service quality based on tourist evaluation, comprising:

[0006] Step 101: Collecting tourist evaluation statistics in the supervised tourist attraction area, wherein the tourist evaluation statistics include tourist evaluation service attitude, facility conditions, transportation convenience, tour guide service, cleanliness and sanitation, catering quality, price rationality, and overall experience;

[0007] Step 102: perform information position encoding and softmax function normalization processing on the tourist evaluation statistical information according to the big data analysis model to obtain tourist evaluation feedback results;

[0008] Step 103: Score and count the service categories in the supervised tourist attraction area based on the feedback results of tourists' evaluations, and obtain service category score statistics;

[0009] Step 104: integrating the service category difference characteristic tourist evaluation statistical information in the process of service category difference characteristic in different time periods through service category scoring statistical data, monitoring the change of complaint concentration point of service category according to the service category difference characteristic tourist evaluation statistical information, and obtaining the service category complaint concentration point change monitoring information;

[0010] Step 105: Based on the monitoring information of the concentrated point jump of service category complaints, the service quality of different tourist groups in the supervised tourist attraction area is dynamically optimized to obtain the dynamic optimization order of service defects of the service category;

[0011] Step 106, using the service defect dynamic optimization order of the service category to generate the tourist attraction area service adjustment steps and the tourist attraction area service defect improvement plan for the service category;

[0012] Step 107: Use the improvement plan to upgrade and improve the tourism service, and repeat steps 102-106 to dynamically optimize the service quality for different tourist groups.

[0013] In this solution, the step 101 collects tourist evaluation statistics in the supervised tourist attraction area, and the tourist evaluation statistics include tourist evaluation service attitude, facility conditions, transportation convenience, tour guide service, cleanliness and hygiene, catering quality, price rationality, and overall experience, specifically:

[0014] Collecting tourist evaluation samples in the supervised tourist attraction area according to preset unit time using the scoring scales issued in advance, wherein the scoring scales issued in advance include SERVQUAL scale, LQI scale, HOLSAT scale, TSI scale, and IPA scale;

[0015] The tourist evaluation samples are integrated according to the preset unit time using the scoring scale issued in advance to obtain tourist evaluation statistical information, which includes tourists' evaluation of service attitude, facility conditions, transportation convenience, tour guide service, cleanliness, catering quality, price rationality, and overall experience.

[0016] Said service attitude includes the friendliness, politeness and professionalism of the staff;

[0017] The facility conditions include whether the facilities of the tourist attraction or hotel are complete, comfortable and safe;

[0018] The transportation convenience includes the comfort, punctuality and convenience of transportation;

[0019] The tour guide services include the tour guide's knowledge level, interpretation ability and service attitude;

[0020] The cleanliness and sanitation mentioned above include the cleanliness and sanitary conditions of scenic spots, hotels and restaurants;

[0021] The catering quality includes the taste, variety and hygiene of the food;

[0022] The reasonableness of the price includes whether the price of the tourism service is consistent with its quality;

[0023] The overall experience includes tourists' overall satisfaction with the entire travel itinerary, including entertainment and activity arrangements.

[0024] In this solution, the step 102 is to perform information position encoding and softmax function normalization processing on the tourist evaluation statistical information according to the big data analysis model to obtain the tourist evaluation feedback result, which is specifically:

[0025] Establish an attention mechanism based on the big data analysis model, and use the information content of tourist evaluation statistics to determine the data input similarity attention score of the attention mechanism's query vector and key vector;

[0026] Screening each type of statistical information in the tourist evaluation statistical information as data to be processed, and setting the difference features of the data to be processed, and sequentially setting the difference features at the input and output of the attention mechanism until the data input similarity of the attention mechanism reaches the determined data input similarity attention score, thereby obtaining an information attention mechanism;

[0027] When there is a difference feature of the missing information attention mechanism, a new information attention mechanism is established for the difference feature of the missing attention mechanism to obtain an attention mechanism of the difference feature, and the attention mechanism of the difference feature is formed into a big data analysis model;

[0028] Calculate the value vector of each differential feature in the differential feature information attention mechanism, and determine the weights of different scales according to the value vector;

[0029] Setting fluctuation ranges of different scales affected by different groups of people, determining statistical errors of the scales according to the fluctuation ranges of different scales affected by different groups of people and weights of different scales, compensating and optimizing the statistical errors of the scales, and obtaining tourist evaluation statistical information after information position coding;

[0030] The tourist evaluation statistical information after information position encoding is normalized by softmax function to obtain the tourist evaluation feedback result.

[0031] In this solution, step 103, the service categories in the supervised tourist attractions area are scored and counted according to the feedback results of tourists' evaluations, and service category scoring statistics are obtained, which are specifically:

[0032] The tourist evaluation service attitude, facility conditions, transportation convenience, and tour guide service information in the tourist evaluation feedback results are plotted as a change curve of tourist evaluation influencing factors;

[0033] Integrate the change curves of tourist evaluation influencing factors of different tourist groups in the service category, perform change node analysis on the change curves of tourist evaluation influencing factors and the change curves of tourist evaluation influencing factors of different tourist groups, and concentrate the types of tourist dissatisfaction in the change curves of tourist evaluation influencing factors whose variances with the change curves of tourist evaluation influencing factors of different tourist groups exceed the set threshold, so as to obtain the concentrated tourist dissatisfaction service characteristics;

[0034] The concentrated features of dissatisfied services for tourists are evaluated into categories of dissatisfied tourists in different service categories, and different labels are used to mark the categories of dissatisfied tourists in different service categories, so as to obtain the categories of dissatisfied tourists in different service categories;

[0035] According to the types of services that tourists are dissatisfied with, the service categories within the supervised tourist attractions area are scored and counted to obtain service category scoring statistical data.

[0036] In this scheme, the step 104 integrates the service category difference characteristic tourist evaluation statistical information in the process of integrating the difference characteristics of the service categories in different time periods through the service category scoring statistical data, and monitors the change of the complaint concentration point of the service category according to the service category difference characteristic tourist evaluation statistical information, and obtains the service category complaint concentration point change monitoring information, which is specifically:

[0037] Through the statistical data of service category scores and the characteristic annotation of the types of tourists' dissatisfaction with different service categories, the statistical information of tourists' evaluation on the service category differences in the process of supervising the regional differences of tourist attractions in different periods of time is extracted;

[0038] Integrate the fluctuation range of tourist evaluation statistics of service category complaint concentration points of different tourist groups, and obtain the fluctuation range information of tourist evaluation at service category complaint concentration points;

[0039] Performing a change node analysis on the statistical information of tourists' evaluations with different characteristics of the service categories and the fluctuation range information of tourists' evaluations at the concentrated points of complaints of the service categories, and obtaining tourists' satisfaction information with different characteristics of the service categories within the supervised tourist attractions area;

[0040] The difference characteristic tourist satisfaction information of the service category in the supervised tourist attraction area is linearly calculated through a position-by-position feedforward network, and a function operation is activated based on the difference characteristic tourist satisfaction information and the difference characteristic tourist evaluation statistical information of the service category after the linear calculation, so as to identify the changes between the service categories in different time periods and obtain the service category improvement point information;

[0041] Based on the difference characteristic tourist satisfaction information and service category improvement point information, the service category complaint concentration point jump monitoring is performed to obtain the service category complaint concentration point jump monitoring information.

[0042] In this solution, step 105, based on the monitoring information of the concentrated point jump of service category complaints, dynamically optimizes the service quality of different tourist groups in the supervised tourist attraction area, and obtains the dynamic optimization order of service defects of the service category, which is specifically:

[0043] Integrated supervision of tourist attraction area conditions and service time data in tourist attraction areas;

[0044] According to the tourist attraction area situation and service period data of the supervised tourist attraction area, the subsequent service satisfaction prediction is conducted for each difference characteristic complaint concentration point of the service category with different characteristics within the supervised tourist attraction area, and a subsequent service satisfaction prediction report for the service defect complaint concentration point is obtained;

[0045] Through the service category complaint concentration point jump monitoring information and the service defect complaint concentration point subsequent step service satisfaction prediction report, the service defect complaint concentration point subsequent step service satisfaction prediction is carried out for the service category, and the dynamic optimization order of service defects in the service category is obtained.

[0046] In this solution, the step 106, using the service defects of the service category to dynamically optimize the order to generate the tourist attraction area service adjustment steps and the tourist attraction area service defect improvement plan for the service category, is specifically:

[0047] Feeding back the dynamically optimized order of service defects of the service categories to the project evaluation table of scenic area managers to obtain comprehensive service quality information of the service categories;

[0048] Analyze the comprehensive service quality information of service categories, dynamically optimize and supervise the safety level of the facilities in the tourist attraction area, and generate a rectification and improvement plan for the tourist attraction area based on the safety level to obtain the service adjustment steps for the tourist attraction area;

[0049] Classify the service quality of the service category by using the comprehensive service quality information of the service category to obtain the service quality category of the service defect complaint concentration point of the service category, wherein the service quality category of the service defect complaint concentration point of the service category includes service categories with different characteristics such as poor service quality, general service quality, and good complex service quality;

[0050] Generating a tourist attraction area service defect improvement plan for service categories of different service quality categories, and sending the tourist attraction area service defect improvement plan to the scenic area management personnel information integration center of the corresponding service category;

[0051] Continuously monitor the concentration points of service defect complaints of service categories, and dynamically optimize the order of service defects of service categories.

[0052] Beneficial effects:

[0053] The present invention discloses a method for dynamically optimizing tourism service quality based on tourist evaluation, which is used to monitor the service category complaint concentration points in tourist attraction areas to improve the service safety of tourist attraction areas. The method first integrates and supervises the tourist evaluation statistical information in the tourist attraction area, adopts a big data analysis model to encode and fuse the information position of the tourist evaluation statistical information, and evaluates the service category at the same time. Through the service category scoring statistical data, the service category complaint concentration points are analyzed. The service category service defect dynamic optimization order is calculated through the service category complaint concentration points. According to the service defect dynamic optimization order, the tourist attraction area service adjustment steps and the tourist attraction area service defect improvement plan for the service category are generated. The method combines information comprehensive processing and dynamic optimization, which helps to improve the service quality and the efficiency of tourist attraction area services and protect the interests of tourists. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flow chart of a method for dynamically optimizing tourism service quality based on visitor evaluations of the present invention is shown. DETAILED DESCRIPTION

[0055] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0057] like Figure 1 As shown, the first aspect of the present invention provides a method for dynamically optimizing tourism service quality based on tourist evaluation, comprising:

[0058] Step 101: Collecting tourist evaluation statistics in the supervised tourist attraction area, wherein the tourist evaluation statistics include tourist evaluation service attitude, facility conditions, transportation convenience, tour guide service, cleanliness and sanitation, catering quality, price rationality, and overall experience;

[0059] Step 102: perform information position encoding and softmax function normalization processing on the tourist evaluation statistical information according to the big data analysis model to obtain tourist evaluation feedback results;

[0060] Step 103: Score and count the service categories in the supervised tourist attraction area based on the feedback results of tourists' evaluations, and obtain service category score statistics;

[0061] Step 104: integrating the service category difference characteristic tourist evaluation statistical information in the process of service category difference characteristic in different time periods through service category scoring statistical data, monitoring the change of complaint concentration point of service category according to the service category difference characteristic tourist evaluation statistical information, and obtaining the service category complaint concentration point change monitoring information;

[0062] Step 105: Based on the monitoring information of the concentrated point jump of service category complaints, the service quality of different tourist groups in the supervised tourist attraction area is dynamically optimized to obtain the dynamic optimization order of service defects of the service category;

[0063] Step 106, using the service defect dynamic optimization order of the service category to generate the tourist attraction area service adjustment steps and the tourist attraction area service defect improvement plan for the service category;

[0064] Step 107: Use the improvement plan to upgrade and improve the tourism service, and repeat steps 102-106 to dynamically optimize the service quality for different tourist groups.

[0065] It should be noted that, by utilizing the integrated tourist evaluation statistical information in the tourist attraction area, the big data analysis model is used to encode and fuse the information position of the integrated tourist evaluation samples, the fused tourist evaluation statistical information is used for service category scoring statistics, and the complaint concentration point jump monitoring of the service category is performed through the scoring statistical data to obtain the complaint concentration point jump monitoring information, and the complaint concentration point jump monitoring information is used to dynamically optimize the service defect dynamic optimization order of the service category, and finally form the tourist attraction area service adjustment steps and the tourist attraction area service defect improvement plan of the service category in the supervision tourist attraction area; using the embodiment of the present invention, the service category service defect complaint concentration point of the service category with different characteristics in the supervision tourist attraction area can be monitored in real time, the service quality of the service defect complaint concentration point of the service category can be dynamically optimized, and the service defect improvement plan of the service category can be formed, which can effectively reduce the service quality of the service category with different characteristics in the supervision tourist attraction area, and at the same time, it is judged whether there is unreasonable setting of tourist attraction area facilities in the supervision tourist attraction area, improve the safety of tourist attraction area facility setting, improve the real-time monitoring of service categories, and ensure the progress of services in the tourist attraction area.

[0066] like Figure 1As shown, through the embodiment of the present invention, the step 101 collects tourist evaluation statistics in the supervised tourist attraction area, and the tourist evaluation statistics include tourist evaluation service attitude, facility conditions, transportation convenience, tour guide service, cleanliness and hygiene, catering quality, price rationality, and overall experience, specifically:

[0067] Collecting tourist evaluation samples in the supervised tourist attraction area according to preset unit time using the scoring scales issued in advance, wherein the scoring scales issued in advance include SERVQUAL scale, LQI scale, HOLSAT scale, TSI scale, and IPA scale;

[0068] The tourist evaluation samples are integrated according to the preset unit time using the scoring scale issued in advance to obtain tourist evaluation statistical information, which includes tourists' evaluation of service attitude, facility conditions, transportation convenience, tour guide service, cleanliness, catering quality, price rationality, and overall experience.

[0069] It should be noted that the SERVQUAL scale includes:

[0070] Tangibles: the modernity of the facilities and equipment, the visual appeal of the physical facilities, the appearance and appearance of the staff, the intuitiveness of the promotional materials. Reliability: the reliability and consistency of the service, the degree to which the promised services are fulfilled. Responsiveness: the willingness of the staff to serve, the willingness to help customers, and the willingness to provide prompt service. Assurance: the knowledge level of the staff, the courtesy and respect of the staff, the credibility of the staff and the sense of security of the customers. Empathy: the care and personalization of the staff, the understanding of the needs of the customers, and the attention paid to the specific needs of the customers.

[0071] The LQI (Lodging Quality Index) scale includes:

[0072] Front desk service: the ease of booking, check-in, check-out and the professionalism of the staff. Room cleanliness: the cleanliness of the room, the hygiene of the bedding and bathroom. Food and beverage: the quality of service in the restaurant, the quality and variety of food. Amenities: the quality and availability of facilities such as the gym, swimming pool, business center, etc. Room facilities: the modernization and functionality of the equipment in the room.

[0073] The HOLSAT (Holiday Satisfaction Model) scale includes:

[0074] Expectations and actual performance: evaluate the gap between expectations before the trip and the actual experience. Satisfaction dimension: includes specific satisfaction with accommodation, catering, activity arrangements, tour guide services, etc.

[0075] The TSI (Tourist Satisfaction Index) scale includes:

[0076] Experience satisfaction: Satisfaction with specific tourism activities and projects. Service satisfaction: Satisfaction with tour guides, service personnel, transportation and other services. Overall satisfaction: Comprehensive satisfaction with the entire tourism experience.

[0077] The IPA (Importance-Performance Analysis) scale includes:

[0078] Importance: The importance that tourists attach to each service and facility. Performance: The performance evaluation of each service and facility in actual experience. Gap analysis: Identify areas that need improvement by analyzing the gap between importance and performance.

[0079] Service attitude includes the friendliness, politeness, and professionalism of the staff;

[0080] Facility conditions include whether the facilities of tourist attractions or hotels are complete, comfortable and safe;

[0081] Transportation convenience includes the comfort, punctuality and convenience of transportation;

[0082] Tour guide services include the tour guide’s knowledge level, interpretation ability and service attitude;

[0083] Cleanliness and sanitation include the cleanliness and sanitary conditions of tourist attractions, hotels, and restaurants;

[0084] Catering quality includes the taste, variety and hygiene of food;

[0085] Price reasonableness includes whether the price of tourism services is consistent with their quality;

[0086] The overall experience includes tourists' overall satisfaction with the entire travel itinerary, including entertainment and activity arrangements.

[0087] According to the embodiment of the present invention, in step 102, the tourist evaluation statistical information is encoded with information position and normalized with a softmax function according to the big data analysis model to obtain tourist evaluation feedback results, which are specifically:

[0088] Establish an attention mechanism based on the big data analysis model, and use the information content of tourist evaluation statistics to determine the data input similarity attention score of the attention mechanism's query vector and key vector;

[0089] Screening each type of statistical information in the tourist evaluation statistical information as data to be processed, and setting the difference features of the data to be processed, and sequentially setting the difference features at the input and output of the attention mechanism until the data input similarity of the attention mechanism reaches the determined data input similarity attention score, thereby obtaining an information attention mechanism;

[0090] When there is a difference feature of the missing information attention mechanism, a new information attention mechanism is established for the difference feature of the missing attention mechanism to obtain an attention mechanism of the difference feature, and the attention mechanism of the difference feature is formed into a big data analysis model;

[0091] Calculate the value vector of each differential feature in the differential feature information attention mechanism, and determine the weights of different scales according to the value vector;

[0092] Setting fluctuation ranges of different scales affected by different groups of people, determining statistical errors of the scales according to the fluctuation ranges of different scales affected by different groups of people and weights of different scales, compensating and optimizing the statistical errors of the scales, and obtaining tourist evaluation statistical information after information position coding;

[0093] The tourist evaluation statistical information after information position encoding is normalized by softmax function to obtain the tourist evaluation feedback result.

[0094] It should be noted that since the monitoring information of tourist evaluation may be affected by climate interference and terrain, the integrated tourist evaluation samples may have noise or information jumps. The big data analysis model is used to encode the information position of the tourist evaluation statistical information, which can effectively evaluate and delete the information of different scales in the tourist evaluation statistical information being affected by different groups of people, greatly improving the accuracy of the integrated tourist evaluation samples. Then, the softmax function is used for normalization processing to finally obtain the tourist evaluation feedback result, ensuring the credibility and robustness of the tourist evaluation statistical information; the value vector is the data input similarity from the root node to the leaf node, and the shorter value vector indicates that the difference feature is relatively easy to be isolated, which is usually considered to be different scales affected by different groups of people; the tourist evaluation feedback result is the feedback result after the tourist evaluation statistical information is processed by the different scales affected by different groups of people and normalized by the softmax function.

[0095] According to the embodiment of the present invention, step 103, scoring and counting the service categories in the supervised tourist attraction area according to the tourist evaluation feedback results, and obtaining service category scoring statistical data, specifically:

[0096] The tourist evaluation service attitude, facility conditions, transportation convenience, and tour guide service information in the tourist evaluation feedback results are plotted as a change curve of tourist evaluation influencing factors;

[0097] Integrate the change curves of tourist evaluation influencing factors of different tourist groups in the service category, perform change node analysis on the change curves of tourist evaluation influencing factors and the change curves of tourist evaluation influencing factors of different tourist groups, and concentrate the types of tourist dissatisfaction in the change curves of tourist evaluation influencing factors whose variances with the change curves of tourist evaluation influencing factors of different tourist groups exceed the set threshold, so as to obtain the concentrated tourist dissatisfaction service characteristics;

[0098] The concentrated features of dissatisfied services for tourists are evaluated into categories of dissatisfied tourists in different service categories, and different labels are used to mark the categories of dissatisfied tourists in different service categories, so as to obtain the categories of dissatisfied tourists in different service categories;

[0099] According to the types of services that tourists are dissatisfied with, the service categories within the supervised tourist attractions are scored and counted to obtain service category scoring statistical data.

[0100] It should be noted that converting the tourist evaluation feedback results into a tourist evaluation influencing factor change curve can intuitively observe the changes in tourist evaluation during the transmission and reception process; integrating the tourist evaluation influencing factor change curves of different tourist groups in the service category and the tourist evaluation influencing factor change curve for change node analysis can accurately evaluate the types of tourist dissatisfaction formed by the service category in the tourist evaluation influencing factor change curve, and at the same time evaluate multiple service categories through the differences between the types of tourist dissatisfaction; by converting the tourist evaluation feedback results into a scatter plot, it is possible to more accurately evaluate the tourist evaluation statistical information formed by the service category, and then evaluate the service category, which greatly improves the evaluation efficiency and evaluation accuracy of the service category, and even if there is interference from a large number of tourist evaluation statistical information of other objects, the service category can be accurately evaluated; the service category includes all service categories with difference characteristics in the supervision tourist attraction area through the scoring scale issued in advance, and the scoring scale issued in advance can collect the tourist evaluation statistical information of all the service categories.

[0101] According to the embodiment of the present invention, in step 104, the service category difference characteristic tourist evaluation statistical information in the process of integrating the difference characteristics of the service categories in different time periods is integrated through the service category scoring statistical data, and the service category is monitored for the change of the complaint concentration point according to the service category difference characteristic tourist evaluation statistical information, and the service category complaint concentration point change monitoring information is obtained, which is specifically:

[0102] Through the statistical data of service category scores and the characteristic annotation of the types of tourists' dissatisfaction with different service categories, the statistical information of tourists' evaluation on the service category differences in the process of supervising the regional differences of tourist attractions in different periods of time is extracted;

[0103] Integrate the fluctuation range of tourist evaluation statistics of service category complaint concentration points of different tourist groups, and obtain the fluctuation range information of tourist evaluation at service category complaint concentration points;

[0104] Performing a change node analysis on the statistical information of tourists' evaluations with different characteristics of the service categories and the fluctuation range information of tourists' evaluations at the concentrated points of complaints of the service categories, and obtaining tourists' satisfaction information with different characteristics of the service categories within the supervised tourist attractions area;

[0105] The difference characteristic tourist satisfaction information of the service category in the supervised tourist attraction area is linearly calculated through a position-by-position feedforward network, and a function operation is activated based on the difference characteristic tourist satisfaction information and the difference characteristic tourist evaluation statistical information of the service category after the linear calculation, so as to identify the changes between the service categories in different time periods and obtain the service category improvement point information;

[0106] Based on the difference characteristic tourist satisfaction information and service category improvement point information, the service category complaint concentration point jump monitoring is performed to obtain the service category complaint concentration point jump monitoring information.

[0107] It should be noted that, by analyzing the statistical information of tourists' evaluations on difference characteristics of service categories, the concentration points of complaints with difference characteristics of service categories within the supervised tourist attractions are evaluated, and jump monitoring of the concentration points of complaints with difference characteristics is performed to obtain jump monitoring information of the concentration points of complaints with difference characteristics of service categories. By using the embodiments of the present invention, it is possible to simultaneously evaluate the concentration points of complaints with difference characteristics of service categories, and by analyzing the changes between service categories, it is possible to provide information on the interaction and security between service categories, which is helpful to monitor the concentration points of complaints with difference characteristics of service categories within the supervised tourist attractions.

[0108] According to the embodiment of the present invention, step 105, based on the monitoring information of the concentrated point jump of service category complaints, dynamically optimizes the service quality of different tourist groups in the supervised tourist attraction area, and obtains the dynamic optimization order of service defects of the service category, which is specifically:

[0109] Integrated supervision of tourist attraction area conditions and service time data in tourist attraction areas;

[0110] According to the tourist attraction area situation and service period data of the supervised tourist attraction area, the subsequent service satisfaction prediction is conducted for each difference characteristic complaint concentration point of the service category with different characteristics within the supervised tourist attraction area, and a subsequent service satisfaction prediction report for the service defect complaint concentration point is obtained;

[0111] Through the service category complaint concentration point jump monitoring information and the service defect complaint concentration point subsequent step service satisfaction prediction report, the service defect complaint concentration point subsequent step service satisfaction prediction is carried out for the service category, and the dynamic optimization order of service defects in the service category is obtained.

[0112] It should be noted that, by predicting the satisfaction level at the concentrated points of service defect complaints of service categories within the regulated tourist attractions, a dynamic optimization order of service defects of service categories is obtained. The dynamic optimization order of service defects can be used to determine whether there is potential safety service quality in the difference characteristics of service categories, improve the safety of the difference characteristics of tourist attractions and the real-time monitoring of tourist attractions. The dynamic optimization order of service defects of service categories is the satisfaction prediction of the subsequent steps of the service at the concentrated points of service defect complaints of service categories.

[0113] According to the embodiment of the present invention, the step 106, using the service defect dynamic optimization order of the service category to generate the tourist attraction area service adjustment step and the tourist attraction area service defect improvement plan of the service category, is specifically as follows:

[0114] Feeding back the dynamically optimized order of service defects of the service categories to the project evaluation table of scenic area managers to obtain comprehensive service quality information of the service categories;

[0115] Analyze the comprehensive service quality information of service categories, dynamically optimize and supervise the safety level of the facilities in the tourist attraction area, and generate a rectification and improvement plan for the tourist attraction area based on the safety level to obtain the service adjustment steps for the tourist attraction area;

[0116] Classify the service quality of the service category by using the comprehensive service quality information of the service category to obtain the service quality category of the service defect complaint concentration point of the service category, wherein the service quality category of the service defect complaint concentration point of the service category includes service categories with different characteristics such as poor service quality, general service quality, and good complex service quality;

[0117] Generating a tourist attraction area service defect improvement plan for service categories of different service quality categories, and sending the tourist attraction area service defect improvement plan to the scenic area management personnel information integration center of the corresponding service category;

[0118] Continuously monitor the concentration points of service defect complaints of service categories, and dynamically optimize the order of service defects of service categories.

[0119] It should be noted that by feeding back the dynamic optimization order of service defects within the preset standard value range, the overall difference characteristics of service categories with regional difference characteristics in the supervision of tourist attractions can be comprehensively observed; by analyzing the comprehensive service quality information of service categories and generating a tourist attraction area management method at the same time, it is helpful to evaluate the setting of unreasonable facilities in tourist attraction areas, and at the same time, through the tourist attraction area service adjustment step, it is possible to propose an adjustment and improvement plan for the facilities in the tourist attraction area, thereby improving the real-time monitoring of service categories; different tourist attraction area service defect improvement plans are formed for service categories of different service quality categories, providing more personalized difference characteristic improvement plans, thereby improving the accuracy of service defect improvement plans and the difference characteristic safety of service categories in the supervision of tourist attractions; by continuously monitoring the service defect complaint concentration points of service categories, iterating the dynamic optimization order of service defects, and iterating the difference characteristic improvement plans of service categories in real time, so that service categories can receive the latest and accurate service defect improvement plans; the scenic area management personnel project evaluation table is a pre-set statistical table, and the dynamic optimization order of service defects is fed back to the statistical table, which can intuitively observe the overall service quality of different tourist groups of service categories with difference characteristics in the supervision of tourist attractions.

[0120] The embodiment of the present invention also includes:

[0121] By monitoring the statistical information of tourists' evaluation in the tourist attraction area, a scatter plot of the statistical information of tourists' evaluation in the tourist attraction area is drawn;

[0122] Extracting information on types of tourists' dissatisfaction that have normal differences in service categories within the tourist attraction area through a scatter plot of tourist evaluation statistics in the tourist attraction area, analyzing the information on types of tourists' dissatisfaction, and learning the normal fluctuation range of the information on types of tourists' dissatisfaction;

[0123] Generating a scatter plot of tourist evaluation statistics in tourist attractions in real time, performing a change node analysis by extracting the fluctuation range of the scatter plot of tourist evaluation statistics in tourist attractions in real time and the normal fluctuation range, and obtaining the jump situation of the scatter plot of tourist evaluation statistics in tourist attractions;

[0124] A jump situation processing plan is generated through the event type and sent to the communication equipment of the scenic area management personnel of the tourist attraction area. At the same time, a tourist attraction area difference feature improvement plan is generated and sent to the service category scenic area management personnel information integration center that supervises the tourist attraction area difference features.

[0125] It should be noted that, since jumps may occur in the supervised tourist attraction area, the fluctuation range of the types of tourists' dissatisfaction with the normal difference characteristics of the service categories in the supervised tourist attraction area is used to study, and then the jumps in the tourist evaluation statistical information in the tourist attraction area are analyzed, and finally the jumps are evaluated, and the corresponding jump situation processing plan and the tourist attraction area difference characteristic improvement plan are generated at the same time; the present invention can effectively evaluate the jump situation in the tourist attraction area, and the jump situation processing plan can enable the tourist attraction area management personnel to quickly handle the jump situation, thereby improving the real-time monitoring of the tourist attraction area; the jump situation processing plan includes the jump situation type, the time and place of the jump situation; the tourist attraction area difference characteristic improvement plan can remind service category service personnel to deal with the jump situation in time, so that the service personnel can make defensive service defects in advance and improve service safety.

[0126] The present invention discloses a method for dynamic optimization of tourism service quality based on tourist evaluation, which is used for real-time jump monitoring of service category complaint concentration points in tourist attraction areas to improve the safety of tourist attraction areas and the stability of service categories. The method first integrates and supervises the statistical information of tourist evaluations in tourist attraction areas, adopts a big data analysis model to encode and fuse the information position of tourist evaluation statistics, and evaluates the service categories at the same time. Subsequently, the service category complaint concentration points are analyzed through the service category scoring statistics. Further, the service category service defect dynamic optimization order is calculated through the service category complaint concentration points. Finally, according to the service defect dynamic optimization order, the tourist attraction area service adjustment steps and the tourist attraction area service defect improvement plan for the service category are generated. The method combines comprehensive information processing and dynamic optimization, which helps to improve the service quality and the efficiency of tourist attraction area services and protect the interests of tourists.

[0127] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for dynamic optimization of tourism service quality based on tourist evaluation, characterized in that: The method includes: Step 101: Collecting tourist evaluation statistics in the supervised tourist attraction area, wherein the tourist evaluation statistics include tourist evaluation service attitude, facility conditions, transportation convenience, tour guide service, cleanliness and sanitation, catering quality, price rationality, and overall experience; Step 102: perform information position encoding and softmax function normalization processing on the tourist evaluation statistical information according to the big data analysis model to obtain tourist evaluation feedback results; Step 103: Score and count the service categories in the supervised tourist attraction area based on the feedback results of tourists' evaluations, and obtain service category score statistics; Step 104: integrating the service category difference characteristic tourist evaluation statistical information in the process of service category difference characteristic in different time periods through service category scoring statistical data, monitoring the change of complaint concentration point of service category according to the service category difference characteristic tourist evaluation statistical information, and obtaining the service category complaint concentration point change monitoring information; Step 105: Based on the monitoring information of the concentrated point jump of service category complaints, the service quality of different tourist groups in the supervised tourist attraction area is dynamically optimized to obtain the dynamic optimization order of service defects of the service category; Step 106, using the service defect dynamic optimization order of the service category to generate the tourist attraction area service adjustment steps and the tourist attraction area service defect improvement plan for the service category; Step 107: Use the improvement plan to upgrade and improve the tourism service, and repeat steps 102-106 to dynamically optimize the service quality for different tourist groups; The step 102 includes: Establish an attention mechanism based on the big data analysis model, and use the information content of tourist evaluation statistics to determine the data input similarity attention score of the attention mechanism's query vector and key vector; Screening each type of statistical information in the tourist evaluation statistical information as data to be processed, and setting the difference features of the data to be processed, and sequentially setting the difference features at the input and output of the attention mechanism until the data input similarity of the attention mechanism reaches the determined data input similarity attention score, thereby obtaining an information attention mechanism; When there is a difference feature of the missing information attention mechanism, a new information attention mechanism is established for the difference feature of the missing attention mechanism to obtain an attention mechanism of the difference feature, and the attention mechanism of the difference feature is formed into a big data analysis model; Calculate the value vector of each differential feature in the differential feature information attention mechanism, and determine the weights of different scales according to the value vector; Setting fluctuation ranges of different scales affected by different groups of people, determining statistical errors of the scales according to the fluctuation ranges of different scales affected by different groups of people and weights of different scales, compensating and optimizing the statistical errors of the scales, and obtaining tourist evaluation statistical information after information position coding; The tourist evaluation statistics after information position encoding are normalized by softmax function to obtain the tourist evaluation feedback result; The step 103 comprises: The tourist evaluation service attitude, facility conditions, transportation convenience, and tour guide service information in the tourist evaluation feedback results are plotted as a change curve of tourist evaluation influencing factors; Integrate the change curves of tourist evaluation influencing factors of different tourist groups in the service category, perform change node analysis on the change curves of tourist evaluation influencing factors and the change curves of tourist evaluation influencing factors of different tourist groups, and concentrate the types of tourist dissatisfaction in the change curves of tourist evaluation influencing factors whose variances with the change curves of tourist evaluation influencing factors of different tourist groups exceed the set threshold, so as to obtain the concentrated tourist dissatisfaction service characteristics; The concentrated features of dissatisfied services for tourists are evaluated into categories of dissatisfied tourists in different service categories, and different labels are used to mark the categories of dissatisfied tourists in different service categories, so as to obtain the categories of dissatisfied tourists in different service categories; According to the types of services that tourists are dissatisfied with, scoring and statistics are given for the service categories in the supervised tourist attractions area to obtain statistical data on the service category scores; The step 104 includes: Through the statistical data of service category scores and the characteristic annotation of the types of tourists' dissatisfaction with different service categories, the statistical information of tourists' evaluation on the service category differences in the process of supervising the regional differences of tourist attractions in different periods of time is extracted; Integrate the fluctuation range of tourist evaluation statistics of service category complaint concentration points of different tourist groups, and obtain the fluctuation range information of tourist evaluation at service category complaint concentration points; Performing a change node analysis on the statistical information of tourists' evaluations with different characteristics of the service categories and the fluctuation range information of tourists' evaluations at the concentrated points of complaints of the service categories, and obtaining tourists' satisfaction information with different characteristics of the service categories within the supervised tourist attractions area; The difference characteristic tourist satisfaction information of the service category in the supervised tourist attraction area is linearly calculated through a position-by-position feedforward network, and a function operation is activated based on the difference characteristic tourist satisfaction information and the difference characteristic tourist evaluation statistical information of the service category after the linear calculation, so as to identify the changes between the service categories in different time periods and obtain the service category improvement point information; Based on the difference characteristic tourist satisfaction information and service category improvement point information, the service category complaint concentration point jump monitoring is performed to obtain the service category complaint concentration point jump monitoring information.

2. A method for dynamic optimization of tourism service quality based on tourist evaluation according to claim 1, characterized in that: The step 101 comprises: Collecting tourist evaluation samples in the supervised tourist attraction area according to preset unit time using the scoring scales issued in advance, wherein the scoring scales issued in advance include SERVQUAL scale, LQI scale, HOLSAT scale, TSI scale, and IPA scale; Using the scoring scale issued in advance and integrating the tourist evaluation samples according to the preset unit time, the tourist evaluation statistical information is obtained, and the tourist evaluation statistical information includes the tourist evaluation service attitude, facility conditions, transportation convenience, tour guide service, cleanliness and sanitation, catering quality, price rationality, and overall experience; Said service attitude includes the friendliness, politeness and professionalism of the staff; The facility conditions include whether the facilities of the tourist attraction or hotel are complete, comfortable and safe; The transportation convenience includes the comfort, punctuality and convenience of transportation; The tour guide services include the tour guide's knowledge level, interpretation ability and service attitude; The cleanliness and sanitation mentioned above include the cleanliness and sanitary conditions of scenic spots, hotels and restaurants; The catering quality includes the taste, variety and hygiene of the food; The reasonableness of the price includes whether the price of the tourism service is consistent with its quality; The overall experience includes tourists' overall satisfaction with the entire travel itinerary, including entertainment and activity arrangements.

3. A method for dynamic optimization of tourism service quality based on tourist evaluation according to claim 1, characterized in that: The step 105 comprises: Integrated supervision of tourist attraction area conditions and service time data in tourist attraction areas; According to the tourist attraction area situation and service period data of the supervised tourist attraction area, the subsequent service satisfaction prediction is conducted for each difference characteristic complaint concentration point of the service category with different characteristics within the supervised tourist attraction area, and a subsequent service satisfaction prediction report for the service defect complaint concentration point is obtained; Through the service category complaint concentration point jump monitoring information and the service defect complaint concentration point subsequent step service satisfaction prediction report, the service defect complaint concentration point subsequent step service satisfaction prediction is carried out for the service category, and the dynamic optimization order of service defects in the service category is obtained.

4. A method for dynamic optimization of tourism service quality based on tourist evaluation according to claim 1, characterized in that: The step 106 includes: Feeding back the dynamically optimized order of service defects of the service categories to the project evaluation table of scenic area managers to obtain comprehensive service quality information of the service categories; Analyze the comprehensive service quality information of service categories, dynamically optimize and supervise the safety level of the facilities in the tourist attraction area, and generate a rectification and improvement plan for the tourist attraction area based on the safety level to obtain the service adjustment steps for the tourist attraction area; Classify the service quality of the service category by using the comprehensive service quality information of the service category to obtain the service quality category of the service defect complaint concentration point of the service category, wherein the service quality category of the service defect complaint concentration point of the service category includes service categories with different characteristics such as poor service quality, general service quality, and good complex service quality; Generating a tourist attraction area service defect improvement plan for service categories of different service quality categories, and sending the tourist attraction area service defect improvement plan to the scenic area management personnel information integration center of the corresponding service category; Continuously monitor the concentration points of service defect complaints of service categories, and dynamically optimize the order of service defects of service categories.

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