A method, system, medium, and processor for detecting tourist satisfaction.

Abstract

This application provides a method for detecting tourist satisfaction, comprising: collecting first facial expression data of a first tourist in a first area of ​​a first route within a tourist area; analyzing and judging the first tourist's emotions based on the first facial expression data; collecting second facial expression data of the first tourist in a second area of ​​the first route; analyzing and judging the first tourist's emotions based on the second facial expression data; and judging the first tourist's initial satisfaction with the route from the first area to the second area based on the change in the first tourist's emotions from the first area to the second area. This application's solution can conduct surveys on the vast majority of tourists entering a tourist area, improving its accessibility and accuracy. Furthermore, using emotional changes for judgment reduces the probability of misjudgment and improves survey accuracy.

Description

Technical Field

[0001] This invention relates to the field of tourism satisfaction survey technology, and in particular to a method, system, medium, and processor for detecting tourist satisfaction. Background Technology

[0002] As people's living standards improve, traveling has become synonymous with fashion and vacation. Tourist satisfaction is the lifeline of every tourist area, concerning its future development and revenue. Therefore, every tourist area adopts various methods to conduct tourist satisfaction surveys.

[0003] Traditional satisfaction surveys include questionnaires, interviews, observation, and data analysis. Questionnaires are distributed to the target audience in specific locations for on-the-spot completion and collection. However, this method is not only labor-intensive, time-consuming, and costly in terms of paper, but also only reaches a small portion of tourists. Although online surveys are possible with technological advancements, their reach and relevance remain poor, failing to accurately reflect tourists' evaluations of the tourist area. In addition, other methods such as interviews, observation, and data analysis (e.g., complaint data analysis) all suffer from low reach and an inability to accurately reflect the true thoughts of the overall tourist population (interviews often involve polite language, etc.).

[0004] With the development of information technology, various new survey methods have emerged. For example, Chinese invention patent application number CN202210986455.X discloses a tourism satisfaction survey system and method based on sentiment analysis. By obtaining and filtering the review texts of tourist attractions from various platforms, the system obtains the satisfaction level of the tourist attractions' related facilities corresponding to the sentiment classification results. However, this method can only obtain the evaluations of some tourists who have commented on the platforms, and the popularization of the survey is still limited.

[0005] Therefore, there is a need for a method, system, medium, and processor for measuring tourist satisfaction. Summary of the Invention

[0006] To address the issue that existing surveys can only reach a small portion of tourists, thus failing to accurately reflect tourists' evaluations of tourist areas, this invention provides a method, system, medium, and processor for detecting tourist satisfaction. This method can conduct surveys on the vast majority of visitors entering a tourist area, improving both reach and accuracy. The specific technical solution is as follows:

[0007] A method for measuring tourist satisfaction includes:

[0008] A camera was installed in the first area of ​​the first route in the tourist area to collect the first facial expression data of the first tourist.

[0009] The emotions of the first tourist are determined based on the analysis of the first facial expression data;

[0010] Cameras were installed in the second area of ​​the first route to collect data on the second facial expressions of the first tourist.

[0011] The emotions of the first tourist were determined based on the analysis of the second facial expression data;

[0012] Based on the emotional changes of the first tourist from the first area to the second area, the survey can determine the first tourist's initial satisfaction with the route between the first and second areas. This method can be used to conduct surveys of tourists entering the tourist area, improving its accessibility and accuracy. At the same time, using emotional changes for judgment reduces the probability of misjudgment and improves the accuracy of the survey.

[0013] Furthermore, the formula for calculating the first satisfaction level is as follows:

[0014] ;

[0015] In the above formula, The highest satisfaction score; The satisfaction value is the value of changing from a non-i-th emotion to the i-th emotion; Age coefficient; is the gender coefficient; n is the type of emotional change.

[0016] Furthermore, it also includes the following steps:

[0017] Record the time when the first tourist enters the first area and the second area respectively. Calculate the time spent on the route between the two areas based on the time difference between the two areas. Based on the time spent on the route and changes in emotions, determine the second level of satisfaction with the route between the first and second areas.

[0018] Furthermore, it also includes the following steps:

[0019] The formula for calculating the second level of satisfaction is as follows:

[0020] ;

[0021] In the above formula, This is the second highest satisfaction rating. This is the route dwell time coefficient; This is the highest satisfaction score.

[0022] Furthermore, it also includes the following steps:

[0023] The process involves detecting the time spent by the first tourist at each attraction and calculating the proportion of time spent at each attraction relative to the total time spent along the route, thus ranking the tourist's satisfaction with each attraction. Further steps include:

[0024] Based on changes in emotions, we can determine the direction for improving the route from the first area to the second area.

[0025] Furthermore, the improved direction for obtaining the route from the first area to the second area based on emotional changes includes the following steps:

[0026] If the mood change is from non-fatigue to fatigue, then it is determined that the route should include more rest areas and facilities.

[0027] If the emotional change is from non-confused to confused, it is determined that more interpretive facilities should be added at the attraction.

[0028] If the emotional change is from non-curiosity to curiosity, it is determined that interactive facilities should be added to the attraction.

[0029] If the emotional change shifts from non-disappointment to disappointment, it is determined that a compensatory activity should be added.

[0030] A tourist satisfaction testing system, applied to the tourist satisfaction testing method described above, includes:

[0031] The first data acquisition module is used to collect the first facial expression data of the first tourist in the first area of ​​the first route of the tourist area.

[0032] The first judgment module is used to analyze and judge the emotions of the first tourist based on the first expression data;

[0033] The second acquisition module is used to acquire the second facial expression data of the first tourist in the second area of ​​the first route;

[0034] The first judgment module is used to analyze and judge the emotions of the first tourist based on the second expression data;

[0035] The satisfaction assessment module is used to determine the first tourist's initial satisfaction with the route between the first and second areas based on the first tourist's emotional changes from the first area to the second area.

[0036] A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the tourist satisfaction detection method described above.

[0037] A processor for running a program, wherein the program executes the tourist satisfaction detection method described above.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] 1. It can detect and survey the vast majority of tourists entering the tourist area, improving its accessibility and accuracy.

[0040] 2. High objectivity: Compared to traditional questionnaires or verbal interviews, facial expression detection is a relatively objective method. Tourists may provide inaccurate information in questionnaires, such as answering casually out of politeness or to avoid spending too much time, or giving untruthful answers during verbal interviews due to their current mood or the interviewer's guidance. Facial expressions, however, are a natural emotional response, and by detecting them through technological means (such as cameras and facial expression recognition software), they can truthfully reflect the tourist's current emotional state.

[0041] 3. Real-time feedback: Facial expression detection provides immediate feedback. During tourists' experiences with tourism products or services, facial expressions can be monitored through cameras installed in scenic spots, hotels, and other locations. If a negative expression is detected, staff can intervene promptly to understand the situation and resolve the issue on-site, thus preventing tourists from continuing their trip with dissatisfaction and affecting their overall experience.

[0042] 4. Rich Data: Facial expressions contain a variety of emotional information, such as happiness, anger, sadness, surprise, and disgust. Through facial expression detection, a wealth of emotional data about tourists can be collected. This data can not only be used to assess satisfaction but also to further analyze tourists' specific feelings about various aspects of tourism products or services. For example, by analyzing tourists' facial expressions in different areas of a tourist attraction, we can understand which areas are more popular and which areas may need improvement.

[0043] 5. Multi-dimensional analysis: By combining facial expression detection with other data (such as tourist behavior data, visit time, etc.), multi-dimensional satisfaction analysis can be conducted. For example, by combining tourists' facial expressions at a particular attraction with their stay time and activities participated in, a more comprehensive understanding of the tourist experience can be gained. If tourists stay at an attraction for a long time and their expressions are mostly pleasant, it can be judged that they are highly satisfied with that attraction; conversely, if their stay time is short and their expressions are unpleasant, further analysis of the reasons is needed.

[0044] 6. Large-scale data collection: Installing facial expression detection equipment in public areas of tourist attractions (such as scenic area entrances and hotel lobbies) allows for the simultaneous monitoring of a large number of tourists. This enables the rapid collection of large data samples for statistical analysis and trend research. Compared to traditional one-on-one surveys, this significantly improves the efficiency of data collection and can cover a wider range of tourists, including those who are unwilling to participate in questionnaires or interviews. Attached Figure Description

[0045] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0046] Figure 1 This is a flowchart illustrating a method for measuring tourist satisfaction.

[0047] Figure 2 This is a schematic diagram of a tourist satisfaction testing system. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0050] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0051] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0052] Example 1

[0053] like Figure 1 The diagram shown is a flowchart of a method for measuring tourist satisfaction, including:

[0054] S1: Collect the first facial expression data of the first tourist in the first area of ​​the first route of the tourist area.

[0055] Furthermore, the facial expression data includes facial expression images.

[0056] In specific implementation, the collection of the first tourist's first facial expression data can be achieved in the following ways:

[0057] Camera-based data collection methods: High-definition cameras are installed near the first area. These cameras can continuously record tourists' facial expressions. For example, the cameras can capture expressions such as anticipation, excitement, or confusion when tourists first enter the scenic area. To ensure high-quality facial expression data collection, the cameras must be installed in a reasonable location, avoiding backlighting, obstructions, etc. Sufficient resolution must also be ensured to clearly identify facial expression details. Furthermore, mobile cameras (such as smart glasses or portable cameras) can be used. Staff wearing smart glasses can collect facial expression data at close range during interactions with tourists (such as tour guide explanations or assistance provided by scenic area staff). Smart glasses can record changes in tourists' expressions from their perspective, which is particularly suitable for capturing tourists' immediate reactions to specific services or explanations. Portable cameras can also be used in special scenarios, such as filming at tourist activities (such as folk performances or outdoor adventure activities) to collect tourists' facial expressions during the activities. However, when using mobile devices to collect data, it is necessary to obtain tourists' consent or use explicit recording devices to make tourists aware that they are being recorded, in order to avoid infringing on their privacy.

[0058] Furthermore, based on image collection methods (tourist-initiated), interesting photo spots can be set up in tourist attractions or hotels to encourage tourists to take photos and share them on social media platforms using specific hashtags. For example, a scenic area could launch an activity called "The Most Beautiful Smile is at [Scenic Area Name]," guiding tourists to share selfies with smiles. Offering incentives such as raffles or small gifts can attract tourist participation. These selfies can serve as supplementary data on facial expressions, allowing for analysis of tourists' happiness and satisfaction levels within the scenic area.

[0059] S2: Analyze the first tourist's emotions based on the first facial expression data.

[0060] In specific implementation, the step of analyzing and judging the first tourist's emotions based on the first facial expression data includes the following steps:

[0061] S21: Image preprocessing. The acquired images need to be screened to remove blurry images (possibly due to tourists moving too fast, poor lighting, or low device resolution) and partially occluded images (such as hats or hair covering key facial features) to ensure the accuracy of subsequent analysis.

[0062] S22: Image standardization involves standardizing the selected images. This includes adjusting image size, resolution, and color balance. For example, all images are resized to the same size to ensure consistent placement and proportion of facial features, facilitating subsequent feature extraction. Simultaneously, color balance correction prevents interference from varying lighting conditions (such as yellowish indoor lighting or excessively bright outdoor sunlight) in expression assessment.

[0063] S23: Facial Feature Extraction. Using facial recognition technology, key facial feature points are located, such as the corners of the eyes, pupil position, the start and end points of the eyebrows, and the corners of the mouth and lip contour. These feature points are the foundation of expression analysis. For example, by comparing the changes in the position of the corners of the mouth feature points in smiling and non-smiling states, it is possible to determine whether a tourist is in a pleasant mood.

[0064] S24: Expression-Related Feature Extraction. In addition to feature points, other features related to facial expressions need to be extracted, such as changes in facial muscle texture, the degree of eye opening and closing, and the angle of eyebrow tilt. For example, when a person is angry, their eyebrows will show a clear downward tilt and convergence, and their eyes may widen. Extracting these features can provide more evidence for judging emotions.

[0065] S25: Construction of an emotion classification model.

[0066] Data annotation and training set preparation: To enable computers to recognize different emotions, facial expression images need to be labeled. Professional psychologists or trained annotators can be invited to label a large number of facial expression images according to standard facial expression classifications (such as happiness, anger, sadness, surprise, disgust, etc.) to build a training set. During the annotation process, it is crucial to ensure the accuracy and consistency of the annotations.

[0067] Choosing a suitable classification model: Utilize machine learning or deep learning algorithms to build an emotion classification model. Commonly used models include Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). For example, CNNs excel at processing image data, automatically learning the complex relationship between facial features and emotions. By inputting a labeled training set into the model for training, the model learns how to determine emotions based on facial features.

[0068] Model Evaluation and Optimization: The trained model is evaluated using an independent test set to check performance metrics such as accuracy, recall, and F1 score. If the model performance is unsatisfactory, it can be optimized by adjusting model parameters, increasing training data, and using data augmentation techniques (such as rotating and flipping images) to improve the accuracy of sentiment judgment.

[0069] S26: Emotion Judgment and Analysis.

[0070] Single-image emotion assessment: The image of the expression to be analyzed is input into a trained emotion classification model, which outputs the probability of the corresponding emotion category. For example, the model might determine that there is an 80% probability that the tourist in an image is happy, a 10% probability that they are surprised, and a 10% probability that they are experiencing other emotions. The most likely emotion category is determined based on the probability, serving as the assessment of the tourist's emotion in the image.

[0071] In addition, sensors in wearable devices (such as smart bracelets and smartwatches) can be used to assist in collecting facial expression data. These devices can detect physiological signals such as heart rate and skin conductance. When tourists feel excited, nervous, or angry, these physiological signals change. By analyzing these changes, it is possible to infer the tourist's emotional state and thus help determine the true emotions behind their facial expressions.

[0072] For example, when tourists are experiencing thrilling rides, the smart bracelet can detect an increase in their heart rate and skin conductivity. Combined with facial expressions such as wide-open mouth and wide-open eyes captured by the camera, it can more accurately determine whether the tourist is in a state of excitement and pleasure.

[0073] S3: Collect the second facial expression data of the first tourist in the second area of ​​the first route. The specific collection steps can be as described in step S1, or other collection methods with the same function can be used.

[0074] S4: Analyze the second facial expression data to determine the first tourist's emotion. The specific determination steps can be as described in step S2, or other determination methods with the same effect can be used.

[0075] S5: Based on the first tourist's emotional changes from area one to area two, determine the first tourist's initial satisfaction with the route between areas one and two. Using emotional changes for judgment can eliminate situations where the tourist's emotions are affected by external factors. For example, if a tourist is in a bad mood before entering the first route due to external factors and remains in a bad mood throughout the tour, this situation can be eliminated, reducing the probability of misjudgment and improving the accuracy of the survey. At the same time, if a tourist is in a good mood before entering and maintains a good mood after the tour, although the influence of external factors may still exist, at least the tour did not ruin their good mood, so the route design still deserves at least some affirmation. In conclusion, using emotional changes (from good to bad mood, from bad to good mood, etc.) as a criterion for judging the satisfaction of a tour route can largely maintain accuracy and reflect the true situation.

[0076] For a continuous sequence of facial images (such as a series of images taken from when tourists enter a scenic area to when they leave), it is possible to analyze the changing trends of emotions. For example, observing that tourists' emotions change from calm to excitement while viewing a certain attraction, and then turn to impatience while waiting in line for the next attraction, this dynamic change in emotions can help understand tourists' experiences at different stages, thereby better assessing tourism satisfaction.

[0077] Furthermore, positive emotions include happiness, surprise, satisfaction, curiosity, and excitement; furthermore, calmness is also a positive emotion, representing inner peace and emotional balance, a relatively comfortable and relaxed psychological state.

[0078] Furthermore, negative emotions include: anger, anxiety, sadness, disgust, fear, tension, disappointment, fatigue, and confusion.

[0079] Furthermore, based on the first tourist's emotional changes from area one to area two, the first tourist's initial satisfaction with the route between area one and area two is determined, including the following:

[0080] When a bad mood turns into a good mood, the satisfaction value becomes positive.

[0081] When a good mood turns into a bad mood, the satisfaction value becomes negative.

[0082] If the negative mood remains unchanged, then this situation should be eliminated from consideration.

[0083] If you maintain a positive mood, you can consider setting your satisfaction level to a positive value.

[0084] Therefore, the formula for calculating the first satisfaction level is as follows:

[0085] ;

[0086] In the above formula, The highest satisfaction score; Let $\frac{i}{i}$ be the satisfaction value for changing from a non-$i$ emotion to the $i$ emotion. If the emotion does not change to the $i$ emotion, then $\frac{i}{i}$. The value is 0; Age coefficient; is the gender coefficient; n is the type of emotional change.

[0087] Furthermore, the types of emotional changes and their corresponding... The range of values ​​includes:

[0088] Turning bad moods into good moods, satisfaction level It is a positive value of 1;

[0089] Good mood turns into bad mood, satisfaction level It is a negative value of -1;

[0090] Maintaining a positive mood and a high level of satisfaction. It can be a positive value of 1 or 0;

[0091] Negative mood remains unchanged, satisfaction level It is 0.

[0092] Furthermore, the age coefficient One of the possible values ​​for includes the following:

[0093] The first visitor was an infant who couldn't walk. Set to 0;

[0094] The first group of tourists is defined as children who can walk to adults (generally 18 years old). Take 0.96;

[0095] The first age group for tourists is defined as those between adulthood and old age (generally 65 years old). Take 0.7;

[0096] The first tourist is a senior citizen or older. Take 0.8.

[0097] Furthermore, the above age coefficients The age coefficient is determined by conducting actual surveys and interviews with tourists to see what percentage of their current emotional state is caused by the tourist area. For example, among 100 children interviewed, 96 children's emotions (whether positive or negative) were related to the tourist routes and attractions, while 4 children's emotions were caused by external factors, such as being scolded by their parents. Therefore, the children's age coefficient... Take 0.96. Similarly, the coefficients for adults and the elderly are... The method for determining this is the same. Meanwhile, after sampling at a certain time, the age coefficient was discovered. When changes occur, the age coefficient should be adjusted when calculating satisfaction. Updates will be made to prevent the influence of social factors from leading to inaccurate survey results.

[0098] Furthermore, the gender coefficient One of the possible values ​​for is as follows:

[0099] female coefficient The value is 0.92;

[0100] Male coefficient The value is 0.85.

[0101] Furthermore, the gender coefficient The determination method can be based on the aforementioned age coefficient. The method of determination.

[0102] Furthermore, it also includes the following steps:

[0103] S6: Record the time when the first tourist enters the first area and the second area respectively. Calculate the time spent on the route between the two areas based on the time difference between the two areas. Based on the time spent on the route and the change in emotions, judge the second level of satisfaction with the route between the first area and the second area.

[0104] Furthermore, it also includes the following steps:

[0105] The formula for calculating the second level of satisfaction is as follows:

[0106] ;

[0107] In the above formula, This is the second highest satisfaction rating. This is the route dwell time coefficient; The first satisfaction score is the average satisfaction score. The overall satisfaction score is calculated by combining the satisfaction scores of each first visitor.

[0108] Furthermore, the formula for calculating the route dwell time coefficient is as follows:

[0109] ;

[0110] In the above formula, The time it takes for the first visitor to travel from the first area to the second area; The total time spent by the first tourist in the entire tourist area.

[0111] Furthermore, it also includes the following steps:

[0112] Calculate the overall satisfaction level of the route from the first area to the second area; the formula for calculating the overall satisfaction level is as follows:

[0113] ;

[0114] In the above formula, For overall satisfaction; Let m be the second satisfaction level of the j-th tourist; m be the total number of tourists tested. The value varies between m and -m to reflect the overall change in tourist satisfaction with the route.

[0115] Furthermore, it also includes the following steps:

[0116] S7: Detect the time the first tourist spends at each attraction and calculate the proportion of time spent at each attraction to the total time spent along the route. This yields a ranking of tourist satisfaction with each attraction, which is used for specific analysis of the attractions to determine which attractions should be prioritized for improvement. This helps in determining the overall improvement strategy for the tourist area and allows for the initial improvement of attractions with low satisfaction rankings.

[0117] Furthermore, it also includes the following steps:

[0118] S8: Based on changes in mood, determine the direction for improving the route from the first area to the second area.

[0119] Furthermore, the improved direction for obtaining the route from the first area to the second area based on emotional changes includes the following steps:

[0120] Obtain the specific emotional type of the first visitor in the first and second areas;

[0121] If the mood change is from non-fatigue to fatigue, then it is determined that the route should include more rest areas and facilities.

[0122] If the emotional change is from non-confused to confused, it is determined that more interpretive facilities should be added at the attraction.

[0123] If the emotional change is from non-curiosity to curiosity, it is determined that interactive facilities should be added to the attraction.

[0124] If the emotional change shifts from non-disappointment to disappointment, it is determined that a compensatory activity should be added.

[0125] The beneficial effects of this application are as follows:

[0126] 1. It can detect and survey the vast majority of tourists entering the tourist area, improving its accessibility and accuracy.

[0127] 2. Highly objective

[0128] Background and Details: Compared to traditional questionnaires or verbal interviews, facial expression detection is a relatively objective method. Tourists may provide inaccurate information in questionnaires, such as answering casually out of politeness or to avoid spending too much time, or giving untruthful answers during verbal interviews due to their current mood or the interviewer's guidance. Facial expressions, however, are a natural emotional response, and by detecting them through technological means (such as cameras and facial expression recognition software), they can accurately reflect the tourist's current emotional state.

[0129] Example: For instance, after visiting a popular attraction, tourists might check the "satisfied" option on a questionnaire, but an facial expression detection system might detect that they have furrowed brows and downturned corners of their mouths, which could suggest that they are not actually that satisfied. This objective detection method can help tourism professionals more accurately grasp tourists' true feelings.

[0130] Real-time feedback

[0131] Background and Details: Facial expression detection provides instant feedback. During tourists' experiences with tourism products or services, facial expressions can be monitored through cameras installed in scenic spots, hotels, and other locations. If a negative facial expression is detected, staff can intervene promptly to understand the situation and resolve the issue on-site, thus preventing tourists from continuing their trip with dissatisfaction and affecting their overall experience.

[0132] Example: In a hotel restaurant, when the facial expression detection system detects a guest's displeasure during their meal, the waiter can immediately approach them to inquire whether the food is not to their liking or the service is inadequate, and provide solutions on the spot, such as changing the dishes or improving the service.

[0133] Data richness

[0134] Background and Details: Facial expressions contain a variety of emotional information, such as happiness, anger, sadness, surprise, and disgust. Facial expression detection allows for the collection of rich emotional data from tourists. This data can not only be used to assess satisfaction but also to further analyze tourists' specific feelings about various aspects of tourism products or services. For example, by analyzing tourists' facial expressions in different areas of a tourist attraction, we can understand which areas are more popular and which areas may need improvement.

[0135] Example: In a large theme park, an expression detection system detected that visitors frequently displayed frustrated expressions in the queue areas for certain rides. This suggests that park management needs to optimize the queuing process or provide a better queuing experience (such as adding more rides in the queue areas). Meanwhile, areas where visitors frequently show expressions of surprise and excitement (such as particularly exciting performance areas) can be further enhanced and promoted.

[0136] Multidimensional analysis

[0137] Background and Details: By combining other data (such as visitor behavior data, visit duration, etc.), facial expression detection can enable multi-dimensional satisfaction analysis. For example, by linking tourists' facial expressions at a particular attraction with their dwell time and activities participated in, a more comprehensive understanding of their experience can be gained. If tourists spend a long time at an attraction and their expressions are mostly pleasant, it can be concluded that they are highly satisfied with that attraction; conversely, if their dwell time is short and their expressions are unpleasant, a deeper analysis of the reasons is needed.

[0138] Example: In a history museum, visitor A spends a long time in a certain exhibition hall, looking relaxed and happy, and interacts with the exhibits extensively (such as taking photos and carefully reading the exhibit descriptions), indicating that he is very satisfied with this exhibition hall. Visitor B, while also spending a long time there, looks somewhat confused, possibly indicating that he is not satisfied with the way the exhibits are displayed or the explanations, and needs to learn more, such as whether he / she requires a more detailed guided tour.

[0139] Large-scale data collection

[0140] Background and Details: Installing facial expression detection equipment in public areas of tourist attractions (such as scenic area entrances and hotel lobbies) allows for the simultaneous monitoring of large numbers of tourists. This enables the rapid collection of substantial data samples for statistical analysis and trend research. Compared to traditional one-on-one surveys, this significantly improves data collection efficiency and can cover a wider range of tourists, including those who are unwilling to participate in questionnaires or interviews.

[0141] Example: In a popular tourist attraction, tens of thousands of visitors come daily. By installing facial expression detection systems at the entrance and main passageways, a large amount of facial expression data can be collected. After a period of accumulation, the satisfaction trends of different seasons, time periods, and visitor groups can be analyzed, providing strong data support for improving the management and services of the attraction.

[0142] Example 2

[0143] like Figure 2 The diagram shows a structural schematic of a tourist satisfaction testing system, applied to the tourist satisfaction testing method described above, including:

[0144] The first data acquisition module is used to collect the first facial expression data of the first tourist in the first area of ​​the first route of the tourist area.

[0145] The first judgment module is used to analyze and judge the emotions of the first tourist based on the first expression data;

[0146] The second acquisition module is used to acquire the second facial expression data of the first tourist in the second area of ​​the first route;

[0147] The first judgment module is used to analyze and judge the emotions of the first tourist based on the second expression data;

[0148] The satisfaction assessment module is used to determine the first tourist's initial satisfaction with the route between the first and second areas based on the first tourist's emotional changes from the first area to the second area.

[0149] Example 3

[0150] A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the tourist satisfaction detection method described above.

[0151] Example 4

[0152] A processor for running a program, wherein the program executes the tourist satisfaction detection method described above.

[0153] This application provides a method for detecting tourist satisfaction, comprising: collecting first facial expression data of a first tourist in a first area of ​​a first route within a tourist area; analyzing and judging the first tourist's emotions based on the first facial expression data; collecting second facial expression data of the first tourist in a second area of ​​the first route; analyzing and judging the first tourist's emotions based on the second facial expression data; and judging the first tourist's initial satisfaction with the route from the first area to the second area based on the emotional changes of the first tourist from the first area to the second area. This application's solution can conduct surveys on the vast majority of tourists entering a tourist area, improving its accessibility and accuracy. Furthermore, using emotional changes for judgment reduces the probability of misjudgment and improves survey accuracy.

[0154] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0155] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0156] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0157] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for detecting tourist travel satisfaction, characterized by, Comprise: Install a camera in a first area of a first route in a tourist area to collect first facial expression data of a first tourist; Analyze and determine the emotion of the first tourist according to the first facial expression data; Install a camera in a second area of the first route to collect second facial expression data of the first tourist; The facial expression data comprises facial expression images; Analyze and determine the emotion of the first tourist according to the second facial expression data; Determine the first satisfaction of the first tourist to the route between the first area and the second area according to the emotion change of the first tourist from the first area to the second area; The emotion change comprises a change from a good emotion to a bad emotion and a change from a bad emotion to a good emotion; The good emotion comprises happiness, surprise, satisfaction, curiosity, excitement and calmness, and the bad emotion comprises anger, anxiety, sadness, disgust, fear, nervousness, disappointment, fatigue and confusion; The calculation formula of the first satisfaction is as follows: ； In the above formula, is the first satisfaction value; is the satisfaction value of changing from the non-i-th emotion to the i-th emotion; is the age coefficient, determined according to actual investigation and interview; is the gender coefficient, determined according to actual investigation and interview; n is the emotion change type; satisfaction value The values are as follows: When the emotion changes from a bad emotion to a good emotion, the satisfaction value is positive; When the emotion changes from a good emotion to a bad emotion, the satisfaction value is negative; When the bad emotion remains unchanged, the situation is excluded; When the good emotion remains unchanged, the satisfaction value is positive; Record the time when the first tourist enters the first area and the second area respectively, calculate the route staying time between the two areas according to the time difference between the two areas, and determine the second satisfaction of the first tourist to the route between the first area and the second area according to the emotion change and the route staying time; the calculation formula of the second satisfaction is as follows: ； In the above formulae, is a second satisfaction value; is a route dwell time coefficient; is a first satisfaction value; The calculation formula of the route staying time coefficient is as follows: ； In the above formula, is the time for the first visitor from the first area to the second area; is the total time for the first visitor in the entire tourist area; Further comprising the following steps: calculating the comprehensive satisfaction of the route between the first area and the second area; the calculation formula of the comprehensive satisfaction is as follows: ； In the above formula, is the overall satisfaction; is the second satisfaction of the jth visitor; m is the total number of detected visitors; The method can detect and investigate the tourists entering the tourist area, improve the popularity and accuracy, and use the emotion change to determine the satisfaction, so as to reduce the probability of misjudgment and improve the investigation accuracy.

2. The tourist travel satisfaction detection method according to claim 1, characterized in that, Further comprising the following steps: Detect the staying time of the first tourist in front of each scenic spot, calculate the proportion of the staying time of each scenic spot in the route staying time, and obtain the satisfaction ranking of the first tourist to each scenic spot.

3. The tourist travel satisfaction detection method according to claim 1, characterized in that, Further comprising the following steps: Obtain the improvement direction of the route between the first area and the second area according to the emotion change.

4. The tourist travel satisfaction detection method according to claim 3, characterized in that, The improvement direction of the route between the first area and the second area according to the emotion change comprises the following steps: If the emotion change is from a non-fatigue emotion to a fatigue emotion, it is determined that a rest area and facilities should be added to the route; If the emotion change is from a non-confusion emotion to a confusion emotion, it is determined that explanation facilities should be added to the scenic spot; If the emotion change is from a non-curiosity emotion to a curiosity emotion, it is determined that interactive facilities should be added to the scenic spot; If the emotion change is from a non-disappointment emotion to a disappointment emotion, it is determined that a compensation link activity should be added.

5. A tourist travel satisfaction detection system characterized by, The method is applied to the tourist satisfaction detection method according to any one of claims 1 to 4, comprising: A first collection module is arranged to collect first facial expression data of a first tourist in a first area of a first route in a tourist area; A first determination module is arranged to analyze and determine the emotion of the first tourist according to the first facial expression data; A second collection module is arranged to collect second facial expression data of the first tourist in a second area of the first route; The first determination module is arranged to analyze and determine the emotion of the first tourist according to the second facial expression data; A satisfaction degree judging module is configured to judge a first satisfaction degree of the first tourist to the route from the first area to the second area according to the emotional change of the first tourist from the first area to the second area.

6. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored program, and the program controls a device where the computer readable storage medium is located to perform the tourist travel satisfaction degree detection method in any one of claims 1 to 4 when the program is running.

7. A processor, comprising: The processor is configured to run a program, and the program performs the tourist travel satisfaction degree detection method in any one of claims 1 to 4 when the program is running.

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