Tourist satisfaction detection method and system, medium and processor

Abstract

The invention provides a tourist travel satisfaction detection method. The method comprises the steps of collecting first expression data of a first tourist in a first area of a first route of a tourist area; analyzing and judging the emotion of the first tourist according to the first expression data; collecting second expression data of the first tourist in a second area of the first route; analyzing and judging the emotion of the first tourist according to the second expression data; and according to the emotion change of the first tourist from the first area to the second area, judging a first satisfaction degree of the first tourist on the route from the first area to the second area. According to the scheme, most passengers entering the tourist area can be detected and investigated, universality and accuracy are improved, meanwhile, judgment is conducted through emotion changes, the probability of misjudgment is reduced, and investigation accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tourism satisfaction survey, and in particular to a method, system, medium and processor for detecting tourist tourism satisfaction. Background Art

[0002] With the improvement of people's living standards, traveling has become synonymous with fashion and vacation, and tourism satisfaction is the lifeline of every tourist area, which is related to the future development and income of the tourist area. Therefore, each tourist area adopts various methods to conduct tourist satisfaction surveys.

[0003] Traditional satisfaction surveys include questionnaire surveys, interviews, observations, and data analysis. Questionnaire surveys are distributed to target audiences at specific locations, and they are asked to fill out and collect them on the spot. However, this method not only consumes manpower, time, and paper costs, but also the number of collection and distribution can only be popularized to a small number of tourists. Although online surveys can be used with technological advances, their popularity and pertinence are still poor, and they cannot truly reflect tourists' evaluation of tourist areas. In addition, the remaining methods, interviews, observations, and data analysis (such as complaint data analysis), have the disadvantages of low popularity and inability to accurately reflect the true thoughts of the overall tourists (interviews are concerned about politeness, so they are expressed in euphemistic language, etc.).

[0004] With the development of information technology, various new survey methods have been developed. For example, the Chinese invention patent application CN202210986455.X discloses a tourism satisfaction survey system and method based on sentiment analysis. By obtaining the comment texts of various platforms of tourist attractions and screening and analyzing them, the satisfaction level of the relevant facilities of the tourist attractions corresponding to the sentiment classification results is obtained. This method can only obtain the evaluations of some tourists who have commented on the platform, and the popularity of the survey is still limited.

[0005] In view of this, a method, system, medium and processor for detecting tourist satisfaction are needed. Summary of the invention

[0006] In view of the problems of low survey popularity and poor authenticity in the prior art, the present invention provides a tourist satisfaction detection method, system, medium and processor, which can detect and survey most tourists entering the tourist area, improving the popularity and accuracy. The specific technical solution is as follows:

[0007] A method for detecting tourist satisfaction, comprising:

[0008] Collecting first expression data of a first tourist in a first area of ​​a first route in a tourist area;

[0009] Determine the emotion of the first visitor according to the first expression data analysis;

[0010] collecting second expression data of the first tourist in the second area of ​​the first route;

[0011] Determine the emotion of the first visitor based on the second expression data analysis;

[0012] According to the emotion change of the first tourist from the first area to the second area, the first satisfaction of the first tourist with the route between the first area and the second area is judged, and the emotion change is used to make the judgment at the same time to reduce the probability of misjudgment and improve the accuracy of the investigation.

[0013] Furthermore, the calculation formula of the first satisfaction level is as follows:

[0014]

[0015] In the above formula, Y1 is the first satisfaction value; X i is the satisfaction value of changing from a non-i-th emotion to the i-th emotion; ε is the age coefficient; is the gender coefficient; n is the type of emotional change.

[0016] Furthermore, the method further comprises the following steps:

[0017] The time when the first tourist enters the first area and the second area is recorded respectively, the stay time on the route between the two areas is calculated according to the time difference between the two areas, and the second satisfaction with the route between the first area and the second area is judged according to the stay time on the route combined with the emotion change.

[0018] Furthermore, the method further comprises the following steps:

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

[0020] Y2=θY1;

[0021] In the above formula, Y2 is the second satisfaction value; θ is the route stay time coefficient; Y1 is the first satisfaction value.

[0022] Furthermore, the method further comprises the following steps:

[0023] Detect the first tourist's stay time at each scenic spot, and calculate the proportion of the stay time at each scenic spot to the stay time along the route, and obtain the tourist's satisfaction ranking for each scenic spot. Further, the following steps are included:

[0024] An improved direction of a route from the first area to the second area is obtained based on the change in emotion.

[0025] Further, obtaining an improved direction of a route from the first area to the second area according to the emotion change comprises the following steps:

[0026] If the mood changes from a non-fatigued mood to a fatigued mood, then it is judged that the route should have additional rest areas and facilities;

[0027] If the emotion changes from non-confused to confused, it is judged that more explanation facilities should be added to the scenic spot;

[0028] If the emotion changes from non-curiosity to curiosity, it is judged that interactive facilities should be added to the scenic spot;

[0029] If the emotion changes from non-disappointment to disappointment, it is judged that the compensatory activities should be increased.

[0030] A tourist travel satisfaction detection system, applied to the above-mentioned tourist travel satisfaction detection method, comprises:

[0031] A first collection module, which is used to collect first expression data of a first tourist in a first area of ​​a first route in a tourist area;

[0032] A first judgment module, which is used to analyze and judge the emotion of the first tourist according to the first expression data;

[0033] A second collection module, which is used to collect second expression data of the first tourist in the second area of ​​the first route;

[0034] A first judgment module, which is used to analyze and judge the emotion of the first tourist according to the second expression data;

[0035] The satisfaction determination module is used to determine the first satisfaction of the first tourist with respect to the route from the first area to the second area according to the emotion change of the first tourist from the first area to the second area.

[0036] A computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned tourist travel satisfaction detection method.

[0037] A processor is used to run a program, wherein the program executes the above-mentioned tourist travel satisfaction detection method when running.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. It is capable of conducting testing and survey on the vast majority of passengers entering tourist areas, thus improving the popularity and accuracy.

[0040] 2. Strong objectivity. Compared with traditional questionnaires or verbal inquiries, facial expression detection is a relatively objective method. Tourists may provide inaccurate information in the questionnaire, such as answering casually because of politeness or not wanting to spend too much time, or giving untrue answers when being verbally asked due to the influence of emotions at the time, the guidance of the investigator, and other factors. Facial expressions are a natural emotional response. Through technical means (such as cameras, facial expression recognition software) for detection, it can truly reflect the current emotional state of tourists.

[0041] 3. Real-time feedback: Expression detection can provide instant feedback. When tourists are experiencing tourism products or services, their expressions can be monitored through cameras installed in scenic spots, hotels and other places. Once a tourist shows a negative expression, the staff can intervene in time to understand the situation and solve the problem on the spot, thus preventing tourists from continuing to travel with dissatisfaction and affecting the entire travel experience.

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

[0043] 5. Multi-dimensional analysis. Combined with other data (such as tourists' behavior data, tour time, etc.), expression detection can conduct multi-dimensional satisfaction analysis. For example, combining tourists' expressions at a certain scenic spot with their stay time at the scenic spot, the activities they participate in, etc., can provide a more comprehensive understanding of tourists' experience. If tourists stay at a scenic spot for a long time and their expressions are mostly pleasant, then it can be judged that they are highly satisfied with the scenic spot; conversely, if the stay time is short and the expression is not good, it is necessary to analyze the reasons in depth.

[0044] 6. Large-scale data collection: Installing expression detection equipment in public areas of tourist sites (such as scenic spot entrances, hotel lobbies, etc.) can monitor a large number of tourists at the same time. This can quickly collect a large number of data samples for statistical analysis and trend research. Compared with the traditional one-to-one survey method, it greatly improves the efficiency of data collection and can cover a wider range of tourist groups, including those who are unwilling to participate in questionnaires or interviews. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. 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 according to the actual scale.

[0046] Figure 1 It is a flow chart of a method for detecting tourist satisfaction;

[0047] Figure 2 This is a structural diagram of a tourist satisfaction detection system. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work 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 "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

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

[0051] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0052] Embodiment 1

[0053] like Figure 1 The figure shows a flow chart of a tourist satisfaction detection method, including:

[0054] S1: Collecting first expression data of a first tourist in a first area of ​​a first route in a tourist area.

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

[0056] In a specific implementation, the first expression data of the first visitor may be collected in the following manner:

[0057] Camera-based collection method: Install high-definition cameras near the first area. These cameras can continuously record tourists' facial expressions. For example, the camera can capture the expressions of expectation, excitement or confusion when tourists first enter the scenic area. In order to ensure the collection of high-quality expression data, the camera should be installed in a reasonable position to avoid backlighting, occlusion, etc. At the same time, sufficient resolution should be guaranteed so that facial expression details can be clearly identified. Furthermore, mobile cameras (such as smart glasses and portable cameras) can also be used, so that staff can wear smart glasses to collect expression data at close range during the interaction with tourists (such as when tour guides explain and scenic area service staff provide help). Smart glasses can record their expression changes from the perspective of tourists. This method is particularly suitable for capturing tourists' immediate reactions to specific services or explanations. Portable cameras can also be used in some special scenarios, such as shooting at tourist activities (such as folk performances, outdoor adventure activities) to collect tourists' expressions during the activities. However, when using mobile devices to collect data, it is necessary to obtain the consent of tourists or use explicit camera devices to let tourists know that the scene is being recorded to avoid privacy violations.

[0058] Based on the image collection method (tourists actively provide), some interesting photo-taking spots can be set up in tourist attractions or hotels to encourage tourists to take photos and share them on social media platforms, while using specific topic tags. For example, a scenic spot can launch an activity called "The most beautiful smile is in [name of scenic spot]" to guide tourists to share selfies with smiles. Provide certain reward mechanisms, such as lucky draws, small gifts, etc., to attract tourists to participate. These selfies can be used as a supplementary source of expression data, from which tourists' happiness and satisfaction in the scenic spot can be analyzed.

[0059] S2: Analyze and determine the emotion of the first visitor according to the first expression data.

[0060] In a specific implementation, the step of analyzing and judging the emotion of the first tourist based on the first expression data includes the following steps:

[0061] S21: Image preprocessing. The collected images need to be screened to remove blurry (possibly due to tourists moving too fast, dark light, or low device pixels, etc.) and partially blocked (such as hats and hair covering key parts of the face) images to ensure the accuracy of subsequent analysis.

[0062] S22: Image standardization, standardize the filtered images. This includes adjusting the size, resolution, and color balance of the images. For example, all images are adjusted to the same size so that the position and proportion of facial features in the image are relatively consistent, which is convenient for subsequent feature extraction. At the same time, correcting the color balance can avoid interference with expression judgment due to different lighting conditions (such as yellowish indoor lighting and too bright outdoor sunlight).

[0063] S23: Facial feature extraction. Using facial recognition technology, locate the key feature points of the face, such as the corners of the eyes, the position of the pupils, the starting and ending points of the eyebrows, the corners of the mouth, the outline of the lips, etc. These feature points are the basis for expression analysis. For example, by comparing the changes in the position of the corners of the mouth feature points in the smiling and non-smiling states, it can be determined whether the tourist is in a happy mood.

[0064] S24: Extraction of expression-related features. In addition to feature points, other features related to expression need to be extracted, such as changes in the texture of facial muscles, the degree of eye opening and closing, the tilt angle of eyebrows, etc. For example, when a person is angry, the eyebrows will have a clear downward and convergent trend, and the eyes may be wide open. By extracting these features, more basis can be provided for emotional judgment.

[0065] S25: Emotion classification model construction.

[0066] Data labeling and training set preparation: In order for computers to recognize different emotions, facial expression images need to be labeled. Professional psychologists or trained labelers can be invited to label a large number of facial expression images according to the standard classification of facial expressions (such as happiness, anger, sadness, surprise, disgust, etc.) to build a training set. During the labeling process, the accuracy and consistency of the labeling must be ensured.

[0067] Choose a suitable classification model: Use machine learning or deep learning algorithms to build an emotion classification model. Commonly used models include support vector machines (SVM), convolutional neural networks (CNN), etc. For example, CNN has an advantage in processing image data, and it can automatically learn the complex relationship between facial features and emotions. By inputting the labeled training set into the model for training, the model learns how to judge emotions based on facial features.

[0068] Model evaluation and optimization: Use an independent test set to evaluate the trained model and check the model's accuracy, recall rate, F1 value and other performance indicators. If the model performance is not ideal, you can optimize the model by adjusting model parameters, increasing training data, and using data enhancement techniques (such as rotating and flipping images) to improve the accuracy of emotion judgment.

[0069] S26: Emotional judgment and analysis.

[0070] Single image emotion judgment: Input the expression image to be analyzed into the trained emotion classification model, and the model will output the emotion category probability corresponding to the image. For example, the model may judge that the tourists in a certain image have an 80% probability of being happy, 10% of being surprised, and 10% of other emotions. Determine the most likely emotion category based on the probability as the judgment of the emotion of the tourists in this image.

[0071] In addition, sensors in wearable devices (such as smart bracelets and smart watches) can be used to assist in collecting expression data. These devices can detect physiological signals such as tourists' heart rate and skin galvanic response. When tourists feel excited, nervous or angry, these physiological signals will change. By analyzing these changes, the emotional state of tourists can be inferred, and then the true emotions behind the expressions can be judged.

[0072] For example, when tourists are experiencing thrilling amusement rides, the smart bracelet detects that their heart rate is accelerated and skin conductivity is increased. Combined with facial expressions such as open mouth and wide eyes captured by the camera, it can more accurately determine that the tourists are in a state of excitement and pleasure at this time.

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

[0074] S4: Analyze and judge the emotion of the first tourist according to the second expression data. The specific judging steps may be as described in step S2, or other judging methods with the same effect may be adopted.

[0075] S5: According to the emotional changes of the first tourist from the first area to the second area, the first satisfaction of the first tourist on the route between the first area and the second area is judged. By using emotional changes to make judgments, some situations where tourists' emotions are affected by external factors can be eliminated. For example, before entering the first route, the tourists' emotions are bad due to external factors, and they can't raise their emotions during the whole travel process. This situation can be eliminated to reduce the probability of misjudgment and improve the accuracy of the survey. At the same time, the tourists are in a good mood before entering and still maintain a good mood after the play. Although it is possible that external factors still exist, at least this tour did not destroy his good mood, so the design of the route is still worthy of the least affirmation. In summary, using emotional changes (from good emotions to bad emotions, from bad emotions to good emotions, etc.) as the judgment standard for satisfaction with the travel route can maintain accuracy to a large extent and reflect the real situation.

[0076] For a continuous sequence of facial expression images (such as a series of images taken from the time a tourist enters a scenic spot to the time the tourist leaves the scenic spot), the changing trend of emotions can be analyzed. For example, if a tourist's emotions change from calm to excited when viewing a certain attraction, and then turn to irritable when waiting in line for the next item, this dynamic change of emotions can help understand the tourist's experience at different stages, thereby better evaluating travel satisfaction.

[0077] Furthermore, good emotions include: happiness, surprise, satisfaction, curiosity and excitement, etc.; further, calmness is also an example of a good emotion, which represents inner peace and emotional balance, and is a relatively comfortable and relaxed psychological state.

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

[0079] Further, judging the first tourist's first satisfaction with the route from the first area to the second area according to the first tourist's emotional change from the first area to the second area includes the following situations:

[0080] From bad mood to good mood, the satisfaction value is positive;

[0081] When the mood changes from good to bad, the satisfaction value is negative;

[0082] If the bad mood remains unchanged, this situation is eliminated;

[0083] Good emotions remain unchanged, and satisfaction values ​​can be considered positive.

[0084] Therefore, the calculation formula of the first satisfaction level is as follows:

[0085]

[0086] In the above formula, Y1 is the first satisfaction value; X i is the satisfaction value of changing from a non-i-th emotion to the i-th emotion. If it does not change to the i-th emotion, then X i The value is 0; ε is the age coefficient; is the gender coefficient; n is the type of emotional change.

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

[0088] Bad mood turns into good mood, satisfaction value X i is a positive value of 1;

[0089] Good mood turns into bad mood, satisfaction value X i is a negative value -1;

[0090] Good mood remains unchanged, satisfaction value X i A positive value of 1 or 0;

[0091] Bad mood remains unchanged, satisfaction value X i is 0.

[0092] Furthermore, one of the value ranges of the age coefficient ε includes the following:

[0093] The first tourist is a baby who cannot walk and ε is 0;

[0094] The first tourist is between the age of a walking child and an adult (generally 18 years old), and ε is 0.96;

[0095] The first tourist is between the age of adults and the elderly (generally 65 years old), and ε is 0.7;

[0096] The first tourist is older than the elderly, and ε is 0.8.

[0097] Furthermore, the above age coefficient ε is determined by conducting an actual survey and interview of the tourist group to see what proportion of the tourists' current emotional state is caused by the tourist area, and this proportion is the age coefficient ε. For example, among the 100 children interviewed, 96 children's emotions (whether good or bad) are related to tourist routes and attractions, and 4 children's emotions are caused by external factors, such as being scolded by their parents, then the children's age coefficient ε is 0.96. Similarly, the method for determining the coefficient ε of adults and the elderly is the same. At the same time, after sampling at a certain time, if the age coefficient ε is found to have changed, the age coefficient ε is updated when calculating satisfaction to prevent the influence of social factors from causing untrue survey results.

[0098] Furthermore, the gender coefficient One of the possible value ranges is as follows:

[0099] The coefficient of female The value is 0.92;

[0100] Coefficient for men The value is 0.85.

[0101] Furthermore, the gender coefficient The method for determining can adopt the method for determining the age coefficient ε mentioned above.

[0102] Furthermore, the method further comprises the following steps:

[0103] S6: Record the time when the first tourist enters the first area and the second area respectively, calculate the stay time on the route between the two areas according to the time difference between the two areas, and judge the second satisfaction with the route between the first area and the second area according to the stay time on the route combined with the emotion change.

[0104] Furthermore, the method further comprises the following steps:

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

[0106] Y2=θY1;

[0107] In the above formula, Y2 is the second satisfaction value; θ is the route stay time coefficient; Y1 is the first satisfaction value. The overall satisfaction value is calculated by combining the satisfaction values ​​of each first tourist.

[0108] Furthermore, the calculation formula of the route dwell time coefficient is as follows:

[0109]

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

[0111] Furthermore, the method further comprises the following steps:

[0112] The comprehensive satisfaction of the route from the first area to the second area is calculated; the comprehensive satisfaction calculation formula is as follows:

[0113]

[0114] In the above formula, Y z For overall satisfaction; is the second satisfaction of the jth tourist; m is the total number of detected tourists, so Y z It varies between m and -m to reflect the changes in tourists' overall satisfaction with the route.

[0115] Furthermore, the method further comprises the following steps:

[0116] S7: Detect the first tourist's stay time in front of each scenic spot, and calculate the proportion of the stay time at each scenic spot to the stay time along the route, so as to obtain the tourists' satisfaction ranking for each scenic spot, which is used for specific analysis of the scenic spots to determine which scenic spots should be improved first, so as to facilitate the determination of the improvement strategy for the entire tourist area and to facilitate the improvement of the scenic spots with low satisfaction rankings first.

[0117] Furthermore, the method further comprises the following steps:

[0118] S8: Obtaining an improved direction of the route from the first area to the second area according to the change in emotion.

[0119] Further, obtaining an improved direction of a route from the first area to the second area according to the emotion change comprises the following steps:

[0120] Obtaining specific emotion types of the first tourist in the first area and the second area;

[0121] If the mood changes from a non-fatigued mood to a fatigued mood, then it is judged that the route should have additional rest areas and facilities;

[0122] If the emotion changes from non-confused to confused, it is judged that more explanation facilities should be added to the scenic spot;

[0123] If the emotion changes from non-curiosity to curiosity, it is judged that interactive facilities should be added to the scenic spot;

[0124] If the emotion changes from non-disappointment to disappointment, it is judged that the compensatory activities should be increased.

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

[0126] 1. It is capable of conducting testing and survey on the vast majority of passengers entering tourist areas, thus improving the popularity and accuracy.

[0127] 2. Strong objectivity

[0128] Background and details: Compared with traditional questionnaires or verbal inquiries, facial expression detection is a relatively objective method. Tourists may provide inaccurate information in the questionnaire, such as answering casually because of politeness or not wanting to spend too much time, or giving untrue answers when being verbally asked due to factors such as emotions at the time and the guidance of the investigator. Facial expressions are a natural emotional response, and detection through technical means (such as cameras, facial expression recognition software) can truly reflect the current emotional state of tourists.

[0129] Example: For example, after a tourist visits a popular attraction, they may check the "satisfied" option in the questionnaire, but the expression detection system finds that their brows are furrowed and the corners of their mouths are drooping, which may suggest that they are not actually that satisfied. This objective detection method can help tourism practitioners more accurately grasp the real feelings of tourists.

[0130] Real-time feedback

[0131] Background and details: Expression detection can provide instant feedback. When tourists are experiencing tourism products or services, their expressions can be monitored through cameras installed in scenic spots, hotels and other places. Once a tourist shows a negative expression, staff can intervene in time to understand the situation and solve the problem on the spot, thus preventing tourists from continuing to travel with dissatisfaction and affecting the entire travel experience.

[0132] Example: In a hotel restaurant, when the facial expression detection system captures a guest showing an unhappy expression during a meal, the waiter can immediately come forward to ask whether the food is not to his taste or the service is not up to standard, and provide solutions to the guest on the spot, such as changing the dish or improving the service method.

[0133] Data richness

[0134] Background and details: Expressions contain a variety of emotional information, such as happiness, anger, sadness, surprise, disgust, etc. Through expression detection, rich emotional data of tourists can be collected. This data can not only be used to judge satisfaction, but also to further analyze tourists' specific feelings about various aspects of tourism products or services. For example, by analyzing tourists' expressions in different scenic spots, we can understand which areas are more popular with tourists and which areas may need improvement.

[0135] Example: In a large theme park, the expression detection system found that visitors frequently showed annoyed expressions in the queue area of ​​certain amusement facilities, which prompted the park managers to optimize the queue process or provide a better queue experience (such as adding entertainment facilities in the queue area). At the same time, areas where visitors frequently showed surprise and excitement (such as particularly exciting performance venues) can be further strengthened and promoted.

[0136] Multi-dimensional analysis

[0137] Background and details: Combined with other data (such as tourists' behavioral data, visit time, etc.), expression detection can be used to conduct multi-dimensional satisfaction analysis. For example, combining tourists' expressions at a certain attraction with their stay time at the attraction, the activities they participated in, etc., can provide a more comprehensive understanding of tourists' experience. If tourists stay at an attraction for a long time and their expressions are mostly pleasant, then it can be judged that they are highly satisfied with the attraction; conversely, if the stay time is short and the expression is not good, it is necessary to analyze the reasons in depth.

[0138] Example: In a history museum, visitor A stayed in a certain exhibition hall for a long time, with a relaxed and happy expression, and interacted with the exhibits a lot (such as taking photos and reading the exhibits carefully), which shows that he is very satisfied with the exhibition hall. Although visitor B also stayed for a long time, his expression was confused, which may mean that he was not satisfied with the display or explanation of the exhibits and needed to know more about the situation, such as whether he needed a more detailed guided tour service.

[0139] Large-scale data collection

[0140] Background and details: Installing expression detection equipment in public areas of tourist sites (such as scenic spot entrances, hotel lobbies, etc.) can monitor a large number of tourists at the same time. This can quickly collect a large number of data samples for statistical analysis and trend research. Compared with the traditional one-to-one survey method, it greatly 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.

[0141] Example: In a popular tourist attraction, there are tens of thousands of tourists every day. By installing expression detection systems at the entrance and main passages of the scenic spot, a large amount of tourists' expression data can be collected. After a period of accumulation, the satisfaction trend of different seasons, different time periods, and different tourist groups can be analyzed, providing strong data support for the management and service improvement of the scenic spot.

[0142] Embodiment 2

[0143] like Figure 2 The figure shows a schematic diagram of the structure of a tourist travel satisfaction detection system, which is applied to the tourist travel satisfaction detection method described above, and includes:

[0144] A first collection module, which is used to collect first expression data of a first tourist in a first area of ​​a first route in a tourist area;

[0145] A first judgment module, which is used to analyze and judge the emotion of the first tourist according to the first expression data;

[0146] A second collection module, which is used to collect second expression data of the first tourist in a second area of ​​the first route;

[0147] A first judgment module, which is used to analyze and judge the emotion of the first visitor according to the second expression data;

[0148] The satisfaction determination module is used to determine the first satisfaction of the first tourist with respect to the route from the first area to the second area according to the emotion change of the first tourist from the first area to the second area.

[0149] Embodiment 3

[0150] A computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned tourist travel satisfaction detection method.

[0151] Embodiment 4

[0152] A processor is used to run a program, wherein the program executes the above-mentioned tourist travel satisfaction detection method when running.

[0153] The present application provides a method for detecting tourist satisfaction, including: collecting first expression data of a first tourist in a first area of ​​a first route in a tourist area; analyzing and judging the first tourist's emotions based on the first expression data; collecting second 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 expression data; judging the first tourist's first satisfaction with the route between the first area and the second area based on the first tourist's emotional changes from the first area to the second area. The present application scheme can conduct detection and investigation on most of the tourists entering the tourist area, improves the popularity and accuracy, and uses emotional changes to make judgments, so as to reduce the probability of misjudgment and improve the accuracy of the investigation.

[0154] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

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

[0156] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0157] If the integrated unit is implemented in the form of 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, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A tourist travel satisfaction detection method, characterized in that: include: Collecting first expression data of a first tourist in a first area of ​​a first route in a tourist area; Determine the emotion of the first visitor according to the first expression data analysis; collecting second expression data of the first tourist in the second area of ​​the first route; Determine the emotion of the first visitor based on the second expression data analysis; According to the emotion change of the first tourist from the first area to the second area, the first satisfaction of the first tourist with the route between the first area and the second area is judged, and the emotion change is used to make the judgment at the same time to reduce the probability of misjudgment and improve the accuracy of the investigation.

2. The method for detecting tourist satisfaction according to claim 1, characterized in that: The calculation formula of the first satisfaction level is as follows: In the above formula, Y1 is the first satisfaction value; X i is the satisfaction value of changing from a non-i-th emotion to the i-th emotion; ε is the age coefficient; is the gender coefficient; n is the type of emotional change.

3. The method for detecting tourist satisfaction according to claim 1, characterized in that: The following steps are also included: The time when the first tourist enters the first area and the second area is recorded respectively, the stay time on the route between the two areas is calculated according to the time difference between the two areas, and the second satisfaction with the route between the first area and the second area is judged according to the stay time on the route combined with the emotion change.

4. The method for detecting tourist satisfaction according to claim 3, characterized in that: The following steps are also included: The calculation formula of the second satisfaction level is as follows: Y2=θY1; In the above formula, Y2 is the second satisfaction value; θ is the route stay time coefficient; Y1 is the first satisfaction value.

5. The method for detecting tourist satisfaction according to claim 3, characterized in that: The following steps are also included: The first tourist's stay time in front of each scenic spot is detected, and the proportion of the stay time at each scenic spot to the stay time along the route is calculated to obtain the tourist's satisfaction ranking for each scenic spot.

6. The method for detecting tourist satisfaction according to claim 1, characterized in that: The following steps are also included: An improved direction of a route from the first area to the second area is obtained based on the change in emotion.

7. The method for detecting tourist satisfaction according to claim 6, characterized in that: The step of obtaining an improved direction of a route from the first area to the second area according to the emotion change comprises the following steps: If the mood changes from a non-fatigued mood to a fatigued mood, then it is judged that the route should have additional rest areas and facilities; If the emotion changes from non-confused to confused, it is judged that more explanation facilities should be added to the scenic spot; If the emotion changes from non-curiosity to curiosity, it is judged that interactive facilities should be added to the scenic spot; If the emotion changes from non-disappointment to disappointment, it is judged that the compensatory activities should be increased.

8. A tourist satisfaction detection system, characterized in that: The tourist satisfaction detection method applied to any one of claims 1 to 7 comprises: A first collection module, which is used to collect first expression data of a first tourist in a first area of ​​a first route in a tourist area; A first judgment module, which is used to analyze and judge the emotion of the first tourist according to the first expression data; A second collection module, which is used to collect second expression data of the first tourist in a second area of ​​the first route; A first judgment module, which is used to analyze and judge the emotion of the first visitor according to the second expression data; The satisfaction determination module is used to determine the first satisfaction of the first tourist with respect to the route from the first area to the second area according to the emotion change of the first tourist from the first area to the second area.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the tourist satisfaction detection method described in any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the tourist travel satisfaction detection method described in any one of claims 1 to 7.

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