A cultural tourism visitor satisfaction survey system

Abstract

This invention provides a cultural tourism visitor satisfaction survey system, relating to the field of tourism creative service technology. It includes: a regional division and data collection module: systematically dividing the entire cultural tourism area into survey regions and collecting visitor satisfaction scores via QR codes; an option tag collection module: collecting option tag data selected by visitors in each survey region; an influence weight conversion module: constructing a conversion function to calculate and generate the influence weight of negative experiences; and an influence weight mean calculation and output module: calculating the mean influence weight of each option tag and establishing an optimization mechanism based on the mean influence weight of each option tag. This invention can distinguish between high-frequency problems that are accompanied by high satisfaction and are therefore not serious, and low-frequency problems that, once they occur, will cause strong dissatisfaction. Ultimately, it enables scenic area management to clearly identify which areas and specific problems should be prioritized for resolution.

Description

Technical Field

[0001] This invention relates to the field of tourism creative service technology, specifically a cultural tourism tourist satisfaction survey system. Background Technology

[0002] In cultural tourism visitor satisfaction surveys, providing an overall score and a selection of negative experience tags are complementary: the overall score can quickly reflect the overall satisfaction level of visitors in a quantitative way, facilitating horizontal comparison and trend analysis; while the pre-set negative experience tags guide visitors to specifically point out pain points in aspects such as service, environment, and cultural display. This avoids the ambiguity of open-ended responses and accurately captures key negative factors affecting satisfaction, providing scenic spots or cultural institutions with actionable improvement guidelines, thereby more effectively optimizing the visitor experience.

[0003] In existing technology, patent CN106156287A discloses a method for analyzing public opinion and satisfaction based on tourism demand templates in scenic area evaluation data. This technology includes three steps: constructing a keyword template library based on tourism demand templates, expanding the keyword template library, and calculating public opinion and satisfaction analysis based on scenic area evaluation data. This method solves the problem that unstructured content such as tourist travelogues and reviews is difficult for other tourists to efficiently search and utilize. It can not only provide tourists with a comprehensive satisfaction value for a scenic area, but also provide specific satisfaction values ​​for six aspects of the scenic area: eating, accommodation, transportation, sightseeing, shopping, and entertainment, allowing tourists to quickly understand the various evaluation parameters of the scenic area.

[0004] However, the existing technologies mentioned above only independently count the average score given by tourists or simply calculate the total number of times each negative issue is checked. They cannot determine whether a problem is common but not serious or rare but has a huge impact. Moreover, they often conduct general surveys of the entire scenic area without distinguishing between areas, resulting in different problems in different locations being mixed together and difficult to pinpoint. The final result is either a bunch of numbers and charts or vague conclusions. Therefore, conventional survey systems force managers to decide the order of improvement based on feelings or average scores, which easily leads to spending resources on problems that seem frequent but are not actually serious, while some problems that cause extreme dissatisfaction among a few tourists are ignored.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a cultural tourism visitor satisfaction survey system to solve the problems mentioned in the background. This invention can distinguish between high-frequency problems that are accompanied by high satisfaction and are therefore not serious, and low-frequency problems that, once they occur, will cause strong dissatisfaction. Ultimately, this allows scenic area management to clearly identify which areas and specific problems should be prioritized for resolution.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A cultural tourism visitor satisfaction survey system includes: Regional Division and Data Collection Module: The entire cultural tourism area is systematically divided into survey areas. An independent satisfaction rating QR code is placed at the center of each survey area. Tourist satisfaction rating scores are collected through the satisfaction rating QR code. The tourist satisfaction rating scores obtained in each survey area are all measured using a uniform scale. Option label collection module: By scanning the satisfaction rating QR code, negative option labels are also provided. The system collects the option label data selected by tourists in each survey area. The survey system automatically records the correlation between each option label and the corresponding tourist satisfaction rating score, and records the timestamp of each scan and the identification of the survey area. Impact weight conversion module: Construct a conversion function, which is used to quantify the tourist satisfaction evaluation score generated by the same tourist in the same survey area and the selected option label, and calculate the negative experience impact weight for each selected option label; Impact weight mean calculation and output module: In each survey area, the negative experience impact weight of each option tag generated in the satisfaction rating QR code collection evaluation is summarized, the impact weight mean of each option tag is calculated, and an optimization mechanism is established based on the impact weight mean of each option tag.

[0008] Furthermore, the process of systematically surveying and dividing the entire cultural tourism area within the regional division data collection module includes: Obtain complete boundary and internal spatial data of the cultural tourism area, divide the overall area into multiple independent survey areas that do not overlap based on the distribution of attractions, and ensure that each survey area has independent representativeness of experience evaluation; assign a unique geographic identifier to each survey area, and determine the data collection point at the center of each survey area; all subsequent satisfaction rating QR codes are bound to the geographic identifier of the corresponding survey area. A unified scale is established as the measurement standard for tourist satisfaction evaluation scores to ensure the comparability of data across regions.

[0009] Furthermore, in the option label collection module, when a tourist selects one or more negative option labels, the system records each selection operation in real time and establishes a one-to-one correspondence between each selected option label and the tourist satisfaction rating score just given by the tourist.

[0010] Furthermore, the system automatically adds a timestamp for each scan, a geographic identifier of the survey area, and an anonymized session sequence number, so that the data linking each option label and tourist satisfaction rating score can be traced back to an independent evaluation event. If tourists do not select any negative option tags, the current evaluation will only record the satisfaction score and will not generate tag-related data; all collected raw data will be stored according to the region identifier, which will only provide structured input for subsequent calculation of the weight of negative experience impact.

[0011] Furthermore, in the influence weight conversion module, the conversion function used to calculate the negative experience influence weight for each selected option label is as follows: in: In the r-th QR code scan evaluation, the negative experience influence weight corresponding to a certain option label t selected by the tourist is given. The larger the negative experience influence weight, the more serious the bad experience problem reflected by the corresponding option label t is in the evaluation. Let be the actual satisfaction score given by the tourist in the r-th evaluation, with a value range of . ; The maximum value of the satisfaction evaluation score is a uniform constant. This represents the total number of negative option tags selected by tourists in the r-th evaluation. If no tags are selected, this function will not be included in the calculation. It is a natural constant; α is a power-law adjustment factor that controls the sensitivity of the satisfaction score deviation to the increase of the weight of negative experience, and satisfies α≥1; k is a global scaling factor used to map the weight values ​​of negative experience impacts to a numerical range that facilitates subsequent statistical summarization, and k≥0.

[0012] Furthermore, in the transformation function, through The item distributes the negative experience across multiple selected option tags in the same evaluation: when a tourist selects multiple negative option tags, it indicates that the negative experience is spread across multiple issues, and the weight of the negative experience borne by each tag is determined by... The number of items is reduced, and based on the diminishing marginal property of the logarithmic function, the amortization effect gradually flattens as the number of option labels increases.

[0013] Furthermore, it also includes the process for calculating and obtaining the global scaling factor k: Global scaling factor k: Defines a pilot area within the cultural tourism scope, and calculates the unscaled negative experience impact weight based on all valid evaluation data collected in the pilot area. Set a target upper limit value T, select the target statistic, and then take the overall mean of the weights of all unscaled negative experience impacts. Then k is calculated using the following formula: .

[0014] Furthermore, it also includes the process for calculating and obtaining the power-law adjustment factor α: Power-order adjustment factor α: A set of data including tourist satisfaction scores was collected in the pilot area. The sample data for the corresponding option labels are used to pre-determine a series of candidate α values, with a fixed pre-determined step size. For each candidate α value, the weight of the intermediate negative experience impact is calculated without considering the scaling factor. Based on the severity ranking of problems under different scores, a set of reference weight sequences is defined, and the candidate α values ​​are selected to correspond to... The α value that minimizes the root mean square error between the reference weight sequence and the reference weight sequence is used as the final power-law adjustment factor.

[0015] Furthermore, the formula used to calculate the average influence weight of each option label is as follows: in: For the survey area Inside, option tab Negative experiences affect the weighted mean, used to quantify option labels. The negative experiences represented in the survey area The average severity in the middle; For the survey area Within, in all QR code review events, there are option tags. Total number of times checked by tourists; For the survey area Includes option tabs The set of all evaluation events, the number of elements is . ; r is A single evaluation event index in the database, each event Each corresponds to a unique scan record.

[0016] Furthermore, the optimization mechanism includes: sorting all selected option labels from high to low according to the average influence weight, and making a comprehensive decision based on the frequency of occurrence of each option label; The logic of the comprehensive decision-making is as follows: collect the median of all the influence weights that affect the mean of the influence weights, and then the influence weight mean that is greater than or equal to the median of the influence weights is determined to be a high mean, and the influence weight mean that is less than the median of the influence weights is determined to be a low mean. Collect the median frequency of all selected occurrences. Frequency occurrences greater than or equal to the median frequency are considered high frequencies, and frequency occurrences less than the median frequency are considered low frequencies. Option labels that meet the criteria of high mean and high frequency are immediately prioritized for processing. Option labels that meet the criteria of high mean and low frequency are considered as the second priority. Option tags that meet the criteria of low mean and high frequency are considered for general attention. Option labels that meet the criteria of low mean and low frequency are determined to have the lowest priority.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This application links the specific scores given by tourists with the negative experience tags they selected, calculating the impact weight of each issue in the actual evaluation. This allows for the differentiation between high-frequency issues that are accompanied by high satisfaction and are therefore not serious, and low-frequency issues that, once they occur, will cause strong dissatisfaction. Furthermore, this invention can analyze issues independently by region, and by combining the frequency and average weight of each issue, it uses the median to categorize issues into four types: immediate priority, secondary priority, routine concern, and lowest priority. This avoids the drawbacks of conventional surveys that only look at scores or the number of complaints, failing to scientifically prioritize improvement priorities. Ultimately, this allows scenic area management to clearly understand which areas and specific issues should be addressed with the utmost priority. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a cultural tourism visitor satisfaction survey system according to the present invention. Figure 2 This is a schematic diagram illustrating the operation process of a cultural tourism visitor satisfaction survey system according to the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0020] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] Example: Please see Figures 1-2 The present invention provides the following technical solutions: A cultural tourism visitor satisfaction survey system, designed for refined experience management in creative tourism services, capable of in-depth quantitative analysis of visitor feedback from different areas within a scenic spot, including: Regional Division and Data Collection Module: This module systematically divides the entire cultural tourism area into survey regions, ensuring that each region independently reflects the actual tourist experience. A unique satisfaction rating QR code is placed at the center of each survey region. Tourists can scan the QR code to rate their overall satisfaction with that region, and the system collects their satisfaction scores accordingly. To ensure the comparability of evaluation results across different regions, a standardized measurement scale is used for all tourist satisfaction scores obtained from each survey region, thus eliminating biases caused by different rating standards. The process of systematically surveying and dividing the entire cultural tourism area includes: First, complete boundary and internal spatial data of the cultural tourism area were acquired, including topography, roads, and the distribution of attractions. Based on the density of attractions, visitor routes, and functional zoning, the overall area was divided into multiple non-overlapping independent survey areas. Each survey area should be representative of the independent experience evaluation, meaning it contains relatively complete visitor nodes and service facilities. A unique geographic identifier was then assigned to each survey area, which can be used for subsequent data tracing and regional comparison. A data collection point was determined at the center of each survey area to ensure the exposure and ease of scanning of QR codes, while avoiding cross-interference between QR codes from different areas. All subsequently collected satisfaction rating QR codes were linked to the geographic identifier of the corresponding survey area, ensuring that each rating data point could be accurately attributed to a specific area.

[0022] The system can also be applied to digital conference and exhibition service scenarios. For example, in large-scale cultural exhibitions or temporary exhibition halls, the survey area can be divided according to the booths or functional halls to achieve precise spatial positioning of exhibition satisfaction.

[0023] A standardized scale is established as the measurement standard for tourist satisfaction evaluation scores. In this embodiment, an integer score range of 0 to 10 is used, and the corresponding satisfaction level description for each score is clearly defined, ensuring that tourists follow the same psychological scale when rating in different areas. The establishment of a standardized scale can effectively eliminate subjective bias in cross-regional ratings.

[0024] Option Tag Collection Module: By scanning the satisfaction rating QR code, tourists are provided with a series of negative option tags. These tags cover typical negative experiences that may be encountered during cultural tourism, specifically including poor environmental hygiene, unclear signage, poor service attitude, damaged facilities, and overcrowding. After giving a satisfaction rating, tourists can freely select one or more corresponding negative option tags based on their actual feelings. The system collects the option tag data selected by tourists in each survey area in real time, and automatically records the correlation between each selected option tag and the satisfaction rating score just given by the tourist. It also records the timestamp of each scan and the geographical identifier of the survey area, ensuring that each data point has a clear temporal and spatial attribution.

[0025] Specifically, the system records every selection in real time and establishes a one-to-one correspondence between each selected option and the tourist's recently submitted satisfaction rating score. This correlation reflects the occurrence of different negative experience issues under different satisfaction score conditions, providing basic data for subsequent analysis of the severity of different issues.

[0026] The system automatically appends a timestamp, a geographic identifier for the survey area, and an anonymized session sequence number to each scan. The anonymized session sequence number is used to distinguish different evaluation events. Through these three dimensions of identification, the correlation data between each option label and the tourist satisfaction rating score can be traced back to an independent evaluation event.

[0027] If tourists do not select any negative option tags during the evaluation process, the current evaluation only records the satisfaction score and does not generate any tag-related data. This avoids interference from invalid data and ensures data purity. All collected raw data is classified and stored according to the identifier of its respective survey area, forming a structured dataset. This survey system is not only suitable for traditional scenic spots, but can also be flexibly deployed in digital tourism projects and sports and exhibition service sites, such as tourist service areas accompanying marathon events and sports and cultural exhibition halls, providing satisfaction evaluation support for diversified cultural, tourism and sports integration scenarios.

[0028] The influence weight transformation module constructs a transformation function. The core function of this function is to quantitatively link the tourist satisfaction score generated by the same tourist in a single evaluation within the same survey area with the negative option tags they actively selected. Through this linked quantification, the intrinsic relationship between the tourist's satisfaction level and the specific negative experience issue selected is considered, and a corresponding negative experience influence weight is independently calculated for each selected option tag. This weight reflects the actual impact of the negative experience issue represented by that option tag on the tourist's satisfaction in the current evaluation event, allowing for comparisons between different negative issues on a unified scale. The introduction of the transformation function transforms the original scoring data and tag selection behavior into quantifiable severity indicators. The transformation function used to calculate the negative experience impact weight for each selected option label is as follows: in: In the r-th QR code scan evaluation, the negative experience influence weight corresponding to a certain option label t selected by the tourist is given. The larger the negative experience influence weight, the more serious the bad experience problem reflected by the corresponding option label t is in the evaluation. Let be the actual satisfaction score given by the tourist in the r-th evaluation, with a value range of . ; The maximum value of the satisfaction rating scale is a uniform constant, first used... Calculate the difference between the actual tourist satisfaction score and the maximum score, then divide by... Standardize to make the results fall into Within the range; This represents the total number of negative option tags selected by tourists in the r-th evaluation. If no tags are selected, this function does not participate in the calculation. As the denominator increases, the rate of increase gradually slows down due to the logarithmic function. If a tourist selects only one negative label, that label will bear the weight of the entire negative experience, and therefore has a higher weight; if a tourist selects multiple labels at the same time, it means that the negative feelings are distributed across different issues, and the weight allocated to each label individually will decrease. Let be a natural constant, and introduce... The purpose is to ensure that the independent variable of the logarithmic function is always greater than 1, and at the same time, when At this time, the denominator will not be too small, maintaining a reasonable range of weight values; In the transformation function, through The item distributes the negative experience across multiple selected option tags in the same evaluation: when a tourist selects multiple negative option tags, it indicates that the negative experience is spread across multiple issues, and the weight of the negative experience borne by each tag is determined by... The number of items is reduced, and based on the diminishing marginal property of the logarithmic function, the amortization effect gradually flattens as the number of option labels increases.

[0029] k is a global scaling factor used to map the weight values ​​of negative experience impacts to a numerical range that facilitates subsequent statistical summarization, and k≥0.

[0030] The process for calculating and obtaining the global scaling factor k: Global scaling factor k: Defines a pilot area within the cultural tourism scope, and calculates the unscaled negative experience impact weight based on all valid evaluation data collected in the pilot area. Set a target upper limit value T, select the target statistic, and then take the overall mean of the weights of all unscaled negative experience impacts. Then k is calculated using the following formula: .

[0031] α is a power-law adjustment factor that controls the sensitivity of the satisfaction score deviation to the increase of the weight of negative experience, and satisfies α≥1; The process for calculating and obtaining the power-law adjustment factor α is as follows: Power-order adjustment factor α: A set of data including tourist satisfaction scores was collected in the pilot area. The sample data for the corresponding option labels are used to pre-determine a series of candidate α values, with a fixed pre-determined step size. For each candidate α value, the weight of the intermediate negative experience impact is calculated without considering the scaling factor. Based on the severity ranking of problems under different scores, a set of reference weight sequences is defined, and the candidate α values ​​are selected to correspond to... The α value that minimizes the root mean square error between the reference weight sequence and the reference weight sequence is used as the final power-law adjustment factor.

[0032] The influence weight mean calculation and output module: Within each survey area, the system aggregates the negative experience influence weights corresponding to each option tag generated from all evaluations collected via satisfaction rating QR codes. These weights originate from multiple selections and ratings of the same option tag by different tourists at different times. Subsequently, for each option tag, the system sums up the negative experience influence weights generated when it was selected and divides them by the total number of times that tag appeared in that area to calculate the mean influence weight of each option tag. This mean can objectively reflect the average severity of the negative experience problem represented by that option tag in the survey area.

[0033] Based on the average impact weight of each option label, an optimization mechanism is established to generate a list of problems ranked by severity for different regions. When the average value of a label exceeds a specific range, an automatic warning is triggered. Through this optimization mechanism, management can rationally allocate resources based on quantified data results, prioritizing and addressing the most prominent negative impacts, thereby continuously improving visitor satisfaction in cultural tourism areas.

[0034] The formula used to calculate the average influence weight of each option label is: in: For the survey area Inside, option tab Negative experiences affect the weighted mean, used to quantify option labels. The negative experiences represented in the survey area The average severity level is used as a core indicator of the severity of problems at the regional level for comparison across labels and regions, and is further input into comprehensive decision-making. For the survey area Within, in all QR code review events, there are option tags. Total number of times checked by tourists The magnitude of does not directly determine the rise or fall of the mean, but rather affects the statistical stability of the mean. The larger the mean, the better the mean can represent the true severity of the label in the region, and the less it is affected by individual extreme values; For the survey area Includes option tabs The set of all evaluation events, the number of elements is . ; r is A single evaluation event index in the database, each event Each corresponds to a unique scan record.

[0035] The system sorts all selected negative option tags from highest to lowest according to their average impact weight. This directly shows the average severity ranking of the negative experience problem represented by each tag in the corresponding survey area. Furthermore, the system considers the frequency of each option tag to make a comprehensive decision, avoiding ignoring common problems based solely on severity or overlooking less severe issues based solely on frequency. The logic of the comprehensive decision-making process is as follows: First, the average of all influence weights is collected, and the median is calculated as the dividing line between high and low averages. Any average value greater than or equal to the median of the influence weight is considered a high average, indicating that the problem's severity is at least moderate to high; while an average value less than the median is considered a low average, indicating that the problem's average severity is relatively low. Simultaneously, the system collects the frequency of all selected option tags and calculates the median frequency, which is used as the dividing line between high and low frequency. Tags with a frequency greater than or equal to the median frequency are considered high frequency, indicating that the problem is mentioned by many visitors; tags with a frequency less than the median frequency are considered low frequency, indicating that the problem is selected by relatively few visitors. Specifically: The option labels that meet the criteria of "high average value + high frequency" indicate that these problems are both serious and widespread, and therefore should be identified as matters that should be addressed immediately and require management to allocate resources to resolve them as soon as possible. The option labels that meet the criteria of "high average value + low frequency" indicate that although these problems do not occur often, they can have a significant negative impact on the visitor experience once they do occur. Therefore, they are classified as secondary priority issues and should be rectified as soon as possible after the first type of issues are resolved. The option labels that meet the criteria of "low mean + high frequency" indicate that although these problems are not extremely serious, they occur very frequently and are likely to accumulate negative feelings. Therefore, they are judged as routine concerns and included in the daily maintenance and continuous improvement plan. Option labels that meet the criteria of low mean and low frequency indicate that these issues have a relatively low impact and frequency of occurrence. Therefore, they are classified as the lowest priority and will be addressed when resources are sufficient, or they can be retained as long-term observation items.

[0036] The aforementioned tiered optimization mechanism is particularly suitable for digital creative cultural exhibition services. In such exhibitions, visitor experience issues are often highly dynamic and diverse. By using a four-quadrant classification based on weighted averages and frequencies, potential problems in innovative display elements such as digital interactive installations and virtual tours can be quickly identified. This system can also be extended to online cultural service scenarios such as online libraries and digital libraries. By linking and quantifying user satisfaction ratings and negative labels in their digital reading experiences, it helps digital cultural institutions optimize interface design, resource retrieval, and the quality of virtual reading services.

[0037] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0038] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0039] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0040] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A cultural tourism visitor satisfaction survey system, characterized in that, include: Regional Division and Data Collection Module: The entire cultural tourism area is systematically divided into survey areas. An independent satisfaction rating QR code is placed at the center of each survey area. Tourist satisfaction rating scores are collected through the satisfaction rating QR code. The tourist satisfaction rating scores obtained in each survey area are all measured using a uniform scale. Option label collection module: By scanning the satisfaction rating QR code, negative option labels are also provided. The system collects the option label data selected by tourists in each survey area. The survey system automatically records the correlation between each option label and the corresponding tourist satisfaction rating score, and records the timestamp of each scan and the identification of the survey area. Impact weight conversion module: Construct a conversion function, which is used to quantify the tourist satisfaction evaluation score generated by the same tourist in the same survey area and the selected option label, and calculate the negative experience impact weight for each selected option label; Impact weight mean calculation and output module: In each survey area, the negative experience impact weight of each option tag generated in the satisfaction rating QR code collection evaluation is summarized, the impact weight mean of each option tag is calculated, and an optimization mechanism is established based on the impact weight mean of each option tag.

2. The cultural tourism visitor satisfaction survey system according to claim 1, characterized in that: The process of systematically surveying and dividing the entire cultural tourism area in the regional division data collection module includes: Obtain complete boundary and internal spatial data of the cultural tourism area, divide the overall area into multiple independent survey areas that do not overlap based on the distribution of attractions, and ensure that each survey area has independent representativeness of experience evaluation; assign a unique geographic identifier to each survey area, and determine the data collection point at the center of each survey area; all subsequent satisfaction rating QR codes are bound to the geographic identifier of the corresponding survey area. A unified scale is established as the measurement standard for tourist satisfaction evaluation scores to ensure the comparability of data across regions.

3. The cultural tourism visitor satisfaction survey system according to claim 1, characterized in that: In the option label collection module, tourists select one or more negative option labels. The system records each selection operation in real time and establishes a one-to-one correspondence between each selected option label and the tourist satisfaction rating score just given by the tourist.

4. The cultural tourism visitor satisfaction survey system according to claim 1, characterized in that: The system automatically adds a timestamp for each scan, a geographic identifier of the survey area, and an anonymous session sequence number, so that the data linking each option label and tourist satisfaction rating score can be traced back to an independent evaluation event. If tourists do not select any negative option tags, the current evaluation will only record the satisfaction score and will not generate tag-related data; all collected raw data will be stored according to the region identifier, which will only provide structured input for subsequent calculation of the weight of negative experience impact.

5. The cultural tourism visitor satisfaction survey system according to claim 1, characterized in that: In the influence weight conversion module, the conversion function used to calculate the negative experience influence weight for each selected option label is as follows: in: In the r-th QR code scan evaluation, the negative experience influence weight corresponding to a certain option label t selected by the tourist is given. The larger the negative experience influence weight, the more serious the bad experience problem reflected by the corresponding option label t is in the evaluation. Let be the actual satisfaction score given by the tourist in the r-th evaluation, with a value range of . ; The maximum value of the satisfaction evaluation score is a uniform constant. This represents the total number of negative option tags selected by tourists in the r-th evaluation. If no tags are selected, this function will not be included in the calculation. It is a natural constant; α is a power-law adjustment factor that controls the sensitivity of the satisfaction score deviation to the increase of the weight of negative experience, and satisfies α≥1; k is a global scaling factor used to map the weight values ​​of negative experience impacts to a numerical range that facilitates subsequent statistical summarization, and k≥0.

6. The cultural tourism visitor satisfaction survey system according to claim 5, characterized in that: In the transformation function, through The item distributes the negative experience across multiple selected option tags in the same evaluation: when a tourist selects multiple negative option tags, it indicates that the negative experience is spread across multiple issues, and the weight of the negative experience borne by each tag is determined by... The number of items is reduced, and based on the diminishing marginal property of the logarithmic function, the amortization effect gradually flattens as the number of option labels increases.

7. The cultural tourism visitor satisfaction survey system according to claim 5, characterized in that: It also includes the process for calculating and obtaining the global scaling factor k: Global scaling factor k: Defines a pilot area within the cultural tourism scope, and calculates the unscaled negative experience impact weight based on all valid evaluation data collected in the pilot area. Set a target upper limit value T, select the target statistic, and then take the overall mean of the weights of all unscaled negative experience impacts. Then k is calculated using the following formula: 。 8. The cultural tourism visitor satisfaction survey system according to claim 7, characterized in that: It also includes the process for calculating and obtaining the power-law adjustment factor α: Power-order adjustment factor α: A set of data including tourist satisfaction scores was collected in the pilot area. The sample data for the corresponding option labels are used to pre-determine a series of candidate α values, with a fixed pre-determined step size. For each candidate α value, the weight of the intermediate negative experience impact is calculated without considering the scaling factor. Based on the severity ranking of problems under different scores, a set of reference weight sequences is defined, and the candidate α values ​​are selected to correspond to... The α value that minimizes the root mean square error between the reference weight sequence and the reference weight sequence is used as the final power-law adjustment factor.

9. A cultural tourism visitor satisfaction survey system according to claim 5, characterized in that: The formula used to calculate the average influence weight of each option label is as follows: in: For the survey area Inside, option tab Negative experiences affect the weighted mean, used to quantify option labels. The negative experiences represented in the survey area The average severity in the middle; For the survey area Within, in all QR code review events, there are option tags. Total number of times checked by tourists; For the survey area Includes option tabs The set of all evaluation events, the number of elements is . ; r is A single evaluation event index in the database, each event Each corresponds to a unique scan record.

10. A cultural tourism visitor satisfaction survey system according to claim 9, characterized in that: The optimization mechanism includes: sorting all selected option labels from high to low according to the average influence weight, and making a comprehensive decision based on the frequency of occurrence of each option label; The logic of the comprehensive decision-making is as follows: collect the median of all the influence weights that affect the mean of the influence weights, and then the influence weight mean that is greater than or equal to the median of the influence weights is determined to be a high mean, and the influence weight mean that is less than the median of the influence weights is determined to be a low mean. Collect the median frequency of all selected occurrences. Frequency occurrences greater than or equal to the median frequency are considered high frequencies, and frequency occurrences less than the median frequency are considered low frequencies. Option labels that meet the criteria of high mean and high frequency are determined to be processed immediately with priority. Option labels that meet the criteria of high mean and low frequency are considered as the second priority. Option tags that meet the criteria of low mean and high frequency are considered for general attention. Option labels that meet the criteria of low mean and low frequency are determined to have the lowest priority.

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