Tourism resource investigation and evaluation method and system based on big data

Abstract

The invention relates to a tourism resource investigation and evaluation method and system based on big data, and relates to the technical field of big data, and the method comprises the steps: obtaining the evaluation data of tourism resources, and determining the data quality scoring index of the evaluation data; extracting interaction data among tourists, scenic spots and time from the tourism evaluation data, and determining a tourist preference tensor based on the interaction data; extracting index data of the tourism resources from the tourism evaluation data, and calculating regional regulation factors of the tourism resources based on the index data of the tourism resources; extracting a sustainability index of the tourism resource from the tourism evaluation data, and calculating a sustainable development index of the tourism resource based on the sustainability index of the tourism resource; based on the tourist preference tensor, the data quality scoring index, the regional regulation factor of the tourism resource and the sustainable development index, determining an evaluation score of the tourism resource; and based on the evaluation score of the tourism resource, performing adaptive optimization and feedback on the evaluation of the tourism resource.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a tourism resource investigation and evaluation method, system, electronic device and non-transient computer-readable storage medium based on big data. Background Art

[0002] Nowadays, the methods of tourism resource investigation and evaluation mainly rely on data collection, data analysis and the construction of evaluation models. Traditionally, the main means of tourism resource investigation include questionnaire surveys, on-site observations, expert reviews and statistical analysis. These methods rely on manual data collection and analysis, and then obtain the evaluation results of tourism resources by sorting and evaluating data on various aspects such as tourist behavior, consumption patterns, and scenic environment. At the same time, with the advancement of big data technology, some studies have begun to combine multi-source data such as social media data, tourism platform reviews, and geographic information system (GIS) data to conduct intelligent evaluation of tourism resources through data mining and machine learning algorithms, which improves the accuracy and efficiency of investigation and evaluation.

[0003] However, traditional survey methods often have time and space limitations and cannot capture tourists' dynamic behaviors and preferences in real time and comprehensively. Secondly, although the introduction of big data technology has improved the richness and multidimensionality of data, the existing evaluation models still cannot fully consider complex factors such as tourists' personalized needs, regional differences, and the sustainability of tourism resources. In addition, differences in data quality, the diversity of data sources, and the complexity of data processing may also lead to inaccurate or biased results. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a tourism resource investigation and evaluation method, system, electronic device and non-transitory computer-readable storage medium based on big data, which can improve the accuracy and comprehensiveness of tourism resource investigation and evaluation.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: The present invention provides a tourism resource investigation and evaluation method based on big data, the method comprising: Acquire evaluation data of tourism resources and determine data quality scoring indicators of the evaluation data; the evaluation data of tourism resources includes environmental data, social media data, OTA platform comments, GPS tracks and mobile phone signaling data; Extracting interaction data among tourists, scenic spots and time from the tourism evaluation data, and determining a tourist preference tensor based on the interaction data; Extracting index data of the tourism resources from the tourism evaluation data, and calculating the regional adjustment factor of the tourism resources based on the index data of the tourism resources; Extracting the sustainability index of the tourism resources from the tourism evaluation data, and calculating the sustainable development index of the tourism resources based on the sustainability index of the tourism resources; Determining the evaluation score of the tourism resources based on the tourist preference tensor, the data quality scoring index, the regional adjustment factor of the tourism resources and the sustainable development index; Based on the evaluation scores of the tourism resources, adaptive optimization and feedback are performed on the evaluation of the tourism resources.

[0006] Optionally, determining a data quality scoring indicator of the evaluation data includes: Obtaining a data integrity weight, a timeliness weight, a timeliness score, an accuracy weight, and an accuracy score for each of the evaluation data; Obtaining a first balance coefficient, a second balance coefficient, and a third balance coefficient for calculating the data quality scoring indicator; The data quality scoring index of the evaluation data is determined based on the first balance coefficient, the second balance coefficient and the third balance coefficient of the data quality scoring index, as well as the data integrity weight, timeliness weight, timeliness score, accuracy weight and accuracy score of each evaluation data.

[0007] Optionally, the data quality scoring indicator is expressed as: ; in, is a data quality scoring indicator. is the data integrity weight of the i-th evaluation data, is the completeness score of the ith evaluation data, is the timeliness weight of the i-th evaluation data, is the timeliness score of the ith evaluation data, is the accuracy weight of the ith evaluation data, is the accuracy score of the ith evaluation data, is the first balance coefficient, is the second balance coefficient, is the third balance coefficient.

[0008] Optionally, determining a tourist preference tensor based on the interaction data includes: Obtaining a tourist feature vector of the tourist, a scenic spot feature vector of the scenic spot, and a time feature vector of the tourist's visit to the scenic spot; According to the historical data of the tourists playing in the scenic spot, construct a first interaction function between tourists and scenic spots, a second interaction function between scenic spots and time, and a third interaction function between tourists and time; The first interaction function, the second interaction function and the third interaction function are weighted according to the first weight, the second weight and the third weight to obtain the tourist preference tensor.

[0009] Optionally, the tourist preference tensor is expressed as: ; in, is the tourist preference tensor, u is the tourist feature vector, v is the scenic spot feature vector, t is the time feature vector, F represents the first interaction function between tourists and scenic spots, G represents the second interaction function between scenic spots and time, and H represents the third interaction function between tourists and time. is the first weight, is the second weight, It is the third weight.

[0010] Optionally, the tourist feature vector includes the tourist's age, gender, income level, travel frequency, spending power, and interest preference; The scenic spot feature vector includes the geographical location, climate conditions, transportation convenience, reception capacity, service facility perfection, and cultural characteristics of the scenic spot of the tourism resources.

[0011] Optionally, the calculating the regional adjustment factor of the tourism resources based on the index data of the tourism resources includes: Obtaining the regional development index, environmental carrying capacity index and regional weight coefficient of each of the scenic spots of the tourism resources; Obtaining the inter-regional difference measure of each of the scenic spots and the adjustment coefficient used to calculate the regional adjustment factor; The regional adjustment factor of the tourism resources is determined according to the regional development index, environmental carrying capacity index and regional weight coefficient of each scenic spot, the inter-regional difference metric, and the adjustment coefficient.

[0012] Optionally, the calculating of the sustainable development index of the tourism resources based on the sustainability index of the tourism resources includes: Obtaining economic sustainability indicators, social sustainability indicators and environmental sustainability indicators of the tourism resources, as well as weights of each of the sustainability indicators; Obtaining the attenuation coefficient, resource utilization rate, and predicted time span for calculating the sustainable development index; The sustainable development index of the tourism resources is calculated based on the economic sustainability index, social sustainability index and environmental sustainability index of the tourism resources, the weight of each sustainability index, the attenuation coefficient, the resource utilization rate and the predicted time span.

[0013] Optionally, determining the evaluation score of the tourism resources based on the tourist preference tensor, the data quality scoring index, the regional adjustment factor of the tourism resources and the sustainable development index includes: Respectively obtain the weights corresponding to the tourist preference tensor, the regional adjustment factor and the sustainable development index; Obtaining the innovation index used to calculate the evaluation score, as well as the innovation factor, innovation score and innovation weight of the innovation index; After weighted summing of the tourist preference tensor, the regional adjustment factor, the sustainable development index, the innovation factor and the innovation score, the evaluation score of the tourism resource is determined in combination with the data quality scoring index of the tourism resource.

[0014] The present invention also provides a tourism resource investigation and evaluation system based on big data, the system comprising: A data acquisition module is used to acquire evaluation data of tourism resources and determine data quality scoring indicators of the evaluation data; the evaluation data of tourism resources includes environmental data, social media data, OTA platform comments, GPS tracks and mobile phone signaling data; A tensor determination module, used to extract interaction data among tourists, scenic spots and time from the tourism evaluation data, and determine a tourist preference tensor based on the interaction data; A regional adjustment module, used to extract the index data of the tourism resources from the tourism evaluation data, and calculate the regional adjustment factor of the tourism resources based on the index data of the tourism resources; A development index module, used to extract the sustainability index of the tourism resources from the tourism evaluation data, and calculate the sustainable development index of the tourism resources based on the sustainability index of the tourism resources; An evaluation score module, used to determine the evaluation score of the tourism resources based on the tourist preference tensor, the data quality score index, the regional adjustment factor of the tourism resources and the sustainable development index; The optimization feedback module is used to adaptively optimize and provide feedback on the evaluation of the tourism resources based on the evaluation scores of the tourism resources.

[0015] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing computer software programs; a processor for reading and executing the computer software programs, thereby realizing a tourism resource investigation and evaluation method based on big data as described above.

[0016] In addition, to achieve the above-mentioned purpose, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, it implements a tourism resource investigation and evaluation method based on big data as described above.

[0017] The beneficial effects of the present invention are:

[0018] (1) The present invention uses a data quality index (DQI) to comprehensively evaluate the quality of data from multiple sources (such as social media, OTA platform reviews, GPS trajectories, mobile phone signals, etc.), thereby ensuring the reliability and validity of the data. Data quality control can avoid errors caused by data problems and ensure the accuracy of subsequent analysis and decision-making. This comprehensive evaluation mechanism provides a solid data foundation for big data-driven tourism resource evaluation.

[0019] (2) The present invention constructs a dynamic model (P) of tourist preferences, combining tourist characteristics, scenic spot characteristics and time characteristics. This method can reflect the changes in tourist preferences in different time periods and scenic spots in real time. Compared with the traditional static evaluation method, this solution has stronger adaptability and can dynamically adjust the recommendation system according to the preferences of tourists, providing more personalized and accurate recommendations for tourism products and services, thereby improving tourist satisfaction and experience.

[0020] (3) The regional adjustment factor (R) introduced in this invention can effectively compensate for the differences in resources, environment, development level, etc. between different regions. The design of the regional adjustment factor solves the problem that traditional evaluation methods cannot accurately reflect regional differences by combining regional social, economic and environmental indicators. This makes the evaluation results more fair and comprehensive, adapting to the actual conditions of different regions, and providing decision makers with more scientific and reasonable resource allocation and development strategies.

[0021] In summary, the present invention aims to achieve a more accurate, efficient and sustainable evaluation of tourism resources by introducing a series of innovative methods such as multi-source data quality control, dynamic tourist preference modeling, regional difference compensation, and sustainable development assessment. These beneficial effects can not only improve the overall management level of the tourism industry, promote smart tourism and sustainable development, but also provide scientific decision-making support for governments at all levels, scenic area managers and investors, and ultimately achieve the optimal allocation of tourism resources and long-term sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of a tourism resource investigation and evaluation method based on big data provided by the present invention; Figure 2 A schematic diagram of the structure of a tourism resource investigation and evaluation system based on big data provided by the present invention; Figure 3 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention; Figure 4 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0023] 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 only 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0025] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0026] See also Figure 1 , provides a flow chart of a tourism resource investigation and evaluation method based on big data of the present invention, comprising the following steps: Step 201: Acquire evaluation data of tourism resources and determine data quality scoring indicators of the evaluation data.

[0027] Among them, the evaluation data of tourism resources include environmental data, social media data, OTA platform reviews, GPS trajectories and mobile phone signaling data.

[0028] In some embodiments, step 201 may include: Obtaining a data integrity weight, a timeliness weight, a timeliness score, an accuracy weight, and an accuracy score for each of the evaluation data; Obtaining a first balance coefficient, a second balance coefficient, and a third balance coefficient for calculating the data quality scoring indicator; The data quality scoring index of the evaluation data is determined based on the first balance coefficient, the second balance coefficient and the third balance coefficient of the data quality scoring index, as well as the data integrity weight, timeliness weight, timeliness score, accuracy weight and accuracy score of each evaluation data.

[0029] In some embodiments, the data quality score indicator is expressed as: ; in, is a data quality scoring indicator. is the data integrity weight of the i-th evaluation data, is the completeness score of the ith evaluation data, is the timeliness weight of the i-th evaluation data, is the timeliness score of the ith evaluation data, is the accuracy weight of the ith evaluation data, is the accuracy score of the ith evaluation data, is the first balance coefficient, is the second balance coefficient, is the third balance coefficient.

[0030] Specifically, DQI (Data Quality Index) is an indicator that comprehensively measures the quality of multi-source data. It is a quantitative score of data quality that combines the three dimensions of data integrity, timeliness, and accuracy.

[0031] is the data integrity weight of the i-th evaluation data. It reflects the importance of data integrity in the total score. Each data item is assigned a weight according to its importance in the data set. The higher the weight, the greater the contribution of the data item to the integrity score.

[0032] is the integrity score of the i-th evaluated data. It measures the integrity level of the data item and is usually scored based on whether the data has missing values, null values, etc. The higher the score, the better the data integrity.

[0033] is the timeliness weight of the ith evaluation data. It indicates the relative importance of timeliness in the total score. Some data may have higher requirements for timeliness and be given a higher weight. For example, for real-time monitoring data, its timeliness is more important than historical data.

[0034] is the timeliness score of the i-th evaluation data. It reflects the timeliness level of the data and is usually scored based on the update frequency and update delay of the data. A high score means that the data is updated in a timely manner and can represent the latest status.

[0035] is the accuracy weight of the i-th evaluation data. It indicates the importance of data accuracy in the overall score. If the accuracy of a data item has a greater impact on the final evaluation result, it will be given a higher weight.

[0036] is the accuracy score of the i-th evaluation data. It reflects the degree of deviation between the data and the actual situation or expected target. The higher the score, the more accurate the data and the smaller the deviation.

[0037] , , is a balance factor that controls the relative importance of each dimension (completeness, timeliness, accuracy) in the final score. Each factor ranges from 0 to 1 and must satisfy + + =1.

[0038] Through these balancing factors, the influence of each quality dimension can be dynamically adjusted according to the actual situation. For example, if the timeliness of data is critical to the project, a larger weight can be assigned to If data accuracy is more important, you can enhance proportion.

[0039] DQI can comprehensively evaluate the quality of data by weighting the completeness, timeliness and accuracy of data. The quality score (completeness, timeliness, accuracy) of each data item is weighted according to its importance in the entire system.

[0040] By adjusting , , ,This formula allows different importance to be assigned to different data quality dimensions in different ,application scenarios. For example, in real-time monitoring applications, timeliness may be more important than completeness and accuracy, while in historical data analysis,completeness and accuracy may be prioritized.

[0041] By multiplying the scores of each dimension with the corresponding weight and summing them up, a comprehensive consideration of the multi-dimensional data quality is ensured. For example, if a data item has a high completeness score but poor timeliness, the entire DQI may still be low, reflecting its overall quality.

[0042] In application scenarios based on big data analysis (such as tourism resource evaluation, smart city construction, etc.), this formula can help analysts quantify the quality of data and guide subsequent data cleaning, preprocessing and fusion. It is suitable for multi-source data fusion systems and can evaluate the quality of data from different sources (such as social media data, sensor data, government platform data, etc.) to provide accurate support for decision-making.

[0043] The present invention provides a scientific and flexible quality evaluation system for multi-source data by comprehensively evaluating the integrity, timeliness and accuracy of the data. , , The adjustment makes this method have high adaptability and practical value in different application scenarios.

[0044] Step 202: extract interaction data among tourists, scenic spots and time from the tourism evaluation data, and determine a tourist preference tensor based on the interaction data.

[0045] In some embodiments, step 202 may include: Obtaining a tourist feature vector of the tourist, a scenic spot feature vector of the scenic spot, and a time feature vector of the tourist's visit to the scenic spot; According to the historical data of the tourists playing in the scenic spot, construct a first interaction function between tourists and scenic spots, a second interaction function between scenic spots and time, and a third interaction function between tourists and time; The first interaction function, the second interaction function and the third interaction function are weighted according to the first weight, the second weight and the third weight to obtain the tourist preference tensor.

[0046] In some embodiments, the tourist preference tensor is represented as: ; in, is the tourist preference tensor, u is the tourist feature vector, v is the scenic spot feature vector, t is the time feature vector, F represents the first interaction function between tourists and scenic spots, G represents the second interaction function between scenic spots and time, and H represents the third interaction function between tourists and time. is the first weight, is the second weight, It is the third weight.

[0047] The tourist feature vector may include the tourist's age, gender, income level, travel frequency, spending power, and interest preference. The scenic spot feature vector may include the geographical location, climate conditions, transportation convenience, reception capacity, service facility perfection, and cultural characteristics of the scenic spot of the tourism resource.

[0048] In the specific implementation, P represents the preference tensor of tourists, which is a multidimensional matrix used to capture the changes in tourists' preferences in different scenic spots and at different times.

[0049] In this formula, tourists' preferences are not just a simple score, but a complex model formed by the interaction of multiple dimensions (tourist characteristics, scenic spot characteristics, and time characteristics). Its purpose is to simulate tourists' dynamic preferences for scenic spots, taking into account the interactive effects between tourists, scenic spots, and time.

[0050] u represents the characteristic vector of tourists. Tourist characteristics may include age, gender, hobbies, consumption level, travel history and other information. Each tourist has a specific vector u, whose dimensions represent the various characteristics of the tourist. This characteristic vector is the basic data used to predict tourists' preferences for different scenic spots at different times.

[0051] v represents the characteristic vector of the scenic spot. The characteristics of the scenic spot may include information such as the size, popularity, facilities, location, activity type, seasonality, etc. Each scenic spot has a specific characteristic vector v, and these characteristics directly affect tourists' preferences.

[0052] t represents the feature vector of time. Time features may involve different time periods (such as seasons, months, days of the week, specific time periods of the day, etc.), as well as the timing of holidays and special events. Time has an important impact on tourists' preferences, and tourists' behaviors and choices at different times are often different.

[0053] F(u,v) is the first interaction function between tourists and scenic spots. It describes the interaction between tourist characteristics and scenic spot characteristics. Generally speaking, different types of tourists have different preferences for different types of scenic spots. For example, young tourists may prefer scenic spots with strong entertainment, while older tourists may prefer cultural heritage scenic spots. This interaction function models the matching relationship between tourists and scenic spots.

[0054] G(v,t) is the interaction function between scenic spots and time. It describes the relationship between scenic spot feature v and time feature 𝑡. The attractiveness of scenic spots may vary in different time periods. For example, some scenic spots have a large number of tourists in summer and relatively few in winter. This function helps capture the interactive effect of scenic spots and time.

[0055] H(u,t) is the interaction function between tourists and time. It describes the relationship between tourist characteristics 𝑢 and time characteristics 𝑡. For example, some groups of tourists may prefer to travel during holidays or specific periods, while other tourists may travel on weekdays or off-season. This interaction function reflects the matching effect between tourists and time.

[0056] , , are the weight coefficients of the three interaction functions, which are used to adjust the relative importance of each interaction function in the final tourist preference score. It is used to adjust the influence weight of the interaction between tourists and scenic spots. It shows the importance of the matching degree between tourist characteristics and scenic spot characteristics on tourist preferences. It is used to adjust the influence weight of the interaction between scenic spots and time. It reflects the time sensitivity of scenic spots, that is, the attractiveness of scenic spots in different time periods. It is used to adjust the influence weight of the interaction between tourists and time. It indicates the influence of time on tourists' behavior, and some tourist groups have stronger preferences in specific time periods.

[0057] P(u,v,t) constructs a multi-dimensional preference prediction model by comprehensively considering the characteristics of tourists, scenic spots and time. This model can dynamically reflect the changes in tourists' preferences for scenic spots at different times and provide data support for the optimal allocation of tourism resources.

[0058] By introducing three interactive functions, the formula reflects the complex interactive relationship between tourists, scenic spots and time. It can capture the impact of such multi-dimensional factors on tourists' preferences, thus achieving more accurate prediction of tourists' preferences.

[0059] By adjusting the weight coefficient , , ,The system can flexibly adjust the influence of various factors according to the actual situation, so as to adapt to different tourism scenarios.,For example, in the peak tourist season, the relationship between scenic spots and time may be more important, while in the off-season, the matching of tourists and scenic spots may be more critical.

[0060] In practical applications, P can be used to provide personalized travel recommendations based on the personalized characteristics of tourists, the characteristics of scenic spots, and the influence of time. For example, based on tourists' historical behaviors, preferences, and specific time periods, the system can push the most suitable scenic spot recommendations. By analyzing P, scenic spot managers can understand the preferences of different types of tourists in different time periods, thereby optimizing the resource allocation, activity arrangements, and service provision of scenic spots and improving tourists' satisfaction. Based on the modeling of tourist preference tensors, travel companies can better design customized travel products that meet the needs of specific tourist groups.

[0061] The present invention models the tourist preference tensor P(u,v,t), and the formula realizes multi-level interactive modeling between tourists, scenic spots and time, so as to more accurately predict tourists' preferences. Through appropriate weight coefficients, the importance of each factor can be flexibly adjusted according to actual conditions, ultimately achieving the purpose of personalized recommendation and optimized resource allocation.

[0062] Step 203: extract the index data of the tourism resources from the tourism evaluation data, and calculate the regional adjustment factor of the tourism resources based on the index data of the tourism resources.

[0063] In some embodiments, step 203 may include: Obtaining the regional development index, environmental carrying capacity index and regional weight coefficient of each of the scenic spots of the tourism resources; Obtaining the inter-regional difference measure of each of the scenic spots and the adjustment coefficient used to calculate the regional adjustment factor; The regional adjustment factor of the tourism resources is determined according to the regional development index, environmental carrying capacity index and regional weight coefficient of each scenic spot, the inter-regional difference metric, and the adjustment coefficient.

[0064] In some embodiments, the regional adjustment factor can be expressed as: , Where R is the regional adjustment factor, is the regional development index of the jth scenic spot, is the environmental carrying capacity index of the jth scenic spot, is the regional weight coefficient of the jth scenic spot, Z is the difference measure between regions, is the adjustment coefficient.

[0065] In the specific implementation, R is the regional adjustment factor, which is an adjustment factor based on the regional development status and environmental carrying capacity, used to measure the impact of the comprehensive environment and development status of each scenic spot in a region. It is mainly affected by the following factors: the regional development index, environmental carrying capacity index and regional weight coefficient of each scenic spot. This factor is further adjusted through the inter-regional difference measurement and adjustment coefficient to reflect the comprehensive impact between different regions.

[0066] is the regional development index of the jth scenic spot, indicating the comprehensive level of economic development, infrastructure, transportation convenience, etc. of the area where the scenic spot is located. The regional development index usually reflects the overall development potential of the region. A value of indicates that the region has strong economic vitality and development potential and may attract more tourists.

[0067] is the environmental carrying capacity index of the jth scenic spot, which represents the carrying capacity of the region in terms of natural environment and ecological resources. It includes the ecological environment quality, resource consumption, environmental protection measures, etc. of the region. Environmental carrying capacity is a key factor in measuring regional sustainable development. The value indicates that the area can support more tourist activities without damaging the ecological environment.

[0068] is the regional weight coefficient of the jth scenic spot, which is used to weigh the importance of different scenic spots in the comprehensive impact of the region. Each scenic spot contributes differently to its region, and the regional weight coefficient reflects this. For example, some scenic spots may be the main tourist destinations in the region and have a higher weight, while other smaller or more distant scenic spots have a lower weight.

[0069] Z is a measure of regional differences, usually measured by comparing the overall development status, environmental carrying capacity, and tourist preferences of different regions. It represents the differences between regions. The larger the value, the greater the regional differences. Factors that may be considered in regional difference measurement include: economic development level, transportation convenience, cultural characteristics, natural landscape, etc. A higher Z value indicates that the differences between regions are large and may require more adjustments.

[0070] It is the adjustment coefficient, which is used to adjust the impact of regional differences on the regional adjustment factor R. This coefficient determines the intensity of the adjustment effect of Z on R. The nonlinear effect of influencing Z makes the impact of the inter-regional difference measure Z more subtle and dynamic.

[0071] It is a typical S-shaped curve function, which has a "smooth interval" and a "rapid change interval". When the inter-regional difference measure Z is small, the value of the function is close to 1, indicating that the adjustment effect of regional differences is small. When the inter-regional difference measure 𝑍 is large, the output value of the Sigmoid function approaches 0, making the adjustment effect of the regional adjustment factor stronger. This form ensures that when the regional difference is small, the adjustment factor has a small impact; when the difference is large, the adjustment effect of the adjustment factor is more significant.

[0072] The formula calculates the regional adjustment factor R by combining the regional development index, environmental carrying capacity and regional weight coefficient, so that the comprehensive performance of scenic spots in different regions is appropriately adjusted. The Sigmoid function in the formula can be dynamically adjusted according to the differences between regions, ensuring that a higher adjustment force can be given when the regional differences are large, thereby more truly reflecting the diversity between regions.

[0073] Through the regional adjustment factor R, the sustainable development level of different regions can be better evaluated and adjusted, especially in tourism resource management and planning, to avoid overexploitation and waste of resources. According to the value of R, the tourism management department can allocate resources to different regions and optimize the configuration of tourism resources and services. For example, more tourism development resources may be invested in regions with better economic development or higher environmental carrying capacity. For the long-term planning of the tourism industry, the regional adjustment factor R can help the government and scenic area managers formulate reasonable development and protection strategies to avoid overexploitation of resources or deterioration of the ecological environment due to large regional differences.

[0074] The present invention dynamically adjusts the regional differences by combining the regional economic development, environmental carrying capacity and scenic spot weight. The final regional adjustment factor R reflects the comprehensive strength of each scenic spot and region, and realizes flexible regional difference adjustment by adjusting the inter-regional difference measure Z and the adjustment coefficient 𝜎. This adjustment factor has important application value in tourism resource planning, management and sustainable development assessment.

[0075] Step 204: extract the sustainability index of the tourism resources from the tourism evaluation data, and calculate the sustainable development index of the tourism resources based on the sustainability index of the tourism resources.

[0076] In some embodiments, step 204 may include: Obtaining economic sustainability indicators, social sustainability indicators and environmental sustainability indicators of the tourism resources, as well as weights of each of the sustainability indicators; Obtaining the attenuation coefficient, resource utilization rate, and predicted time span for calculating the sustainable development index; The sustainable development index of the tourism resources is calculated based on the economic sustainability index, social sustainability index and environmental sustainability index of the tourism resources, the weight of each sustainability index, the attenuation coefficient, the resource utilization rate and the predicted time span.

[0077] In some embodiments, the sustainability index may be expressed as: ; in, is the Sustainable Development Index, is an indicator of economic sustainability. is an indicator of social sustainability, is an indicator of environmental sustainability. is the fourth weight, is the fifth weight, is the sixth weight, is the attenuation coefficient, is the resource utilization, and T is the prediction time span.

[0078] In specific implementation, S is the sustainable development index, which is an indicator to measure the comprehensive sustainability level of a region, scenic spot or tourism resource in the three dimensions of economy, society and environment. The index is a weighted combination of three sub-indicators: economic sustainability, social sustainability and environmental sustainability, and takes into account the utilization efficiency and time decay factors of resources.

[0079] EC is an economic sustainability indicator that measures the sustainability of a region or scenic spot in economic development. It usually includes: the stability and sustainability of economic growth, the source of income from tourism, the relationship between resource utilization and economic benefits, the return on investment, the contribution of tourism to the local economy, etc. A high EC indicates that the economic activities of the region or scenic spot can remain stable in the long term and promote local economic growth without over-reliance on limited resources or causing an imbalance in the economic structure.

[0080] SC is a social sustainability indicator that reflects the degree of sustainable development of a region or scenic spot at the social and cultural level. It usually includes: the welfare and social stability of local residents, the guarantee of education and employment opportunities, the protection and dissemination of cultural heritage, and social fairness and inclusiveness. A higher SC indicates that the region or scenic spot can provide good living conditions and educational opportunities for local residents, and maintain cultural diversity and social stability.

[0081] EN is an environmental sustainability index, which measures the level of sustainable use of the ecological environment of a region or scenic spot. It includes: ecological protection measures, control of environmental pollution, rational use and protection of resources, energy consumption and waste management. A higher EN indicates that the region pays attention to environmental protection, rational use of natural resources and maintains ecological balance in development and tourism activities.

[0082] The fourth, fifth and sixth weights are weight coefficients corresponding to economic, social and environmental sustainability indicators, reflecting the relative importance of these three dimensions in sustainable development. These weight coefficients are generally set according to the actual situation, goals and strategies of specific regions or projects. For example, if the main problem of the region is economic development, the fourth weight may be given a larger weight.

[0083] μ is the decay coefficient, which is used to control the degree to which the impact of resource utilization on the sustainability index decreases over time. That is, the impact of resource utilization on the sustainability index will gradually weaken over time. A larger value of μ means that a decrease in resource utilization efficiency will have a greater negative impact on sustainability over time; conversely, the impact will be smaller.

[0084] ρ is the resource utilization rate, which indicates the efficiency of resource utilization in the current region or scenic spot. It includes: the consumption rate of natural resources, energy use and waste management efficiency, the impact of tourism on the natural environment, etc. The larger 𝜌 is, the higher the utilization efficiency of resources in the region or scenic spot is, the lower the resource consumption is, and the smaller the negative impact on the environment is.

[0085] T is the forecast time span, which refers to the time span from the current moment to a certain moment in the future. It is used to evaluate the changing trend of the sustainable development index in the future. T can be used to simulate the sustainable development performance at different time points and can be predicted based on actual conditions, such as 10 years, 20 years, 30 years, etc.

[0086] It is the weighted sum of the three dimensions of economy, society and environment, reflecting the sustainability level of the region or scenic spot at the current moment.

[0087] By introducing the attenuation coefficient and resource utilization rate, the trend of the decreasing impact of resource utilization rate on the sustainable development index over time is simulated. The larger the value of the product, the stronger the negative impact of resource utilization on the sustainability index, and this impact gradually decays over time.

[0088] In practical applications, this formula can help regions or scenic spots evaluate their level of sustainable development and determine whether they can develop stably in the long term without damaging resources and the environment. Tourism management departments can use this formula to determine the sustainability of different regions or scenic spots, and then formulate reasonable tourism development and resource protection strategies. Based on this sustainable development index, government departments can make long-term plans, such as finding a balance between economic growth and ecological protection. Investors can use the sustainable development index to evaluate the long-term investment value of tourism projects and avoid project failures due to resource depletion or environmental degradation.

[0089] The present invention calculates a comprehensive sustainable development index through a weighted combination of the three dimensions of economy, society and environment, and takes into account the decay effect of resource utilization and time. Its purpose is to evaluate the sustainable development capacity of a region or scenic spot in the long term, help formulate reasonable resource protection, economic development and social policies, ensure the rational use of resources and the sustainability of the environment, and ultimately achieve sustainable tourism development.

[0090] Step 205: Determine the evaluation score of the tourism resources based on the tourist preference tensor, the data quality scoring index, the regional adjustment factor of the tourism resources and the sustainable development index.

[0091] In some embodiments, step 205 may include: Respectively obtain the weights corresponding to the tourist preference tensor, the regional adjustment factor and the sustainable development index; Obtaining the innovation index used to calculate the evaluation score, as well as the innovation factor, innovation score and innovation weight of the innovation index; After weighted summing of the tourist preference tensor, the regional adjustment factor, the sustainable development index, the innovation factor and the innovation score, the evaluation score of the tourism resource is determined in combination with the data quality scoring index of the tourism resource.

[0092] When evaluating tourism resources, we can first determine the weight of each influencing factor, which reflects the importance of each factor in the evaluation model.

[0093] The tourist preference tensor (P) represents the degree of preference of tourists for different scenic spots and different time periods. It takes into account the interactive relationship between tourist characteristics, scenic spot characteristics and time characteristics. To ensure that the model can reflect the relative importance of different factors, it is necessary to assign weights to the interactive functions of tourist characteristics, scenic spot characteristics and time characteristics. Through the weight coefficient, the influence of tourist preferences can be dynamically adjusted to ensure that the preference modeling can fit the real needs of tourists.

[0094] The regional adjustment factor (R) mainly reflects the differences in resources, environment, development level, etc. between different regions. This factor is adjusted through the regional development index, environmental carrying capacity index and regional difference measurement. The weight of the regional adjustment factor reflects the influence of different regions on the overall tourism resource evaluation, which can help balance the resource allocation of each region and make the evaluation results regionally adaptable.

[0095] The Sustainability Index (S) measures the sustainability of tourism resources in economic, social and environmental terms. The index takes into account the sustainability of tourism resources in long-term use, while the weights determine the contribution of each aspect (economic, social, environmental) to the overall evaluation. Depending on the characteristics of tourism resources, the weights can be adjusted according to actual needs to ensure that the sustainability evaluation can truly reflect the long-term impact of resource use.

[0096] Innovation index is a part of the evaluation system, which is used to measure the innovation of tourism resources or management models. The process of obtaining innovation factors, innovation scores and innovation weights is to identify and strengthen the impact of innovation factors on evaluation scores.

[0097] Innovation factors reflect the contribution of certain innovative measures to the improvement of tourism resource management, tourist experience or sustainable development. It can be the application of new technologies, the introduction of new management models, or the development of new products. The introduction of each innovation factor is an improvement of the existing model, which affects the overall evaluation results.

[0098] The innovation score reflects the implementation effect of each innovative measure or plan, which is usually obtained through quantitative and qualitative analysis. The higher the innovation score, the better the implementation effect of the innovative measure, and the more obvious the improvement in the sustainable development of tourism resources and tourist experience.

[0099] Innovation weights determine the importance of each innovation factor in the comprehensive evaluation. If an innovation has a greater impact on the improvement of tourism resources, its weight should be relatively high. The weight setting is allocated according to the specific innovation content and goals to ensure that the final evaluation score can highlight the importance of innovation factors.

[0100] In the above steps, the tourist preference tensor, regional adjustment factor, sustainable development index, innovation factor and innovation score have been assigned corresponding weights. In the weighted summation process, these factors are weighted and summarized to obtain a comprehensive evaluation score, which reflects the comprehensive performance of tourism resources in various dimensions.

[0101] At the same time, the data quality score index (DQI) is also introduced into the evaluation process, which directly affects all subsequent calculations. Data quality is the basis of the evaluation score. Only high-quality data can ensure the accuracy and effectiveness of subsequent analysis. Therefore, in the calculation of the final score, the data quality score index is used as a key factor, together with tourist preferences, regional regulation, sustainability and innovation factors.

[0102] Through this weighted summation method, the final evaluation score can fully reflect the quality, sustainability, innovation, regional adaptability and other dimensions of tourism resources. This score provides decision makers with an accurate reference for subsequent resource management, policy formulation and optimization of marketing strategies.

[0103] Step 206: Based on the evaluation scores of the tourism resources, adaptively optimize and provide feedback on the evaluation of the tourism resources.

[0104] Specifically, adaptive optimization is a dynamic adjustment and optimization process, the purpose of which is to automatically adjust the various weight coefficients in the evaluation model according to the changes in the current evaluation score (V) and the historical score, so as to better adapt to the changing environment and needs. In this scheme, the optimization process is based on the comprehensive evaluation score of tourism resources and is implemented through a time series optimization function.

[0105] The time series optimization function (O) is the core calculation tool in the adaptive optimization process. Its function is to adjust the changing trend of the evaluation score by comparing the current evaluation score (V(t)) with the previous round of evaluation score (V(t-1)) and combining the gradient coefficient (ξ). Specifically, the adjustment process of the optimization function can be divided into two parts: By comparing the difference between the current score and the previous round score, a change (V(t)-V(t-1)) is evaluated. This difference is used to guide the adjustment of the model so that the evaluation model gradually tends to the optimal state.

[0106] The gradient coefficient (ξ) is used to calculate the change gradient of the evaluation score (∇V(t)), that is, the trend of score change. This part helps the model make more precise adjustments in the future evaluation process by analyzing the trend of score increase and decrease.

[0107] Based on the feedback mechanism of adaptive optimization, the evaluation results of the previous round will affect the parameter setting of the next round of evaluation. Specifically, for example, the tourist preference model (P) may be adjusted according to the feedback information of the previous round to ensure that it more accurately reflects the actual needs and behavior changes of tourists.

[0108] Update of regional adjustment factors (R) and sustainability assessment (S): Based on the results of the previous round of optimization, parameters such as the regional development index (D) or environmental carrying capacity index (E) can also be dynamically adjusted to better cope with changes in regional differences.

[0109] As innovative measures are gradually implemented and feedback accumulates, the weights and scores of innovation factors will be continuously adjusted to ensure that new innovative measures can receive reasonable evaluation and sufficient feedback.

[0110] The result of each optimization (O(t+1)) will have an impact on the next round of evaluation, forming a cyclic optimization process. This process ensures that the evaluation model continues to self-correct over time and adapt to changes in the external environment and tourist needs. Specifically: If some factors (such as tourist preferences, regional development, sustainability, etc.) are underestimated or overestimated in the current model, the adaptive optimization process will correct this deviation by adjusting the model weights. The optimization process of the evaluation score accumulates and feedbacks continuously, making the final tourism resource evaluation more accurate, scientific and in line with actual needs.

[0111] The present invention realizes the dynamic adjustment capability of tourism resource management through adaptive optimization and feedback mechanism based on evaluation scores. The optimization process can not only improve the accuracy of evaluation, but also continuously adjust the model according to changes in the environment, changes in tourist demand and the effects of innovative measures, making the management of tourism resources more efficient and flexible, and continuously promoting the sustainable development of resources.

[0112] See also Figure 2 , Figure 2 A structural schematic diagram of a tourism resource investigation and evaluation system based on big data provided by the present invention.

[0113] like Figure 2As shown, a tourism resource investigation and evaluation system based on big data proposed in an embodiment of the present invention includes: The data acquisition module 301 is used to acquire evaluation data of tourism resources and determine data quality scoring indicators of the evaluation data; the evaluation data of tourism resources includes environmental data, social media data, OTA platform comments, GPS tracks and mobile phone signaling data; A tensor determination module 302 is used to extract interaction data between tourists, scenic spots and time from the tourism evaluation data, and determine a tourist preference tensor based on the interaction data; A regional adjustment module 303 is used to extract the index data of the tourism resources from the tourism evaluation data, and calculate the regional adjustment factor of the tourism resources based on the index data of the tourism resources; A development index module 304 is used to extract the sustainability index of the tourism resources from the tourism evaluation data, and calculate the sustainable development index of the tourism resources based on the sustainability index of the tourism resources; An evaluation score module 305 is used to determine the evaluation score of the tourism resource based on the tourist preference tensor, the data quality score index, the regional adjustment factor of the tourism resource and the sustainable development index; The optimization feedback module 306 is used to perform adaptive optimization and feedback on the evaluation of the tourism resources based on the evaluation scores of the tourism resources.

[0114] See also Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented: Acquire evaluation data of tourism resources and determine data quality scoring indicators of the evaluation data; the evaluation data of tourism resources includes environmental data, social media data, OTA platform comments, GPS tracks and mobile phone signaling data; Extracting interaction data among tourists, scenic spots and time from the tourism evaluation data, and determining a tourist preference tensor based on the interaction data; Extracting index data of the tourism resources from the tourism evaluation data, and calculating the regional adjustment factor of the tourism resources based on the index data of the tourism resources; Extracting the sustainability index of the tourism resources from the tourism evaluation data, and calculating the sustainable development index of the tourism resources based on the sustainability index of the tourism resources; Determining the evaluation score of the tourism resources based on the tourist preference tensor, the data quality scoring index, the regional adjustment factor of the tourism resources and the sustainable development index; Based on the evaluation scores of the tourism resources, adaptive optimization and feedback are performed on the evaluation of the tourism resources.

[0115] See also Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented: Acquire evaluation data of tourism resources and determine data quality scoring indicators of the evaluation data; the evaluation data of tourism resources includes environmental data, social media data, OTA platform comments, GPS tracks and mobile phone signaling data; Extracting interaction data among tourists, scenic spots and time from the tourism evaluation data, and determining a tourist preference tensor based on the interaction data; Extracting index data of the tourism resources from the tourism evaluation data, and calculating the regional adjustment factor of the tourism resources based on the index data of the tourism resources; Extracting the sustainability index of the tourism resources from the tourism evaluation data, and calculating the sustainable development index of the tourism resources based on the sustainability index of the tourism resources; Determining the evaluation score of the tourism resources based on the tourist preference tensor, the data quality scoring index, the regional adjustment factor of the tourism resources and the sustainable development index; Based on the evaluation scores of the tourism resources, adaptive optimization and feedback are performed on the evaluation of the tourism resources.

[0116] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0117] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0119] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0121] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0122] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A tourism resource investigation and evaluation method based on big data, characterized in that: The method comprises: Acquire evaluation data of tourism resources and determine data quality scoring indicators of the evaluation data; the evaluation data of tourism resources includes environmental data, social media data, OTA platform comments, GPS tracks and mobile phone signaling data; Extracting interaction data among tourists, scenic spots and time from the tourism evaluation data, and determining a tourist preference tensor based on the interaction data; Extracting index data of the tourism resources from the tourism evaluation data, and calculating the regional adjustment factor of the tourism resources based on the index data of the tourism resources; Extracting the sustainability index of the tourism resources from the tourism evaluation data, and calculating the sustainable development index of the tourism resources based on the sustainability index of the tourism resources; Determining the evaluation score of the tourism resources based on the tourist preference tensor, the data quality scoring index, the regional adjustment factor of the tourism resources and the sustainable development index; Based on the evaluation scores of the tourism resources, adaptive optimization and feedback are performed on the evaluation of the tourism resources.

2. The tourism resource investigation and evaluation method based on big data according to claim 1 is characterized in that: Determining the data quality scoring index of the evaluation data includes: Obtaining a data integrity weight, a timeliness weight, a timeliness score, an accuracy weight, and an accuracy score for each of the evaluation data; Obtaining a first balance coefficient, a second balance coefficient, and a third balance coefficient for calculating the data quality scoring indicator; The data quality scoring index of the evaluation data is determined based on the first balance coefficient, the second balance coefficient and the third balance coefficient of the data quality scoring index, as well as the data integrity weight, timeliness weight, timeliness score, accuracy weight and accuracy score of each evaluation data.

3. The tourism resource investigation and evaluation method based on big data according to claim 2 is characterized in that: The data quality scoring index is expressed as: ； in, is a data quality scoring indicator. is the data integrity weight of the i-th evaluation data, is the completeness score of the ith evaluation data, is the timeliness weight of the i-th evaluation data, is the timeliness score of the ith evaluation data, is the accuracy weight of the ith evaluation data, is the accuracy score of the ith evaluation data, is the first balance coefficient, is the second balance coefficient, is the third balance coefficient.

4. The tourism resource investigation and evaluation method based on big data according to claim 3 is characterized in that: Determining a tourist preference tensor based on the interaction data includes: Obtaining a tourist feature vector of the tourist, a scenic spot feature vector of the scenic spot, and a time feature vector of the tourist's visit to the scenic spot; According to the historical data of the tourists playing in the scenic spot, construct a first interaction function between tourists and scenic spots, a second interaction function between scenic spots and time, and a third interaction function between tourists and time; The first interaction function, the second interaction function and the third interaction function are weighted according to the first weight, the second weight and the third weight to obtain the tourist preference tensor.

5. The tourism resource investigation and evaluation method based on big data according to claim 4 is characterized in that: The tourist preference tensor is expressed as: ； in, is the tourist preference tensor, u is the tourist feature vector, v is the scenic spot feature vector, t is the time feature vector, F represents the first interaction function between tourists and scenic spots, G represents the second interaction function between scenic spots and time, and H represents the third interaction function between tourists and time. is the first weight, is the second weight, It is the third weight.

6. The tourism resource investigation and evaluation method based on big data according to claim 5 is characterized in that: The tourist feature vector includes the tourist's age, gender, income level, travel frequency, spending power, and interest preference; The scenic spot feature vector includes the geographical location, climate conditions, transportation convenience, reception capacity, service facility perfection, and cultural characteristics of the scenic spot of the tourism resources.

7. The tourism resource investigation and evaluation method based on big data according to claim 6 is characterized in that: The calculating the regional adjustment factor of the tourism resources based on the index data of the tourism resources includes: Obtaining the regional development index, environmental carrying capacity index and regional weight coefficient of each of the scenic spots of the tourism resources; Obtaining the inter-regional difference measure of each of the scenic spots and the adjustment coefficient used to calculate the regional adjustment factor; The regional adjustment factor of the tourism resources is determined according to the regional development index, environmental carrying capacity index and regional weight coefficient of each scenic spot, the inter-regional difference metric, and the adjustment coefficient.

8. The tourism resource investigation and evaluation method based on big data according to claim 7 is characterized in that: The calculating of the sustainable development index of the tourism resources based on the sustainability index of the tourism resources comprises: Obtaining economic sustainability indicators, social sustainability indicators and environmental sustainability indicators of the tourism resources, as well as weights of each of the sustainability indicators; Obtaining the attenuation coefficient, resource utilization rate, and predicted time span for calculating the sustainable development index; The sustainable development index of the tourism resources is calculated based on the economic sustainability index, social sustainability index and environmental sustainability index of the tourism resources, the weight of each sustainability index, the attenuation coefficient, the resource utilization rate and the predicted time span.

9. The tourism resource investigation and evaluation method based on big data according to any one of claims 1 to 8, characterized in that: Determining the evaluation score of the tourism resources based on the tourist preference tensor, the data quality scoring index, the regional adjustment factor of the tourism resources and the sustainable development index includes: Respectively obtain the weights corresponding to the tourist preference tensor, the regional adjustment factor and the sustainable development index; Obtaining the innovation index used to calculate the evaluation score, as well as the innovation factor, innovation score and innovation weight of the innovation index; After taking a weighted sum of the tourist preference tensor, the regional adjustment factor, the sustainable development index, the innovation factor and the innovation score, the evaluation score of the tourism resource is determined in combination with the data quality scoring index of the tourism resource.

10. A tourism resource investigation and evaluation system based on big data, characterized in that: The system comprises: A data acquisition module is used to acquire evaluation data of tourism resources and determine data quality scoring indicators of the evaluation data; the evaluation data of tourism resources includes environmental data, social media data, OTA platform comments, GPS tracks and mobile phone signaling data; A tensor determination module, used to extract interaction data among tourists, scenic spots and time from the tourism evaluation data, and determine a tourist preference tensor based on the interaction data; A regional adjustment module, used to extract the index data of the tourism resources from the tourism evaluation data, and calculate the regional adjustment factor of the tourism resources based on the index data of the tourism resources; A development index module, used to extract the sustainability index of the tourism resources from the tourism evaluation data, and calculate the sustainable development index of the tourism resources based on the sustainability index of the tourism resources; An evaluation scoring module, used to determine the evaluation score of the tourism resources based on the tourist preference tensor, the data quality scoring index, the regional adjustment factor of the tourism resources and the sustainable development index; The optimization feedback module is used to adaptively optimize and provide feedback on the evaluation of the tourism resources based on the evaluation scores of the tourism resources.

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