Recommendation method and system for tourism homestay site selection

Abstract

The invention discloses a recommendation method and system for tourism homestay site selection, and relates to the technical field of tourism management. The method comprises the following steps: collecting basic data of each candidate site for each index in a multi-dimensional index system; performing preprocessing and statistical analysis on the basic data to obtain standardized data of each index; allocating weights to the indexes in the multi-dimensional index system by using an analytic hierarchy process; according to the weight and the standardized data of each index, performing comprehensive scoring on each candidate site selection by adopting a weighted scoring method, and selecting a plurality of candidate site selection with the highest comprehensive score as recommended site selection; and generating a site selection recommendation report according to the basic data of the recommended site selection for each index. According to the invention, the accuracy of tourism homestay site selection can be improved.

Description

Technical Field

[0001] The present application relates to the field of tourism management technology, and in particular to a method and system for recommending the site selection of a tourist homestay. Background Art

[0002] As a new form of accommodation, B&Bs are favored by more and more tourists due to their unique cultural atmosphere, personalized services and low consumption costs. According to market research, the B&B industry has experienced significant growth in the past few years, attracting a large number of investors and entrepreneurs to enter this field. However, the successful operation of B&Bs does not only rely on good services and unique decorations, the importance of site selection is self-evident.

[0003] Site selection is a complex and critical decision for B&B operators. Improper site selection may lead to insufficient customer flow, fierce market competition, and even operating losses. Therefore, how to scientifically and systematically select a suitable B&B address has become an urgent problem to be solved. At present, many operators rely on experience and intuition to select sites, but this often lacks data support, resulting in waste of resources and increased risks.

[0004] Most of the existing site selection methods use simple market surveys and peer experience analysis, which lacks systematicity and scientificity. Therefore, a new site selection method is urgently needed to improve the scientificity and accuracy of site selection. Summary of the invention

[0005] The purpose of this application is to provide a method and system for recommending the site selection of tourist homestays, which can improve the accuracy of the site selection of tourist homestays.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for recommending the site selection of tourist homestays, comprising: collecting basic data for each candidate site for each indicator in a multidimensional indicator system; preprocessing and statistically analyzing the basic data to obtain standardized data for each indicator; using a hierarchical analysis method to assign weights to each indicator in the multidimensional indicator system; using a weighted scoring method to comprehensively score each candidate site based on the weight and standardized data of each indicator, and selecting multiple candidate sites with the highest comprehensive scores as recommended sites; generating a site recommendation report based on the basic data of the recommended site for each indicator.

[0008] In a second aspect, the present application provides a recommendation system for site selection of tourist homestays, comprising: a collector, an input device and a processor; the input device is connected to the collector and the processor respectively; the input device is used to input a multi-dimensional indicator system for site selection of tourist homestays into the processor; the collector is used to collect basic data of each candidate site for each indicator in the multi-dimensional indicator system, and input the data into the processor through the input device; the processor is used to pre-process and statistically analyze the basic data to obtain standardized data of each indicator; the analytic hierarchy process is used to assign weights to each indicator in the multi-dimensional indicator system; based on the weight and standardized data of each indicator, a weighted scoring method is used to comprehensively score each candidate site, and multiple candidate sites with the highest comprehensive scores are selected as recommended sites; based on the basic data of the recommended site for each indicator, a site recommendation report is generated.

[0009] According to the specific embodiments provided in this application, this application has the following technical effects:

[0010] The present application provides a method and system for recommending the site selection for tourist homestays. Based on a constructed multi-dimensional indicator system, the recommended site selection is determined by weighting the indicator weights and standardized data. Compared with the existing site selection method that uses simple market surveys and peer experience analysis, the site selection decision-making process is more systematic, and the accuracy of the site selection for tourist homestays is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 A flowchart of a method for recommending a location for a tourist homestay in one embodiment of the present application;

[0013] Figure 2 A flowchart of a method for recommending a location for a tourist homestay provided in another embodiment of the present application. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0015] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0016] In an exemplary embodiment, Figure 1 As shown, the present application provides a method for recommending the location of a tourist homestay, including the following steps 1 to 5. Among them:

[0017] Step 1: Collect basic data for each candidate site for each indicator in the multi-dimensional indicator system.

[0018] Step 2: Preprocess and statistically analyze the basic data to obtain standardized data of each indicator.

[0019] Step 3: Use the analytic hierarchy process to assign weights to each indicator in the multidimensional indicator system.

[0020] Step 4: Based on the weights and standardized data of each indicator, a weighted scoring method is used to comprehensively score each candidate site, and multiple candidate sites with the highest comprehensive scores are selected as recommended sites.

[0021] Step 5: Generate a site selection recommendation report based on the basic data of each indicator of the recommended site selection.

[0022] By implementing steps 1 to 5 above and based on the constructed multi-dimensional indicator system, the recommended site selection is determined by weighting the indicator weights and standardized data. Compared with the existing site selection method that uses simple market research and peer experience analysis, this makes the site selection decision-making process more systematic and improves the accuracy of the site selection of tourist homestays.

[0023] In another exemplary embodiment of the present application, a scientific and reasonable multi-dimensional index system is constructed to help more comprehensively evaluate the advantages and disadvantages of each potential site selection. The above multi-dimensional index system includes: market demand index, traffic convenience index, environmental quality index and competition index.

[0024] Market demand is one of the important factors affecting the location of tourist homestays. Market demand specifically includes tourist flow, occupancy rate and consumption level. Tourist flow reflects the average annual number of tourists in a specific area and is a direct reflection of market attractiveness. High tourist flow usually means a larger source market, which directly affects the occupancy rate and profitability of homestays. Occupancy rate is the average occupancy rate of all homestays in the same area. A high occupancy rate indicates that the market acceptance of homestays in the area is high and the demand is strong, which helps to enhance the attractiveness of new homestays. Consumption level reflects the average consumption level of tourists in the area, which is usually obtained through questionnaires or market research. A higher consumption level indicates that tourists have a stronger ability to pay, which is conducive to the profitability of homestays.

[0025] Traffic convenience directly affects tourists' arrival and travel experience. Traffic convenience indicators include public transportation convenience and road traffic conditions. Public transportation convenience is measured by calculating the average distance (meters) to the nearest public transportation station. Public transportation stations that are closer can improve tourists' arrival convenience and increase potential customer sources. Road traffic conditions refer to the average congestion index of major traffic roads, which is convenient for evaluating traffic flow in the area. A lower congestion index means smoother traffic and better travel experience for tourists.

[0026] Environmental quality directly affects tourists' experience and satisfaction. Environmental quality indicators include climate comfort and environmental pollution index. The climate comfort score calculated based on historical meteorological data includes factors such as temperature, humidity, and precipitation, and good climatic conditions usually attract more tourists. The environmental pollution index evaluates the environmental conditions of the region by combining the Air Quality Index (AQI) and noise pollution levels. A lower environmental pollution index means a healthier living and tourist environment, which helps to enhance the attractiveness of homestays.

[0027] The degree of competition reflects the market saturation and potential profit space. The indicators of the degree of competition include competition density and user evaluation scores. The competition density is reflected by calculating the ratio of the number of homestays in the area to the number of tourists that can be accommodated. A higher competition density may mean that the market is saturated, and vice versa, it means there are potential market opportunities. The user evaluation score is obtained by averaging the user ratings collected by online platforms (such as Ctrip Travel, Qunar Travel, etc.), which can reflect the service quality and customer satisfaction of the homestay. A higher user evaluation score means a better market reputation, which is helpful for the promotion of new homestays.

[0028] In another exemplary embodiment of the present application, the basic data of each candidate site for each indicator includes market demand data, traffic data, environmental factor data, competitor analysis data, etc.

[0029] Market demand data is mainly obtained through data capture and questionnaire surveys, which can help analyze the demand status of tourists in a specific area and understand tourists' preferences and consumption habits. On the one hand, relevant data is captured from major travel websites. These websites usually contain rich homestay information, including the number of listings, occupancy rate, guest reviews, price ranges, etc. For example: the number of homestays, the total number of homestays in the area; the average occupancy rate, the average occupancy rate of each homestay in a specific time period; customer ratings, a comprehensive rating calculated based on user reviews (such as a 5-star system). On the other hand, design and publish online questionnaires to collect tourists' feedback on accommodation preferences, consumption capacity and accommodation experience. The questionnaire should include questions in multiple dimensions, such as: travel purpose: leisure, business, visiting relatives, etc.; accommodation preferences: preferred accommodation types (homestays, hotels, youth hostels, etc.); consumption habits: expected accommodation budget and price range willing to pay.

[0030] Traffic data mainly includes public transportation data and road traffic conditions, which can evaluate the traffic convenience of the site selection area and ensure that tourists can easily reach the homestay. Through the open data of the urban transportation management department, public transportation routes, station distribution, departure frequency and running time and other information can be obtained to obtain public transportation data, which is helpful to evaluate the traffic accessibility of the homestay. Public transportation data is specifically: the distance to the nearest public transportation station (unit: meter); the coverage of major public transportation routes (such as subways and buses). Using map software (Amap, Baidu Map, etc.) to obtain road traffic flow and congestion in a specific time period, the road traffic conditions in the relevant area can be obtained. By analyzing the traffic capacity of the main roads, it can ensure that tourists can arrive smoothly when traveling by car. Road traffic conditions specifically refer to the driving time from major attractions (unit: minutes), or the peak traffic congestion index.

[0031] Environmental factor data include climate data, environmental pollution index and surrounding resource data. Environmental factors have a direct impact on the attractiveness and comfort of homestays. Collecting relevant data can help evaluate the natural and cultural environment of the region. Meteorological data mainly refers to the temperature, humidity, precipitation and other information of the selected area obtained through the historical meteorological data provided by the Meteorological Bureau. These data can be used to calculate climate comfort. For example, the average annual temperature is used to evaluate the comfort of tourists in different seasons; the annual precipitation is a direct factor affecting tourists' travel willingness and homestay operations. The environmental pollution index can effectively evaluate the environmental quality of the selected area by obtaining data such as air quality index and noise pollution level from environmental monitoring agencies. Surrounding resource data: collect data related to natural landscapes, cultural heritage, etc. to evaluate their impact on tourist attraction, which can be obtained through the data resources of the Tourism Bureau or cultural department. For example, the distance to the main attractions: the distance and arrival time from the homestay to the main attractions; cultural activities: information on festivals, exhibitions, etc. in the area.

[0032] Competitor analysis data includes competition density data and user evaluation analysis. The number and distribution of similar homestays are collected through travel platforms, and the intensity of competition in the area is analyzed to obtain competition density data, such as the number of homestays in the same geographical area, or the price range and average price of competitors. User evaluation analysis refers to collecting customer reviews of competitors, analyzing their service quality and customer satisfaction, in order to identify their own advantages and room for improvement. For example, by obtaining the average value of user ratings, the service quality of competitors can be reflected; by collecting common complaints, the main complaints of customers about competitors can be analyzed to find opportunities for improvement.

[0033] In another exemplary embodiment of the present application, the relevant data is analyzed and processed to obtain standardized data, which provides a scientific basis for the site selection of tourist homestays. Then the above step 2 can be replaced by the following steps 21 to 25:

[0034] Step 21: Perform data cleaning, data standardization and data integration on the basic data in turn to obtain preprocessed data.

[0035] Step 22: Perform descriptive statistical analysis on the preprocessed data to obtain basic features, including mean, median and / or variance.

[0036] Step 23: Perform correlation analysis on the preprocessed data to obtain the relationship between indicators.

[0037] Step 24: Perform cluster analysis on the preprocessed data according to the features to obtain cluster results of candidate sites.

[0038] Step 25: Determine the basic characteristics, the relationship between the indicators and the clustering results of the candidate sites as the standardized data of each indicator.

[0039] Among them, the relevant data is preprocessed, which includes data cleaning, data standardization and data integration, which can improve data quality and ensure the accuracy of subsequent analysis. Data cleaning includes removing duplicate data, processing missing values ​​and correcting erroneous data. For questionnaire surveys and web crawled data, user input errors or incomplete information often occur, and the data needs to be carefully checked and cleaned. For example, Python's Pandas library is used for data cleaning, and missing values ​​can be processed by filling the mean or deleting rows with missing values. Data of different dimensions are standardized to ensure data comparability. For example, temperature and humidity can be standardized by Z-score or Min-Max normalization. Data integration refers to integrating data from different sources into a unified data set for subsequent analysis, such as integrating market demand data, traffic data, environmental data and competitor analysis data into a data frame.

[0040] Further in-depth analysis of the relevant data after preprocessing is performed to obtain standardized data. The in-depth analysis mainly includes descriptive statistical analysis, correlation analysis and cluster analysis. Descriptive statistical analysis refers to the use of statistical methods to analyze the basic characteristics of various data, such as mean, median, variance, etc., which can effectively understand the overall situation of the market and the distribution characteristics of the data. Example: Calculate the average occupancy rate and price distribution of homestays in the region and analyze their overall trends. Correlation analysis refers to analyzing the relationship between different variables, such as the relationship between market demand and traffic convenience, by calculating the Pearson correlation coefficient and other methods. Cluster analysis refers to grouping data by characteristics through clustering algorithms (such as K-means clustering) to identify potential site selection areas with similar characteristics, which helps to understand which areas have common characteristics in terms of market demand, environmental conditions and traffic convenience. Example: K-means clustering algorithm, that is, selecting K initial clustering centers, iteratively optimizing until convergence, and obtaining the final clustering results.

[0041] In another exemplary embodiment of the present application, the specific steps of weighting each indicator using the Analytic Hierarchy Process (AHP) are as follows:

[0042] S31. Obtain judgments on the relative importance of each indicator through expert review or questionnaire survey, and construct a judgment matrix. First, form a team of experts in related fields. These experts must have a deep understanding of the tourism market, homestay operations, and site selection factors. Then, based on the previously established multi-dimensional indicator system, determine the various indicators that need to be evaluated, such as market demand, transportation convenience, environmental quality, and degree of competition. Then, through questionnaire surveys or group discussions, let experts evaluate the relative importance of each indicator. Usually, a scale of 1 to 9 is used for judgment, as follows:

[0043] 1: Both are equally important;

[0044] 3: One item is more important than the other;

[0045] 5: One item is clearly more important than the other;

[0046] 7: One item is more important than the other;

[0047] 9: One is extremely more important than the other;

[0048] 2, 4, 6, 8: Indicates the degree between two adjacent numbers;

[0049] Finally, based on the expert evaluation results, a judgment matrix is ​​constructed. For example, if there are three indicators: market demand A, traffic convenience B, and environmental quality C, the judgment matrix can be expressed as:

[0050]

[0051] Among them, a AB Refers to the importance score of market demand A relative to transportation convenience B; a AC Refers to the importance score of market demand A relative to environmental quality C; a BC Refers to the importance score of transportation convenience B relative to environmental quality C;

[0052] S32, calculating the eigenvalues ​​and eigenvectors of the judgment matrix, specifically comprising the following steps:

[0053] S321, calculate the maximum eigenvalue λ of the judgment matrix max , which can be calculated by the eigenvalue decomposition method.

[0054] S322, sum each row of the judgment matrix to form a normalized weight vector. The specific steps are:

[0055] 1. Sum each row of the judgment matrix to obtain the weight vector S:

[0056]

[0057] Among them, S 1 , S 2 and S 3 are the sum of the first, second and third rows respectively, n is the total number of indicators, a 1j is the element in the 1st row and jth column of the judgment matrix, that is, the importance score of indicator 1 relative to indicator j, a 2j is the element in the 2nd row and jth column of the judgment matrix, a 3j It is the element in the 3rd row and jth column of the judgment matrix.

[0058] 2. Calculate the weight W of each indicator i :

[0059]

[0060] Among them, a ij S is the importance score of indicator a relative to indicator j, j is the weight vector of index j, i=1,2,…,n.

[0061] S323. Calculate the consistency ratio (CR) to check the consistency of the judgment matrix to ensure the reliability of the evaluation. The calculation formula of CR is:

[0062]

[0063] in, RI is a random consistency index, which depends on the dimension of the matrix. If CR is less than 0.1, the matrix is ​​judged to have acceptable consistency.

[0064] S33. Finally determine the weight of each indicator, and express all weights in vector form as follows:

[0065]

[0066] Where W represents the weight vector, W 1 represents the tourist flow index, W 2 represents the occupancy rate indicator, W 3 represents the consumption level indicator, W 4 represents the public transportation convenience index, W 5 Indicates the road traffic condition index, W 6 represents the climate comfort index, W 7 Represents the environmental pollution index, W 8 represents the competition density index, W 9 Represents the user evaluation score indicator.

[0067] In another exemplary embodiment of the present application, a weighted scoring method is used to comprehensively score each candidate site, so as to comprehensively evaluate the weights and standardized values ​​of each indicator, and then calculate the comprehensive scores of all candidate sites, and sort them from high to low according to the scores, and then select several sites with the highest scores as recommended sites. The calculation formula of the weighted scoring method is:

[0068]

[0069] Among them, S x The comprehensive score of the xth candidate site reflects the comprehensive performance of the site in all indicators; W i is the weight of the i-th indicator, indicating the importance of this indicator to the site selection decision; Z ix is the standardized value of the xth candidate site on the i-th indicator, ensuring that all indicators are compared within the same scale; n is the number of indicators, representing the different evaluation dimensions considered in the comprehensive score.

[0070] Scoring process: In actual operation, the index values ​​of all candidate sites are first standardized, and then each site is weighted according to the determined weight. The specific process is as follows: Ensure that the index values ​​of each candidate site have been standardized so that data of different dimensions can be reasonably compared; weight each index W i Applied to the corresponding normalized value Z ix In order to ensure that more important indicators have a greater impact on the final score, all indicators of each candidate site are weighted and summed to obtain the comprehensive score S of each site. x .

[0071] Once the comprehensive scores of all candidate sites are calculated, these scores are sorted. Specifically, all candidate sites are sorted from high to low according to the scores, and then several locations with the highest scores are selected as recommended sites based on the ranking of the comprehensive scores.

[0072] In another exemplary embodiment of the present application, a detailed site selection recommendation report is generated based on the site selection results obtained above to help decision makers choose the most suitable location for tourist homestays. The report covers multiple key aspects to ensure a comprehensive assessment of the potential of each candidate site. The site selection recommendation report includes: basic site selection information, market demand analysis, competition analysis, environmental factor assessment, expected operating benefit assessment, and a summary of advantages and disadvantages.

[0073] In another exemplary embodiment of the present application, the basic site selection information includes geographical location, traffic conditions and surrounding facilities; market demand analysis includes statistics on tourist flow, consumption level and occupancy rate; competition analysis includes statistics on competition density and user evaluation; environmental factor assessment includes assessment of environmental pollution index and climate comfort; and expected operating benefit assessment includes profit forecast and risk assessment.

[0074] Specifically, for the above-mentioned site selection recommendation report content, the generation process of the site selection recommendation report is as follows:

[0075] First, basic information about each recommended site is provided, including: geographical location, which clarifies the specific address and coordinates of the site, and provides a map location diagram for quick positioning; traffic conditions, analyzing the surrounding traffic convenience, such as the distance and traffic conditions of major roads and public transportation stations, to help understand the accessibility of the location; surrounding facilities, listing nearby supermarkets, restaurants, medical care, attractions and other facilities to provide convenient services for tourists.

[0076] Next, we conduct an in-depth analysis of market demand. The specific analysis content includes: tourist flow, which provides statistics on the average annual number of tourists in the area to illustrate the potential customer base of the market; consumption level, which analyzes the average consumption level of tourists in the area to help evaluate the profitability of the site; occupancy rate: displays the average occupancy rate of similar B&Bs in the area to determine the market's attractiveness and competition.

[0077] Based on the market demand analysis, further competition analysis is conducted, which includes: competition density, that is, calculating the ratio of the number of homestays in the area to the number of tourists that can be accommodated, and evaluating the intensity of competition; user evaluation, that is, collecting and analyzing user ratings and feedback on online platforms to understand the service quality and customer satisfaction of existing homestays.

[0078] Taking into account the environmental impact of the site selection, an assessment of environmental factors is carried out. The specific assessment contents include: environmental pollution index, which provides the air quality index and noise pollution level of the area, and evaluates the impact of the environment on the tourist experience; climate comfort, based on historical meteorological data, calculates the climate comfort score of the area to ensure that tourists have a good living experience here.

[0079] Finally, based on market demand and competition analysis, conduct an assessment of expected operating benefits, including revenue forecasts and risk assessments. Based on market demand, occupancy rate, and consumption level, use a formula to predict the potential revenue of the site. For example, the revenue can be calculated using the following formula: Expected revenue = tourist flow × average occupancy rate × average consumption level. Analyze potential operating risks, such as seasonal fluctuations, increased competition, etc., and propose corresponding response strategies.

[0080] The last part of the report will summarize the advantages and disadvantages of each recommended location to help decision makers make the final choice. The recommendation report will be accompanied by charts and data for intuitive understanding and analysis, so that decision makers can choose the best tourist homestay location with more confidence.

[0081] Generate detailed site recommendation reports to help decision makers clearly understand the potential of each candidate site and support more informed business decisions.

[0082] In another exemplary embodiment of the present application, in order to adapt to market changes and continuously optimize, after the above step 5, the method may also include: establishing a feedback mechanism, and regularly collecting market feedback and operating data of homestays at recommended locations based on the feedback mechanism; analyzing market feedback and operating data, and updating indicators and / or adjusting indicator weights based on the analysis results.

[0083] First, establish an effective feedback mechanism to regularly collect market feedback and operating data. Obtain tourists' opinions and suggestions on B&Bs through questionnaires, online reviews, and social media. These market feedbacks can involve aspects such as service quality, facility conditions, and overall experience. Regularly summarize operating data, including occupancy rate, customer return rate, revenue, etc., which can reflect the operating status and market performance of B&Bs.

[0084] Secondly, we conduct in-depth analysis of the collected feedback and operating data to evaluate the actual effect of the site selection decision. The analysis includes:

[0085] Business performance: Evaluate the difference between expected and actual earnings and identify the factors that cause the difference.

[0086] Customer satisfaction: By collating and analyzing tourist feedback, we can determine the level of tourist satisfaction and identify areas for improvement.

[0087] Market trends: Observe market changes, such as tourism seasonality and changes in competition, to help understand industry dynamics.

[0088] Finally, based on the analysis results, the comprehensive scoring formula is optimized in a timely manner to ensure its effectiveness and accuracy. Specific measures include adjusting weight distribution and updating indicator settings.

[0089] Adjust weight allocation: adjust the weight of each indicator based on market feedback and changes in operating data. For example, if market demand changes significantly, it may be necessary to increase the weight of the market demand indicator.

[0090] Update indicator settings: Regularly review and update the indicator system to ensure that it reflects the current market environment. For example, add new market trend indicators or remove indicators that are no longer applicable to keep the comprehensive scoring formula forward-looking.

[0091] In another exemplary embodiment of the present application, Figure 2 Another process of a method for recommending the site selection of a tourist homestay of the present application is shown: obtaining relevant data about the target area and its market environment; preprocessing and analyzing the relevant data; constructing a multidimensional indicator system; using the hierarchical analysis method to assign weights to each indicator in the multidimensional indicator system; weighting each candidate site to obtain a comprehensive scoring formula, and then sorting them from high to low according to the score, and selecting several locations with the highest scores as recommended sites; generating a detailed site recommendation report based on the above site selection results; establishing a feedback mechanism, regularly collecting and analyzing various types of data, and optimizing the comprehensive scoring formula.

[0092] The recommended method for selecting a site for a tourist homestay in the present application has the following beneficial effects:

[0093] (1) Provide B&B operators with a scientific basis for site selection, reduce operating risks, and improve the efficiency of resource allocation.

[0094] (2) Regularly collect market feedback and operating data, and be able to timely adjust the parameters and indicator settings in the comprehensive formula to ensure the real-time effectiveness of site selection decisions and adapt to market changes.

[0095] (3) Comprehensive consideration of multiple influencing factors makes site selection recommendations more comprehensive, identifies potential risks and opportunities, and improves the success rate of investment.

[0096] (4) Generate detailed site recommendation reports to help decision makers clearly understand the potential of each candidate site and support more informed business decisions.

[0097] Based on the same inventive concept, the embodiment of the present application also provides a tourist homestay site selection recommendation system for implementing the tourist homestay site selection recommendation method involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more tourist homestay site selection recommendation system embodiments provided below can refer to the limitations of the tourist homestay site selection recommendation method above, and will not be repeated here.

[0098] In an exemplary embodiment, a recommendation system for the site selection of a tourist homestay is provided, comprising: a collector, an input device, and a processor. The input device is connected to the collector and the processor respectively; the input device is used to input a multi-dimensional index system for the site selection of a tourist homestay into the processor; the collector is used to collect basic data of each candidate site for each indicator in the multi-dimensional index system, and input the data into the processor through the input device; the processor is used to pre-process and statistically analyze the basic data to obtain standardized data of each indicator; the analytic hierarchy process is used to assign weights to each indicator in the multi-dimensional index system; the weighted scoring method is used to comprehensively score each candidate site according to the weight and standardized data of each indicator, and multiple candidate sites with the highest comprehensive scores are selected as recommended sites; a site selection recommendation report is generated according to the basic data of the recommended site for each indicator.

[0099] As an optional implementation, the tourist homestay site selection recommendation system further includes: a display. The display is connected to the processor; the display is used to display the site selection recommendation report generated by the processor.

[0100] As another optional implementation, the recommendation system for the site selection of tourist homestays also includes: a memory. The memory is connected to the collector, the input device and the processor respectively; the memory is used to store the multi-dimensional indicator system, the basic data of each candidate site for each indicator and the site selection recommendation report.

[0101] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0102] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for recommending the location of a tourist homestay, characterized in that: include: Collect basic data for each candidate site for each indicator in the multi-dimensional indicator system; Preprocessing and statistically analyzing the basic data to obtain standardized data of each indicator; Using the analytic hierarchy process to assign weights to each indicator in the multi-dimensional indicator system; According to the weights and standardized data of each indicator, a weighted scoring method is used to comprehensively score each candidate site, and multiple candidate sites with the highest comprehensive scores are selected as recommended sites; Generate a site selection recommendation report based on the basic data of each indicator of the recommended site selection.

2. The method for recommending the location of a tourist homestay according to claim 1, characterized in that: The multi-dimensional indicator system includes: market demand indicators, traffic convenience indicators, environmental quality indicators and competition level indicators.

3. The method for recommending the location of a tourist homestay according to claim 1, characterized in that: The basic data is preprocessed and statistically analyzed to obtain standardized data of each indicator, including: Performing data cleaning, data standardization and data integration on the basic data in sequence to obtain pre-processed data; Performing descriptive statistical analysis on the preprocessed data to obtain basic characteristics; the basic characteristics include mean, median and / or variance; Performing correlation analysis on the preprocessed data to obtain the relationship between the indicators; Performing cluster analysis on the preprocessed data according to the features to obtain cluster results of candidate sites; The basic characteristics, the relationships between indicators and the clustering results of candidate sites are determined as the standardized data of each indicator.

4. The method for recommending the location of a tourist homestay according to claim 1, characterized in that: The calculation formula of the weighted scoring method is: Among them, S x is the comprehensive score of the xth candidate site, W i is the weight of the ith indicator, Z ix is the normalized value of the x-th candidate site on the ith indicator, and n is the number of indicators.

5. The method for recommending the location of a tourist homestay according to claim 1, characterized in that: The site recommendation report includes: basic site information, market demand analysis, competition analysis, environmental factor assessment, expected operating benefit assessment and summary of advantages and disadvantages.

6. The method for recommending the location of a tourist homestay according to claim 5, characterized in that: The basic site selection information includes geographical location, traffic conditions and surrounding facilities; the market demand analysis includes statistics on tourist flow, consumption level and occupancy rate; the competition analysis includes statistics on competition density and user evaluation; the environmental factor assessment includes an assessment of the environmental pollution index and climate comfort; and the expected operating benefit assessment includes profit forecast and risk assessment.

7. The method for recommending the location of a tourist homestay according to claim 1, characterized in that: Generate a site recommendation report based on the basic data of each indicator of the recommended site selection, and then include: Establish a feedback mechanism and regularly collect market feedback and operating data of the recommended B&Bs based on the feedback mechanism; Analyze market feedback and operating data, and update indicators and / or adjust indicator weights based on the analysis results.

8. A recommendation system for selecting a location for a tourist homestay, characterized in that: The recommendation system for selecting a location for a tourist homestay comprises: a collector, an input device and a processor; The input device is connected with the collector and the processor respectively; The input device is used to input a multi-dimensional indicator system for the site selection of tourist homestays into the processor; The collector is used to collect basic data of each candidate site for each indicator in the multi-dimensional indicator system and input it into the processor through the input device; The processor is used to preprocess and statistically analyze the basic data to obtain standardized data of each indicator; use the hierarchical analysis method to assign weights to each indicator in the multi-dimensional indicator system; use the weighted scoring method to comprehensively score each candidate site based on the weight and standardized data of each indicator, and select multiple candidate sites with the highest comprehensive scores as recommended sites; generate a site recommendation report based on the basic data of each indicator for the recommended site.

9. The recommendation system for selecting a location for a tourist homestay according to claim 8, characterized in that: The tourist homestay location recommendation system also includes: a display; The display is connected to the processor; The display is used to display the site selection recommendation report generated by the processor.

10. The recommendation system for selecting a location for a tourist homestay according to claim 8, characterized in that: The tourist homestay location recommendation system also includes: a memory; The memory is connected to the collector, the input device and the processor respectively; The memory is used to store a multi-dimensional indicator system, basic data of each candidate site for each indicator, and a site recommendation report.

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