Hotel comfort index evaluation method based on big data

Abstract

The invention relates to the technical field of hotel evaluation, in particular to a hotel comfort index evaluation method based on big data, and the method comprises the following steps: based on a social media platform, collecting evaluation data of a user for a hotel, evaluating the credit score of the user, and obtaining a user credibility evaluation result. According to the method, the reputation score of the user is evaluated by integrating the account creation time, the evaluation release frequency, the evaluation detail degree and the text similarity of the user, the credibility of user evaluation is effectively improved, high-reputation evaluation is screened, the accuracy and practicability of the evaluation result are ensured, and the user experience is improved. By evaluating the modernization and functional integrity of hotel facilities and analyzing the ratio of the room size to the occupied space of the facilities, the actual comfort of the hotel is comprehensively evaluated, comfort evaluation is more objective and scientific, more accurate accommodation experience reference can be provided for consumers, and customer satisfaction and market competitiveness are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hotel evaluation, and in particular to a hotel comfort index evaluation method based on big data. Background Art

[0002] The field of hotel evaluation technology focuses on the development and application of algorithms and methods to evaluate and improve the quality of hotel services and facilities. This field covers everything from basic customer satisfaction surveys to complex data analysis techniques, including user feedback analysis, service quality monitoring, and facility operation efficiency evaluation. Through the application of these technologies, hotel services can be evaluated quantitatively and qualitatively, thereby guiding hotel management to make targeted improvements to improve customer satisfaction and optimize operational efficiency.

[0003] Among them, the hotel comfort index evaluation method is a technical solution for quantifying the comfort level of hotel accommodation experience. This method evaluates multiple dimensions of the hotel, such as the environmental quality of the guest rooms, the completeness of the facilities, the response speed of the service staff, and other customer experience factors, to form a comprehensive measurement index. The index is designed to provide consumers with an intuitive reference to help them make more informed decisions when choosing a hotel. In addition, this evaluation method also provides hotel management with a specific basis for improving services and facilities, which helps to improve overall customer satisfaction and market competitiveness.

[0004] Traditional evaluation methods rely on direct user feedback in actual operation. When processing large-scale data, especially unstructured data from social media, they are unable to effectively distinguish the credibility of users and are easily affected by biased or false evaluations. In addition, when evaluating hotel comfort, traditional methods ignore the comprehensive evaluation of the modernity and functional integrity of facilities, as well as the quantitative analysis of the actual available space in the room, which limits the comprehensiveness and accuracy of the evaluation results. This results in the hotel management being unable to obtain sufficiently accurate basis for improvement, affecting the efficiency of improving the hotel's service quality and customer satisfaction. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a hotel comfort index evaluation method based on big data.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a hotel comfort index evaluation method based on big data, comprising the following steps:

[0007] S1: Based on the social media platform, collect users’ evaluation data on hotels, evaluate users’ credibility scores, and obtain user credibility evaluation results;

[0008] S2: Based on the user credibility evaluation result, user evaluations with credibility scores below a threshold are eliminated, and the remaining evaluations are collected and sorted to obtain user evaluation screening results;

[0009] S3: Based on the user evaluation screening results, extract the user's ratings of the hotel service staff, combine the number of evaluations, and evaluate the service quality of the hotel staff through average calculation to obtain a service quality evaluation result;

[0010] S4: Based on the hotel equipment catalog, extract the equipment's service life and maintenance status information, evaluate the equipment's functional integrity and modernity according to the equipment type, purchase date, and frequency of use, and obtain the equipment status evaluation results;

[0011] S5: Based on the equipment status evaluation result, according to the room size data provided by the hotel, the ratio of the total area of ​​the hotel room to the space occupied by the facilities is analyzed, the spaciousness of the hotel room is evaluated, and the hotel spaciousness evaluation result is obtained;

[0012] S6: Based on the service quality evaluation result, the equipment status evaluation result and the hotel spaciousness evaluation result, the hotel comfort index is calculated, the hotel comfort rating is obtained, and the hotel comfort evaluation result is obtained.

[0013] As a further solution of the present invention, the user credibility evaluation result includes a user credibility score, an evaluation validity index and a user activity rating; the user evaluation screening result includes filtered user comments, rating data and user identification information; the service quality evaluation result includes a service staff score, number of evaluations and service quality level; the equipment status evaluation result includes equipment type, average service life and maintenance frequency; the hotel spaciousness evaluation result includes an average room area, a bed size index and a bathroom size index; and the hotel comfort evaluation result includes a comfort score and a comfort ranking.

[0014] As a further solution of the present invention, based on the social media platform, the steps of collecting user evaluation data on the hotel, evaluating the user's credibility score, and obtaining the user credibility evaluation result are specifically as follows:

[0015] S101: Based on the social media platform, crawl user evaluation data related to the hotel, extract the evaluation text, publishing time and user account information, and obtain the user evaluation dataset;

[0016] S102: Based on the user evaluation data set, extract the frequency of user evaluation and the level of detail of the evaluation, and analyze the similarity between the user's current evaluation and other users' evaluations through text processing to obtain evaluation similarity analysis results;

[0017] S103: Based on the evaluation similarity analysis result, the reputation score of each user is calculated according to the user's evaluation publishing frequency, evaluation detail, text similarity and account creation time to obtain a user credibility evaluation result.

[0018] As a further solution of the present invention, the formula for calculating the reputation score of each user is:

[0019]

[0020] Among them, S is the user's reputation score, f represents the user's review publishing frequency, and f max represents the highest posting frequency among similar users, w1 represents the weight coefficient of posting frequency, d represents the level of detail of the evaluation, w2 represents the weight coefficient of detail, s represents the text similarity with other users’ evaluations, s max represents the highest similarity among similar users, w3 represents the weight coefficient of text similarity, t represents the account creation time, and w4 represents the weight coefficient of account creation time.

[0021] As a further solution of the present invention, based on the user credibility evaluation result, user evaluations with credibility scores below a threshold are eliminated, and the remaining evaluations are collected and sorted to obtain the user evaluation screening results in the following steps:

[0022] S201: Based on the user credibility evaluation result, extract the credibility scores of all users, sort the credibility scores from high to low, build a user credibility list, and obtain a user credibility ranking result;

[0023] S202: Based on the user reputation ranking result, compare the reputation score of each user with a set reputation threshold, identify users whose reputation scores are lower than the preset reputation threshold, delete user evaluations below the reputation threshold, and obtain a trusted user evaluation data set;

[0024] S203: Based on the trusted user evaluation data set, the evaluations after credibility screening are sorted, the evaluation format is unified, and duplicate items are removed to obtain user evaluation screening results.

[0025] As a further solution of the present invention, based on the user evaluation screening results, extracting the user's rating of the hotel service personnel, combining the number of evaluations, and evaluating the service quality of the hotel staff through average calculation, the steps of obtaining the service quality evaluation result are specifically as follows:

[0026] S301: Based on the user evaluation screening results, screen the reviews that mention hotel service personnel, identify and mark the reviews containing service personnel ratings, collect each user's ratings of the service personnel, and aggregate the rating data to obtain service personnel rating information;

[0027] S302: Based on the service personnel rating information, count the number of occurrences of the rating data, identify the frequency of occurrence of differentiated rating levels, and obtain service rating frequency statistical information;

[0028] S303: Based on the service rating frequency statistical information, the ratings are averaged according to the frequency of each rating level and the corresponding rating value, the hotel staff service quality is evaluated, and a service quality evaluation result is obtained.

[0029] As a further solution of the present invention, based on the hotel equipment catalog, the service life and maintenance status information of the equipment are extracted, and the functional integrity and modernity of the equipment are evaluated according to the equipment type, purchase date and frequency of use. The steps of obtaining the equipment status evaluation result are specifically as follows:

[0030] S401: Based on the hotel equipment catalog, retrieve each equipment information, including equipment type, purchase date and usage frequency, and summarize the information to obtain an equipment information table;

[0031] S402: Based on the equipment information table, the service life of each piece of equipment is calculated, the maintenance frequency and the most recent maintenance time are extracted from the equipment maintenance record, the maintenance status of each piece of equipment is recorded and updated, and the service life and maintenance status data of the equipment are obtained;

[0032] S403: Based on the equipment service life and maintenance status data, by comparing the actual performance of the equipment with the performance of similar new equipment, the functional integrity and modernity of each equipment are evaluated to obtain equipment status assessment results.

[0033] As a further solution of the present invention, based on the equipment status evaluation result, according to the room size data provided by the hotel, the ratio of the total area of ​​the hotel room to the space occupied by the facilities is analyzed to evaluate the spaciousness of the hotel room. The steps of obtaining the hotel spaciousness evaluation result are specifically as follows:

[0034] S501: Based on the equipment status evaluation result, the size data of each facility in the room is extracted from the hotel's equipment management data, including the length and width of the bed, the volume of the furniture and the size of the bathroom equipment, to obtain a room facility size table.

[0035] S502: Based on the room facility size table, analyze the area occupied by each facility, compare it with the total area of ​​each room, evaluate the ratio of the facility area to the total area of ​​the room, and obtain the room facility space ratio analysis result;

[0036] S503: Based on the room facility space ratio analysis result, by analyzing the relationship between the facility occupancy ratio and the space utilization rate, the actual available space of each room is identified, the spaciousness of the room is evaluated, and the hotel spaciousness evaluation result is obtained.

[0037] As a further solution of the present invention, based on the service quality evaluation results, the equipment status evaluation results and the hotel spaciousness evaluation results, the hotel comfort index is calculated to obtain the hotel comfort rating. The steps of obtaining the hotel comfort evaluation results are specifically as follows:

[0038] S601: Based on the service quality evaluation results, the equipment status evaluation results and the hotel spaciousness evaluation results, a corresponding weight is assigned to each result according to the influence of the service quality, the equipment status and the hotel spaciousness on the user comfort, to obtain a weight assignment result;

[0039] S602: Based on the weight distribution result, the evaluation score of each result is integrated according to the corresponding weight, and the hotel comfort index is calculated to obtain the hotel comfort score;

[0040] S603: Based on the hotel comfort score, according to the preset comfort rating standard, the obtained comfort index is converted into a corresponding rating category, and the hotel's comfort level is rated from low to high to obtain a hotel comfort evaluation result.

[0041] As a further solution of the present invention, the formula for calculating the hotel comfort index is:

[0042]

[0043] Among them, C is the hotel comfort index, Q represents the hotel staff service quality score, F represents the functional integrity score of the hotel equipment, M represents the modernity score of the hotel equipment, W represents the hotel spaciousness score, and w q 、w f 、w m and w w is the weight coefficient.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, the user's reputation score is evaluated by comprehensively considering the user's account creation time, evaluation publishing frequency, evaluation detail and text similarity, which effectively improves the credibility of user evaluations, and screens high-reputation evaluations to ensure the accuracy and practicality of the evaluation results. By evaluating the modernity and functional integrity of hotel facilities and analyzing the ratio of room size to facility occupied space, the actual comfort of the hotel is comprehensively evaluated. By screening and analyzing data in various dimensions, the comfort evaluation is made more objective and scientific, which can provide consumers with a more accurate reference for accommodation experience. At the same time, it also provides specific improvement directions for hotel management to enhance customer satisfaction and market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0047] Figure 2 It is a schematic diagram of the refinement of S1 of the present invention;

[0048] Figure 3 It is a schematic diagram of the refinement of S2 of the present invention;

[0049] Figure 4 It is a schematic diagram of the refinement of S3 of the present invention;

[0050] Figure 5 It is a schematic diagram of the refinement of S4 of the present invention;

[0051] Figure 6 It is a detailed schematic diagram of S5 of the present invention;

[0052] Figure 7 It is a detailed schematic diagram of S6 of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0055] See also Figure 1 The present invention provides a technical solution: a hotel comfort index evaluation method based on big data, comprising the following steps:

[0056] S1: Based on the social media platform, collect users' evaluation data on hotels, evaluate the user's credibility score according to each user's account creation time, evaluation frequency, evaluation detail, and text similarity between the evaluation and other users' evaluations, and obtain the user credibility evaluation result;

[0057] S2: Based on the user credibility evaluation results, extract the credibility scores of all users, compare the credibility scores with the pre-set credibility threshold, eliminate user reviews with credibility scores below the threshold, collect and organize the remaining reviews, and obtain user review screening results;

[0058] S3: Based on the user evaluation screening results, extract the user's rating of the hotel service staff, combine the number of evaluations, and evaluate the service quality of the hotel staff through average calculation to obtain the service quality evaluation result;

[0059] S4: Based on the hotel equipment catalog, extract the equipment's service life and maintenance status information, evaluate the equipment's functional integrity and modernity according to the equipment type, purchase date, and frequency of use, and obtain the equipment status evaluation results;

[0060] S5: Based on the equipment status evaluation result, according to the room size data provided by the hotel, including the room size, bed size, bathroom size and the size data of the facilities in the hotel room, the ratio of the total area of ​​the hotel room to the space occupied by the facilities is analyzed, and the spaciousness of the hotel room is evaluated to obtain the hotel spaciousness evaluation result;

[0061] S6: Based on the service quality evaluation results, the equipment status evaluation results and the hotel spaciousness evaluation results, a weight is assigned to each evaluation result, and through weighted analysis, the hotel's comfort index is calculated to obtain the hotel's comfort rating and obtain the hotel's comfort evaluation result.

[0062] The user credibility assessment results include user credibility score, evaluation validity index and user activity rating; the user evaluation screening results include filtered user comments, rating data and user identification information; the service quality assessment results include service staff rating, number of evaluations and service quality level; the equipment status assessment results include equipment type, average service life and maintenance frequency; the hotel spaciousness assessment results include average room area, bed size index and bathroom size index; the hotel comfort assessment results include comfort score and comfort ranking.

[0063] See also Figure 2 ,Based on the social media platform, we collect users’ evaluation data on hotels, and evaluate the user’s credibility score according to each user’s account creation time, evaluation publishing frequency, evaluation detail, and text similarity between the evaluation and other users’ evaluations. The specific steps to obtain the user credibility evaluation results are as follows:

[0064] S101: Based on the social media platform, crawl user evaluation data related to the hotel, extract the evaluation text, publishing time and user account information, and obtain the user evaluation dataset;

[0065] Based on social media platforms, by defining and presetting data capture rules, the structure of the target data is established, including key fields such as evaluation text, release time, and user account information. API calls or web crawler technology are used to extract data from social media platforms. Then, the captured raw data is formatted through preset data capture rules, such as removing useless characters and unifying the time format. The processed data is then stored in the database to prepare for data analysis. The processed user evaluation data set will include neat evaluation text, standardized release time, and complete user account information.

[0066] S102: Based on the user evaluation data set, extract the frequency of user evaluation and the level of detail of the evaluation, and analyze the similarity between the user's current evaluation and other users' evaluations through text processing to obtain the evaluation similarity analysis result;

[0067] Based on the user evaluation data set, the frequency and detail extraction are performed, which mainly includes counting the number of posts of each user within the set time, calculating the average length of the evaluation text, querying the user data in the database, and implementing data filtering and aggregation through SQL statements. At the same time, natural language processing technology, such as the TF-IDF algorithm, is used to analyze text data and calculate the similarity between evaluation texts. In this process, text similarity calculation formulas, such as the cosine similarity formula, are used to compare user evaluations to obtain evaluation similarity analysis results.

[0068] S103: Based on the evaluation similarity analysis results, the reputation score of each user is calculated according to the user's evaluation publishing frequency, evaluation detail, text similarity and account creation time to obtain the user credibility evaluation result.

[0069] The formula for calculating each user's reputation score is:

[0070]

[0071] Among them, S is the user's reputation score, f represents the user's review publishing frequency, and f max represents the highest posting frequency among similar users, w1 represents the weight coefficient of posting frequency, d represents the level of detail of the evaluation, w2 represents the weight coefficient of detail, s represents the text similarity with other users’ evaluations, s max represents the highest similarity among similar users, w3 represents the weight coefficient of text similarity, t represents the account creation time, and w4 represents the weight coefficient of account creation time.

[0072] formula:

[0073]

[0074] Parameter meaning and acquisition method:

[0075] f: The user's review posting frequency, calculated by dividing the total number of reviews posted by the user in the past year by 365 days. For example, if the user posted 50 reviews in a year, the posting frequency f is times / day.

[0076] f max : The highest posting frequency among similar users. Usually, the highest number of posts of all users in a year is extracted from the database and divided by 365 for normalization. Assuming that the most active user in the database posts 100 times in a year, f max for

[0077] d: The level of detail of the review, which is a value calculated based on the number of words in the review text. For example, if the review text contains 200 words, then d = 200 words.

[0078] s: Text similarity with other user reviews, which is usually obtained through text matching software and calculated as the average text similarity percentage between user reviews and other user reviews. For example, if the average similarity between the text and other reviews is 30%, then s = 0.30.

[0079] s max : The highest text similarity among similar users. The highest similarity value among all user reviews is extracted from the database. For example, if the highest similarity is 50%, then s max =0.50.

[0080] t: Account creation time, calculated in years. For example, if the user's account was created 5 years ago, then t = 5 years.

[0081] w1, w2, w3, w4: weight coefficients, preset according to the impact of each parameter on credibility.

[0082] Calculation example:

[0083] The weight coefficients are set to w1=0.25, w2=0.001, w3=0.25, and w4=0.1.

[0084] Calculate the user's reputation score:

[0085] Posting Frequency Score Section:

[0086]

[0087] Level of detail score section:

[0088] d·w2=200·0.001=0.2

[0089] Text similarity score part:

[0090]

[0091] Account creation time score section:

[0092]

[0093] Calculate the reputation score S:

[0094] S=0.0585+0.2+0.1+0.0707

[0095] =0.4292

[0096] The calculation result S = 0.4292 represents the user's comprehensive reputation score, which reflects the user's reputation level based on their activity, evaluation quality, originality and account history. A higher score means that the user has a better reputation and can enjoy a higher credibility on the platform.

[0097] See also Figure 3 , based on the user credibility evaluation results, extract the credibility scores of all users, compare the credibility scores with the pre-set credibility threshold, eliminate user reviews with credibility scores below the threshold, collect and organize the remaining reviews, and obtain the user review screening results in the following steps:

[0098] S201: based on the user credibility evaluation result, extract the credibility scores of all users, sort the credibility scores from high to low, build a user credibility list, and obtain the user credibility sorting result;

[0099] Based on the user credibility assessment results, data retrieval is performed according to the comprehensive credibility score field of each user, and then a sorting algorithm, such as quick sort or merge sort, is used to sort the retrieved credibility scores from high to low. This process involves comparing the credibility scores of each user and re-adjusting the data structure to reflect the sorted order. After the sorting is completed, the sorting results are stored in a new data structure to form a user credibility list, which displays the user rankings from high to low according to the credibility score.

[0100] S202: Based on the user reputation ranking result, the reputation score of each user is compared with the set reputation threshold, the users whose reputation scores are lower than the preset reputation threshold are identified, and the user evaluations lower than the reputation threshold are deleted to obtain a trusted user evaluation data set;

[0101] Based on the user reputation ranking results, the reputation score is compared with the preset threshold. A loop is used to traverse each user record in the reputation list, and each user's reputation score is compared with the reputation threshold set by the system. In this process, conditional judgment statements are used to identify users whose reputation scores are lower than the threshold. Once a user with a reputation score lower than the threshold is found, the data deletion command is immediately executed to remove the evaluation records of these users from the user evaluation database to obtain a trusted user evaluation data set.

[0102] S203: Based on the trusted user evaluation data set, the evaluations after credibility screening are sorted, the evaluation format is unified, and duplicate items are removed to obtain user evaluation screening results.

[0103] Based on the trusted user evaluation dataset, the evaluation data format unification operation is performed. Various evaluation formats are standardized through data conversion functions, such as unifying the date representation method and the encoding format of the evaluation text. Next, the evaluation data is deduplicated, and data structures such as hash tables are used to check and remove duplicate evaluation entries to ensure that each evaluation is unique. In addition, the evaluation text is grammatically and spelled to improve the quality of the dataset. The entire process is automated through scripts to obtain user evaluation screening results.

[0104] See also Figure 4 Based on the user evaluation screening results, extract the user's rating of the hotel service staff, combine the number of evaluations, and evaluate the service quality of the hotel staff through average calculation. The specific steps to obtain the service quality evaluation results are as follows:

[0105] S301: based on the user evaluation screening results, screening the reviews that mention the hotel service staff, identifying and marking the reviews containing the service staff ratings, collecting each user's ratings of the service staff, and summarizing the rating data to obtain the service staff rating information;

[0106] Based on the user evaluation screening results, the evaluation text is scanned by setting keyword searches, such as "service staff", "front desk" and "service attitude". Once relevant keywords are detected, the evaluation is marked as a review involving service staff. At the same time, the service staff ratings explicitly mentioned in the evaluation are collected, and text parsing technology is used to extract digital ratings from the evaluation content. Each user-submitted evaluation is processed individually to ensure the accuracy and completeness of the rating data. After the collection is completed, all rating data are summarized to obtain the service staff rating information.

[0107] S302: Based on the service personnel rating information, count the number of occurrences of the rating data, identify the frequency of occurrence of differentiated rating levels, and obtain service rating frequency statistics information;

[0108] Based on the service personnel rating information, the frequency statistics of the rating data are performed, and the collected rating data is subjected to frequency analysis to count the number of occurrences of each rating level. The process involves iterative traversal of the data set, calculating the specific frequency of each rating level, and storing it in a new data structure, such as a key-value pair or a data frame. This can intuitively display the distribution of each rating level. After the statistics are completed, the service rating frequency statistics information obtained reflects the popularity of each rating level and the performance distribution of service personnel in user evaluations.

[0109] S303: Based on the service rating frequency statistics information, the ratings are averaged according to the frequency of each rating level and the corresponding rating value, the hotel staff service quality is evaluated, and a service quality evaluation result is obtained.

[0110] Based on the service rating frequency statistics, an average calculation of the ratings is performed to evaluate the service quality of hotel service staff. The frequency of occurrence of each rating level and the corresponding rating value are considered, and the rating data are summarized and averaged. The calculation process ensures that each rating value is given an appropriate weight according to its frequency of occurrence, thereby obtaining an accurate service quality rating. The rating reflects the user's true opinion of the overall satisfaction with the hotel service staff, and the service quality evaluation result is obtained.

[0111] See also Figure 5 ,Based on the hotel equipment catalog, extract the equipment's age and maintenance status information, and evaluate the equipment's functional integrity and modernity according to the equipment type, purchase date and frequency of use.,The specific steps to obtain the equipment status evaluation results are as follows:

[0112] S401: Based on the hotel equipment catalog, retrieve each equipment information, including equipment type, purchase date and usage frequency, and summarize the information to obtain an equipment information table;

[0113] Based on the hotel equipment catalog, a detailed search is conducted on each equipment listed in the catalog, including records of equipment type, purchase date, and frequency of use. Equipment information is extracted by accessing relevant tables in the database. The information is obtained by querying the database, and it is ensured that each data extracted from the database is the latest and most accurate. The collected data includes the equipment model, purchase time, and daily usage statistics. The data is sorted and summarized into an equipment information table, which will be used to further analyze the equipment usage status and maintenance needs.

[0114] S402: Based on the equipment information table, the service life of each piece of equipment is calculated, the maintenance frequency and the most recent maintenance time are extracted from the equipment maintenance record, the maintenance status of each piece of equipment is recorded and updated, and the service life and maintenance status data of the equipment are obtained;

[0115] Based on the equipment information table, the service life of each piece of equipment is determined by calculating the time difference between the equipment's purchase date and the current date. Then, the relevant maintenance frequency and the most recent maintenance time are extracted from the equipment maintenance record database, and the maintenance status of each piece of equipment is recorded and updated. During this process, automated tools are used to regularly check and update maintenance records to ensure that all data is accurate and up-to-date, and to obtain equipment service life and maintenance status data.

[0116] S403: Based on the equipment age and maintenance status data, the functional integrity and modernity of each piece of equipment are evaluated by comparing the actual performance of the equipment with the performance of similar new equipment, and the equipment status evaluation results are obtained;

[0117] Based on the equipment age and maintenance status data, the functional integrity and modernity of each device are evaluated. By comparing the actual performance of each device with the performance of new devices of the same type on the market, the comparison can be made through performance test reports or equipment specifications. The operating performance, energy efficiency standards and technical parameters of the equipment are analyzed in detail. During the evaluation process, the timeliness and quality of equipment maintenance are also considered, and this is used as a basis to judge the modernity and functional integrity of the equipment. After completing the analysis, all data and evaluation results are summarized to form the equipment status evaluation results.

[0118] See also Figure 6 Based on the equipment status evaluation results, according to the room size data provided by the hotel, including room size, bed size, bathroom size and hotel room facility size data, the ratio of the total area of ​​the hotel room to the space occupied by the facilities is analyzed to evaluate the spaciousness of the hotel room. The specific steps for obtaining the hotel spaciousness evaluation results are as follows:

[0119] S501: Based on the equipment status assessment result, the size data of each facility in the room is extracted from the hotel's equipment management data, including the length and width of the bed, the volume of the furniture, and the size of the bathroom equipment, to obtain a room facility size table.

[0120] Based on the equipment status assessment results, the specific size data of various facilities in each room are extracted from the hotel's equipment management database, including the length and width of the bed, the volume of the furniture, and the size of the bathroom equipment. Through database query operations, detailed size information of each facility is obtained to ensure the accuracy and update of all size data. The data is organized and summarized into a room facility size table, which lists the facility sizes in each room in detail, providing the necessary basic data for subsequent space analysis.

[0121] S502: Based on the room facility size table, analyze the area occupied by each facility, compare it with the total area of ​​each room, evaluate the ratio of the facility area to the total area of ​​the room, and obtain the room facility space ratio analysis result;

[0122] Based on the room facility size table, calculate the area occupied by each facility. For example, calculate the floor area through the bed size and furniture volume. Then, compare these area data with the total area of ​​each room, and analyze the ratio of the area occupied by the facility to the total area of ​​the room. The calculation process involves simple mathematical formulas, such as the calculation of area ratio, to obtain the analysis results of the room facility space ratio. The results show the spatial distribution and proportion of various facilities in the room.

[0123] S503: Based on the room facility space ratio analysis result, by analyzing the relationship between the facility occupancy ratio and the space utilization rate, the actual available space of each room is identified, the spaciousness of the room is evaluated, and the hotel spaciousness evaluation result is obtained.

[0124] Based on the results of the room facility space ratio analysis, the relationship between the facility occupancy ratio and space utilization is analyzed. The actual available space in each room is calculated, which involves subtracting the area occupied by the facilities from the total area to evaluate the spaciousness of each room. This is achieved through data visualization and ratio calculation to ensure an accurate assessment of the space efficiency of each room. After completing these analyses, the hotel spaciousness assessment results obtained provide hotel management with quantitative indicators of the spaciousness of each room, supporting the hotel to make more effective decisions on room layout and facility configuration.

[0125] See also Figure 7 , based on the service quality evaluation results, equipment status evaluation results and hotel spaciousness evaluation results, each evaluation result is weighted, and through weighted analysis, the hotel's comfort index is calculated to obtain the hotel's comfort rating. The specific steps for obtaining the hotel's comfort evaluation results are:

[0126] S601: Based on the service quality evaluation results, the equipment status evaluation results and the hotel spaciousness evaluation results, a corresponding weight is assigned to each result according to the influence of the service quality, the equipment status and the hotel spaciousness on the user comfort, and a weight assignment result is obtained;

[0127] Based on the service quality assessment results, equipment status assessment results and hotel spaciousness assessment results, through expert meetings and historical data analysis, the impact of the three factors on user comfort is determined, and corresponding weights are assigned to each assessment result. This involves the use of decision analysis techniques, such as hierarchical analysis method, to assess the relative importance of each factor. This method is used to calculate the weight ratio of service quality, equipment status and hotel spaciousness, ensuring that each factor is appropriately weighted according to its actual impact on user comfort, and obtaining the weight allocation result.

[0128] S602: Based on the weight distribution result, the evaluation score of each result is integrated according to the corresponding weight, and the hotel comfort index is calculated to obtain the hotel comfort score;

[0129] The formula for calculating the hotel comfort index is:

[0130]

[0131] Among them, C is the hotel comfort index, Q represents the hotel staff service quality score, F represents the functional integrity score of the hotel equipment, M represents the modernity score of the hotel equipment, W represents the hotel spaciousness score, and w q 、w f 、w m and w w is the weight coefficient.

[0132] formula:

[0133]

[0134] Parameter meaning and acquisition method:

[0135] Q: represents the service quality score of hotel staff, which is based on the aggregated data of user satisfaction surveys or online rating platforms. After screening, the data is averaged to obtain the quality score.

[0136] F: represents the functional integrity score of the hotel equipment, which is calculated based on the data analysis of equipment maintenance records and usage frequency. These data usually come from the hotel's equipment management system, which records the maintenance times and fault reports of each equipment.

[0137] M: represents the modernity score of the hotel's equipment, which is evaluated against industry standards and the adoption of new technologies. This score reflects whether the equipment uses the latest technology, and the data can be obtained from equipment procurement records and technical specifications.

[0138] W: stands for the hotel's spaciousness score, which is calculated by measuring the ratio of the room area to the facilities area. This usually involves on-site measurements or analysis of room design drawings to determine the actual usable space size of the room.

[0139] w q 、w f 、w m and w w : Weighting coefficients for service quality, equipment functional completeness, equipment modernity and spaciousness. The coefficients are set through statistical analysis based on industry average data and historical performance indicators, and are intended to reflect the contribution of each aspect to the total comfort.

[0140] Calculation example:

[0141] Set the parameters as follows:

[0142] Q = 85 (indicates that the service quality is very high), F = 75 (indicates that the equipment is well maintained but there is room for improvement), M = 60 (indicates that the equipment is newer but not completely updated), W = 90 (indicates that the room is very spacious), w q =0.3, w f =0.2, w m =0.2, w w =0.3 (the weight reflects the importance of each factor).

[0143] The hotel comfort index C is calculated as follows:

[0144]

[0145] The calculation result C = 54.9153, which represents the hotel's comfort score, which reflects the hotel's overall performance in terms of service quality, functionality and modernity of equipment, and spaciousness of space. A higher score means that the hotel performs well in terms of comprehensive comfort, which is an important indicator for attracting customers.

[0146] S603: Based on the hotel comfort score, according to the preset comfort rating standard, the obtained comfort index is converted into a corresponding rating category, and the hotel's comfort level is rated from low to high to obtain the hotel comfort evaluation result.

[0147] Based on the hotel comfort score, the hotel comfort index is converted into a specific rating category. According to the comfort rating standards preset by the hotel management department, these standards usually include several levels, such as excellent, good, average and poor. By comparing the comfort index with the thresholds of these preset levels, the hotel's comfort level is determined to ensure that each comfort index can be accurately classified into the corresponding level to obtain the hotel comfort evaluation result.

[0148] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A hotel comfort index evaluation method based on big data, characterized in that: The following steps are involved: S1: Based on the social media platform, collect users’ evaluation data on hotels, evaluate users’ credibility scores, and obtain user credibility evaluation results; S2: Based on the user credibility evaluation result, user evaluations with credibility scores below a threshold are eliminated, and the remaining evaluations are collected and sorted to obtain user evaluation screening results; S3: Based on the user evaluation screening results, extract the user's ratings of the hotel service staff, combine the number of evaluations, and evaluate the service quality of the hotel staff through average calculation to obtain a service quality evaluation result; S4: Based on the hotel equipment catalog, extract the equipment's service life and maintenance status information, evaluate the equipment's functional integrity and modernity according to the equipment type, purchase date, and frequency of use, and obtain the equipment status evaluation results; S5: Based on the equipment status evaluation result, according to the room size data provided by the hotel, the ratio of the total area of ​​the hotel room to the space occupied by the facilities is analyzed, the spaciousness of the hotel room is evaluated, and the hotel spaciousness evaluation result is obtained; S6: Based on the service quality evaluation result, the equipment status evaluation result and the hotel spaciousness evaluation result, the hotel comfort index is calculated, the hotel comfort rating is obtained, and the hotel comfort evaluation result is obtained.

2. The hotel comfort index evaluation method based on big data according to claim 1 is characterized in that: The user credibility evaluation result includes the user reputation score, evaluation validity index and user activity rating, the user evaluation screening result includes filtered user comments, rating data and user identification information, the service quality evaluation result includes service staff rating, evaluation times and service quality level, the equipment status evaluation result includes equipment type, average service life and maintenance frequency, the hotel spaciousness evaluation result includes average room area, bed size index and bathroom size index, and the hotel comfort evaluation result includes comfort score and comfort ranking.

3. The hotel comfort index evaluation method based on big data according to claim 1 is characterized in that: Based on the social media platform, the steps to collect users' evaluation data on hotels and evaluate users' credibility scores are as follows: S101: Based on the social media platform, crawl user evaluation data related to the hotel, extract the evaluation text, publishing time and user account information, and obtain the user evaluation dataset; S102: extracting the frequency of user evaluation and the level of detail of the evaluation based on the user evaluation data set, and analyzing the similarity between the user's current evaluation and other users' evaluations through text processing to obtain evaluation similarity analysis results; S103: Based on the evaluation similarity analysis result, the reputation score of each user is calculated according to the user's evaluation publishing frequency, evaluation detail, text similarity and account creation time to obtain a user credibility evaluation result.

4. The hotel comfort index evaluation method based on big data according to claim 3 is characterized in that: The formula for calculating the reputation score of each user is: Among them, S is the user's reputation score, f represents the user's review publishing frequency, and f max represents the highest posting frequency among similar users, w1 represents the weight coefficient of posting frequency, d represents the level of detail of the evaluation, w2 represents the weight coefficient of detail, s represents the text similarity with other users’ evaluations, s max represents the highest similarity among similar users, w3 represents the weight coefficient of text similarity, t represents the account creation time, and w4 represents the weight coefficient of account creation time.

5. The hotel comfort index evaluation method based on big data according to claim 1 is characterized in that: Based on the user credibility evaluation result, user reviews with credibility scores below a threshold are eliminated, and the remaining reviews are collected and sorted to obtain the user review screening results in the following steps: S201: Based on the user credibility evaluation result, extract the credibility scores of all users, sort the credibility scores from high to low, build a user credibility list, and obtain a user credibility ranking result; S202: Based on the user reputation ranking result, compare the reputation score of each user with a set reputation threshold, identify users whose reputation scores are lower than the preset reputation threshold, delete user evaluations below the reputation threshold, and obtain a trusted user evaluation data set; S203: Based on the trusted user evaluation data set, the evaluations after credibility screening are sorted, the evaluation format is unified, and duplicate items are removed to obtain user evaluation screening results.

6. The hotel comfort index evaluation method based on big data according to claim 1 is characterized in that: Based on the user evaluation screening results, extract the user's rating of the hotel service staff, combine the number of evaluations, and evaluate the service quality of the hotel staff through average calculation. The specific steps of obtaining the service quality evaluation result are as follows: S301: Based on the user evaluation screening results, screen the reviews that mention hotel service personnel, identify and mark the reviews containing service personnel ratings, collect each user's ratings of the service personnel, and aggregate the rating data to obtain service personnel rating information; S302: Based on the service personnel rating information, count the number of occurrences of the rating data, identify the frequency of occurrence of differentiated rating levels, and obtain service rating frequency statistical information; S303: Based on the service rating frequency statistical information, the ratings are averaged according to the frequency of each rating level and the corresponding rating value, the hotel staff service quality is evaluated, and a service quality evaluation result is obtained.

7. The hotel comfort index evaluation method based on big data according to claim 1 is characterized in that: Based on the hotel equipment catalog, extract the equipment's age and maintenance status information, and evaluate the equipment's functional integrity and modernity based on the equipment type, purchase date, and frequency of use. The specific steps to obtain the equipment status evaluation results are as follows: S401: Based on the hotel equipment catalog, retrieve each equipment information, including equipment type, purchase date and usage frequency, and summarize the information to obtain an equipment information table; S402: Based on the equipment information table, the service life of each piece of equipment is calculated, the maintenance frequency and the most recent maintenance time are extracted from the equipment maintenance record, the maintenance status of each piece of equipment is recorded and updated, and the service life and maintenance status data of the equipment are obtained; S403: Based on the equipment service life and maintenance status data, by comparing the actual performance of the equipment with the performance of similar new equipment, the functional integrity and modernity of each equipment are evaluated to obtain an equipment status assessment result.

8. The hotel comfort index evaluation method based on big data according to claim 1 is characterized in that: Based on the equipment status evaluation result, according to the room size data provided by the hotel, the ratio of the total area of ​​the hotel room to the space occupied by the facilities is analyzed to evaluate the spaciousness of the hotel room. The steps of obtaining the hotel spaciousness evaluation result are specifically as follows: S501: Based on the equipment status evaluation result, the size data of each facility in the room is extracted from the hotel's equipment management data, including the length and width of the bed, the volume of the furniture and the size of the bathroom equipment, to obtain a room facility size table. S502: Based on the room facility size table, analyze the area occupied by each facility, compare it with the total area of ​​each room, evaluate the ratio of the facility area to the total area of ​​the room, and obtain the room facility space ratio analysis result; S503: Based on the room facility space ratio analysis result, by analyzing the relationship between the facility occupancy ratio and the space utilization rate, the actual available space of each room is identified, the spaciousness of the room is evaluated, and the hotel spaciousness evaluation result is obtained.

9. The hotel comfort index evaluation method based on big data according to claim 1 is characterized in that: Based on the service quality evaluation results, the equipment status evaluation results and the hotel spaciousness evaluation results, the hotel comfort index is calculated to obtain the hotel comfort rating. The steps of obtaining the hotel comfort evaluation results are as follows: S601: Based on the service quality evaluation results, the equipment status evaluation results and the hotel spaciousness evaluation results, a corresponding weight is assigned to each result according to the influence of the service quality, the equipment status and the hotel spaciousness on the user comfort, to obtain a weight assignment result; S602: Based on the weight distribution result, the evaluation score of each result is integrated according to the corresponding weight, and the hotel comfort index is calculated to obtain the hotel comfort score; S603: Based on the hotel comfort score, according to the preset comfort rating standard, the obtained comfort index is converted into a corresponding rating category, and the hotel's comfort level is rated from low to high to obtain a hotel comfort evaluation result.

10. The hotel comfort index evaluation method based on big data according to claim 9 is characterized in that: The formula for calculating the hotel comfort index is: Among them, C is the hotel comfort index, Q represents the hotel staff service quality score, F represents the functional integrity score of the hotel equipment, M represents the modernity score of the hotel equipment, W represents the hotel spaciousness score, and w q 、w f 、w m and w w is the weight coefficient.

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