Hotel management quality evaluation method and system based on operation big data

Abstract

The invention relates to the technical field of hotel management evaluation, in particular to a hotel management quality evaluation method and system based on operation big data. The method comprises the following steps: acquiring a hotel multi-dimensional operation monitoring log, extracting customer historical feedback information and performing adaptive shielding processing so as to obtain standardized customer feedback information; performing hotel service link analysis and full-link sequential evolution on the hotel multi-dimensional operation monitoring log, and constructing a full-cycle service link relation graph; performing abnormal link response evaluation on the full-cycle service link relation graph according to the standardized customer feedback information to obtain an abnormal link response evaluation value; historical customer check-in behavior analysis is performed on the hotel multi-dimensional operation monitoring log, and then satisfaction association trend mining is performed, so that a customer satisfaction association rule is generated. According to the invention, comprehensive and objective quality evaluation is realized, hotel management decision making is facilitated, and customer experience is improved.

Description

Technical Field

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

[0002] Hotel management not only involves the cleaning and maintenance of guest rooms, but also includes front desk reception, customer service, catering management, facility maintenance and other links. As consumers' requirements for accommodation quality increase, the complexity and difficulty of hotel management also increase. Traditional hotel management quality assessment methods usually rely on manual inspections and regular customer feedback, but this method has many problems such as low efficiency, narrow coverage, and delayed feedback, making it difficult to fully and accurately assess the overall level of hotel management.

[0003] In recent years, with the continuous advancement and application of big data technology, the hotel industry has gradually begun to try to conduct management quality assessment based on operational big data. By collecting and analyzing various data in hotel operations, such as customer check-in data, consumption behavior, service feedback, employee work status, passenger flow, etc., hotel managers can grasp the current status of operations more comprehensively and accurately. However, most of the existing evaluation methods based on operational data still face problems such as data silos, insufficient analysis dimensions, and incomplete evaluation indicators, which make it impossible to accurately reflect the overall picture of hotel management and may even lead to wrong management decisions.

[0004] Especially in the context of big data, the amount of data for hotel operations is huge and highly complex. How to mine effective information from data of many different dimensions and conduct scientific analysis is a problem that needs to be solved urgently. Traditional manual analysis methods can no longer meet this demand. Therefore, developing an intelligent and automated hotel management quality assessment method that can process and analyze these big data in real time and comprehensively, thereby providing a scientific basis for hotel management decisions, has become an urgent need for the hotel industry to improve management levels. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a hotel management quality assessment method and system based on operational big data to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a hotel management quality assessment method based on operational big data, comprising the following steps: Step S1: Obtain the hotel's multi-dimensional operation monitoring log, extract historical customer feedback information and perform adaptive shielding processing to obtain standardized customer feedback information; Step S2: Analyze the hotel service links and the sequential evolution of all links on the hotel multi-dimensional operation monitoring log, and construct a full-cycle service link relationship diagram; Step S3: Conduct an abnormal link response assessment on the full-cycle service link diagram based on the standardized customer feedback information to obtain an abnormal link response assessment value; Step S4: Analyze the historical customer check-in behaviors in the hotel's multi-dimensional operation monitoring logs, and then conduct satisfaction correlation trend mining to generate customer satisfaction correlation rules; Step S5: Conduct real-time customer flow analysis and customer flow change trend prediction based on the hotel's multi-dimensional operation monitoring logs to generate customer flow trend prediction information; Step S6: Predict the customer satisfaction situation based on the customer satisfaction correlation rules and customer flow trend prediction information, and conduct a comprehensive hotel management quality assessment based on the abnormal link response assessment value to obtain a hotel management quality assessment report.

[0007] By extracting the multi-dimensional operation monitoring logs of the hotel, the whole process data from customer check-in to check-out can be comprehensively obtained, including equipment status, room usage, employee service efficiency, etc., which provides comprehensive basic data for subsequent quality assessment. Adaptive shielding processing of historical customer feedback information can effectively identify and remove malicious evaluations. Through machine learning and sentiment analysis technologies, this processing can ensure that the final obtained data is more accurate, avoid human subjective interference, and improve the authenticity of customer feedback. After the customer feedback information is shielded and filtered, standardization processing can eliminate differences in the scoring system, sentiment expression, and language usage, ensure that subsequent analysis can be carried out under the same standard, and ensure the reliability of the evaluation results. By analyzing the multi-dimensional operation data of the hotel, each service link can be systematically sorted out, including the complete service process from customer reservation, check-in, dining, entertainment, check-out, etc. This comprehensive analysis of service links helps to discover the problems and their interrelationships existing in the service chain. The analysis of the sequential evolution of the whole link can reveal bottlenecks or repetitive links in the service process, help hotel managers identify optimization spaces, and improve service quality by simplifying the process or adding certain key links. By constructing a relationship diagram of the whole-cycle service links, hotel managers can clearly see the relationship between the service process and each link, providing an intuitive tool to assist decision-making and improvement. By combining the standardized customer feedback information with the relationship diagram of the whole-cycle service links, the links that perform poorly in the whole service chain can be accurately identified. This analysis can help hotel operators quickly locate the parts with low service quality, such as long customer waiting times, poor service attitudes, or frequent facility failures. By evaluating the response quality of each link, the hotel can optimize its service strategy. For example, strengthen training, equipment maintenance, or increase human resources for the poorly performing links, so as to improve the overall service quality. This evaluation process is based on actual customer feedback data, effectively avoiding the subjectivity of traditional reliance on sensory evaluation and manual inspection, and improving the accuracy and execution of quality assessment. Through the analysis of historical customer check-in behaviors, the behavioral patterns of customers at different times, seasons, or during specific events can be discovered, such as preferred room types, consumption habits, and check-in frequencies. These data can help the hotel deeply understand customer needs and preferences. Through the trend analysis of historical data, the correlation rules of customer satisfaction can be mined, that is, which factors (such as check-in time, room type, service items, etc.) have the greatest impact on customer satisfaction. This analysis provides important decision-making support for hotel management, helping it foresee potential customer problems and take measures in future services. Through the satisfaction correlation rules, the hotel can provide more personalized services according to the behavioral patterns and preferences of customers, further improve customer satisfaction, and enhance customer loyalty.Real-time customer flow analysis can help hotels dynamically understand the distribution of customers (such as the number of check-ins and the flow direction during different time periods), so that they can reasonably allocate manpower and resources during peak hours and optimize customer service. Through the prediction of customer flow change trends, hotels can predict in advance the changes in customer flow during a certain period in the future (such as during holidays or special events), so as to make preparations such as room reservation, catering arrangement, and service staff scheduling, and avoid waste or shortage of resources. Through flow trend prediction, hotels can scientifically manage rooms, dispatch personnel, and allocate resources to maximize the use efficiency of resources and service quality. By combining the customer satisfaction correlation law and the customer flow trend prediction information, the change trend of customer satisfaction within a certain period in the future can be predicted. This can help hotels anticipate problems in service quality and take timely measures to avoid customer dissatisfaction. Considering various data such as customer satisfaction, flow changes, and service link response evaluation comprehensively can provide a comprehensive and objective quality assessment for hotel management. This assessment not only helps management decision-making but also helps improve the work efficiency of employees and the customer service experience. By analyzing the management quality assessment report, hotels can identify weak links in management and make targeted adjustments and optimizations.

[0008] In this specification, a hotel management quality assessment system based on operation big data is provided for implementing the hotel management quality assessment method based on operation big data as described above, including: A data processing module, configured to obtain hotel multi-dimensional operation monitoring logs, extract historical customer feedback information and perform adaptive shielding processing, so as to obtain standardized customer feedback information; A service link module, configured to perform hotel service link analysis and full-link sequential evolution on the hotel multi-dimensional operation monitoring logs, and construct a full-cycle service link relationship graph; A response evaluation module, configured to perform abnormal link response evaluation on the full-cycle service link relationship graph according to the standardized customer feedback information to obtain an abnormal link response evaluation value; A satisfaction correlation module, configured to perform historical customer check-in behavior analysis on the hotel multi-dimensional operation monitoring logs, and then perform satisfaction correlation trend mining to generate customer satisfaction correlation laws; A flow trend prediction module, configured to perform real-time customer flow analysis and customer flow change trend prediction based on the hotel multi-dimensional operation monitoring logs, and generate customer flow trend prediction information; A quality assessment module, configured to perform customer satisfaction situation prediction according to the customer satisfaction correlation law and the customer flow trend prediction information, and perform a comprehensive hotel management quality assessment according to the abnormal link response evaluation value to obtain a hotel management quality assessment report.

[0009] The present invention provides comprehensive and accurate data support for subsequent analysis by acquiring and processing various monitoring data of hotel operations (such as room status, equipment conditions, service times, etc.) and combining with historical feedback information of customers. Adaptive shielding processing of customer feedback can effectively filter out malicious, false or bad evaluations, thus ensuring that the obtained customer feedback is more real and effective. This helps to eliminate the interference of subjective factors and improve the reliability of data. Through the standardization processing of customer feedback information, the differences in the scoring system and customer language expressions can be eliminated, enabling feedback data from different sources and formats to be uniformly and standardly used for subsequent analysis. This provides a unified basis for data analysis and ensures the fairness and comparability of subsequent evaluations. Through the service process module, the hotel can comprehensively understand how each service process (such as reception, check-in, catering, room cleaning, etc.) works together and their mutual influences during the whole process from customer reservation to check-out. This helps to discover potential problems in the service process and provides a basis for optimizing the service process. By drawing a relationship diagram of the full-cycle service process, hotel managers can clearly see the dependency relationships and evolution processes of each service process, improving the transparency of overall operations. This enables the hotel to better control the key nodes of the service and ensure seamless connection. Through process analysis, the hotel can promptly identify which service processes are bottlenecks or weak links, and thus can specifically optimize the processes and conduct personnel training to improve the overall service quality. By analyzing customer feedback and service process data, it can help the hotel quickly identify those underperforming and slow-responsive processes (for example, long check-in waiting times, delayed cleaning services, etc.), thus effectively locating potential problems in hotel management. The response evaluation module can give specific response evaluation values for abnormal processes, and these evaluation values provide clear improvement directions for hotel managers. For example, if the response speed of a certain process is slow, more personnel can be invested or the process can be adjusted to improve the service quality. Timely identification and improvement of problems in service processes can significantly enhance the overall customer experience, reduce customer complaints, and enhance customer loyalty and satisfaction. By analyzing the historical check-in behaviors of customers, the hotel can deeply understand the behavior patterns, consumption preferences and demand changes of customers. This helps the hotel to make more targeted service improvements and predict the future demand trends of customers. By revealing the correlation rules between customer satisfaction and their check-in behaviors, the hotel can provide personalized services according to the needs of different customer groups. For example, recommend suitable room types, catering or other additional services based on the customer's check-in history. The correlation rules between satisfaction and check-in behaviors can help hotel managers clarify which factors most affect customer satisfaction, and thus specifically optimize these key factors to improve overall customer satisfaction. By monitoring the real-time customer flow, the hotel can dynamically understand the distribution of customers at different times and regions, ensure the reasonable allocation of service resources, and avoid customer backlogs during peak hours.Through traffic trend prediction, hotels can predict changes in room demand and peak customer arrival times in the future. This helps hotels make advance arrangements and preparations for resources such as staff, facilities, and rooms. Based on traffic prediction, hotels can accurately predict room demand for each time period, avoiding shortages or surpluses of resources, thereby improving overall operational efficiency and service quality. By comprehensively evaluating various types of data such as customer satisfaction, traffic trends, and service link responses, hotels can obtain a comprehensive quality assessment report. This report can comprehensively reflect the hotel's operational status, customer experience, and service quality, providing objective data support for managers. The data report provided by the quality assessment module helps hotel managers make more scientific and accurate decisions, identify weak links in services, optimize operational processes, and improve service levels. Through regular quality assessment reports, hotels can continuously track changes in service quality and customer satisfaction, adjust strategies and optimize processes in a timely manner, and continuously enhance the competitiveness of the hotel. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a schematic flowchart of the steps of a hotel management quality assessment method based on operation big data according to the present invention; Figure 2 is a detailed implementation step flowchart of step S1; Figure 3 is a detailed implementation step flowchart of step S2; Figure 4 is a detailed implementation step flowchart of step S3. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0011] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0012] The embodiments of the present application provide a hotel management quality assessment method and system based on operation big data. The execution subjects of the hotel management quality assessment method and system based on operation big data include but are not limited to: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that can be regarded as general computing nodes of the present application. The data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0013] Please refer to Figures 1 to 4 , the present invention provides a hotel management quality assessment method based on operation big data. The hotel management quality assessment method based on operation big data includes the following steps: Step S1: Obtain hotel multi-dimensional operation monitoring logs, extract historical customer feedback information and perform adaptive shielding processing to obtain standardized customer feedback information; Step S2: Analyze the hotel service links and the sequential evolution of all links in the hotel multi-dimensional operation monitoring log, and construct a relationship diagram of the full-cycle service links; Step S3: Evaluate the response to abnormal links in the relationship diagram of the full-cycle service links according to the standardized customer feedback information to obtain the evaluation value of the response to abnormal links; Step S4: Analyze the historical customer check-in behavior in the hotel multi-dimensional operation monitoring log, and then mine the correlation trend of satisfaction, so as to generate the correlation law of customer satisfaction; Step S6: Based on the hotel multi-dimensional operation monitoring log, conduct real-time customer flow analysis and prediction of the customer flow change trend, and generate customer flow trend prediction information; Step S6: Predict the customer satisfaction situation according to the correlation law of customer satisfaction and the customer flow trend prediction information, and comprehensively evaluate the hotel management quality according to the evaluation value of the response to abnormal links to obtain the hotel management quality evaluation report.

[0014] Through extracting the multi-dimensional operation monitoring logs of hotels, the whole process data from customer check-in to check-out can be comprehensively obtained, including equipment status, room usage, employee service efficiency, etc., which provides comprehensive basic data for subsequent quality assessment. Adaptive shielding processing of historical customer feedback information can effectively identify and remove malicious evaluations. Through machine learning and sentiment analysis technologies, this processing can ensure that the final obtained data is more accurate, avoid human subjective interference, and improve the authenticity of customer feedback. The standardized processing of the customer feedback information after shielding and filtering can eliminate the differences in the scoring system, sentiment expression, and language usage, ensure that subsequent analysis can be carried out under the same standard, and ensure the reliability of the evaluation results. By analyzing the multi-dimensional operation data of hotels, each service link can be systematically sorted out, including the complete service process from customer reservation, check-in, dining, entertainment, to check-out. This all-round analysis of service links helps to discover the problems and their interrelationships existing in the service chain. The analysis of the sequential evolution of all links can reveal the bottlenecks or repetitive links in the service process, help hotel managers identify the space for optimization, and improve service quality by simplifying the process or adding certain key links. By constructing a relationship diagram of the full-cycle service links, hotel managers can clearly see the relationship between the service process and each link, providing an intuitive tool to assist decision-making and improvement. By combining the standardized customer feedback information with the relationship diagram of the full-cycle service links, the links that perform poorly in the entire service chain can be accurately identified. This analysis can help hotel operators quickly locate the parts with low service quality, such as long customer waiting times, poor service attitudes, or frequent facility failures. By evaluating the response quality of each link, the hotel can optimize its service strategy. For example, strengthen training, equipment maintenance, or increase human resources for the poorly performing links, so as to improve the overall service quality. This evaluation process is based on actual customer feedback data, effectively avoiding the subjectivity of traditional reliance on sensory evaluation and manual inspection, and improving the accuracy and execution of quality assessment. Through the analysis of historical customer check-in behaviors, the behavioral patterns of customers at different time periods, seasons, or during specific activities can be discovered, such as preferred room types, consumption habits, and check-in frequencies. These data can help hotels deeply understand customer needs and preferences. Through the trend analysis of historical data, the correlation laws of customer satisfaction can be mined, that is, which factors (such as check-in time, room type, service items, etc.) have the greatest impact on customer satisfaction. This analysis provides important decision-making support for hotel management, helping it foresee potential customer problems and take measures in future services. Through the satisfaction correlation laws, hotels can provide more personalized services according to the behavioral patterns and preferences of customers, further improving customer satisfaction and enhancing customer loyalty.Real-time customer flow analysis can help hotels dynamically understand the distribution of customers (such as the number of check-ins and the flow direction during different time periods), so that they can reasonably allocate manpower and resources during peak hours and optimize customer service. Through the prediction of customer flow change trends, hotels can predict in advance the changes in customer flow during a certain period in the future (such as during holidays or special events), so as to make preparations such as room reservation, catering arrangement, and service staff scheduling, and avoid waste or shortage of resources. Through flow trend prediction, hotels can scientifically manage rooms, schedule personnel, and allocate resources to maximize the use efficiency of resources and service quality. By combining the customer satisfaction correlation law and the customer flow trend prediction information, the change trend of customer satisfaction within a certain period in the future can be predicted. This can help hotels anticipate problems in service quality and take timely measures to avoid customer dissatisfaction. Considering various data such as customer satisfaction, flow changes, and service link response evaluation comprehensively can provide a comprehensive and objective quality assessment for hotel management. This assessment not only helps management decision-making but also improves the work efficiency of employees and the customer service experience. By analyzing the management quality assessment report, hotels can identify weak links in management and make targeted adjustments and optimizations. Whether it is to improve service quality, improve facility management, or adjust customer service strategies, scientific decisions can be made based on the data-driven assessment results.

[0015] In an embodiment of the present invention, refer to Figure 1 , which is a schematic diagram of the step flow of a hotel management quality assessment method based on operation big data according to the present invention. In this example, the steps of the method include: Step S1: Obtain the multi-dimensional operation monitoring logs of the hotel, extract the historical customer feedback information and perform adaptive shielding processing to obtain standardized customer feedback information; In this embodiment, data related to individuals requires user permission or consent when applied to specific products or technologies, and the collection, use, and processing of relevant data need to comply with relevant laws, regulations, and standards in relevant countries and regions. Extract customer historical feedback information from the multi-dimensional operation monitoring logs of the hotel. Data sources include customer satisfaction surveys, online reviews, complaint records, and social media feedback, etc. Ensure that the extracted data covers a reasonable time range, such as feedback records in the past year. Standardize the format of the extracted data to ensure that all feedback information contains necessary fields, such as customer ID, feedback time, feedback content, rating, etc. Perform adaptive masking processing on the extracted customer feedback information to protect customer privacy and sensitive information. This can be achieved through text processing technologies. For example, use regular expressions or natural language processing (NLP) methods to identify and mask personal information (such as names, contact information) and sensitive content (such as specific words in negative reviews). Set masking criteria. For example, completely obscure fields related to personal information, while perform sensitive word replacement or fuzzing on general comment content to ensure the privacy and security of feedback information. Standardize the feedback information after masking processing to ensure the consistency and analyzability of the information. Standardization steps include converting the feedback content into a unified format, unifying the rating standard (such as converting the 5-point scale to a 1-10 point scale), etc. Generate a dataset of standardized customer feedback information, record the processing status of each feedback information, and ensure that the source and processing process of each feedback can be traced.

[0016] Step S2: Analyze the hotel service links and the sequential evolution of all links in the multi-dimensional operation monitoring log of the hotel, and construct a full-cycle service link relationship diagram; In this embodiment, data related to service processes are extracted from the multi-dimensional operation monitoring logs of the hotel. These data include customer check-in, check-out, service requests, complaint handling, customer feedback, and employee service records, etc. Ensure that the entire service cycle is covered, starting from customer reservation to departure, and detailed information of each process is recorded. Clean the extracted data to remove duplicate and invalid information, and ensure that all data formats are consistent. For example, unify the timestamps into the ISO8601 format for subsequent time series analysis. Define each service process of the hotel based on the extracted data. These processes include reservation confirmation, customer check-in, room inspection, customer service (such as catering, cleaning), complaint handling, and customer check-out, etc. The start and end flags of each process should be clearly defined. Classify the service processes. For example, the processes can be classified into front desk service, housekeeping service, and logistics support, etc., for subsequent analysis and relationship diagram construction. Use time series analysis to analyze the sequential evolution of each service process. By calculating the average duration of each process, the transition probability between processes, etc., identify the critical path and bottleneck processes in the service flow. Set parameters. For example, calculate the average processing time and the maximum processing time of each process, and record the transition frequency between processes to analyze the flow trend of customers in the service chain. Based on the analysis results, use a graphical tool to construct a full-cycle service process relationship diagram. In the diagram, each service process is represented by a node, the relationship between processes is represented by an edge, and the thickness of the edge can reflect the transition frequency or processing time. Ensure that the relationship diagram can intuitively reflect the logical relationship and sequential evolution between service processes, and mark the key indicators of each process, such as average processing time, customer satisfaction, etc.

[0017] Step S3: Evaluate the response to abnormal processes for the full-cycle service process relationship diagram based on the standardized customer feedback information to obtain the abnormal process response evaluation value; In this embodiment, it is ensured that the standardized customer feedback information extracted and processed previously has been completed and includes the evaluations of customers in various service links. The feedback information should include scores, comment contents, complaint types, etc., for subsequent analysis. Classify the customer feedback information to identify the feedback related to service links. For example, classify it as check-in experience, service response, room cleaning, food and beverage service, etc., in order to more accurately evaluate the performance of each link. Set the definition of abnormal links according to industry standards and the service objectives of this hotel. For example, it is set that if the customer satisfaction score of a certain service link is lower than 3 points (out of 5 points) or the complaint rate exceeds a certain threshold, then this link is regarded as an abnormal link. Determine the evaluation parameters, such as the number of customer feedbacks, link processing time, customer complaint ratio, etc., for comprehensive evaluation. Conduct a response evaluation for each link in the full-cycle service link relationship diagram. Use the standardized customer feedback information to calculate the customer satisfaction score and complaint ratio of each service link. Combine the processing time of the link and customer feedback to evaluate the response ability of each link. For example, if the response time of a certain link is long and the customer score is low, the response evaluation value of this link should be marked as abnormal. Calculate the abnormal link response evaluation value for each service link according to the set standards. A weighted scoring system can be used to combine customer satisfaction, complaint ratio, and processing time to generate a comprehensive evaluation value. Set the evaluation formula, for example: Abnormal response evaluation value = (Customer satisfaction score × Weight 1) + (Complaint ratio × Weight 2) + (Processing time × Weight 3). The weights are set according to actual business needs to reflect the importance of different factors.

[0018] Step S4: Analyze the historical customer check-in behaviors in the hotel's multi-dimensional operation monitoring logs, and then mine the satisfaction correlation trends to generate customer satisfaction correlation rules; In this embodiment, historical customer check-in behavior data is extracted from the multi-dimensional operation monitoring logs of the hotel, including the check-in time, length of stay, room type selection, service usage, customer feedback, and satisfaction scores of customers. Ensure that the data coverage is extensive, including at least historical data for the past year, in order to conduct effective trend analysis. Clean the data by removing duplicate records and invalid data to ensure data quality. Pay special attention to the unification of time formats and the handling of missing values for subsequent analysis. Use statistical analysis methods to conduct descriptive analysis of customer check-in behavior. The average length of stay of customers, the occupancy rate of different room types, and the check-in distribution of customers at different time periods can be calculated. Set analysis parameters. For example, group check-in behavior by quarter or month to observe the changing trends of customer behavior at different time periods. At the same time, use clustering analysis methods to divide customers into different groups (such as business customers, leisure customers, etc.) to better understand the check-in preferences of different types of customers. Conduct correlation analysis on the customer check-in behavior data and satisfaction score data. Use correlation analysis (such as the Pearson correlation coefficient) to confirm the relationship between different check-in behaviors (such as room type, length of stay, service usage) and customer satisfaction. Set the threshold for correlation analysis. For example, a relationship with a correlation coefficient greater than 0.5 is considered significantly correlated. Record the correlation between each behavior and satisfaction to identify the key factors affecting customer satisfaction. According to the results of the correlation analysis, extract the correlation rules of customer satisfaction. For example, it is found that the customer satisfaction of those who use room service is significantly higher than those who do not, or the customer satisfaction of those with a long length of stay is generally higher. Visualize these rules. For example, use charts to show the relationship between different check-in behaviors and satisfaction scores for intuitive understanding.

[0019] Step S5: Based on the multi-dimensional operation monitoring logs of the hotel, conduct real-time customer flow analysis and prediction of customer flow change trends to generate customer flow trend prediction information; In this embodiment, real-time customer flow data is extracted from the multi-dimensional operation monitoring logs of the hotel, including customer arrival time, departure time, reservation status, customer type (such as business, leisure), length of stay, etc. Ensure the integrity and timeliness of the data so as to reflect the current customer flow status. Clean the data, remove duplicate and invalid records, and ensure that all time data formats are consistent. Pay special attention to the timestamps of the data to ensure accurate analysis of the timeliness of the customer flow. Use descriptive statistical analysis methods to analyze the real-time customer flow data to understand the current customer flow situation. Calculate key indicators such as the customer arrival rate, departure rate, and number of customers staying during the current time period. Set key parameters. For example, the customer arrival rate can be defined as the number of customers arriving per hour, and the departure rate as the number of customers leaving per hour. Compare these data with historical averages to identify abnormal fluctuations in the current customer flow. Based on the results of the real-time customer flow analysis, apply time series analysis methods (such as ARIMA model or exponential smoothing method) to predict the change trend of the customer flow. Use historical data to build a model to predict the customer flow in the next few hours, days or weeks. In the model, consider seasonal and cyclical factors affecting the customer flow, such as the change trend of the customer flow during holidays and weekends. By introducing these factors, improve the accuracy of the prediction model. According to the prediction results, generate customer flow trend prediction information. This information should include the expected future customer flow, potential peak hours, and the amplitude of change in customer flow. Through visualization tools, present the prediction results in the form of charts. For example, a line chart shows the change trend of the customer flow in the next few days, enabling management to intuitively understand the dynamic changes in the customer flow.

[0020] Step S6: Predict the customer satisfaction situation according to the customer satisfaction association rule and the customer flow trend prediction information, and comprehensively evaluate the hotel management quality according to the abnormal link response evaluation value to obtain a hotel management quality evaluation report.

[0021] In this embodiment, using the previously extracted correlation rules of customer satisfaction and the predicted information of customer traffic trends, the prediction of the customer satisfaction situation is prepared. First, review the customer satisfaction rules, determine which check-in behaviors and service links have a significant impact on customer satisfaction, and combine these factors with the changes in customer traffic. Set the main input variables of the prediction model, including customer traffic, check-in behaviors of different customer types, historical satisfaction scores, etc., for subsequent prediction analysis. Apply multiple regression analysis or machine learning algorithms (such as random forest, support vector machine, etc.) to predict the customer satisfaction situation. These models can make full use of the complex relationship between customer traffic and satisfaction to predict the future customer satisfaction level. Introduce the evaluation value of the abnormal link response into the model as a key influencing factor. This value can reflect the quality of the service link and directly affect customer satisfaction. The training process of the model will include training with historical data to improve the prediction accuracy. Based on the prediction of the customer satisfaction situation, combine the evaluation value of the abnormal link response to conduct a comprehensive hotel management quality assessment. Set a comprehensive evaluation formula, for example: Hotel management quality evaluation value = Predicted value of customer satisfaction × Weight 1 + Evaluation value of abnormal link response × Weight 2. Through weighted calculation, comprehensively consider the impacts of customer satisfaction and service quality to generate an evaluation value reflecting the overall hotel management quality. When setting the weights, they can be adjusted according to the actual operation objectives and historical data. Organize the prediction results of the customer satisfaction situation and the hotel management quality evaluation value to generate a hotel management quality evaluation report. The report should include the prediction of future customer satisfaction, the management quality evaluation value, and potential improvement suggestions. The analysis results should clearly point out the main factors affecting customer satisfaction and the weak links in management quality, and provide targeted improvement measures. For example, if the evaluation value of the abnormal response of a certain service link is high, it is recommended to strengthen employee training and optimize the service process. The generated hotel management quality evaluation report should include charts and data visualization for the management to quickly understand and make decisions. The report should clearly present the trend of customer satisfaction, the management quality evaluation results and their changing factors. Record the parameters, methods, assumptions and their rationality in the evaluation process to provide an important basis for subsequent hotel management decisions and ensure the transparency and reproducibility of the report.

[0022] In this embodiment, refer to Figure 2 , which is the schematic diagram of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Obtain the hotel's multi-dimensional operation monitoring logs; perform outlier filtering and optimization on the hotel's multi-dimensional operation monitoring logs to construct outlier-optimized monitoring logs; Step S12: Extract the customer's historical feedback information according to the outlier-optimized monitoring logs; Step S13: Perform in-depth semantic parsing on the customer's historical feedback information to generate in-depth semantic features of the feedback information; Step S14: Based on the in-depth semantic features of the feedback information, perform intelligent detection of malicious evaluations and extract malicious evaluation information; Step S15: Perform adaptive shielding processing on the malicious evaluation information to obtain standardized customer feedback information.

[0023] In this embodiment, after obtaining the authorization from the hotel and the customer, multi-dimensional monitoring logs are extracted from the hotel's operation management system. These logs generally include data such as customer check-in information, consumption records, service evaluations, room status, activity arrangements, etc. Ensure that the obtained data covers all relevant dimensions for subsequent analysis. After the data is extracted, a preliminary check is carried out to ensure the integrity and accuracy of the data. Mark the missing values and outliers to prepare for subsequent outlier processing. Format the collected monitoring logs to unify the data structure for subsequent processing. This involves operations such as data type conversion, field renaming, and missing value filling. Use data cleaning tools (such as the Pandas library in Python or Excel) to handle errors and inconsistencies in the data to ensure that the data quality meets the analysis standards. Select a suitable outlier detection method according to the characteristics of the data. Common methods include the Z-score, IQR (Interquartile Range) method, LOF (Local Outlier Factor), etc. These methods can effectively identify outliers that are significantly different from the normal data distribution. When selecting a method, consider the distribution characteristics of the data and business requirements to ensure that the selected method can accurately capture outliers. Use the selected outlier detection method to analyze the monitoring logs, identify the outliers and mark them. For the detected outliers, decide whether to delete, replace, or retain them for subsequent analysis. Record the outliers in detail and analyze their causes to facilitate future optimization of operation strategies. Ensure the transparency and traceability of outlier processing. After outlier filtering, organize the optimized monitoring logs. This log should contain the processed valid data to ensure the integrity and consistency of the data. Save the constructed outlier-optimized monitoring logs to the database to provide a reliable data basis for subsequent steps. Identify the fields related to customer feedback from the outlier-optimized monitoring logs. Generally, it includes information such as customer evaluations, ratings, suggestions, and complaints. These fields will be used for subsequent analysis and processing. According to business requirements, it is necessary to classify the feedback information, such as positive feedback, negative feedback, and neutral feedback, for subsequent semantic parsing. Use data processing tools (such as SQL queries or data analysis software) to extract customer feedback information. Ensure that the extracted data covers all relevant dimensions and remove duplicate and invalid records. Organize the extracted data to form a structured data table, including information such as customer ID, feedback content, timestamp, etc., to prepare for subsequent in-depth analysis. Select a suitable deep learning technology for semantic parsing. Common models include BERT, GPT, etc. Selecting these models can effectively capture the context information in the text and improve the accuracy of parsing. Considering the specific requirements of semantic parsing, it is necessary to fine-tune the model to adapt to the specific language and expression methods in the hotel industry. Use the labeled customer feedback data to train the model to ensure that the model can learn effective semantic features. During the training process, monitor the loss function and accuracy of the model for necessary adjustments.The performance of the model is evaluated using the cross - validation method to ensure the generalization ability of the model on unseen data. By adjusting hyperparameters and training strategies, the performance of the model is optimized. The trained model is applied to the customer historical feedback information to extract deep semantic features. The features include sentiment tendency, topic vocabulary, and context relationships, etc. The semantic features of each feedback message are recorded for subsequent analysis and report generation. An appropriate malicious review detection algorithm is selected according to the characteristics of the feedback information. Common methods include rule - based detection, machine - learning classification algorithms (such as SVM, random forest), and deep - learning models (such as LSTM). Combining business requirements and data characteristics, the most suitable detection strategy is determined to ensure that the model can effectively identify malicious reviews. The detection model is trained using labeled malicious and normal review data to ensure that the model can learn effective features. During the training process, monitor the performance metrics of the model (such as accuracy, recall) for adjustment. The generalization ability of the model is evaluated using the validation set to ensure that it can effectively identify malicious reviews in actual applications. The trained malicious review detection model is applied to the extracted customer feedback information to identify potential malicious reviews. The review information marked as malicious is recorded to ensure the traceability of this information. Analyze the identified malicious review information to understand its characteristics and patterns, providing a basis for subsequent processing and optimization. According to business requirements and the characteristics of malicious reviews, an adaptive shielding processing standard is formulated. This includes directly deleting malicious reviews, marking them as pending review, or modifying them. Ensure the transparency and consistency of the processing standard for subsequent review and management. According to the formulated standard, process the identified malicious review information. Ensure that the processing process is automated to improve efficiency and accuracy. Record the decision basis and results of each processing for subsequent review and analysis. Organize the processed customer feedback information into a standardized format, including valid customer reviews and meaningful feedback content. Ensure the integrity and consistency of the information. Record the processed feedback information for subsequent analysis and report generation. Generate a report on the standardized customer feedback information, which should include the processing process, results, and statistical analysis of the feedback information. This will provide an important basis for the hotel's operation and management. Summarize the lessons learned from the entire process to provide references and suggestions for subsequent customer feedback management and operation optimization.

[0024] In this embodiment, the specific steps of step S12 are as follows: Extract parametric data according to the hotel multi - dimensional operation monitoring log; Calculate the normal parameter range of the parametric data; Detect abnormal outlier data points according to the normal parameter range and mark multiple abnormal outlier data points; Evaluate the repairable data for multiple abnormal outlier data points to obtain repairable outlier data points and non - repairable outlier data points; Calculate the average value of the parametric data; Perform average value interpolation on the repairable outlier data points according to the average value to obtain average value interpolation optimized data points; Perform outlier filtering on the irreparable outlier data points, and perform standardized reconstruction on the hotel multi-dimensional operation monitoring log based on the average value interpolation optimized data points to construct an outlier optimized monitoring log.

[0025] In this embodiment, each data source in the hotel operation monitoring log is identified, including the room occupancy rate, customer evaluation, energy consumption, employee working hours, financial data, etc. These data usually exist in a structured or semi-structured form in a database or log file. Collect the required monitoring log data to ensure the integrity and consistency of the data. Usually, the data is extracted from multiple systems, such as a property management system (PMS), a customer relationship management system (CRM), and an energy consumption monitoring system. Clean the extracted data to remove duplicate records, missing values, and irrelevant information to ensure the accuracy of subsequent analysis. Data cleaning tools or scripts can be used to process it. Organize the data format to ensure that all parametric data adopts a unified format, for example, convert the time stamp to a standard format and ensure that the units of numerical data are consistent. According to the key indicators of hotel operation, convert the cleaned data into parametric data. Define the indicators and dimensions of the parametric data, such as converting the occupancy rate, customer score, revenue per available room (RevPAR), etc. into analyzable parameters. Store the parametric data in a data frame (such as a Pandas DataFrame) to prepare for subsequent statistical analysis and anomaly detection. Perform statistical analysis on the extracted parametric data to calculate the basic statistical characteristics of each parameter, including the mean, standard deviation, minimum value, maximum value, and quartiles, etc. These statistical characteristics provide a basis for subsequent anomaly detection.

[0026] Use descriptive statistical analysis methods to ensure a comprehensive understanding of the data distribution, identify the normal range and outliers of the data. Based on the results of the statistical analysis, set the normal parameter range for each parameter. For example, the normal range can be defined as the mean ± 2 standard deviations, or the upper and lower limits can be defined according to quartiles. Record the normal range of each parameter for subsequent outlier detection. Organize the calculation results and generate a report on the normal parameter range. The report should include the statistical characteristics of each parameter and its normal range to ensure the integrity and easy understanding of the information. Record the parameters and assumptions used in the analysis process to provide a basis for subsequent outlier detection and data processing. Select a suitable outlier detection method, such as the Z-score method, the IQR method, or an outlier detection algorithm based on machine learning. These methods can effectively identify data points that exceed the set normal range. Determine the identification criteria for outliers, such as the absolute value of the Z-score being greater than 3 or the data point falling outside the upper and lower limits of the IQR. Apply the selected outlier detection method to the parameterized data to identify outlier data points. Mark all data points that do not conform to the normal parameter range according to the set criteria. Record the detailed information of the outlier data points, including the time they are located, the parameter name, and their specific values. Set the reparability assessment criteria according to business requirements and data characteristics. For example, it can be judged based on the context information of the data, the degree of missingness, and its impact on the overall analysis. Identify the characteristics of repairable data points, such as whether the data point is a temporary error and whether it can be filled in by interpolation or other methods.

[0027] Analyze each of the marked outlier data points one by one to determine whether it meets the reparability criteria. Classify and record the repairable data points and the non-repairable data points. Record the evaluation results of each data point, including its repair suggestions and processing methods for subsequent operations. Calculate the mean of the cleaned parameterized data to ensure that the calculation covers all relevant parameters. Use appropriate statistical tools to calculate the mean of each parameter. Record the mean of each parameter for subsequent interpolation processing. Organize the mean calculation results and generate a report on the mean of the parameterized data. The report should include the mean of all parameters and its calculation basis to ensure the integrity and understandability of the information. Record the parameters and methods used in the calculation process to provide a basis for subsequent data processing. Select a suitable interpolation method to process the repairable outlier data points. Commonly used methods include linear interpolation, Lagrange interpolation, or spline interpolation, etc. Select an interpolation method suitable for the data characteristics. Set the parameters of the interpolation according to the distribution and characteristics of the data, such as the interpolation interval and the weights of nearby data points. Perform interpolation processing on the repairable outlier data points and replace them with the interpolated data obtained through the mean calculation. Ensure that the interpolated data is consistent with the overall trend of the original data. Record the detailed information of each interpolated optimized data point, including the original value, the interpolated value, and its calculation basis.

[0028] Filter the irreparable outlier data points, delete or mark these data points to ensure the accuracy of subsequent analysis. You can choose to directly delete these data points or replace them with NaN values. Record the results of the filtering process, including information on the deleted or marked data points, for subsequent analysis and auditing. Based on the interpolated repairable data points and the data after outlier filtering, perform a standardized reconstruction of the hotel's multi-dimensional operation monitoring log. Normalize all parameters to the same scale for easy comparison and analysis. Ensure that the reconstructed monitoring log can accurately reflect the hotel's operation status and reduce the misleading caused by outlier data. Organize the data of the reconstructed monitoring log to generate an outlier optimization monitoring log report. The report should include the statistical characteristics of the monitoring log, the processing process, and its improvement of the overall data quality. Record the parameters and methods used in the reconstruction process to ensure the transparency and traceability of the results, providing a basis for subsequent data analysis and decision-making.

[0029] In this embodiment, refer to Figure 3 , which is a schematic diagram of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: Analyze the hotel service links of the hotel's multi-dimensional operation monitoring log to obtain multiple hotel service link information; Step S22: Conduct a logical correlation analysis before and after the multiple hotel service link information to obtain the logical correlation characteristics between the links; Step S23: Analyze the trigger conditions of each link based on the multiple hotel service link information to generate the trigger condition for each link; Step S24: Perform a sequential evolution of all links according to the trigger condition of each link and the logical correlation characteristics between the links to construct a full-cycle service link relationship diagram.

[0030] In this embodiment, a suitable logical association analysis method is selected, such as Association Rule Learning or Sequential Pattern Mining. These methods can effectively identify the sequential relationship between processes. Set the analysis parameters, such as support and confidence thresholds, to ensure that the identified association relationships are statistically significant. Use the selected method to analyze the organized service process information and identify the logical association features between processes. For example, determine whether the "room cleaning" process always follows the "customer registration" process, and record the frequency and strength of this relationship. Visualize the analysis results to generate a relationship diagram between processes, which helps to identify key service processes and their impacts. Based on the service process information, clarify the trigger conditions for each process. The trigger conditions include time factors, customer behaviors (such as occupancy rate, reservation status), external factors (such as weather), etc. Set the key performance indicators (KPIs) for each service process, such as customer satisfaction, response time, etc., to help evaluate the effectiveness of the trigger conditions. Analyze each service process one by one to identify the specific conditions that affect the process start. For example, through the analysis of the customer registration process, it is found that the customer's arrival time and advance reservation status are the two main conditions affecting the registration speed. Record the trigger conditions for each process and their related data to ensure a full understanding of the interactions between processes. Based on the analysis results in steps S22 and S23, design a full-process sequential evolution model. This model should be able to comprehensively consider the trigger conditions and logical association features of each process and reflect the overall structure of the service process. Determine the input parameters of the model, including the trigger conditions, logical association features, and service time of each process, to ensure the accuracy of the model. Through a modeling tool or simulation software, implement the full-process sequential evolution. Simulate the hotel service process and observe the operation of each process under different trigger conditions. Generate a relationship diagram of the full-cycle service processes, which clearly shows the relationships and processes between each process, helping to identify key processes and potential bottlenecks. Organize the results of the full-process sequential evolution to generate a report on the relationship diagram of the full-cycle service processes. The report should include the logical relationship diagram of the service processes, the process trigger conditions, and their impacts on service quality. Record the parameters and assumptions in the modeling and evolution processes to ensure the transparency and traceability of the results, providing a basis for subsequent service process optimization.

[0031] In this embodiment, refer to Figure 4 , which is a schematic diagram of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: Identify historical hotel feedback problems from the standardized customer feedback information and extract the historical hotel feedback problems; Step S32: Classify the historical hotel feedback problems to obtain the problem type of each feedback; Step S33: Based on each feedback problem type, trace the problem links in the full-cycle service link diagram and extract abnormal service links; Step S34: Calculate the problem-solving response speed for historical hotel feedback problems to obtain the problem-solving response speed; Step S35: Evaluate the response of abnormal service links based on the problem-solving response speed to obtain the abnormal link response evaluation value.

[0032] In this embodiment, standardized customer feedback information data is collected, including customer satisfaction surveys, online evaluations, and complaint records. Ensure the integrity and consistency of the data for in-depth analysis. Clean the data, remove redundant information and invalid records, and standardize the format, such as unifying feedback information from different sources into a standardized text format. Text mining technology (such as natural language processing) is used to analyze customer feedback and identify recurring problems. This can be achieved through methods such as keyword extraction, topic modeling, or sentiment analysis. The identified feedback issues include service quality, cleanliness, facility maintenance, etc., ensuring that all issues related to customer experience are covered. According to the characteristics of historical feedback issues, problem classification standards are set. These standards include the nature of the problem, the service links affected, and the severity of customer perception. The classification types include service problems, facility problems, hygiene problems, and other problems, ensuring that each problem can be accurately classified. Use classification algorithms (such as decision trees, support vector machines, or deep learning) to automatically classify historical feedback issues. Compare the feedback issues with the set classification standards to identify the type of each problem. For issues that cannot be automatically classified, manual review and adjustment can be performed to ensure the accuracy of the classification. Arrange the classification results and generate a report for each type of feedback problem. The report should include the number and proportion of each type of problem and its impact on overall customer satisfaction. Record the data and assumptions in the classification process to provide a basis for subsequent service link traceability analysis. Based on the full-cycle service link relationship diagram constructed in step S22, design a problem link traceability model. The model should be able to associate each feedback problem with a specific service link. Determine the data input format, including the problem type, service link and its logical relationship to ensure the accuracy of the model. Use the traceability model to analyze each feedback problem and determine its corresponding service link. Identify the abnormal service links related to customer feedback problems. For example, if the guest complains about insufficient cleaning, the performance of the cleaning service link needs to be tracked. Extract abnormal service links and record the key links that affect customer satisfaction to ensure the pertinence of subsequent analysis. Collect solutions and response time data related to historical feedback problems. These data should include the problem submission time, solution time and its corresponding service link. Clean the data to ensure consistency in time format, remove invalid records, and ensure data accuracy. Calculate the solution response speed of each historical feedback problem, usually through the formula: response speed = solution time - submission time. Ensure that the calculation covers all relevant issues and record the distribution of response time. Count the basic characteristics of response speed, such as mean, standard deviation and quantile, for subsequent evaluation. Based on the abnormal service links extracted in step S33 and the response speed data in step S34, establish an abnormal link response evaluation model. The model should be able to comprehensively consider the importance of response speed and feedback issues. Determine evaluation indicators, such as a weighted combination of response speed and the severity of customer feedback issues, to ensure the objectivity of the evaluation results.Conduct a response evaluation for each abnormal service link and calculate its response evaluation value. The evaluation value can be calculated by weighted average or other methods to ensure that it reflects the actual performance of the link. Record the data and assumptions during the evaluation process and identify the links with poor performance for subsequent improvement.

[0033] In this embodiment, step S4 includes the following steps: Step S41: Analyze the historical customer check-in behavior in the hotel multi-dimensional operation monitoring log and extract the historical customer check-in behavior data; Step S42: Conduct a statistical analysis of the historical occupancy rate for the historical customer check-in behavior data and extract the historical customer occupancy rate; Step S43: Extract the check-in timestamps of the historical customer check-in behavior data; Step S44: Calculate the customer satisfaction of hotel services for the same time period based on the check-in timestamps in the hotel multi-dimensional operation monitoring log and extract the customer satisfaction for the same time period; Step S45: Mine the satisfaction correlation trend for the customer satisfaction in the same time period based on the historical customer occupancy rate, thereby generating the customer satisfaction correlation rule.

[0034] In this embodiment, the calculation formula for determining the occupancy rate is generally: Occupancy Rate = (Number of Actually Occupied Rooms / Number of Rentable Rooms) × 100%. The number of rentable rooms is the total number of rooms in the hotel during that time period minus the number of rooms that cannot be rented due to maintenance or other reasons. Set a time range, for example, statistics are carried out monthly or weekly to facilitate observing the change trend of the occupancy rate. Use historical customer check-in behavior data to calculate the occupancy rate month by month or week by week. Compare the number of actually occupied rooms with the number of rentable rooms and record the occupancy rate for each time period. Calculate the basic statistical characteristics of the occupancy rate, including mean, standard deviation, and range of change, etc., to help understand the fluctuations of the occupancy rate. Extract check-in timestamps from the historical customer check-in behavior data and ensure that the format of the timestamps is consistent, for example, unified as the ISO8601 format (YYYY - MM - DD HH:MM:SS). Ensure the accuracy of the timestamp data, remove missing or invalid timestamp records, and ensure the data quality for subsequent analysis. Analyze the extracted timestamps to identify peak check-in time periods for customers, such as weekends, holidays, or specific seasons. Data aggregation methods can be used to conduct statistics by hour, day, or week. Record the number of customer check-ins in different time periods to facilitate subsequent customer satisfaction calculation and trend analysis. Organize the extracted check-in timestamp data to generate a timestamp analysis report. The report should include peak check-in periods for customers, time distribution characteristics, and their impact on service provision. Record the parameters and assumptions in the analysis process to provide a basis for subsequent customer satisfaction analysis. Extract data related to customer satisfaction from the hotel's multi-dimensional operation monitoring logs, including feedback scores, customer comments, processing times, etc. Ensure that the data covers the time period corresponding to the check-in timestamps. Clean the satisfaction data to ensure the standardization of the scores (for example, unify the scores to 1 - 5 points), and remove invalid and missing feedback records. According to the extracted check-in timestamps, aggregate the customer satisfaction data within the same time period and calculate the average satisfaction score for that time period. For example, the average satisfaction of customers checking in on weekends can be calculated. Statistically analyze the satisfaction distribution characteristics of each time period, such as mean, standard deviation, and range of satisfaction change, etc., for subsequent analysis. Select an appropriate association analysis method, such as Pearson correlation coefficient or Spearman rank correlation coefficient, to evaluate the relationship between historical customer occupancy rate and customer satisfaction. Set analysis parameters, such as a correlation threshold (for example, a correlation coefficient ≥ 0.5 is a significant association), and determine the time range of the data. Use statistical analysis software to conduct an association analysis on the historical customer occupancy rate and customer satisfaction in the same time period and calculate the correlation between the two. Identify time periods with significant associations and draw scatter plots or trend charts to visualize the relationship between customer satisfaction and occupancy rate. Organize the association analysis results to generate a customer satisfaction association rule report. The report should include the correlation analysis between occupancy rate and satisfaction, trend charts, and their impact on hotel operation decisions.Record the data and assumptions during the analysis process to provide a basis for subsequent formulation of operation strategies and improvement of customer satisfaction.

[0035] In this embodiment, step S5 includes the following steps: Step S51: Conduct real-time customer flow analysis based on the hotel's multi-dimensional operation monitoring log to obtain the hotel's real-time customer flow data; Step S52: Calculate the actual number of check-ins at multiple time periods for the hotel's real-time customer flow data to generate the actual number of customers in multiple time periods; Step S53: Conduct customer flow time-series fluctuation analysis on the actual number of customers in multiple time periods to generate customer flow time-series fluctuation characteristics; Step S54: Predict the customer flow change trend for the customer flow time-series fluctuation characteristics to generate customer flow trend prediction information.

[0036] In this embodiment, a data analysis tool is used to calculate the current customer flow in real time. For example, count the number of customers arriving within a specific time period (such as every hour or every minute), record the check-in and check-out times to ensure that the customer flow can be reflected in real time. Set parameters such as the time window (for example, update the flow data every 10 minutes) to monitor the changes in customer flow in real time. Organize the real-time customer flow data and generate a customer flow analysis report. The report should include the statistical data of real-time customer flow, the flow change trend chart, and its impact on hotel operations. Record the parameters and assumptions used in the analysis process to provide a basis for subsequent customer flow period calculation and fluctuation analysis. According to business requirements, divide the customer flow data into multiple time periods, for example, count by hour, day, or week. The length of each time period should be reasonably set according to the customer check-in pattern and hotel operation characteristics. Set parameters such as the start and end times of the time period to ensure that the data in each time period can accurately reflect the customer flow. Statistically analyze the customer flow data within each time period and calculate the actual number of check-ins. This includes the number of customers who checked in during that time period and the number of customers who are currently staying. Record the number of check-ins for each time period and calculate the corresponding occupancy rate (for example, the ratio of the actual number of check-ins to the number of available rooms). Select appropriate statistical analysis methods, such as the moving average method, weighted moving average, or exponential smoothing method, to analyze the fluctuation characteristics of the actual customer numbers. Set parameters such as the window size (for example, a sliding window of 3 time periods). Determine the time range of the analysis to ensure that all relevant time period data can be covered to facilitate observing the changes in customer flow. Use the selected analysis method to perform a fluctuation analysis on the actual customer numbers for multiple time periods. Calculate the fluctuation amplitude, fluctuation frequency, and change trend to identify peak and trough periods. Record the results of the fluctuation analysis. For example, if the customer flow fluctuation amplitude in a certain time period reaches 20%, it indicates that the customer liquidity is strong during that time period. Select a suitable time series prediction model, such as ARIMA (Autoregressive Integrated Moving Average Model), SARIMA (Seasonal Autoregressive Integrated Moving Average Model), or Prophet model. Consider the characteristics and periodicity of the data when making the selection. Set the parameters of the model, such as the seasonal period (for example, week, month) and the time window of historical data (for example, data from the past 12 weeks). Use the selected prediction model to predict the trend of the customer flow time series fluctuation characteristics. When training the model, use historical customer flow data and verify and adjust the model to ensure the accuracy of the prediction. Generate prediction data for future customer flow, for example, predict the weekly customer flow changes for the next month and record the prediction confidence interval. Organize the prediction results and generate a customer flow trend prediction report. The report should include the prediction data for future flow, the prediction trend chart, and an analysis of its impact on hotel business decisions. Record the parameters and assumptions used in the prediction process to provide a basis for subsequent operation strategy formulation and customer flow management.

[0037] In this embodiment, step S6 includes the following steps: Step S61: Perform real-time monitoring and analysis of the work of hotel service staff based on the hotel's multi-dimensional operation monitoring log, and extract each personnel work monitoring parameter; Step S62: Calculate the peak value of the whole-link service operation according to each personnel work monitoring parameter, so as to generate the peak value of the whole-link operation; Step S63: Based on the peak value of the whole-link operation, perform customer service capacity scheduling analysis on the customer flow trend prediction information, so as to generate the whole-link customer service capacity scheduling value; Step S64: Based on the customer satisfaction correlation law and the whole-link customer service capacity scheduling value, predict the customer satisfaction situation, so as to generate a real-time customer satisfaction situation prediction map; Step S65: Conduct a comprehensive hotel management quality assessment on the abnormal link response evaluation value and the real-time customer satisfaction situation prediction map to obtain a hotel management quality assessment report.

[0038] In this embodiment, data related to the work of service staff is extracted from the hotel's multi-dimensional operation monitoring log, including the employee number, working hours, service category, customer feedback, work efficiency, etc. of each employee. These data are usually updated in real time through the hotel management system. Ensure the integrity and consistency of the data, remove duplicate records and invalid data, and pay special attention to the time range covered by the data for real-time monitoring of the work status of service staff. Analyze the extracted employee work data to identify key work monitoring parameters, such as: the service duration of each employee, the customer satisfaction score, the number of service items completed, the number of customer complaints, etc. Set the standard values of the monitoring parameters. For example, the service duration should be within the preset range for each shift (such as each employee serves 6 hours per day) for subsequent performance evaluation. Determine the calculation standard for the peak value of the whole-link service operation, generally using the combination of service efficiency and customer flow. For example, set the peak value calculation formula as: operation peak value = (number of served customers / service duration) × customer satisfaction. Set the time period (such as every hour, daily or weekly) to facilitate the calculation of service peaks in different time periods. Use the extracted service staff work monitoring parameters to calculate the operation peak value of each service link one by one. Combine the number of served customers, service duration and customer satisfaction of each link to finally obtain the operation peak value data of the whole link. Record the operation peak value of each time period and analyze its change trend. For example, the operation peak value of a certain period reaches 80%, indicating that the service capacity of this period reaches a high point. According to the peak value of the whole-link operation, set the standard for customer service capacity scheduling analysis. For example, the service capacity scheduling value = (actual customer flow / operation peak value) × 100%. Set the threshold value, such as the scheduling value should be between 70% - 90% as normal. Determine the analysis time period to facilitate the comparison of the predicted customer flow with the operation peak value.

[0039] Compare the customer traffic trend prediction information for each time period with the peak value of the overall operation, and calculate the customer service capacity scheduling value for each link. Record the results of the scheduling analysis. For example, if the customer traffic in a certain time period is 100 people and the operation peak value is 120 people, then the scheduling value is 83.3%, indicating that the service capacity is reasonable. Using the correlation law of customer satisfaction analyzed in the early stage, set the main factors affecting customer satisfaction, such as service response time, service quality, employee attitude, etc. Determine the weight of each factor. For example, service quality accounts for 60%, response time accounts for 30%, and employee attitude accounts for 10% for quantitative analysis. According to the overall customer service capacity scheduling value and the factors affecting customer satisfaction, conduct a prediction of the customer satisfaction situation. Regression analysis or time series analysis methods can be used to predict the future customer satisfaction score. Generate future customer satisfaction prediction data. For example, predict that the customer satisfaction score next week is 4.2 points (out of 5 points) and record the relevant data. Organize the prediction results and generate a real-time customer satisfaction situation prediction chart. The chart should include the future customer satisfaction trend, influencing factors and their changes. Record the parameters and assumptions in the prediction process to provide a basis for subsequent hotel management quality evaluation. Determine the criteria for hotel management quality evaluation, and comprehensively consider the abnormal link response evaluation value and the customer satisfaction situation prediction. Set an evaluation formula. For example: management quality score = (customer satisfaction score - abnormal response evaluation value) × weight coefficient. Set the weight coefficient. For example, the customer satisfaction score accounts for 70% and the abnormal response evaluation value accounts for 30% to quantify the evaluation results. Conduct a comprehensive management quality evaluation for each service link and calculate the management quality score. Record the evaluation results for each link. For example, if the customer satisfaction score is 4.5 points and the abnormal link response evaluation value is 3.0, then the management quality score is 4.0. Record the data and assumptions in the evaluation process to ensure the objectivity and repeatability of the evaluation results. Organize the evaluation results and generate a hotel management quality evaluation report. The report should include the management quality scores of each service link, factor analysis and its impact on the overall hotel operation. Record the parameters and methods in the evaluation process to provide a basis for subsequent management strategy optimization and customer satisfaction improvement.

[0040] In this embodiment, the specific steps of step S62 are as follows: Quantify the work efficiency of each personnel work monitoring parameter to generate the work efficiency quantification value of each personnel; Calculate the service response speed according to each personnel work monitoring parameter and extract the service response speed of each personnel; Extract customer complaint handling information from the hotel multi-dimensional operation monitoring log; Based on the full-cycle service link relationship diagram, conduct a service link analysis for each personnel to generate the service link of each personnel; Evaluate the service operation capabilities of each individual one by one based on the work efficiency quantification value of each person, the service response speed of each person, and the customer complaint handling letter to generate the service operation capability value of each person; Calculate the peak value of the service operation in the whole process for the service operation capability value of each person according to the service links of each person, so as to generate the peak value of the whole process operation.

[0041] In this embodiment, work monitoring parameters of each service staff are extracted from the multi-dimensional operation monitoring logs of the hotel, including service time, the number of service items completed, customer feedback, etc. These data can be obtained in real time through the Property Management System (PMS). Ensure the integrity and consistency of the data, remove invalid records, and ensure that the work parameters of each employee cover a complete work cycle (such as daily working hours). Determine the calculation criteria for work efficiency. For example, work efficiency can be defined as: Work Efficiency = Number of Service Items Completed / Service Time (hours). Set a reasonable benchmark value. For example, it is expected to complete at least 3 service items per hour. Set the outlier judgment criteria. For example, if an employee's work efficiency is less than 1.5 service items per hour, it is marked as inefficient. Use the extracted work parameters to calculate the work efficiency quantification value of each employee one by one. Record the efficiency value of each employee and compare it with the set benchmark value. Determine the calculation criteria for service response speed. For example: Service Response Speed = Time from Customer Request to Service Start (minutes). Ensure that the data is sourced from real-time customer feedback and service records. Set the ideal range of response speed. For example, the response time for high-quality service should be controlled within 5 minutes. Extract the customer request time and service start time from the work monitoring parameters, and calculate the service response speed of each employee. Ensure that the time format of the data is consistent to avoid analysis errors caused by format differences. For missing request records, fill in the data or mark them to ensure the accuracy of the calculation. Use the extracted data to calculate the service response speed of each employee one by one and record this data. Analyze the distribution of response speeds to identify efficient and inefficient response cases. Extract customer complaint handling information from the hotel operation monitoring logs, including complaint type, handling time, handling result, responsible person, etc. Ensure that the extracted data covers a certain historical time range, such as complaint records in the past three months. Clean the data to ensure the integrity and consistency of the complaint records, and filter out duplicate and invalid records. Determine the calculation criteria for customer complaint handling efficiency. For example: Handling Efficiency = Number of Successfully Handled Complaints / Total Number of Complaints. Set an ideal handling efficiency target. For example, the handling efficiency should reach over 85%. Record the statistical data of handling time and analyze the differences in handling time for different types of complaints. Analyze the extracted complaint handling information to identify the main complaint types and their handling effects, and record the associated information with each employee. Mark the handling status of the complaint according to the handling result (such as resolved, unresolved). Generate a customer complaint handling information report, which should include the distribution of complaint types, handling efficiency, and an analysis of the impact on customer satisfaction. Based on the full-cycle service link relationship diagram of the hotel, clarify the start and end times, participating personnel, and their responsibilities of each service link. Ensure that the relationship diagram can reflect each link in the service process. Set the logical relationships between the links for subsequent service analysis. For example, after a customer checks in, room inspection should be carried out first, and then customer registration.Analyze the work of each employee one by one to identify their roles and responsibilities in the service process. Record the performance and participation of each employee in different service processes. Combine the work monitoring parameters of the employees to analyze their efficiency and response speed in each service process, and evaluate their overall performance in the service chain. Organize the analysis results to generate a service process report for each employee. The report should include the service processes participated by each employee, the performance of the processes, and their impact on the overall service quality. Record the data and assumptions in the analysis process to provide a basis for subsequent service operation ability evaluation. Set a calculation formula for service operation ability, for example: Service operation ability = (Quantified work efficiency value × Weight 1) + (Service response speed × Weight 2) + (Complaint handling efficiency × Weight 3). The weights can be set according to the actual situation, for example, work efficiency accounts for 50%, response speed accounts for 30%, and complaint handling accounts for 20%. Ensure the standardization of all parameters to avoid evaluation errors caused by different dimensions. Calculate the service operation ability value of each employee one by one according to the set evaluation criteria. Record the score of each employee and compare it with the set ability standard. Identify high-efficiency and low-efficiency employees, and analyze the performance differences and reasons. Determine the calculation standard for the peak value of the whole-process service operation. For example, the peak value of the whole-process operation can be defined as: Peak operation value = ∑(Service operation ability value of each employee) / Total number of employees. Set a calculation period (such as daily, weekly) to observe the change trend of the peak operation value. Use the service operation ability value of each employee to calculate the peak value of the whole-process operation one by one. Record the peak operation value of each time period and analyze its change trend. Identify peak and trough periods and analyze their impact on service quality and customer satisfaction. Organize the calculation results to generate a report on the peak value of the whole-process service operation. The report should include the peak operation value of each time period and the analysis of its impact on the overall service quality. Record the data and assumptions in the calculation process to provide a basis for subsequent optimization of operation strategies and improvement of service quality.

[0042] In this embodiment, a hotel management quality evaluation system based on operation big data is provided for implementing the hotel management quality evaluation method based on operation big data as described above, including: A data processing module, configured to obtain hotel multi-dimensional operation monitoring logs, extract customer historical feedback information and perform adaptive shielding processing to obtain standardized customer feedback information; A service process module, configured to perform hotel service process analysis and full-process sequential evolution on the hotel multi-dimensional operation monitoring logs to construct a full-cycle service process relationship diagram; A response evaluation module, configured to perform abnormal process response evaluation on the full-cycle service process relationship diagram according to the standardized customer feedback information to obtain an abnormal process response evaluation value; The satisfaction correlation module is used to analyze the historical customer check-in behaviors in the hotel's multi-dimensional operation monitoring logs, and then mine the satisfaction correlation trends, so as to generate the customer satisfaction correlation rules; The traffic trend prediction module is used to conduct real-time customer flow analysis and customer traffic change trend prediction based on the hotel's multi-dimensional operation monitoring logs, and generate customer traffic trend prediction information; The quality assessment module is used to predict the customer satisfaction situation according to the customer satisfaction correlation rules and the customer traffic trend prediction information, and comprehensively evaluate the hotel management quality according to the abnormal link response evaluation value, so as to obtain the hotel management quality assessment report.

[0043] The present invention provides comprehensive and accurate data support for subsequent analysis by acquiring and processing various monitoring data of hotel operations (such as room status, equipment conditions, service times, etc.) and combining with historical feedback information of customers. Adaptive shielding processing of customer feedback can effectively filter out malicious, false or bad evaluations, thus ensuring that the obtained customer feedback is more real and effective. This helps to eliminate the interference of subjective factors and improve the reliability of data. Through the standardization processing of customer feedback information, the differences in the scoring system and customer language expressions can be eliminated, enabling feedback data from different sources and formats to be uniformly and normatively used for subsequent analysis. This provides a unified basis for data analysis and ensures the fairness and comparability of subsequent evaluations. Through the service process module, the hotel can comprehensively understand how each service process (such as reception, check-in, catering, room cleaning, etc.) works together and their mutual influences during the whole process from customer reservation to check-out. This helps to discover potential problems in the service process and provide a basis for optimizing the service process. By drawing a relationship diagram of the full-cycle service process, hotel managers can clearly see the dependency relationships and evolution processes of each service process, improving the transparency of overall operations. This enables the hotel to better control the key nodes of the service and ensure seamless connection. Through process analysis, the hotel can promptly identify which service processes are bottlenecks or weak links, so as to targetedly optimize the processes and conduct personnel training to improve the overall service quality. By analyzing customer feedback and service process data, it can help the hotel quickly identify those underperforming and slow-responsive processes (for example, long check-in waiting times, delayed cleaning services, etc.), thus effectively locating potential problems in hotel management. The response evaluation module can give specific response evaluation values for abnormal processes, and these evaluation values provide clear improvement directions for hotel managers. For example, if the response speed of a certain process is slow, more personnel can be invested or the process can be adjusted to improve the service quality. Timely identification and improvement of problems in service processes can significantly enhance the overall customer experience, reduce customer complaints, and increase customer loyalty and satisfaction. By analyzing the historical check-in behaviors of customers, the hotel can deeply understand the behavior patterns, consumption preferences and demand changes of customers. This helps the hotel to make more targeted service improvements and predict future demand trends of customers. By revealing the correlation rules between customer satisfaction and their check-in behaviors, the hotel can provide personalized services according to the needs of different customer groups. For example, recommend suitable room types, catering or other additional services based on the customer's check-in history. The correlation rules between satisfaction and check-in behaviors can help hotel managers clarify which factors most affect customer satisfaction, so as to targetedly optimize these key factors and improve overall customer satisfaction. By monitoring the real-time customer flow, the hotel can dynamically understand the distribution of customers at different times and regions, ensure the reasonable allocation of service resources, and avoid customer backlogs during peak hours.Through traffic trend prediction, hotels can forecast changes in room demand and peak customer arrival times over a period of time in the future. This helps hotels make advance arrangements and preparations for resources such as staff, facilities, and rooms. Based on traffic prediction, hotels can accurately forecast room demand for each time period, avoiding shortages or surpluses of resources, thereby improving overall operational efficiency and service quality. By comprehensively evaluating various types of data such as customer satisfaction, traffic trends, and service link responses, hotels can obtain a comprehensive quality assessment report. This report can fully reflect the hotel's operational status, customer experience, and service quality, providing objective data support for managers. The data report provided by the quality assessment module helps hotel managers make more scientific and accurate decisions, identify weak links in services, optimize operational processes, and improve service levels. Through regular quality assessment reports, hotels can continuously track changes in service quality and customer satisfaction, adjust strategies and optimize processes in a timely manner, and continuously enhance the competitiveness of the hotel.

[0044] Therefore, in any regard, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0045] As described above, these are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A hotel management quality assessment method based on operational big data, characterized in that: The following steps are involved: Step S1: Obtain the hotel's multi-dimensional operation monitoring log, extract historical customer feedback information and perform adaptive shielding processing to obtain standardized customer feedback information; Step S2: Analyze the hotel service links and the sequential evolution of all links on the hotel multi-dimensional operation monitoring log, and construct a full-cycle service link relationship diagram; Step S3: performing abnormal link response evaluation on the full-cycle service link relationship diagram according to the standardized customer feedback information to obtain an abnormal link response evaluation value; Step S4: Analyze the historical customer check-in behavior of the hotel's multi-dimensional operation monitoring log, and then mine the satisfaction correlation trend to generate customer satisfaction correlation rules; Step S5: Perform real-time customer flow analysis and customer flow change trend prediction based on the hotel's multi-dimensional operation monitoring log to generate customer flow trend prediction information; Step S6: Perform customer satisfaction situation prediction based on customer satisfaction association rules and customer flow trend prediction information, and perform a comprehensive hotel management quality assessment based on the abnormal link response evaluation value to obtain a hotel management quality assessment report.

2. The hotel management quality assessment method based on operational big data according to claim 1 is characterized in that: The specific steps of step S1 are: Step S11: obtaining a hotel multi-dimensional operation monitoring log; performing outlier filtering optimization on the hotel multi-dimensional operation monitoring log to construct an outlier optimization monitoring log; Step S12: extracting customer historical feedback information based on the outlier optimization monitoring log; Step S13: performing deep semantic analysis on the customer's historical feedback information to generate deep semantic features of the feedback information; Step S14: performing intelligent detection of malicious reviews based on deep semantic features of feedback information, and extracting malicious review information; Step S15: Adaptively shield malicious evaluation information to obtain standardized customer feedback information.

3. The hotel management quality assessment method based on operational big data according to claim 2 is characterized in that: The specific steps of step S12 are: Extracting parameterized data according to the multi-dimensional operation monitoring log of the hotel; calculating conventional parameter ranges for the parameterized data; Detect abnormal outlier data points according to the conventional parameter range and mark multiple abnormal outlier data points; Performing repairable data evaluation on multiple abnormal outlier data points to obtain repairable outlier data points and unrepairable outlier data points; calculating a mean value of the parameterized data; Performing mean value interpolation processing on the repairable outlier data points according to the mean value to obtain mean value interpolation optimized data points; The irreparable outlier data points are filtered out, and the hotel's multi-dimensional operation monitoring logs are standardized and reconstructed based on the average value interpolation optimization data points to construct outlier optimization monitoring logs.

4. The hotel management quality assessment method based on operational big data according to claim 1 is characterized in that: The specific steps of step S2 are: Step S21: Analyze the hotel service links of the hotel multi-dimensional operation monitoring log to obtain information on multiple hotel service links; Step S22: performing a logical correlation analysis on the information of multiple hotel service links to obtain the logical correlation characteristics between the links; Step S23: Analyze the trigger conditions of each link based on the information of multiple hotel service links, and generate the trigger conditions of each link; Step S24: Evolve all links sequentially according to the triggering conditions of each link and the logical association characteristics between links, and construct a full-cycle service link relationship diagram.

5. The hotel management quality assessment method based on operational big data according to claim 1 is characterized in that: The specific steps of step S3 are: Step S31: Identify historical hotel feedback issues from the standardized customer feedback information and extract historical hotel feedback issues; Step S32: classifying the historical hotel feedback questions to obtain the type of each feedback question; Step S33: based on each feedback problem type, the problem link is traced back to the full-cycle service link relationship diagram, and abnormal service links are extracted; Step S34: the problem solving response speed is calculated for the historical hotel feedback problems, so as to obtain the problem solving response speed; Step S35: Perform an abnormal link response evaluation on the abnormal service link according to the problem solving response speed to obtain an abnormal link response evaluation value.

6. The hotel management quality assessment method based on operational big data according to claim 1 is characterized in that: The specific steps of step S4 are: Step S41: Analyze the historical customer check-in behavior of the hotel's multi-dimensional operation monitoring log and extract the historical customer check-in behavior data; Step S42: Perform historical occupancy rate statistics on historical customer check-in behavior data and extract historical customer occupancy rates; Step S43: extracting the check-in timestamp of the historical customer check-in behavior data; Step S44: Calculate the hotel service customer satisfaction in the same time period on the hotel multi-dimensional operation monitoring log according to the check-in timestamp, and extract the customer satisfaction in the same time period; Step S45: performing satisfaction correlation trend mining on customer satisfaction in the same time period based on historical customer occupancy rates, thereby generating customer satisfaction correlation rules.

7. The hotel management quality assessment method based on operational big data according to claim 1 is characterized in that: The specific steps of step S5 are: Step S51: Perform real-time customer flow analysis based on the hotel's multi-dimensional operation monitoring log to obtain the hotel's real-time customer flow data; Step S52: Calculate the actual number of guests in multiple time periods based on the hotel's real-time customer flow data to generate the actual number of guests in multiple time periods; Step S53: performing customer flow time series fluctuation analysis on the actual number of customers in multiple time periods to generate customer flow time series fluctuation characteristics; Step S54: Predict the customer traffic change trend based on the customer traffic time series fluctuation characteristics to generate customer traffic trend prediction information.

8. The hotel management quality assessment method based on operational big data according to claim 1 is characterized in that: The specific steps of step S6 are: Step S61: Perform real-time hotel service staff work monitoring and analysis based on the hotel's multi-dimensional operation monitoring log, and extract each staff member's work monitoring parameters; Step S62: Calculate the peak value of all-link service operation according to the work monitoring parameters of each personnel, so as to generate the peak value of all-link operation; Step S63: Perform customer service capacity scheduling analysis on customer flow trend prediction information based on all-link operation peaks, thereby generating all-link customer service capacity scheduling values; Step S64: Predicting the customer satisfaction situation based on the customer satisfaction correlation law and the full-link customer service capability scheduling value, thereby generating a real-time customer satisfaction situation prediction graph; Step S65: Conduct a comprehensive hotel management quality assessment on the abnormal link response evaluation value and the real-time customer satisfaction situation prediction diagram to obtain a hotel management quality assessment report.

9. The hotel management quality assessment method based on operational big data according to claim 8 is characterized in that: The specific steps of step S62 are: Quantify the work efficiency of each personnel's work monitoring parameters and generate a quantitative value of each personnel's work efficiency; Calculate the service response speed based on each personnel's work monitoring parameters and extract the service response speed of each personnel; Extract customer complaint handling information based on the hotel's multi-dimensional operation monitoring logs; Analyze each personnel's service link based on the full-cycle service link relationship diagram to generate each personnel's service link; Evaluate the service operation capability of each staff member based on their work efficiency, service response speed and customer complaint handling information to generate the service operation capability value of each staff member; The service operation peak value of all links is calculated for each person’s service operation capability value according to each person’s service link, thereby generating the operation peak value of all links.

10. A hotel management quality assessment system based on operational big data, characterized in that: The method for evaluating hotel management quality based on operational big data as claimed in claim 1 comprises: The data processing module is used to obtain the hotel's multi-dimensional operation monitoring logs, extract historical customer feedback information and perform adaptive shielding processing to obtain standardized customer feedback information; A service link module is used to analyze the hotel service links and the sequential evolution of all links of the hotel multi-dimensional operation monitoring log, and to construct a full-cycle service link relationship diagram; A response evaluation module is used to evaluate the abnormal link response of the full-cycle service link relationship diagram according to the standardized customer feedback information to obtain the abnormal link response evaluation value; The satisfaction correlation module is used to analyze the historical customer check-in behavior of the hotel's multi-dimensional operation monitoring logs, and then to mine the satisfaction correlation trend, so as to generate the customer satisfaction correlation law; The traffic trend prediction module is used to perform real-time customer flow analysis and customer flow change trend prediction based on the hotel's multi-dimensional operation monitoring logs, and generate customer flow trend prediction information; The quality assessment module is used to predict the customer satisfaction situation based on the customer satisfaction correlation law and customer flow trend prediction information, and to conduct a comprehensive hotel management quality assessment based on the abnormal link response evaluation value to obtain a hotel management quality assessment report.

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