Tourism management system and method based on big data

Abstract

The invention is suitable for the field of tourism management systems, and provides a tourism management system and method based on big data, and the system comprises a demand analysis and data management module, a tourism planning service and facility management module, an intelligent assistance and professional service module, and a monitoring and feedback system module. According to the method, personalized barrier-free routes are recommended, the equipment requirements in different time periods can be accurately predicted, the equipment inventory is optimized, the out-of-stock and excessive inventory phenomena are reduced, and the inventory management efficiency is improved. The equipment distribution is dynamically adjusted, and the equipment is reasonably distributed among a plurality of service points, so that enough equipment is ensured to meet the requirements of tourists in peak hours. Through the innovative design of the intelligent assistance and professional service module, personalized, precise and efficient management of barrier-free tourism is realized, the tourism experience of tourists having difficulty in moving and the management efficiency of scenic spots are remarkably improved, and the system has obvious beneficial effects and progressive advantages.

Description

Technical Field

[0001] The present invention belongs to the field of tourism management systems, and in particular relates to a tourism management system and method based on big data. Background Art

[0002] With the continuous growth of the tourism industry and the booming global tourism market, many tourism management systems are emerging. First, the degree of digitization of tourism management systems is constantly improving. Various tourism management systems can better manage and analyze tourism data and provide more accurate tourism product recommendations and personalized services by using advanced technologies such as cloud computing, big data, and artificial intelligence. Secondly, the functions of tourism management systems are becoming more and more complete. In addition to traditional booking and payment functions, modern tourism management systems have also added functions such as itinerary planning, attraction recommendations, navigation and guidance, transportation and accommodation information inquiries, etc., to provide tourists with a full range of services. In addition, tourism management systems are gradually integrating with other related fields. For example, combined with geographic information systems (GIS), accurate location positioning and navigation functions can be achieved; combined with social media and online review platforms, real user evaluations and travel experience sharing can be provided.

[0003] The current tourism management services and facilities for people with limited mobility are not perfect enough, and there is still a lot of room for improvement. This includes but is not limited to insufficient construction and maintenance of barrier-free facilities, inconvenient information access channels, lack of relevant training for staff, and lack of targeted tourism products and services. These problems limit the equal, convenient and comfortable experience of people with limited mobility during tourism. Therefore, it is necessary to further improve the level of tourism management, comprehensively consider and meet the special needs of this group of people, and achieve inclusive tourism. Summary of the invention

[0004] The purpose of the present invention is to provide a tourism management system based on big data, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0005] The present invention is implemented in this way: a tourism management system based on big data, the system comprising: Demand analysis and data management module, which is used to collect tourist feedback data, collect usage data of scenic spots, transportation and accommodation barrier-free facilities, identify the problems and needs of people with limited mobility, and count and analyze the frequency and distribution of use of barrier-free facilities; The tourism planning service and facility management module is used to establish a barrier-free itinerary recommendation model, recommend travel itineraries based on tourists' mobility and interests, collect information on barrier-free facilities in scenic spots, formulate barrier-free facility optimization plans, establish a facility maintenance database, and monitor maintenance needs in real time; Intelligent assistance and professional service module, which is used to establish barrier-free journey navigation model, provide barrier-free route planning and real-time navigation, establish auxiliary equipment rental service points, calculate the number and distribution of rental equipment, establish equipment demand prediction and optimization model, and service point optimal distribution and dynamic adjustment model; The monitoring and feedback system module is used to establish a real-time monitoring system, track the travel activities of tourists with limited mobility, monitor the operating status of barrier-free facilities, establish a tourist feedback mechanism, formulate improvement measures, and regularly evaluate the effectiveness of improvement measures.

[0006] As a further solution of the present invention, the demand analysis and data management module specifically includes: Data collection and processing unit, used to implement data collection, collect real-time feedback from mobile applications, use automated tools to collect barrier-free facility usage data for scenic spots, transportation and accommodation, and clean and format the collected data; The data analysis and demand identification unit is used to conduct preliminary analysis of the collected data, analyze visitor feedback and facility usage data, and build a demand identification model.

[0007] As a further solution of the present invention, the tourism planning service and facility management module specifically includes: The barrier-free journey recommendation and planning unit is used to collect and integrate tourists' mobility data and interest information, and develop a barrier-free journey recommendation model. The specific formula is: ; in, is the final recommendation score, is the number of factors considered, It is The weight of the factors, It is The matching function of factors, is the input variable of this factor, Specific to each factor: represents the matching degree of tourists’ action ability, where It is the tourists’ mobility data; Indicates the matching degree of tourists’ interests and hobbies, where It is the tourists’ hobbies and interests data; Indicates the convenience of facilities, among which It is the barrier-free facilities data of the tourist destination; Indicates the convenience of transportation, It is the accessibility data of the transportation mode; represents seasonal suitability, where It is the weather and activity suitability data for the tourist season; Among them, the convenience of facilities The facility convenience evaluates the completeness and availability of barrier-free facilities in the tourist destination: the availability of ramps and elevators, barrier-free toilets and changing rooms, barrier-free parking spaces, barrier-free information and signs, barrier-free dining and accommodation facilities. A scoring standard is set for each facility, ranging from level 1 for very inconvenient to level 5 for fully barrier-free. Each item is scored based on the facilities of the tourist destination, and then a weighted average score is calculated as an indicator of facility convenience. Transportation convenience Transportation convenience evaluates the convenience and accessibility of transportation modes to tourist destinations: accessibility facilities of public transportation, accessibility services of taxis, convenience of parking facilities, and accessibility facilities of transportation hubs. Scoring standards are set for each mode of transportation, and scores are given based on the degree of perfection of accessibility facilities. The facility management and maintenance unit is used to collect and analyze the location, status and usage frequency of barrier-free facilities in scenic areas, formulate facility optimization plans, facility upgrades and layout adjustments, establish a facility maintenance database, record the maintenance history and current status of the facilities, implement a real-time monitoring system, and automatically detect the operating status and maintenance needs of the facilities.

[0008] As a further solution of the present invention, the intelligent assistance and professional service module specifically includes: The barrier-free journey navigation model unit is used to collect and integrate scenic area maps, barrier-free facility locations, and traffic route data, screen suitable barrier-free routes based on tourists' mobility data, calculate the optimal barrier-free route, update route information in real time, and provide route planning results and real-time navigation guidance; The auxiliary equipment rental service unit is used to collect and analyze tourists' demand data for auxiliary equipment, predict the peak and valley values ​​of equipment rental based on the demand data, calculate the type and quantity of required equipment, and the optimal distribution of service points, establish an equipment rental database, record equipment status and rental history, monitor equipment inventory and service point status in real time, and adjust rental strategies. The specific formula of the equipment demand prediction and optimization model is: ; ; in: It is The optimal number of devices. It is The unit cost of the equipment, is the risk aversion coefficient, which is used to balance inventory cost and out-of-stock risk. is the variance between demand and supply, representing the risk of stock-outs, It is The expected demand for this type of equipment, is a standard normal distribution Quantiles, used to determine service levels, is the service level. A 95% service level means that there will be no shortage of goods 95% of the time. is the total number of types of equipment; The optimal distribution and dynamic adjustment model of service points is as follows: ; ; It is Moment Equipment demand for each service point, It is Moment The actual demand for this equipment, It is Moment The inventory of the equipment It is The adjustment coefficient of each device reflects its impact on the demand for service points. It is The weight of the equipment, It is Moment The service capabilities of each service point, It is the speed parameter for service capacity adjustment.

[0009] As a further solution of the present invention, the monitoring and feedback system module specifically includes: The real-time monitoring unit is used to collect data on barrier-free facilities and tourist activities in the scenic area in real time through monitoring equipment, transmit the real-time data to the central monitoring system through a wireless network, process and analyze the collected data in real time, trigger an early warning mechanism based on the analysis results, and record log files; The feedback analysis unit is used to collect tourists' feedback on barrier-free facilities and services through mobile applications, text messages, telephone calls, and social media, organize, classify, and store the collected feedback data, conduct sentiment analysis on tourists' feedback, identify positive and negative opinions and suggestions, perform statistical analysis on feedback data, identify common problems and needs, and generate feedback reports.

[0010] Another object of the present invention is to provide a tourism management method based on big data, the method comprising: Collect tourist feedback data, collect usage data of scenic spots, transportation and accommodation barrier-free facilities, identify the problems and needs of people with limited mobility, and count and analyze the frequency and distribution of use of barrier-free facilities; Establish a barrier-free itinerary recommendation model to recommend travel itineraries based on tourists’ mobility and interests, collect information on barrier-free facilities in scenic spots, develop barrier-free facility optimization plans, establish a facility maintenance database, and monitor maintenance needs in real time; Establish an accessible journey navigation model, provide accessible route planning and real-time navigation, establish auxiliary equipment rental service points, calculate the number and distribution of rental equipment, establish equipment demand prediction and optimization models, and service point optimal distribution and dynamic adjustment models; Establish a real-time monitoring system to track the travel activities of tourists with limited mobility, monitor the operating status of barrier-free facilities, establish a tourist feedback mechanism, formulate improvement measures, and regularly evaluate the effectiveness of the improvement measures.

[0011] As a further solution of the present invention, the collection of tourist feedback data and the collection of usage data of scenic spots, transportation and accommodation barrier-free facilities specifically include: Implement data collection, collect real-time feedback from mobile applications, use automated tools to collect data on the use of barrier-free facilities in scenic spots, transportation and accommodation, and clean and format the collected data; Conduct preliminary analysis of the collected data, analyze visitor feedback and facility usage data, and build a demand identification model.

[0012] As a further solution of the present invention, the establishment of a barrier-free journey recommendation model specifically includes: Collect and integrate tourists' mobility data and hobbies information to develop a barrier-free journey recommendation model. The specific formula is:

[0013] in, is the final recommendation score, is the number of factors considered, It is The weight of the factors, It is The matching function of factors, is the input variable of this factor, Specific to each factor: represents the matching degree of tourists’ action ability, where It is the tourists’ mobility data; Indicates the matching degree of tourists’ interests and hobbies, where It is the tourists’ hobbies and interests data; Indicates the convenience of facilities, among which It is the barrier-free facilities data of the tourist destination; Indicates the convenience of transportation, It is the accessibility data of the transportation mode; represents seasonal suitability, where It is the weather and activity suitability data for the tourist season; Among them, the convenience of facilities The facility convenience evaluates the completeness and availability of barrier-free facilities in the tourist destination: the availability of ramps and elevators, barrier-free toilets and changing rooms, barrier-free parking spaces, barrier-free information and signs, barrier-free dining and accommodation facilities. A scoring standard is set for each facility, ranging from level 1 for very inconvenient to level 5 for fully barrier-free. Each item is scored based on the facilities of the tourist destination, and then a weighted average score is calculated as an indicator of facility convenience. Transportation convenience Transportation convenience evaluates the convenience and accessibility of transportation modes to tourist destinations: accessibility facilities of public transportation, accessibility services of taxis, convenience of parking facilities, and accessibility facilities of transportation hubs. Scoring standards are set for each mode of transportation, and scores are given based on the degree of perfection of accessibility facilities. Collect and analyze the location, status and usage frequency of barrier-free facilities in scenic areas, formulate facility optimization plans, facility upgrades and layout adjustments, establish a facility maintenance database, record the maintenance history and current status of the facilities, implement a real-time monitoring system, and automatically detect the operating status and maintenance needs of the facilities.

[0014] As a further solution of the present invention, the establishment of equipment demand prediction and optimization model, service point optimal distribution and dynamic adjustment model specifically includes: Collect and integrate scenic area maps, barrier-free facility locations, and traffic route data, select suitable barrier-free routes based on tourists' mobility data, calculate the optimal barrier-free route, update route information in real time, and provide route planning results and real-time navigation guidance; Collect and analyze tourists' demand data for auxiliary equipment, predict the peak and valley values ​​of equipment rental based on the demand data, calculate the type and quantity of required equipment, and the optimal distribution of service points, establish an equipment rental database, record equipment status and rental history, monitor equipment inventory and service point status in real time, and adjust rental strategies. The specific formula of the equipment demand prediction and optimization model is: ; ; in: It is The optimal number of devices. It is The unit cost of the equipment, is the risk aversion coefficient, which is used to balance inventory cost and out-of-stock risk. is the variance between demand and supply, representing the risk of stock-outs, It is The expected demand for this type of equipment, is a standard normal distribution Quantiles, used to determine service levels, is the service level. A 95% service level means that there will be no shortage of goods 95% of the time. is the total number of types of equipment; The optimal distribution and dynamic adjustment model of service points is as follows: ; ; It is Moment Equipment demand for each service point, It is Moment The actual demand for this equipment, It is Moment The inventory of the equipment It is The adjustment coefficient of each device reflects its impact on the demand for service points. It is The weight of the equipment, It is Moment The service capabilities of each service point, It is the speed parameter for service capacity adjustment.

[0015] As a further solution of the present invention, the real-time monitoring system is established to track the travel activities of tourists with limited mobility, monitor the operating status of barrier-free facilities, and establish a tourist feedback mechanism, which specifically includes: Through monitoring equipment, real-time data on barrier-free facilities and tourist activities in the scenic area are collected, and the real-time data is transmitted to the central monitoring system through the wireless network. The collected data is processed and analyzed in real time. According to the analysis results, the early warning mechanism is triggered and the log file is recorded; Collect tourists’ feedback on accessible facilities and services through mobile applications, text messages, phone calls, and social media, organize, classify, and store the collected feedback data, conduct sentiment analysis on tourists’ feedback, identify positive and negative opinions and suggestions, perform statistical analysis on feedback data, identify common problems and needs, and generate feedback reports.

[0016] The beneficial effects of the present invention are: improving barrier-free tourism experience, recommending personalized barrier-free routes, accurately predicting equipment demand in different time periods, optimizing equipment inventory, reducing out-of-stock and over-inventory phenomena, and improving inventory management efficiency. Dynamically adjusting equipment distribution, reasonably allocating equipment between multiple service points, and ensuring that there are enough equipment to meet tourist needs during peak hours. Through the innovative design of intelligent assistance and professional service modules, the present invention realizes personalized, precise and efficient management of barrier-free tourism, significantly improving the travel experience of tourists with limited mobility and the management efficiency of scenic spots, and has obvious beneficial effects and progressive advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A structural block diagram of a tourism management system based on big data provided by an embodiment of the present invention; Figure 2 A structural block diagram of a demand analysis and data management module provided in an embodiment of the present invention; Figure 3 A structural block diagram of a tourism planning service and facility management module provided by an embodiment of the present invention; Figure 4 A structural block diagram of the intelligent assistance and professional service module provided by an embodiment of the present invention; Figure 5 A structural block diagram of a monitoring and feedback system module provided by an embodiment of the present invention; Figure 6 A flow chart of a tourism management method based on big data provided by an embodiment of the present invention; Figure 7 A flowchart for collecting tourist feedback data and collecting usage data of scenic spots, transportation and accommodation barrier-free facilities provided in an embodiment of the present invention; Figure 8 A flowchart of establishing a barrier-free journey recommendation model provided by an embodiment of the present invention; Fig. 9 A flow chart of establishing an equipment demand prediction and optimization model, an optimal distribution of service points and a dynamic adjustment model provided by an embodiment of the present invention; Fig.10 A flowchart of establishing a real-time monitoring system, tracking the travel activities of tourists with limited mobility, monitoring the operating status of barrier-free facilities, and establishing a tourist feedback mechanism is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0020] Figure 1 A structural block diagram of a tourism management system based on big data provided by an embodiment of the present invention, such as Figure 1 As shown, a tourism management system based on big data, the system includes: The demand analysis and data management module 100 is used to collect tourist feedback data, collect usage data of scenic spots, transportation and accommodation barrier-free facilities, identify the problems and needs of people with limited mobility, and count and analyze the usage frequency and distribution of barrier-free facilities; The tourism planning service and facility management module 200 is used to establish a barrier-free itinerary recommendation model, recommend travel itineraries based on tourists' mobility and interests, collect information on barrier-free facilities in scenic spots, formulate barrier-free facility optimization plans, establish a facility maintenance database, and monitor maintenance needs in real time; This module uses advanced algorithms and machine learning technology to provide personalized travel recommendations for tourists with limited mobility, taking into account factors such as tourists' mobility, interests and hobbies, as well as the accessible facilities and transportation convenience of the tourist destination. This unit uses the following formula to calculate the recommendation score: in, is the final recommendation score, is the number of factors considered, It is The weight of the factors, It is The matching function of factors, is the input variable of the factor, specific to each factor: represents the matching degree of tourists’ action ability, where It is the tourists’ mobility data; Indicates the matching degree of tourists’ interests and hobbies, where It is the tourists’ hobbies and interests data; Indicates the convenience of facilities, among which It is the barrier-free facilities data of the tourist destination; Indicates the convenience of transportation, It is the accessibility data of the transportation mode; represents seasonal suitability, where It is the weather and activity suitability data for the tourist season; among them, the convenience of facilities The facility convenience evaluates the completeness and availability of barrier-free facilities in the tourist destination: the availability of ramps and elevators, barrier-free toilets and changing rooms, barrier-free parking spaces, barrier-free information and signs, barrier-free dining and accommodation facilities. A scoring standard is set for each facility, ranging from level 1 for very inconvenient to level 5 for fully barrier-free. Each item is scored based on the facilities of the tourist destination, and then a weighted average score is calculated as an indicator of facility convenience. Transportation convenience Transportation convenience evaluates the convenience and accessibility of transportation modes to tourist destinations: accessibility facilities of public transportation, accessibility services of taxis, convenience of parking facilities, and accessibility facilities of transportation hubs. Scoring standards are set for each mode of transportation, and scores are given based on the degree of perfection of accessibility facilities. For this module's scoring system, here is a specific example: Assume that we have a tourist with limited mobility named Mr. Zhang who wants to take a tour during his upcoming vacation. We use the formula of the barrier-free journey recommendation and planning unit to recommend a suitable travel destination for him.

[0021] Input data: Mobility data Mr. Zhang uses a wheelchair and requires a completely barrier-free environment.

[0022] Hobbies and interests data : Mr. Zhang likes history and culture, and prefers museums and historical sites.

[0023] Accessibility data : We collected data on accessibility facilities in several potential tourist destinations, including ramps, elevators, accessible restrooms, etc.

[0024] Traffic convenience data : Including the accessibility of public transportation, accessible taxi services, etc.

[0025] Seasonal suitability data : Weather and activity suitability for the current season.

[0026] Weight distribution: Mobility : 0.3 (because Mr. Zhang's mobility is a key factor in choosing a travel destination) Hobbies : 0.2 (Hobbies and interests are also important, but they are slightly inferior to action ability) Facilities convenience : 0.25 (The convenience of facilities is very important to Mr. Zhang's travel experience) Transportation convenience : 0.15 (Transportation convenience affects tourism accessibility) Seasonal suitability : 0.1 (Seasonal suitability has an impact on the travel experience, but it is not a decisive factor) Matching function: Action capability matching : If the destination is completely accessible, then = 1 otherwise it decreases according to the accessibility level.

[0027] Interest matching : If the destination is rich in historical and cultural attractions, then =1, otherwise it decreases according to the number and quality of attractions.

[0028] Facilities convenience : Rating based on the completeness of barrier-free facilities, from 1 (very inconvenient) to 5 (completely barrier-free).

[0029] Transportation convenience : Rate the accessibility of transportation modes from 1 (very inconvenient) to 5 (completely accessible).

[0030] Seasonal suitability : Rating based on weather and activity suitability, from 1 (very unsuitable) to 5 (very suitable).

[0031] Calculate the recommendation score: Suppose we have three potential tourist destinations A, B and C, and their matching function scores are as follows: Destination A: =0.9, =0.8, =4.5, =4.0, =4.2; Destination B: =0.7, =0.9, =4.0, =3.5, =4.5; Destination C: =1.0, =0.7, =5.0, =4.5, =3.8; Calculate the recommendation score: Destination A: =0.3×0.9+0.2×0.8+0.25×4.5+0.15×4.0+0.1×4.2=2.91 Destination B: =0.3×0.7+0.2×0.9+0.25×4.0+0.15×3.5+0.1×4.5=2.65 Destination C: =0.3×1.0+0.2×0.7+0.25×5.0+0.15×4.5+0.1×3.8=3.13 According to the calculation results, Destination C has the highest recommendation score, so the system recommends Mr. Zhang to travel to Destination C. This example shows how to use the formula of the barrier-free journey recommendation and planning unit to provide personalized travel recommendations for tourists with limited mobility.

[0032] By comprehensively considering multiple factors, this module can provide highly personalized travel recommendations to meet the special needs of different tourists. Using big data analysis, the system can update and optimize the recommendation model in real time to improve the accuracy and practicality of the recommendations. By providing barrier-free travel planning, the system significantly improves the travel experience of tourists with limited mobility.

[0033] This module is responsible for monitoring and managing barrier-free facilities in scenic areas, ensuring the availability and maintenance status of these facilities. This unit implements its functions through the following steps: Data collection: Real-time collection of data on the location, status, and frequency of use of barrier-free facilities. Analysis and optimization: Analyze the collected data, identify problems and bottlenecks in the use of facilities, and develop optimization plans. Maintenance database: Establish and maintain a facility maintenance database to record the maintenance history and current status of the facilities. Real-time monitoring: Implement a real-time monitoring system to automatically detect the operating status and maintenance needs of the facilities.

[0034] Through real-time monitoring and data analysis, this module enables the system to promptly detect and resolve facility problems and improve the efficiency of facility use. Predictive maintenance reduces the need for sudden failures and emergency repairs, and reduces maintenance costs. It ensures the continuous availability of barrier-free facilities and improves the overall quality of tourism services.

[0035] Intelligent assistance and professional service module 300, used to establish a barrier-free journey navigation model, provide barrier-free route planning and real-time navigation, establish auxiliary equipment rental service points, calculate the number and distribution of rental equipment, establish equipment demand prediction and optimization model, service point optimal distribution and dynamic adjustment model; This module collects and integrates scenic area maps, barrier-free facility locations, and traffic route data. According to tourists' mobility data, suitable barrier-free routes are selected. Graph algorithms and optimization algorithms are used to calculate the optimal barrier-free route. Route information is updated in real time to provide accurate route planning results and navigation guidance. Advanced data collection and integration technologies are used to ensure data accuracy and timeliness. Machine learning and artificial intelligence technologies are used to provide optimal route planning based on tourists' personalized needs and mobility. GPS and real-time data updates are used to ensure the accuracy and timeliness of navigation information, providing a seamless travel experience for tourists with limited mobility.

[0036] This module collects and analyzes tourists' demand data for auxiliary equipment, including historical rental data, real-time demand data, etc. According to the demand data, the peak and valley values ​​of equipment rental are predicted, and the types and quantities of required equipment are calculated. The optimal distribution of service points is calculated, and an equipment rental database is established to record the equipment status and rental history. Real-time monitoring of equipment inventory and service point status is carried out, and rental strategies are adjusted to ensure the balance of equipment supply and demand. Accurately predict equipment demand and reduce equipment idleness and out-of-stock phenomena by using big data analysis and time series prediction models. Calculate the optimal distribution of equipment through optimization algorithms to ensure that equipment is fully utilized during peak demand periods. Real-time monitoring of equipment status and rental status, and timely adjustment of rental strategies using dynamic adjustment models to improve service efficiency and user satisfaction. Through data analysis and modeling, scientific decision-making support is provided to managers, and equipment management and service point distribution are optimized. Through intelligent navigation and planning, personalized travel experiences are provided to meet the needs of different tourists. Using big data and intelligent algorithms, refined management is achieved to improve equipment utilization and service efficiency. Through real-time data monitoring and dynamic adjustment, rapid response to changes in tourist demand is achieved to improve service quality. Through scientific prediction and optimization models, we can achieve reasonable allocation of resources, reduce waste and improve operational efficiency. Through data feedback and analysis, we can continuously improve services and facilities and enhance the overall tourism experience. In order to more specifically explain the equipment demand prediction and optimization model and the service point optimal distribution and dynamic adjustment model, the following is the improved detailed formula: The specific formula of equipment demand forecasting and optimization model is: ; ; in: It is The optimal number of devices. It is The unit cost of the equipment, is the risk aversion coefficient, which is used to balance inventory cost and out-of-stock risk. is the variance between demand and supply, representing the risk of stock-outs, It is The expected demand for this type of equipment, is a standard normal distribution Quantiles, used to determine service levels, is the service level. A 95% service level means that there will be no shortage of goods 95% of the time. is the total number of types of equipment; The optimal distribution and dynamic adjustment model of service points is as follows: ; ; It is Moment Equipment demand for each service point, It is Moment The actual demand for this equipment, It is Moment The inventory of the equipment It is The adjustment coefficient of each device reflects its impact on the demand for service points. It is The weight of the equipment, It is Moment The service capabilities of each service point, It is the speed parameter for service capacity adjustment.

[0037] The monitoring and feedback system module 400 is used to establish a real-time monitoring system, track the travel activities of tourists with limited mobility, monitor the operating status of barrier-free facilities, establish a tourist feedback mechanism, formulate improvement measures, and regularly evaluate the effectiveness of the improvement measures.

[0038] Figure 2 The structural block diagram of the demand analysis and data management module provided by the embodiment of the present invention is as follows: Figure 2 As shown, the demand analysis and data management module specifically includes: The data collection and processing unit 110 is used to implement data collection, collect real-time feedback from mobile applications, use automated tools to collect barrier-free facility usage data for scenic spots, transportation, and accommodation, and clean and format the collected data; The data analysis and demand identification unit 120 is used to perform preliminary analysis on the collected data, analyze visitor feedback and facility usage data, and build a demand identification model.

[0039] Figure 3The structural block diagram of the tourism planning service and facility management module provided by the embodiment of the present invention is as follows: Figure 3 As shown, the tourism planning service and facility management module specifically includes: The barrier-free journey recommendation and planning unit 210 is used to collect and integrate tourists' mobility data and interest information to develop a barrier-free journey recommendation model. The specific formula is: ; in, is the final recommendation score, is the number of factors considered, It is The weight of the factors, It is The matching function of factors, is the input variable of this factor, Specific to each factor: represents the matching degree of tourists’ action ability, where It is the tourists’ mobility data; Indicates the matching degree of tourists’ interests and hobbies, where It is the tourists’ hobbies and interests data; Indicates the convenience of facilities, among which It is the barrier-free facilities data of the tourist destination; Indicates the convenience of transportation, It is the accessibility data of the transportation mode; represents seasonal suitability, where It is the weather and activity suitability data for the tourist season; Among them, the convenience of facilities The facility convenience evaluates the completeness and availability of barrier-free facilities in the tourist destination: the availability of ramps and elevators, barrier-free toilets and changing rooms, barrier-free parking spaces, barrier-free information and signs, barrier-free dining and accommodation facilities. A scoring standard is set for each facility, ranging from level 1 for very inconvenient to level 5 for fully barrier-free. Each item is scored based on the facilities of the tourist destination, and then a weighted average score is calculated as an indicator of facility convenience. Transportation convenience Transportation convenience evaluates the convenience and accessibility of transportation modes to tourist destinations: accessibility facilities of public transportation, accessibility services of taxis, convenience of parking facilities, and accessibility facilities of transportation hubs. Scoring standards are set for each mode of transportation, and scores are given based on the degree of perfection of accessibility facilities. The facility management and maintenance unit 220 is used to collect and analyze the location, status and usage frequency of the barrier-free facilities in the scenic area, formulate facility optimization plans, facility upgrades and layout adjustments, establish a facility maintenance database, record the maintenance history and current status of the facilities, implement a real-time monitoring system, and automatically detect the operating status and maintenance needs of the facilities.

[0040] Figure 4 The structure diagram of the intelligent assistance and professional service module provided by the embodiment of the present invention is as follows: Figure 4 As shown, the intelligent assistance and professional service module specifically includes: The barrier-free journey navigation model unit 310 is used to collect and integrate scenic area maps, barrier-free facility locations, and traffic route data, select suitable barrier-free routes based on tourists' mobility data, calculate the optimal barrier-free route, update route information in real time, and provide route planning results and real-time navigation guidance; The auxiliary equipment placement and rental service unit 320 is used to collect and analyze tourists' demand data for auxiliary equipment, predict the peak and valley values ​​of equipment rental based on the demand data, calculate the type and quantity of required equipment, and the optimal distribution of service points, establish an equipment rental database, record equipment status and rental history, monitor equipment inventory and service point status in real time, and adjust rental strategies. The specific formula of the equipment demand prediction and optimization model is: ; ; in: It is The optimal number of devices. It is The unit cost of the equipment, is the risk aversion coefficient, which is used to balance inventory cost and out-of-stock risk. is the variance between demand and supply, representing the risk of stock-outs, It is The expected demand for this type of equipment, is a standard normal distribution Quantiles, used to determine service levels, is the service level. A 95% service level means that there will be no shortage of goods 95% of the time. is the total number of types of equipment; The optimal distribution and dynamic adjustment model of service points is as follows: ; ; It is Moment Equipment demand for each service point, It is Moment The actual demand for this equipment, It is Moment The inventory of the equipment It is The adjustment coefficient of each device reflects its impact on the demand for service points. It is The weight of the equipment, It is Moment The service capabilities of each service point, It is the speed parameter for service capacity adjustment.

[0041] Figure 5 The structure block diagram of the monitoring and feedback system module provided by the embodiment of the present invention is as follows: Figure 5 As shown, the monitoring and feedback system module specifically includes: The real-time monitoring unit 410 is used to collect data on barrier-free facilities and tourist activities in the scenic area in real time through monitoring equipment, transmit the real-time data to the central monitoring system through a wireless network, process and analyze the collected data in real time, trigger an early warning mechanism based on the analysis results, and record a log file; The feedback analysis unit 420 is used to collect tourists' feedback on barrier-free facilities and services through mobile applications, text messages, telephone calls, and social media, organize, classify, and store the collected feedback data, perform sentiment analysis on tourists' feedback, identify positive and negative opinions and suggestions, perform statistical analysis on feedback data, identify common problems and needs, and generate feedback reports.

[0042] Figure 6 A flow chart of a tourism management method based on big data provided by an embodiment of the present invention, such as Figure 6 As shown, a tourism management method based on big data, the method comprising: S100, collects tourist feedback data, collects usage data of scenic spots, transportation and accommodation barrier-free facilities, identifies the problems and needs of people with limited mobility, and counts and analyzes the frequency and distribution of use of barrier-free facilities; S200, establish a barrier-free itinerary recommendation model, recommend travel itineraries based on tourists’ mobility and interests, collect information on barrier-free facilities in scenic spots, formulate barrier-free facility optimization plans, establish a facility maintenance database, and monitor maintenance needs in real time; S300, establishes an accessible journey navigation model, provides accessible route planning and real-time navigation, establishes auxiliary equipment rental service points, calculates the number and distribution of rental equipment, establishes equipment demand prediction and optimization models, and service point optimal distribution and dynamic adjustment models; S400, establish a real-time monitoring system to track the travel activities of tourists with limited mobility, monitor the operating status of barrier-free facilities, establish a tourist feedback mechanism, formulate improvement measures, and regularly evaluate the effectiveness of the improvement measures.

[0043] Figure 7 A flowchart of collecting tourist feedback data and collecting usage data of scenic spots, transportation and accommodation barrier-free facilities provided by an embodiment of the present invention, such as Figure 7 As shown, the collection of tourist feedback data and the collection of usage data of scenic spots, transportation and accommodation barrier-free facilities specifically include: S110, implement data collection, collect real-time feedback from mobile applications, use automated tools to collect barrier-free facility usage data in scenic spots, transportation and accommodation, and clean and format the collected data; S120, conduct preliminary analysis on the collected data, analyze visitor feedback and facility usage data, and build a demand identification model.

[0044] Figure 8 A flowchart of establishing a barrier-free journey recommendation model provided by an embodiment of the present invention, such as Figure 8 As shown, the establishment of the barrier-free journey recommendation model specifically includes: S210, collect and integrate tourists’ mobility data and hobbies information, and develop a barrier-free journey recommendation model. The specific formula is: ; in, is the final recommendation score, is the number of factors considered, It is The weight of the factors, It is The matching function of factors, is the input variable of this factor, Specific to each factor: represents the matching degree of tourists’ action ability, where It is the tourists’ mobility data; Indicates the matching degree of tourists’ interests and hobbies, where It is the tourists’ hobbies and interests data; Indicates the convenience of facilities, among which It is the barrier-free facilities data of the tourist destination; Indicates the convenience of transportation, It is the accessibility data of the transportation mode; represents seasonal suitability, where It is the weather and activity suitability data for the tourist season; Among them, the convenience of facilities The facility convenience evaluates the completeness and availability of barrier-free facilities in the tourist destination: the availability of ramps and elevators, barrier-free toilets and changing rooms, barrier-free parking spaces, barrier-free information and signs, barrier-free dining and accommodation facilities. A scoring standard is set for each facility, ranging from level 1 for very inconvenient to level 5 for fully barrier-free. Each item is scored based on the facilities of the tourist destination, and then a weighted average score is calculated as an indicator of facility convenience. Transportation convenience Transportation convenience evaluates the convenience and accessibility of transportation modes to tourist destinations: accessibility facilities of public transportation, accessibility services of taxis, convenience of parking facilities, and accessibility facilities of transportation hubs. Scoring standards are set for each mode of transportation, and scores are given based on the degree of perfection of accessibility facilities. S220, collect and analyze the location, status and usage frequency of barrier-free facilities in scenic areas, formulate facility optimization plans, facility upgrades and layout adjustments, establish a facility maintenance database, record the maintenance history and current status of the facilities, implement a real-time monitoring system, and automatically detect the operating status and maintenance needs of the facilities.

[0045] Fig. 9 The flowchart of establishing the equipment demand prediction and optimization model, the service point optimal distribution and dynamic adjustment model provided by the embodiment of the present invention is as follows: Fig. 9 As shown, the establishment of equipment demand prediction and optimization model, service point optimal distribution and dynamic adjustment model specifically includes: S310, collects and integrates scenic area maps, barrier-free facility locations, and traffic route data, selects suitable barrier-free routes based on tourists’ mobility data, calculates the optimal barrier-free route, updates route information in real time, and provides route planning results and real-time navigation guidance; S320, collect and analyze tourists’ demand data for auxiliary equipment, predict the peak and valley values ​​of equipment rental based on the demand data, calculate the types and quantities of required equipment, and the optimal distribution of service points, establish an equipment rental database, record equipment status and rental history, monitor equipment inventory and service point status in real time, and adjust rental strategies. The specific formula of the equipment demand prediction and optimization model is: ; ; in: It is The optimal number of devices. It is The unit cost of the equipment, is the risk aversion coefficient, which is used to balance inventory cost and out-of-stock risk. is the variance between demand and supply, representing the risk of stock-outs, It is The expected demand for this type of equipment, is a standard normal distribution Quantiles, used to determine service levels, is the service level. A 95% service level means that there will be no shortage of goods 95% of the time. is the total number of types of equipment; The optimal distribution and dynamic adjustment model of service points is as follows: ; ; It is Moment Equipment demand for each service point, It is Moment The actual demand for this equipment, It is Moment The inventory of the equipment It is The adjustment coefficient of each device reflects its impact on the demand for service points. It is The weight of the equipment, It is Moment The service capabilities of each service point, It is the speed parameter for service capacity adjustment.

[0046] Fig.10 A flowchart of establishing a real-time monitoring system, tracking the travel activities of tourists with limited mobility, monitoring the operating status of barrier-free facilities, and establishing a tourist feedback mechanism is provided in an embodiment of the present invention, such as Fig.10 As shown, the establishment of a real-time monitoring system to track the travel activities of tourists with limited mobility, monitor the operating status of barrier-free facilities, and establish a tourist feedback mechanism specifically includes: S410, collecting data on barrier-free facilities and tourist activities in the scenic area in real time through monitoring equipment, transmitting the real-time data to the central monitoring system through a wireless network, processing and analyzing the collected data in real time, triggering an early warning mechanism based on the analysis results, and recording a log file; S420, collects tourists’ feedback on barrier-free facilities and services through mobile applications, text messages, phone calls, and social media, organizes, categorizes, and stores the collected feedback data, conducts sentiment analysis on tourists’ feedback, identifies positive and negative opinions and suggestions, performs statistical analysis on feedback data, identifies common problems and needs, and generates feedback reports.

[0047] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0048] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0049] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0050] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0051] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A tourism management system based on big data, characterized in that: The system comprises: Demand analysis and data management module, which is used to collect tourist feedback data, collect usage data of scenic spots, transportation and accommodation barrier-free facilities, identify the problems and needs of people with limited mobility, and count and analyze the frequency and distribution of use of barrier-free facilities; The tourism planning service and facility management module is used to establish a barrier-free itinerary recommendation model, recommend travel itineraries based on tourists' mobility and interests, collect information on barrier-free facilities in scenic spots, formulate barrier-free facility optimization plans, establish a facility maintenance database, and monitor maintenance needs in real time; Intelligent assistance and professional service module, which is used to establish barrier-free journey navigation model, provide barrier-free route planning and real-time navigation, establish auxiliary equipment rental service points, calculate the number and distribution of rental equipment, establish equipment demand prediction and optimization model, and service point optimal distribution and dynamic adjustment model; The monitoring and feedback system module is used to establish a real-time monitoring system, track the travel activities of tourists with limited mobility, monitor the operating status of barrier-free facilities, establish a tourist feedback mechanism, formulate improvement measures, and regularly evaluate the effectiveness of improvement measures.

2. The tourism management system based on big data according to claim 1 is characterized in that: The demand analysis and data management module specifically includes: Data collection and processing unit, used to implement data collection, collect real-time feedback from mobile applications, use automated tools to collect barrier-free facility usage data for scenic spots, transportation and accommodation, and clean and format the collected data; The data analysis and demand identification unit is used to conduct preliminary analysis of the collected data, analyze visitor feedback and facility usage data, and build a demand identification model.

3. The tourism management system based on big data according to claim 2 is characterized in that: The tourism planning service and facility management module specifically includes: The barrier-free journey recommendation and planning unit is used to collect and integrate tourists' mobility data and interest information, and develop a barrier-free journey recommendation model. The specific formula is: ； in, is the final recommendation score, is the number of factors considered, It is The weight of the factors, It is The matching function of factors, is the input variable of this factor, Specific to each factor: represents the matching degree of tourists’ action ability, where It is the tourists’ mobility data; Indicates the matching degree of tourists’ interests and hobbies, where It is the tourists’ hobbies and interests data; Indicates the convenience of facilities, among which It is the barrier-free facilities data of the tourist destination; Indicates the convenience of transportation, It is the accessibility data of the transportation mode; represents seasonal suitability, where It is the weather and activity suitability data for the tourist season; Among them, the convenience of facilities The facility convenience evaluates the completeness and availability of barrier-free facilities in the tourist destination: the availability of ramps and elevators, barrier-free toilets and changing rooms, barrier-free parking spaces, barrier-free information and signboards, barrier-free dining and accommodation facilities. A scoring standard is set for each facility, ranging from level 1 for very inconvenient to level 5 for fully barrier-free. Each item is scored based on the facilities of the tourist destination, and a weighted average score is calculated as an indicator of facility convenience. Transportation convenience Transportation convenience evaluates the convenience and accessibility of transportation modes to tourist destinations: accessibility facilities of public transportation, accessibility services of taxis, convenience of parking facilities, and accessibility facilities of transportation hubs. Scoring standards are set for each mode of transportation, and scores are given based on the degree of perfection of accessibility facilities. The facility management and maintenance unit is used to collect and analyze the location, status and usage frequency of barrier-free facilities in scenic areas, formulate facility optimization plans, facility upgrades and layout adjustments, establish a facility maintenance database, record the maintenance history and current status of the facilities, implement a real-time monitoring system, and automatically detect the operating status and maintenance needs of the facilities.

4. The tourism management system based on big data according to claim 3 is characterized in that: The intelligent assistance and professional service module specifically includes: The barrier-free journey navigation model unit is used to collect and integrate scenic area maps, barrier-free facility locations, and traffic route data, screen suitable barrier-free routes based on tourists' mobility data, calculate the optimal barrier-free route, update route information in real time, and provide route planning results and real-time navigation guidance; The auxiliary equipment rental service unit is used to collect and analyze tourists' demand data for auxiliary equipment, predict the peak and valley values ​​of equipment rental based on the demand data, calculate the type and quantity of required equipment, and the optimal distribution of service points, establish an equipment rental database, record equipment status and rental history, monitor equipment inventory and service point status in real time, and adjust rental strategies. The specific formula of the equipment demand prediction and optimization model is: ； ； in: It is The optimal number of devices, It is The unit cost of the equipment, is the risk aversion coefficient, which is used to balance inventory cost and out-of-stock risk. is the variance between demand and supply, representing the risk of stock-outs, It is The expected demand for this type of equipment, is a standard normal distribution Quantiles, used to determine service levels, is the service level, is the total number of types of equipment; The optimal distribution and dynamic adjustment model of service points is as follows: ； ； It is Moment Equipment demand for each service point, It is Moment The actual demand for this equipment, It is Moment The inventory of the equipment It is The adjustment coefficient of each device reflects its impact on the demand for service points. It is The weight of the equipment, It is Moment The service capabilities of each service point, It is the speed parameter for service capacity adjustment.

5. The tourism management system based on big data according to claim 4 is characterized in that: The monitoring and feedback system module specifically includes: The real-time monitoring unit is used to collect data on barrier-free facilities and tourist activities in the scenic area in real time through monitoring equipment, transmit the real-time data to the central monitoring system through a wireless network, process and analyze the collected data in real time, trigger an early warning mechanism based on the analysis results, and record log files; The feedback analysis unit is used to collect tourists' feedback on barrier-free facilities and services through mobile applications, text messages, telephone calls, and social media, organize, classify, and store the collected feedback data, conduct sentiment analysis on tourists' feedback, identify positive and negative opinions and suggestions, perform statistical analysis on feedback data, identify common problems and needs, and generate feedback reports.

6. A tourism management method based on big data, characterized in that: The method comprises: Collect tourist feedback data, collect usage data of scenic spots, transportation and accommodation barrier-free facilities, identify the problems and needs of people with limited mobility, and count and analyze the frequency and distribution of use of barrier-free facilities; Establish a barrier-free itinerary recommendation model to recommend travel itineraries based on tourists’ mobility and interests, collect information on barrier-free facilities in scenic spots, develop barrier-free facility optimization plans, establish a facility maintenance database, and monitor maintenance needs in real time; Establish an accessible journey navigation model, provide accessible route planning and real-time navigation, establish auxiliary equipment rental service points, calculate the number and distribution of rental equipment, establish equipment demand prediction and optimization models, and service point optimal distribution and dynamic adjustment models; Establish a real-time monitoring system to track the travel activities of tourists with limited mobility, monitor the operating status of barrier-free facilities, establish a tourist feedback mechanism, formulate improvement measures, and regularly evaluate the effectiveness of the improvement measures.

7. The tourism management method based on big data according to claim 6 is characterized in that: The collection of tourist feedback data and the collection of usage data of scenic spots, transportation and accommodation barrier-free facilities specifically include: Implement data collection, collect real-time feedback from mobile applications, use automated tools to collect data on the use of barrier-free facilities in scenic spots, transportation and accommodation, and clean and format the collected data; Conduct preliminary analysis of the collected data, analyze visitor feedback and facility usage data, and build a demand identification model.

8. The tourism management method based on big data according to claim 7 is characterized in that: The establishment of the barrier-free journey recommendation model specifically includes: Collect and integrate tourists' mobility data and hobbies information to develop a barrier-free journey recommendation model. The specific formula is: ； in, is the final recommendation score, is the number of factors considered, It is The weight of the factors, It is The matching function of factors, is the input variable of this factor, Specific to each factor: represents the matching degree of tourists’ action ability, where It is the tourists’ mobility data; Indicates the matching degree of tourists’ interests and hobbies, where It is the tourists’ hobbies and interests data; Indicates the convenience of facilities, among which It is the barrier-free facilities data of the tourist destination; Indicates the convenience of transportation, It is the accessibility data of the transportation mode; represents seasonal suitability, where It is the weather and activity suitability data for the tourist season; Among them, the convenience of facilities The facility convenience evaluates the completeness and availability of barrier-free facilities in the tourist destination: the availability of ramps and elevators, barrier-free toilets and changing rooms, barrier-free parking spaces, barrier-free information and signs, barrier-free dining and accommodation facilities. A scoring standard is set for each facility, ranging from level 1 for very inconvenient to level 5 for fully barrier-free. Each item is scored based on the facilities of the tourist destination, and then a weighted average score is calculated as an indicator of facility convenience. Transportation convenience Transportation convenience evaluates the convenience and accessibility of transportation modes to tourist destinations: accessibility facilities of public transportation, accessibility services of taxis, convenience of parking facilities, and accessibility facilities of transportation hubs. Scoring standards are set for each mode of transportation, and scores are given based on the degree of perfection of accessibility facilities. Collect and analyze the location, status and usage frequency of barrier-free facilities in scenic areas, formulate facility optimization plans, facility upgrades and layout adjustments, establish a facility maintenance database, record the maintenance history and current status of the facilities, implement a real-time monitoring system, and automatically detect the operating status and maintenance needs of the facilities.

9. The tourism management method based on big data according to claim 8 is characterized in that: The establishment of equipment demand prediction and optimization model, service point optimal distribution and dynamic adjustment model specifically includes: Collect and integrate scenic area maps, barrier-free facility locations, and traffic route data, select suitable barrier-free routes based on tourists' mobility data, calculate the optimal barrier-free route, update route information in real time, and provide route planning results and real-time navigation guidance; Collect and analyze tourists' demand data for auxiliary equipment, predict the peak and valley values ​​of equipment rental based on the demand data, calculate the type and quantity of required equipment, and the optimal distribution of service points, establish an equipment rental database, record equipment status and rental history, monitor equipment inventory and service point status in real time, and adjust rental strategies. The specific formula of the equipment demand prediction and optimization model is: ； ； in: It is The optimal number of devices, It is The unit cost of the equipment, is the risk aversion coefficient, which is used to balance inventory cost and out-of-stock risk. is the variance between demand and supply, representing the risk of stock-outs, It is The expected demand for this type of equipment, is a standard normal distribution Quantiles, used to determine service levels, is the service level. A 95% service level means that there will be no shortage of goods 95% of the time. is the total number of types of equipment; The optimal distribution and dynamic adjustment model of service points is as follows: ； ； It is Moment Equipment demand for each service point, It is Moment The actual demand for this equipment, It is Moment The inventory of the equipment It is The adjustment coefficient of each device reflects its impact on the demand for service points. It is The weight of the equipment, It is Moment The service capabilities of each service point, It is the speed parameter for service capacity adjustment.

10. The tourism management method based on big data according to claim 9 is characterized in that: The establishment of a real-time monitoring system to track the travel activities of tourists with limited mobility, monitor the operating status of barrier-free facilities, and establish a tourist feedback mechanism specifically includes: Through monitoring equipment, real-time data on barrier-free facilities and tourist activities in the scenic area are collected, and the real-time data is transmitted to the central monitoring system through the wireless network. The collected data is processed and analyzed in real time. According to the analysis results, the early warning mechanism is triggered and the log file is recorded; Collect tourists’ feedback on accessible facilities and services through mobile applications, text messages, phone calls, and social media, organize, classify, and store the collected feedback data, conduct sentiment analysis on tourists’ feedback, identify positive and negative opinions and suggestions, perform statistical analysis on feedback data, identify common problems and needs, and generate feedback reports.

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