Garden tourist flow intelligent prediction and guidance management system

Abstract

The invention relates to the technical field of garden management, and particularly discloses a garden tourist flow intelligent prediction and guide management system, which is used for predicting and guiding the flow of tourists at each scenic spot in a garden, and comprises the following steps: firstly, numbering each scenic spot in the garden to facilitate statistics, then installing an infrared camera at the entrance and exit of each scenic spot, and finally, carrying out intelligent prediction and guide on the flow of the tourists; the system is used for collecting tourist data of each scenic spot, calculating a real-time congestion coefficient of each scenic spot according to the real-time tourist data of each scenic spot and the actual condition of an entrance and exit channel of each scenic spot, then obtaining a guide coefficient of each scenic spot in combination with historical data of each scenic spot, the bearing capacity of each scenic spot and an emergency, and sending the guide coefficient to the scenic spot. The tourist flow of the scenic spots is predicted according to the guide coefficient of each scenic spot, and the tourists are guided according to the prediction result, so that the overall operation efficiency of the garden is improved, and the tourism experience of the tourists is improved.

Description

Technical Field

[0001] The invention relates to the technical field of garden management, and in particular to an intelligent prediction and guidance management system for garden visitor flow. Background Art

[0002] With the improvement of people's living standards, gardens, as important places for leisure and entertainment, have an increasing number of tourists. However, there are many problems in the management of tourist flow in gardens: on the one hand, it is impossible to accurately predict the tourist flow, resulting in excessive concentration of tourists in a certain scenic spot in the garden while too few tourists in other scenic spots, which not only affects the tourists' experience, but also reduces the overall operation efficiency of the garden. On the other hand, some existing flow monitoring systems can only realize the statistics of the current number of tourists, without considering emergencies, and lack intelligent guidance for tourists. Therefore, it is of great practical significance to develop a system that can accurately predict the flow of garden tourists and conduct effective guidance and management. Summary of the invention

[0003] The purpose of the present invention is to provide a garden tourist flow intelligent prediction and guidance management system to solve the above technical problems:

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A garden tourist flow intelligent prediction and guidance management system, the system comprises: a data collection module, a data processing module, a data analysis module and a flow prediction and guidance management module;

[0006] The data collection module is equipped with infrared sensors at the entrances and exits of various scenic spots in the garden to collect the entry and exit data of tourists in real time;

[0007] The data processing module is used to clean and integrate the collected data and store the processed data in a database for subsequent modules to call;

[0008] The data analysis module is used to analyze the tourist information data in the database;

[0009] The traffic prediction and guidance management module is used to predict and guide the traffic of scenic spots based on the analysis results of the data analysis module.

[0010] As a further description of the technical solution of the present invention, the working process of the data acquisition module includes:

[0011] All scenic spots are numbered in the following order: 1, 2, ..., n;

[0012] Infrared cameras are installed at the entrance and exit of each scenic spot. The cameras should be installed in a position that can cover the corresponding area to ensure the integrity of image acquisition;

[0013] The entry and exit data of the tourists include:

[0014] The flow of people at the entrance and exit of the scenic spot, the average movement speed of people at the entrance and exit of the scenic spot, and the average movement speed of people at the exit of the scenic spot and the average length of stay of people inside the scenic spot during the monitoring period.

[0015] As a further description of the technical solution of the present invention, the working process of the data analysis module includes:

[0016] Calculate the congestion coefficient of each scenic spot according to the in-and-out data of tourists collected by the data collection module;

[0017] The attraction guidance coefficient of each attraction is calculated based on the attraction congestion coefficient of each attraction.

[0018] As a further description of the technical solution of the present invention, the working process of calculating the congestion coefficient of each scenic spot includes:

[0019] Construct a mathematical model of the congestion coefficient of the i-th scenic spot, and the expression is:

[0020] ;

[0021] In the formula, i belongs to n, represents the average length of time people stay in the i-th scenic spot during the monitoring period, represents the flow of people at the exit of the i-th scenic spot during the monitoring period, represents the flow of people at the entrance of the i-th scenic spot during the monitoring period, represents the average moving speed of people at the entrance of the i-th scenic spot during the monitoring period, represents the average moving speed of people at the exit of the i-th scenic spot during the monitoring period, It represents the standard average duration of stay of people in the i-th scenic spot during the monitoring period preset by the system. It represents the standard flow of people at the exit of the i-th scenic spot during the monitoring period preset by the system. It represents the standard flow of people at the entrance of the i-th scenic spot during the monitoring period preset by the system. It represents the standard average moving speed of people at the entrance of the i-th scenic spot during the monitoring period preset by the system. It represents the standard average moving speed of people at the exit of the i-th scenic spot during the monitoring period preset by the system. and Represents the weight coefficient.

[0022] As a further description of the technical solution of the present invention, the weight coefficient The weight coefficient is determined according to the number of entrances to the i-th scenic spot. Determined according to the number of exits of the i-th scenic spot.

[0023] As a further description of the technical solution of the present invention, the working process of calculating the scenic spot guidance coefficient of each scenic spot includes:

[0024] Divide each day into x time periods, and obtain the congestion index of the time period corresponding to the monitoring time period of the i-th scenic spot for y consecutive days in sequence;

[0025] According to the congestion index of the i-th road section for y consecutive days in the monitoring time period, the average congestion index of the i-th scenic spot in the monitoring time period is obtained. ;

[0026] Construct the guidance coefficient mathematical model of the i-th scenic spot, the expression is:

[0027] ;

[0028] In the formula, Represents the guidance coefficient of the i-th scenic spot, and its value range is , There are absolutely no tourists. Indicates that the tourist attraction has reached its maximum capacity; Indicates the maximum real-time passenger flow of tourists during the monitoring period. represents the maximum carrying capacity of tourists at the i-th scenic spot, represents the weight coefficient, represents the dynamic adjustment coefficient, Indicates the conversion factor.

[0029] As a further description of the technical solution of the present invention, Represents the influence weight of historical data on the guidance coefficient, and its value range is , When it means that it is completely dependent on historical data, When means completely relying on real-time data;

[0030] It is used to reflect the impact of emergencies on congestion. The value range is , Indicates no sudden impact. Indicates that the emergency has caused a significant increase in congestion.

[0031] As a further description of the technical solution of the present invention, the working process of the guidance management module includes:

[0032] Compare the guidance coefficient of the i-th scenic spot with the guidance coefficient threshold interval. Belongs to the threshold range , it means that the tourist flow of the i-th scenic spot is idle and tourists can go there for sightseeing;

[0033] When the i-th scenic spot guidance coefficient Belongs to the threshold range , it means that the tourist flow of the i-th scenic spot is moderate, and tourists can visit it, and it is recommended that tourists make plans in advance;

[0034] When the i-th scenic spot guidance coefficient Belongs to the threshold range If , it means that the tourist flow at the i-th scenic spot is extremely congested and tourists cannot visit it.

[0035] As a further description of the technical solution of the present invention, the method for using the system includes the following steps:

[0036] Step S1, real-time monitoring of the flow of people at the entrance of each scenic spot, the flow of people at the exit of the scenic spot, the average speed of people at the entrance of the scenic spot, the average speed of people at the exit of the scenic spot, and the average length of time people stay inside the scenic spot;

[0037] Step S2, calculating the real-time congestion coefficient of each scenic spot according to the real-time data collected in step S1;

[0038] Step S3, according to the real-time congestion coefficient of each scenic spot and in combination with historical data, and taking into account the carrying capacity of each scenic spot and emergencies, the guidance coefficient of each scenic spot is obtained;

[0039] Step S4: predict the subsequent tourist flow of each scenic spot according to the guidance coefficient of each scenic spot, and guide according to the prediction result.

[0040] The beneficial effects of the present invention are as follows: the present invention is used to predict and guide the flow of tourists at various scenic spots in a garden. First, the various scenic spots in the garden are numbered for easy statistics. Then, infrared cameras are installed at the entrances and exits of each scenic spot to collect tourist data at each scenic spot. The real-time congestion coefficient of each scenic spot is calculated based on the real-time tourist data of each scenic spot and the actual conditions of the entrances and exits of each scenic spot. Then, the guidance coefficient of each scenic spot is obtained by combining the historical data of each scenic spot with the carrying capacity of each scenic spot and emergencies. The tourist flow of the scenic spot is predicted based on the guidance coefficient of each scenic spot, and tourists are guided based on the prediction results, which not only improves the overall operation efficiency of the garden, but also improves the travel experience of tourists. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] Figure 1 It is a structural schematic diagram of the garden tourist flow intelligent prediction and guidance management system of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] See also Figure 1 As shown, the present invention provides a garden tourist flow intelligent prediction and guidance management system, the system includes: a data acquisition module, a data processing module, a data analysis module and a flow prediction and guidance management module;

[0045] The data collection module is equipped with infrared sensors at the entrances and exits of various scenic spots in the garden to collect the entry and exit data of tourists in real time;

[0046] The data processing module is used to clean and integrate the collected data and store the processed data in a database for subsequent modules to call;

[0047] The data analysis module is used to analyze the tourist information data in the database;

[0048] The traffic prediction and guidance management module is used to predict and guide the traffic of scenic spots based on the analysis results of the data analysis module.

[0049] Through the above technical scheme, the present invention is used to predict and guide the flow of tourists at various scenic spots in the garden. First, the various scenic spots in the garden are numbered for easy statistics. Then, infrared cameras are installed at the entrances and exits of each scenic spot to collect tourist data of each scenic spot. The real-time congestion coefficient of each scenic spot is calculated according to the real-time tourist data of each scenic spot and the actual situation of the entrances and exits of each scenic spot. Then, the guidance coefficient of each scenic spot is obtained by combining the historical data of each scenic spot with the carrying capacity of each scenic spot and emergencies. The tourist flow of the scenic spot is predicted according to the guidance coefficient of each scenic spot, and tourists are guided according to the prediction results, which not only improves the overall operation efficiency of the garden, but also improves the travel experience of tourists.

[0050] As a further description of the technical solution of the present invention, the working process of the data acquisition module includes:

[0051] All scenic spots are numbered in the following order: 1, 2, ..., n;

[0052] Infrared cameras are installed at the entrance and exit of each scenic spot. The cameras should be installed in a position that can cover the corresponding area to ensure the integrity of image acquisition;

[0053] The entry and exit data of the tourists include:

[0054] The flow of people at the entrance and exit of the scenic spot, the average movement speed of people at the entrance and exit of the scenic spot, and the average movement speed of people at the exit of the scenic spot and the average length of stay of people inside the scenic spot during the monitoring period.

[0055] Through the above technical scheme, this embodiment provides a method for collecting tourist data, by installing infrared cameras at the entrances and exits of each scenic spot, and ensuring that the image data collected by the infrared cameras is very complete, and then obtaining the flow of people at the scenic spot entrance, the flow of people at the scenic spot exit, the average movement speed of people at the scenic spot entrance, the average movement speed of people at the scenic spot exit and the average length of stay of people inside the scenic spot during the monitoring period based on the image data.

[0056] As a further description of the technical solution of the present invention, the working process of the data analysis module includes:

[0057] Calculate the congestion coefficient of each scenic spot according to the in-and-out data of tourists collected by the data collection module;

[0058] The attraction guidance coefficient of each attraction is calculated based on the attraction congestion coefficient of each attraction.

[0059] As a further description of the technical solution of the present invention, the working process of calculating the congestion coefficient of each scenic spot includes:

[0060] Construct a mathematical model of the congestion coefficient of the i-th scenic spot, and the expression is:

[0061] ;

[0062] In the formula, i belongs to n, represents the average length of time people stay in the i-th scenic spot during the monitoring period, represents the flow of people at the exit of the i-th scenic spot during the monitoring period, represents the flow of people at the entrance of the i-th scenic spot during the monitoring period, represents the average moving speed of people at the entrance of the i-th scenic spot during the monitoring period, represents the average moving speed of people at the exit of the i-th scenic spot during the monitoring period, It represents the standard average duration of stay of people in the i-th scenic spot during the monitoring period preset by the system. It represents the standard flow of people at the exit of the i-th scenic spot during the monitoring period preset by the system. It represents the standard flow of people at the entrance of the i-th scenic spot during the monitoring period preset by the system. It represents the standard average moving speed of people at the entrance of the i-th scenic spot during the monitoring period preset by the system. It represents the standard average moving speed of people at the exit of the i-th scenic spot during the monitoring period preset by the system. and Represents the weight coefficient.

[0063] As a further description of the technical solution of the present invention, the weight coefficient The weight coefficient is determined according to the number of entrances to the i-th scenic spot. Determined according to the number of exits of the i-th scenic spot.

[0064] Through the above technical scheme, the present invention provides a method for calculating the real-time congestion coefficient of each scenic spot according to the real-time tourist data of each scenic spot. First, the entrance flow of each scenic spot, the exit flow of each scenic spot, the average moving speed of people at the entrance of the scenic spot, the average moving speed of people at the exit of the scenic spot and the average length of stay of people inside the scenic spot are obtained respectively. Then, the standard entrance flow of each scenic spot, the standard exit flow of each scenic spot, the standard average moving speed of people at the entrance of the scenic spot, the standard average moving speed of people at the exit of the scenic spot and the standard average length of stay of people inside the scenic spot preset by the system are obtained. Then, the measured data is compared with the standard data, and then combined with the actual number of entrances and exits of each scenic spot, the congestion coefficient of each scenic spot is calculated.

[0065] As a further description of the technical solution of the present invention, the working process of calculating the scenic spot guidance coefficient of each scenic spot includes:

[0066] Divide each day into x time periods, and obtain the congestion index of the time period corresponding to the monitoring time period of the i-th scenic spot for y consecutive days in sequence;

[0067] According to the congestion index of the i-th road section for y consecutive days in the monitoring time period, the average congestion index of the i-th scenic spot in the monitoring time period is obtained. ;

[0068] Construct the guidance coefficient mathematical model of the i-th scenic spot, the expression is:

[0069] ;

[0070] In the formula, Represents the guidance coefficient of the i-th scenic spot, and its value range is , There are absolutely no tourists. Indicates that the tourist attraction has reached its maximum capacity; Indicates the maximum real-time passenger flow of tourists during the monitoring period. represents the maximum carrying capacity of tourists at the i-th scenic spot, represents the weight coefficient, represents the dynamic adjustment coefficient, Indicates the conversion factor.

[0071] As a further description of the technical solution of the present invention, Represents the influence weight of historical data on the guidance coefficient, and its value range is , When it means that it is completely dependent on historical data, When means completely relying on real-time data;

[0072] It is used to reflect the impact of emergencies on congestion. The value range is , Indicates no sudden impact. Indicates that the emergency has caused a significant increase in congestion.

[0073] As a further description of the technical solution of the present invention, the guidance management module works by comparing the guidance coefficient of the i-th scenic spot with the guidance coefficient threshold interval. Belongs to the threshold range , it means that the tourist flow of the i-th scenic spot is idle and tourists can go there for sightseeing;

[0074] When the i-th scenic spot guidance coefficient Belongs to the threshold range , it means that the tourist flow of the i-th scenic spot is moderate, and tourists can visit it, and it is recommended that tourists make plans in advance;

[0075] When the i-th scenic spot guidance coefficient Belongs to the threshold range If , then it means that the tourist flow at the i-th scenic spot is extremely congested and tourists cannot visit it.

[0076] Through the above technical solution, the present invention provides a method for predicting and guiding the tourist flow of each scenic spot. First, the historical data of the current time period of each scenic spot is obtained to obtain the average congestion index of the current time period of each scenic spot. Then, according to the actual carrying capacity of the scenic spot and the actual number of people in the current time period, and combined with whether there is an emergency in the current time period, the formula is used to predict and guide the tourist flow of each scenic spot. The guidance coefficient of each scenic spot is calculated, and then the flow of each scenic spot is predicted according to the guidance coefficient of each scenic spot, and guidance is performed according to the prediction results. The guidance coefficient of each scenic spot is compared with the guidance coefficient threshold interval. When the guidance coefficient of each scenic spot belongs to the threshold interval, it means that the current tourist flow of the scenic spot is idle and tourists can go for sightseeing; when the guidance coefficient of each scenic spot is lower than the threshold interval, it means that the current tourist flow of the scenic spot is moderate, tourists can go for sightseeing, and tourists are advised to plan in advance; when the guidance coefficient of each scenic spot is higher than the threshold interval, it means that the current tourist flow of the scenic spot is extremely congested and tourists cannot go for sightseeing.

[0077] As a further description of the technical solution of the present invention, the method for using the system includes the following steps:

[0078] Step S1, real-time monitoring of the flow of people at the entrance of each scenic spot, the flow of people at the exit of the scenic spot, the average speed of people at the entrance of the scenic spot, the average speed of people at the exit of the scenic spot, and the average length of time people stay inside the scenic spot;

[0079] Step S2, calculating the real-time congestion coefficient of each scenic spot according to the real-time data collected in step S1;

[0080] Step S3, according to the real-time congestion coefficient of each scenic spot and in combination with historical data, and taking into account the carrying capacity of each scenic spot and emergencies, the guidance coefficient of each scenic spot is obtained;

[0081] Step S4: predict the subsequent tourist flow of each scenic spot according to the guidance coefficient of each scenic spot, and guide according to the prediction result.

[0082] It should be noted that the calculations in the present invention are all dimensionless calculations, and the standard data, threshold intervals, and weight coefficients set in the present invention are all empirical data and need not be elaborated.

[0083] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A garden tourist flow intelligent prediction and guidance management system, characterized in that: The system includes: a data acquisition module, a data processing module, a data analysis module and a flow prediction and guidance management module; The data collection module is equipped with infrared sensors at the entrances and exits of various scenic spots in the garden to collect the entry and exit data of tourists in real time; The data processing module is used to clean and integrate the collected data and store the processed data in a database for subsequent modules to call; The data analysis module is used to analyze the tourist information data in the database; The traffic prediction and guidance management module is used to predict and guide the traffic of scenic spots based on the analysis results of the data analysis module.

2. According to claim 1, the system for intelligent prediction and guidance of tourist flow in gardens is characterized in that: The working process of the data acquisition module includes: All scenic spots are numbered in the following order: 1, 2, ..., n; Infrared cameras are installed at the entrance and exit of each scenic spot. The cameras should be installed in a position that can cover the corresponding area to ensure the integrity of image acquisition; The entry and exit data of the tourists include: The flow of people at the entrance and exit of the scenic spot, the average movement speed of people at the entrance and exit of the scenic spot, and the average movement speed of people at the exit of the scenic spot and the average length of stay of people inside the scenic spot during the monitoring period.

3. The system for intelligent prediction and guidance of garden visitor flow according to claim 2 is characterized in that: The working process of the data analysis module includes: Calculate the congestion coefficient of each scenic spot according to the in-and-out data of tourists collected by the data collection module; The attraction guidance coefficient of each attraction is calculated based on the attraction congestion coefficient of each attraction.

4. The system for intelligent prediction and guidance of garden visitor flow according to claim 3 is characterized in that: The working process of calculating the congestion coefficient of each scenic spot includes: Construct a mathematical model of the congestion coefficient of the i-th scenic spot, and the expression is: ； In the formula, i belongs to n, represents the average length of time people stay in the i-th scenic spot during the monitoring period, represents the flow of people at the exit of the i-th scenic spot during the monitoring period, represents the flow of people at the entrance of the i-th scenic spot during the monitoring period, represents the average moving speed of people at the entrance of the i-th scenic spot during the monitoring period, represents the average moving speed of people at the exit of the i-th scenic spot during the monitoring period, It represents the standard average duration of stay of people in the i-th scenic spot during the monitoring period preset by the system. It represents the standard flow of people at the exit of the i-th scenic spot during the monitoring period preset by the system. It represents the standard flow of people at the entrance of the i-th scenic spot during the monitoring period preset by the system. It represents the standard average moving speed of people at the entrance of the i-th scenic spot during the monitoring period preset by the system. It represents the standard average moving speed of people at the exit of the i-th scenic spot during the monitoring period preset by the system. and Represents the weight coefficient.

5. The system for intelligent prediction and guidance of garden visitor flow according to claim 4 is characterized in that: The weight coefficient The weight coefficient is determined according to the number of entrances to the i-th scenic spot. Determined according to the number of exits of the i-th scenic spot.

6. The system for intelligent prediction and guidance of garden visitor flow according to claim 4 is characterized in that: The working process of calculating the scenic spot guidance coefficient of each scenic spot includes: Divide each day into x time periods, and obtain the congestion index of the time period corresponding to the monitoring time period of the i-th scenic spot for y consecutive days in sequence; According to the congestion index of the i-th road section for y consecutive days in the monitoring time period, the average congestion index of the i-th scenic spot in the monitoring time period is obtained. ; Construct the guidance coefficient mathematical model of the i-th scenic spot, the expression is: ; In the formula, Represents the guidance coefficient of the i-th scenic spot, and its value range is , There are absolutely no tourists. Indicates that the tourist attraction has reached its maximum capacity; Indicates the maximum real-time passenger flow of tourists during the monitoring period. represents the maximum carrying capacity of tourists at the i-th scenic spot, represents the weight coefficient, represents the dynamic adjustment coefficient, Indicates the conversion factor.

7. The system for intelligent prediction and guidance of garden visitor flow according to claim 6 is characterized in that: Represents the influence weight of historical data on the guidance coefficient, and its value range is , When it means that it is completely dependent on historical data, When means completely relying on real-time data; It is used to reflect the impact of emergencies on congestion. The value range is , Indicates no sudden impact. Indicates that the emergency has caused a significant increase in congestion.

8. The system for intelligent prediction and guidance of garden visitor flow according to claim 7 is characterized in that: The working process of the guidance management module includes: Compare the guidance coefficient of the i-th scenic spot with the guidance coefficient threshold interval. Belongs to the threshold range , it means that the tourist flow of the i-th scenic spot is idle and tourists can go there for sightseeing; When the i-th scenic spot guidance coefficient Belongs to the threshold range , it means that the tourist flow of the i-th scenic spot is moderate, and tourists can visit it, and it is recommended that tourists make plans in advance; When the i-th scenic spot guidance coefficient Belongs to the threshold range If , then it means that the tourist flow at the i-th scenic spot is extremely congested and tourists cannot visit it.

9. The intelligent prediction and guidance management system for forest tourist flow according to any one of claims 1 to 8, characterized in that: The method of using the system comprises the following steps: Step S1, real-time monitoring of the flow of people at the entrance of each scenic spot, the flow of people at the exit of the scenic spot, the average speed of people at the entrance of the scenic spot, the average speed of people at the exit of the scenic spot, and the average length of time people stay inside the scenic spot; Step S2, calculating the real-time congestion coefficient of each scenic spot according to the real-time data collected in step S1; Step S3, according to the real-time congestion coefficient of each scenic spot and in combination with historical data, and taking into account the carrying capacity of each scenic spot and emergencies, the guidance coefficient of each scenic spot is obtained; Step S4: predict the subsequent tourist flow of each scenic spot according to the guidance coefficient of each scenic spot, and guide according to the prediction result.

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