Service management system based on big data

Abstract

A big data-based service management system disclosed by the present invention comprises a big data statistics module, a tour enthusiasm analysis module and a service management module, the big data statistics module is used for collecting and obtaining scenic spot and tour related data, and the tour enthusiasm analysis module is used for analyzing and judging tour emotions of tourists in the scenic spot. The service management module is used for dynamically controlling the scenic spot passenger flow volume, and the big data statistics module, the tourist enthusiasm analysis module and the service management module are electrically connected with one another; through big data analysis and real-time scene monitoring, the influence of the tourist enthusiasm, the played item and the field heat degree on the visitor flow control is creatively considered, the purpose of intelligently and dynamically adjusting the scenic spot visitor flow is achieved, and the scenic spot visitor flow control method has the advantages of dynamically and reasonably controlling the scenic spot visitor flow and being high in practicability.

Description

Technical Field

[0001] The present invention relates to the technical field of scenic spot big data management, and in particular to a service management system based on big data. Background Art

[0002] With the improvement of living standards, the number of tourists is increasing. Every winter, the passenger flow of many ice and snow theme scenic spots in northern China has increased dramatically. Since the carrying capacity of scenic spots is limited, in order to ensure the safety and experience of visitors, each scenic spot will detect the passenger flow of the scenic spot to ensure that the number of visitors entering the scenic spot is adjusted in time. However, the existing scenic spots have fixed control over the flow of people. For ice and snow projects, due to the variety and confusion of the running projects, the different enthusiasm of tourists in different time periods or due to external influences, and the ice and snow ground used for recreational sports is difficult for novices to control. Then, when approaching the upper limit of the control of the flow of people, tourists often collide, push and fall, which may cause skin injuries and equipment damage, and in severe cases, it is very easy to cause major casualties. And due to the particularity of the ice and snow ground, emergency rescue is slow. Therefore, it is necessary to design a service management system based on big data that can dynamically and reasonably control the passenger flow of scenic spots and has strong practicality. Summary of the invention

[0003] The purpose of the present invention is to provide a service management system based on big data to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a service management system based on big data, including a big data statistics module, a tourist enthusiasm analysis module and a service management module, the big data statistics module is used to collect and obtain scenic spot and travel-related data, the tourist enthusiasm analysis module is used to analyze and judge the tourist mood at the scenic spot, and the service management module is used to dynamically control the tourist flow of the scenic spot, and the big data statistics module, the tourist enthusiasm analysis module and the service management module are electrically connected to each other.

[0005] According to the above technical solution, the big data statistics module includes a meteorological acquisition module and an activity performance project data recording module. The meteorological acquisition module is used to obtain the current forecast meteorological data of the scenic spot, and the activity performance project data recording module is used to statistically record the activities and performance related data held in the scenic spot.

[0006] According to the above technical solution, the play enthusiasm analysis module includes a big data analysis and prediction module and an on-site real-time analysis module. The big data analysis and prediction module is used to predict the play enthusiasm of tourists in the scenic area based on the historical big data of the scenic area. The on-site real-time analysis module is used to analyze the real-time play scenes of tourists in the scenic area in real time and judge the real-time play enthusiasm of tourists on site.

[0007] According to the above technical solution, the service management module includes an appointment registration setting module and an on-site management control module. The appointment registration setting module is used to set the upper limit of the scenic spot passenger flow according to the analysis results, and the on-site management control module is used to further perform passenger flow management and control according to the existing actual situation based on the setting of the upper limit of the scenic spot passenger flow.

[0008] According to the above technical solution, the big data analysis and prediction module further includes a data sorting submodule and a data matching submodule. The data sorting submodule is used to sort the scenic spot and travel-related data collected and acquired by the big data statistics module, and the data matching submodule is used to match the sorted data with the historical big data database to obtain the best predicted passenger flow restriction result under the current scenic spot and travel conditions.

[0009] According to the above technical solution, the on-site real-time analysis module further includes an on-site image acquisition submodule, a screen character tracking submodule and a decibel analysis submodule. The on-site image acquisition submodule is used to collect real-time monitoring image data of the scenic spot, the screen character tracking submodule is used to identify the picture in the image, and frame and track the characters in the picture, and the decibel analysis submodule is used to analyze the audio data in the image data and extract the real-time decibel value of the on-site situation.

[0010] According to the above technical solution, the operation method of the service management system includes the following steps:

[0011] Step S1: Start the big data statistics module, which includes a weather acquisition module and an activity performance project data recording module. The weather acquisition module is used to obtain the current forecast weather data of the scenic spot, while the activity performance project data recording module is used to statistically record the activities and performance related data held in the scenic spot;

[0012] Step S2: The system organizes the scenic spot and tour-related data collected by the big data statistics module, and then the data matching submodule matches the organized data with the historical big data database to obtain the best predicted passenger flow restriction result under the current scenic spot and tour conditions;

[0013] Step S3: collect the real-time monitoring image data of the scenic spot, and then the image character tracking submodule identifies the image in the image, frames and tracks the characters in the image; at the same time, the decibel analysis submodule is used to analyze the audio data in the image data and extract the real-time decibel value of the scene;

[0014] Step S4: Analyze the emotions of tourists at the scenic spot through the big data analysis and prediction module and the on-site real-time analysis module; the big data analysis and prediction module predicts the enthusiasm of tourists at the scenic spot based on the historical big data of the scenic spot, while the on-site real-time analysis module analyzes the real-time scenes of tourists at the scenic spot in real time and determines the real-time enthusiasm of tourists at the scenic spot;

[0015] Step S5: Based on the analysis results of tourist enthusiasm, the service management module dynamically controls the tourist flow of the scenic spot; the reservation registration setting module is responsible for setting the upper limit of the tourist flow of the scenic spot according to the analysis results, and the on-site management control module is responsible for dynamically adjusting the tourist flow based on the upper limit of the tourist flow of the scenic spot according to the current actual situation.

[0016] According to the above technical solution, step S4 further includes the following steps:

[0017] Step S41: sorting out the scenic spot and travel-related data collected by the big data statistics module, including meteorological data and activity performance data;

[0018] Step S42: matching the sorted data with the historical big database;

[0019] Step S43: collecting real-time monitoring image data of the scenic spot through camera equipment;

[0020] Step S44: using computer vision technology, identifying, framing and tracking the people in the collected images, and analyzing and calculating the average moving speed v of the tourists;

[0021] Step S45: parsing the audio information in the collected image data, extracting the real-time decibel value of the scene, and calculating the real-time average decibel value f of the scenic area;

[0022] Step S46: The tourist enthusiasm index value Q of the tourists at the scenic spot is calculated by the formula Q=αv+βf, wherein α and β are control parameters of the average moving speed of tourists and the real-time average decibel value of the scenic spot, respectively.

[0023] According to the above technical solution, step S5 further includes:

[0024] Step S51: Matching values ​​according to the historical big database, outputting predicted tourist enthusiasm, and the system converting and setting the number of scenic spot reservation registrants according to the predicted data;

[0025] Step S52: further obtain the enthusiasm index value of the tourists on site of the scenic spot. When the enthusiasm index value Q is higher than the default preset value n%, the current upper limit value of the number of scenic spot reservation registrants is lowered to (1-n%) of the original value through the on-site management control module.

[0026] Compared with the prior art, the beneficial effects achieved by the present invention are: the present invention innovatively considers the influence of tourists' enthusiasm for playing, the projects they play and the heat level on the flow of people through big data analysis and real-time scene monitoring, and achieves the purpose of intelligently and dynamically adjusting the flow of tourists in scenic spots. At the same time, it can reduce the risk of accidents caused by the increase in tourists' excitement in special projects such as ice and snow sports, improve safety, ensure that tourists follow safety regulations while having fun, and thus optimize the management experience of scenic spots. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0028] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0029] 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.

[0030] See also Figure 1 The present invention provides a technical solution: a service management system based on big data, including a big data statistics module, a play enthusiasm analysis module and a service management module. The big data statistics module is used to collect and obtain data related to scenic spots and play, the play enthusiasm analysis module is used to analyze and judge the play emotions of tourists on the spot in the scenic area, and the service management module is used to dynamically control the passenger flow of the scenic area. The big data statistics module, the tourist enthusiasm analysis module and the service management module are electrically connected to each other; through big data analysis and real-time scene monitoring, the influence of tourists' play enthusiasm, the items played and the heat level on the flow of people is innovatively considered, and the purpose of intelligently and dynamically adjusting the passenger flow of the scenic area is achieved. At the same time, it can reduce the accidental risks caused by the increased excitement of tourists in special items such as ice and snow sports, improve safety, and ensure that tourists follow safety regulations while having fun, thereby optimizing the scenic area management experience.

[0031] The big data statistics module includes a weather acquisition module and an activity performance project data recording module. The weather acquisition module is used to obtain the current forecast weather data of the scenic spot, and the activity performance project data recording module is used to statistically record the activities and performance related data held in the scenic spot.

[0032] The tourist enthusiasm analysis module includes a big data analysis and prediction module and an on-site real-time analysis module. The big data analysis and prediction module is used to predict the tourist enthusiasm of the scenic spot based on the historical big data of the scenic spot. The on-site real-time analysis module is used to analyze the real-time tourist scenes in the scenic spot and judge the real-time tourist enthusiasm.

[0033] The service management module includes an appointment registration setting module and an on-site management control module. The appointment registration setting module is used to set the upper limit of the scenic spot passenger flow according to the analysis results, and the on-site management control module is used to further manage and control the passenger flow according to the existing actual situation based on the setting of the upper limit of the scenic spot passenger flow.

[0034] The big data analysis and prediction module further includes a data sorting submodule and a data matching submodule. The data sorting submodule is used to sort the scenic spot and travel-related data collected and obtained by the big data statistics module. The data matching submodule is used to match the sorted data with the historical big data database to obtain the best predicted passenger flow restriction results for the current scenic spot and travel.

[0035] The on-site real-time analysis module further includes an on-site image acquisition submodule, a screen character tracking submodule and a decibel analysis submodule. The on-site image acquisition submodule is used to collect real-time monitoring image data of the scenic spot. The screen character tracking submodule is used to identify the picture in the image and frame and track the characters in the picture. The decibel analysis submodule is used to analyze the audio data in the image data and extract the real-time decibel value of the on-site situation.

[0036] The operation method of the service management system comprises the following steps:

[0037] Step S1: Start the big data statistics module, which includes a weather acquisition module and an activity performance project data recording module. The weather acquisition module is used to obtain the current forecast weather data of the scenic spot, while the activity performance project data recording module is used to statistically record the activities and performance related data held in the scenic spot; the big data statistics module provides the system with basic information about the scenic spot environment and activity status;

[0038] Step S2: The system organizes the scenic spot and tour-related data collected by the big data statistics module, and then the data matching submodule matches the organized data with the historical big data database to obtain the best predicted passenger flow restriction result under the current scenic spot and tour conditions;

[0039] Step S3: collect the real-time monitoring image data of the scenic spot, and then the image character tracking submodule identifies the image in the image, frames and tracks the characters in the image; at the same time, the decibel analysis submodule is used to analyze the audio data in the image data and extract the real-time decibel value of the scene;

[0040] Step S4: Analyze the emotions of tourists at the scenic spot through the big data analysis and prediction module and the on-site real-time analysis module; the big data analysis and prediction module predicts the enthusiasm of tourists at the scenic spot based on the historical big data of the scenic spot, while the on-site real-time analysis module analyzes the real-time scenes of tourists at the scenic spot in real time and determines the real-time enthusiasm of tourists at the scenic spot;

[0041] Step S5: Based on the analysis results of tourist enthusiasm, the service management module dynamically controls the tourist flow of the scenic spot; the reservation registration setting module is responsible for setting the upper limit of the tourist flow of the scenic spot according to the analysis results, and the on-site management control module is responsible for dynamically adjusting the tourist flow based on the upper limit of the tourist flow of the scenic spot according to the current actual situation.

[0042] Step S4 further comprises the following steps:

[0043] Step S41: sorting out the scenic spot and travel-related data collected by the big data statistics module, including meteorological data and activity performance data; and then establishing a historical big data database through the data to support the prediction of travel enthusiasm;

[0044] Step S42: matching the sorted data with the historical big data database; by comparing the data of the current scenic spot with the historical similar scenes, the best predicted passenger flow restriction result is obtained; thereby enabling the system to better understand the current scenic spot situation, thereby more accurately predicting the enthusiasm of tourists to play;

[0045] Step S43: collecting real-time monitoring image data of the scenic spot through camera equipment; these image data provide real-time information on tourist behavior, crowding level, etc.;

[0046] Step S44: using computer vision technology, identifying, framing and tracking the people in the collected images, and analyzing and calculating the average moving speed v of the tourists;

[0047] Step S45: parsing the audio information in the collected image data, extracting the real-time decibel value of the scene, and calculating the real-time average decibel value f of the scenic spot; the audio information can reflect the laughter of tourists, the sound of emergencies, etc., providing a more comprehensive analysis basis for the enthusiasm of tourists;

[0048] Step S46: The tourist enthusiasm index value Q of the tourists at the scenic spot is calculated by the formula Q=αv+βf, wherein α and β are control parameters of the average moving speed of tourists and the real-time average decibel value of the scenic spot, respectively.

[0049] Step S5 further comprises:

[0050] Step S51: Matching values ​​according to the historical big database, outputting predicted tourist enthusiasm, and the system converting and setting the number of scenic spot reservation registrants according to the predicted data;

[0051] Step S52: further obtain the playing enthusiasm index value of the tourists at the scenic spot. When the playing enthusiasm index value Q is higher than the default preset value n%, the current upper limit of the number of people who are currently set to register for the scenic spot is adjusted down to (1-n%) of the original value through the on-site management control module; the flow control is affected by the items played and the heat level at the scene. Through the enthusiasm of tourists, the setting of passenger flow is reflected indirectly, reducing the possibility of accidents caused by the relatively chaotic and uncontrolled characteristics of ice and snow sports. In particular, when tourists are having a lot of fun and are very enthusiastic, they will make some stimulating or relatively challenging actions, thereby increasing the unknown risks. In most cases, the more excited the tourists are, the more likely they are to ignore safety regulations when playing, and the more likely they are to be in danger.

[0052] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0053] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A service management system based on big data, characterized by: The service management system includes a big data statistics module, a tourist enthusiasm analysis module and a service management module. The big data statistics module is used to collect and obtain scenic spot and tourist related data. The tourist enthusiasm analysis module is used to analyze and determine the tourist mood of tourists at the scenic spot. The service management module is used to dynamically control the tourist flow of the scenic spot. The big data statistics module, the tourist enthusiasm analysis module and the service management module are electrically connected to each other.

2. A service management system based on big data according to claim 1, characterized in that: The big data statistics module includes a weather acquisition module and an activity performance project data recording module. The weather acquisition module is used to obtain the current forecast weather data of the scenic spot, and the activity performance project data recording module is used to statistically record the activities and performance related data held in the scenic spot.

3. A service management system based on big data according to claim 2, characterized in that: The play enthusiasm analysis module includes a big data analysis and prediction module and an on-site real-time analysis module. The big data analysis and prediction module is used to predict the play enthusiasm of tourists in the scenic area based on the historical big data of the scenic area. The on-site real-time analysis module is used to analyze the real-time play scenes of tourists in the scenic area in real time and judge the real-time play enthusiasm of tourists on site.

4. A service management system based on big data according to claim 3, characterized in that: The service management module includes an appointment registration setting module and an on-site management control module. The appointment registration setting module is used to set the upper limit of the scenic spot passenger flow according to the analysis results, and the on-site management control module is used to further perform passenger flow management and control according to the existing actual situation based on the setting of the upper limit of the scenic spot passenger flow.

5. A service management system based on big data according to claim 4, characterized in that: The big data analysis and prediction module further includes a data sorting submodule and a data matching submodule. The data sorting submodule is used to sort the scenic spot and travel-related data collected and acquired by the big data statistics module, and the data matching submodule is used to match the sorted data with the historical big data database to obtain the best predicted passenger flow restriction result under the current scenic spot and travel conditions.

6. A service management system based on big data according to claim 5, characterized in that: The on-site real-time analysis module further includes an on-site image acquisition submodule, a screen character tracking submodule and a decibel analysis submodule. The on-site image acquisition submodule is used to collect real-time monitoring image data of the scenic spot, the screen character tracking submodule is used to identify the picture in the image, and frame and track the characters in the picture, and the decibel analysis submodule is used to analyze the audio data in the image data and extract the real-time decibel value of the on-site situation.

7. A service management system based on big data according to claim 6, characterized in that: The operation method of the service management system comprises the following steps: Step S1: Start the big data statistics module, which includes a weather acquisition module and an activity performance project data recording module. The weather acquisition module is used to obtain the current forecast weather data of the scenic spot, while the activity performance project data recording module is used to statistically record the activities and performance related data held in the scenic spot; Step S2: The system organizes the scenic spot and tour-related data collected by the big data statistics module, and then the data matching submodule matches the organized data with the historical big data database to obtain the best predicted passenger flow restriction result under the current scenic spot and tour conditions; Step S3: collect the real-time monitoring image data of the scenic spot, and then the image character tracking submodule identifies the image in the image, frames and tracks the characters in the image; at the same time, the decibel analysis submodule is used to analyze the audio data in the image data and extract the real-time decibel value of the scene; Step S4: Analyze the emotions of tourists at the scenic spot through the big data analysis and prediction module and the on-site real-time analysis module; the big data analysis and prediction module predicts the enthusiasm of tourists at the scenic spot based on the historical big data of the scenic spot, while the on-site real-time analysis module analyzes the real-time scenes of tourists at the scenic spot in real time and determines the real-time enthusiasm of tourists at the scenic spot; Step S5: Based on the analysis results of tourist enthusiasm, the service management module dynamically controls the tourist flow of the scenic spot; the reservation registration setting module is responsible for setting the upper limit of the tourist flow of the scenic spot according to the analysis results, and the on-site management control module is responsible for dynamically adjusting the tourist flow based on the upper limit of the tourist flow of the scenic spot according to the current actual situation.

8. A service management system based on big data according to claim 7, characterized in that: The step S4 further comprises the following steps: Step S41: sorting out the scenic spot and travel-related data collected by the big data statistics module, including meteorological data and activity performance data; Step S42: matching the sorted data with the historical big database; Step S43: collecting real-time monitoring image data of the scenic spot through camera equipment; Step S44: using computer vision technology, identifying, framing and tracking the people in the collected images, and analyzing and calculating the average moving speed v of the tourists; Step S45: parsing the audio information in the collected image data, extracting the real-time decibel value of the scene, and calculating the real-time average decibel value f of the scenic area; Step S46: The tourist enthusiasm index value Q of the tourists at the scenic spot is calculated by the formula Q=αv+βf, wherein α and β are control parameters of the average moving speed of tourists and the real-time average decibel value of the scenic spot, respectively.

9. A service management system based on big data according to claim 8, characterized in that: The step S5 further comprises: Step S51: Matching values ​​according to the historical big database, outputting predicted tourist enthusiasm, and the system converting and setting the number of scenic spot reservation registrants according to the predicted data; Step S52: further obtain the enthusiasm index value of the tourists on site of the scenic spot. When the enthusiasm index value Q is higher than the default preset value n%, the current upper limit value of the number of scenic spot reservation registrants is lowered to (1-n%) of the original value through the on-site management control module.

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