A service management system based on big data

Abstract

The application discloses a service management system based on big data, which comprises a big data statistical module, a playing enthusiasm analysis module and a service management module, the big data statistical module is used for collecting and acquiring scenic spot and playing related data, the playing enthusiasm analysis module is used for analyzing and judging the playing mood of on-site tourists in the scenic spot, and the service management module is used for dynamically controlling the passenger flow of the scenic spot, the big data statistical module, the tourist enthusiasm analysis module and the service management module are electrically connected with each other, through big data analysis and real-time scene monitoring, the influence of the playing enthusiasm of tourists, the played projects and the on-site heat degree on the passenger flow control is innovatively considered, the purpose of intelligently and dynamically adjusting the passenger flow of the scenic spot is achieved, and the application has the characteristics of dynamically and reasonably controlling the passenger flow of the scenic spot and high practicability.

Description

TECHNICAL FIELD

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

[0002] With the improvement of living standards, the number of tourists is increasing, and when winter comes, the number of visitors to many ice and snow theme scenic spots in northern China increases sharply. Since the carrying capacity of the scenic spot is limited, in order to ensure the safety of visitors and the experience of visiting, the number of visitors to the scenic spot will be detected by each scenic spot to ensure timely adjustment of the number of visitors entering the scenic spot. However, the existing scenic spot is fixed for flow control, and for ice and snow projects, due to the variety and chaos of running projects, different time periods or external influences leading to different tourist play enthusiasm, and the ice and snow ground used for recreational sports is also difficult to control for beginners. Further, when approaching the upper limit of the number of visitors, collisions, pushing and falling of tourists often occur, which may cause minor injuries and equipment damage, and in severe cases, major accidents may occur. And due to the particularity of the ice and snow ground, emergency rescue is slow. Therefore, it is necessary to design a kind of service management system based on big data for dynamic and reasonable control of the number of visitors in the scenic spot and high practicability. SUMMARY

[0003] The present application relates to the technical field of scenic spot big data management, in particular to a service management system based on big data.

[0004] In order to solve the above technical problems, the present application provides the following technical scheme: a service management system based on big data, comprising a big data statistical module, a play enthusiasm analysis module and a service management module, the big data statistical module is used for collecting and acquiring scenic spot and play related data, the play enthusiasm analysis module is used for analyzing and judging the play mood of on-site visitors in the scenic spot, the service management module is used for dynamically controlling the number of visitors in the scenic spot, and the big data statistical module, the visitor enthusiasm analysis module and the service management module are electrically connected with each other.

[0005] According to the above technical scheme, the big data statistical module comprises a meteorological acquisition module and an activity performance project data recording module, the meteorological acquisition module is used for acquiring the current forecast meteorological data of the scenic spot, and the activity performance project data recording module is used for statistically recording the activity and performance related data held by the scenic spot.

[0006] According to the above technical scheme, the play enthusiasm analysis module comprises a big data analysis and prediction module and a live analysis module, the big data analysis and prediction module is used for predicting the play enthusiasm degree of visitors in the scenic spot according to the historical big data of the scenic spot, and the live analysis module is used for analyzing the live play scene of on-site visitors in the scenic spot in real time and judging the real-time play enthusiasm degree of on-site visitors.

[0007] According to the technical scheme, the service management module comprises a reservation registration setting module and a live management control module, the reservation registration setting module is used for setting the upper limit of the scenic spot passenger flow according to the analysis result, and the live management control module is used for further managing and controlling the passenger flow according to the existing live on the basis of the upper limit of the scenic spot passenger flow.

[0008] According to the technical scheme, the big data analysis and prediction module further comprises a data arrangement sub-module and a data matching sub-module, the data arrangement sub-module is used for arranging the scenic spot and play related data collected and acquired by the big data statistical module, and the data matching sub-module is used for matching the arranged data with the historical big database to obtain the best predicted passenger flow limit result under the current scenic spot and play condition.

[0009] According to the technical scheme, the live analysis module further comprises a live image acquisition sub-module, a picture character tracking sub-module and a decibel analysis sub-module, the live image acquisition sub-module is used for acquiring live monitoring image data of the scenic spot, the picture character tracking sub-module is used for identifying pictures in the image and framing and tracking characters in the picture, and the decibel analysis sub-module is used for analyzing audio data in the image data to extract real-time decibel values of the live.

[0010] According to the technical scheme, the operation method of the service management system comprises the following steps:

[0011] Step S1: starting a big data statistical module, wherein the big data statistical module comprises a weather acquisition module and an activity performance project data recording module, the weather acquisition module is used for acquiring currently predicted weather data of the scenic spot, and the activity performance project data recording module is used for statistically recording activity and performance related data held in the scenic spot;

[0012] Step S2: the system arranges the scenic spot and play related data collected and acquired by the big data statistical module, then the data matching sub-module matches the arranged data with the historical big database to obtain the best predicted passenger flow limit result under the current scenic spot and play condition;

[0013] Step S3: acquiring live monitoring image data of the scenic spot, then the picture character tracking sub-module frames and tracks characters in the picture by identifying pictures in the image; meanwhile, the decibel analysis sub-module is used for analyzing audio data in the image data to extract real-time decibel values of the live;

[0014] Step S4: analyzing the scenic spot live visitor play mood through the big data analysis and prediction module and the live analysis module; the big data analysis and prediction module predicts the scenic spot visitor play enthusiasm degree according to the scenic spot historical big data, and the live analysis module analyzes the scenic spot live visitor play live scene in real time and judges the live visitor real-time play enthusiasm degree.

[0015] Step S5: Based on the analysis results of visitor enthusiasm, the service management module dynamically controls the visitor flow in the scenic area; the reservation registration setting module is responsible for setting the upper limit of the visitor flow in the scenic area according to the analysis results, while the on-site management control module further adjusts the visitor flow dynamically based on the current situation on the basis of the upper limit of the visitor flow in the scenic area.

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

[0017] Step S41: Organize the scenic area and related data collected by the big data statistics module; including weather data and event / performance data;

[0018] Step S42: Match the organized data with the historical big data database;

[0019] Step S43: Collect real-time monitoring video data of the scenic area using camera equipment;

[0020] Step S44: Using computer vision technology, identify, frame, and track people in the acquired images, and analyze and calculate the average moving speed v of the tourists;

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

[0022] Step S46: Calculate the visitor enthusiasm index value Q of the scenic area using the formula Q=αv+βf, where α and β are the control parameters for the average movement speed of tourists and the real-time average decibel value of the scenic area, respectively.

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

[0024] Step S51: Match values ​​based on historical big data, output predicted tourist enthusiasm, and the system converts and sets the number of people registering for reservations at the scenic spot based on the predicted data;

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

[0026] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention, through big data analysis and real-time scene monitoring, innovatively considers the impact of tourists' enthusiasm for activities, the activities they participate in, and the on-site temperature on visitor flow control, thus achieving the goal of intelligently and dynamically adjusting the visitor flow in scenic areas. At the same time, it can reduce the risk of accidents caused by increased tourist excitement in special activities such as ice and snow sports, improve safety, and ensure that tourists follow safety regulations while enjoying their activities, thereby optimizing the scenic area management experience. Attached Figure Description

[0027] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0028] Figure 1 This is a schematic diagram of the system module composition of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Please see Figure 1 This invention provides a technical solution: a service management system based on big data, comprising a big data statistics module, a visitor enthusiasm analysis module, and a service management module. The big data statistics module collects and acquires data related to the scenic area and its activities; the visitor enthusiasm analysis module analyzes and judges the visitor's mood at the scenic area; and the service management module dynamically controls the visitor flow. The big data statistics module, visitor enthusiasm analysis module, and service management module are electrically connected to each other. Through big data analysis and real-time scene monitoring, this system innovatively considers the impact of visitor enthusiasm, the activities they participate in, and the ambient temperature on visitor flow control, achieving the goal of intelligently and dynamically adjusting visitor flow. Simultaneously, it reduces the risk of accidents caused by increased visitor excitement in special activities such as snow sports, improves safety, and ensures that visitors follow safety regulations while enjoying their activities, thereby optimizing the scenic area management experience.

[0031] The big data statistics module includes a weather acquisition module and an event and performance data recording module. The weather acquisition module is used to acquire the current forecast weather data of the scenic area, and the event and performance data recording module is used to statistically record data related to the events and performances held in the scenic area.

[0032] The visitor enthusiasm analysis module includes a big data analysis and prediction module and a live-site analysis module. The big data analysis and prediction module is used to predict the visitor enthusiasm of the scenic area based on historical big data, while the live-site analysis module is used to analyze the real-time visitor situation in the scenic area and determine the real-time visitor enthusiasm.

[0033] The service management module includes a reservation registration setting module and an on-site management control module. The reservation registration setting module is used to set the upper limit of the scenic area's visitor flow based on the analysis results, while the on-site management control module is used to further manage and control the visitor flow based on the current situation, on the basis of setting the upper limit of the scenic area's visitor flow.

[0034] The big data analysis and prediction module further includes a data processing submodule and a data matching submodule. The data processing submodule is used to process the scenic area and related data collected by the big data statistics module. The data matching submodule is used to match the processed data with the historical big data database to obtain the best predicted visitor flow limit result under the current scenic area and visitor conditions.

[0035] The on-site real-time analysis module further includes an on-site image acquisition submodule, an image person 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 area. The image person tracking submodule is used to identify the image in the image and to frame and track the people in the image. 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 of the service management system includes the following steps:

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

[0038] Step S2: The system organizes the scenic area and related data collected by the big data statistics module. Then, the data matching submodule matches the organized data with the historical big data database to obtain the best predicted visitor flow limit result under the current scenic area and visitor conditions.

[0039] Step S3: Collect real-time monitoring video data of the scenic area. Then, the image person tracking submodule identifies the image and tracks the people 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 visitor sentiment at the scenic spot through the big data analysis and prediction module and the on-site situation analysis module; the big data analysis and prediction module predicts the visitor enthusiasm based on the historical big data of the scenic spot, while the on-site situation analysis module analyzes the on-site visitor situation in real time and judges the real-time visitor enthusiasm.

[0041] Step S5: Based on the analysis results of visitor enthusiasm, the service management module dynamically controls the visitor flow in the scenic area; the reservation registration setting module is responsible for setting the upper limit of the visitor flow in the scenic area according to the analysis results, while the on-site management control module further adjusts the visitor flow dynamically based on the current situation on the basis of the upper limit of the visitor flow in the scenic area.

[0042] Step S4 further includes the following steps:

[0043] Step S41: Organize the scenic area and related data collected by the big data statistics module; including meteorological data and event performance data; and then build a historical big data database to support the prediction of visitor enthusiasm.

[0044] Step S42: Match the organized data with the historical big data database; by comparing the data under the current scenic area conditions with similar historical scenarios, obtain the best predicted visitor flow limit result; thus enabling the system to better understand the current scenic area conditions and more accurately predict the enthusiasm of tourists.

[0045] Step S43: Collect real-time monitoring video data of the scenic area through camera equipment; this video data provides real-time information on tourist behavior, crowding levels, etc.

[0046] Step S44: Using computer vision technology, identify, frame, and track people in the acquired images, and analyze and calculate the average moving speed v of the tourists;

[0047] Step S45: Analyze the audio information in the collected video data, extract the real-time decibel value of the scene, and calculate the real-time average decibel value f of the scenic area; the audio information can reflect the laughter of tourists, the sound of emergencies, etc., and provide a more comprehensive analysis basis for the enthusiasm of tourists.

[0048] Step S46: Calculate the visitor enthusiasm index value Q of the scenic area using the formula Q=αv+βf, where α and β are the control parameters for the average movement speed of tourists and the real-time average decibel value of the scenic area, respectively.

[0049] Step S5 further includes:

[0050] Step S51: Match values ​​based on historical big data, output predicted tourist enthusiasm, and the system converts and sets the number of people registering for reservations at the scenic spot based on the predicted data;

[0051] Step S52: Further obtain the enthusiasm index value of tourists at 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 people registered for reservations in the scenic spot is lowered to (1-n%) of the original value through the on-site management control module. The flow of people is affected by the activities and the temperature of the scene. By observing the enthusiasm of tourists, the flow of people can be indirectly reflected, reducing the possibility of accidents caused by the relatively chaotic and dangerous uncontrollable nature of ice and snow sports. In particular, when tourists are having a great time and are very enthusiastic, they may do some stimulating or relatively challenging actions, which increases the unknown risks. In most cases, the more excited the tourists are, the more likely they are to ignore safety regulations and the more likely they are to have accidents.

[0052] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0053] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A service management system based on big data, characterized in that: The service management system includes a big data statistics module, a visitor enthusiasm analysis module, and a service management module. The big data statistics module is used to collect and acquire data related to the scenic area and visitors. The visitor enthusiasm analysis module is used to analyze and judge the visitor's mood at the scenic area. The service management module is used to dynamically control the visitor flow in the scenic area. The big data statistics module, visitor enthusiasm analysis module, and service management module are electrically connected to each other. The operation method of the service management system includes the following steps: Step S1: Activate the big data statistics module, which includes a weather acquisition module and an event performance data recording module. The weather acquisition module is used to acquire the current forecast weather data of the scenic area, while the event performance data recording module is used to statistically record data related to the events and performances held in the scenic area. Step S2: The system organizes the scenic area and related data collected by the big data statistics module. Then, the data matching submodule matches the organized data with the historical big data database to obtain the best predicted visitor flow limit result under the current scenic area and visitor conditions. Step S3: Collect real-time monitoring video data of the scenic area. Then, the image person tracking submodule identifies the image and tracks the people 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 visitor sentiment at the scenic area using the big data analysis and prediction module and the on-site situation analysis module. The big data analysis and prediction module predicts visitor enthusiasm based on historical big data of the scenic area, while the on-site situation analysis module analyzes the real-time visitor situation and determines the real-time visitor enthusiasm. Step S4 further includes the following steps: Step S41: Organize the scenic area and related data collected by the big data statistics module; including weather data and event / performance data; Step S42: Match the organized data with the historical big data database; Step S43: Collect real-time monitoring video data of the scenic area using camera equipment; Step S44: Using computer vision technology, identify, frame, and track people in the acquired images, and analyze and calculate the average moving speed v of the tourists; Step S45: Analyze the audio information in the collected image data, extract the real-time decibel value of the scene, and calculate the real-time average decibel value f of the scenic area; Step S46: Using the formula The enthusiasm index value Q of tourists at the scenic spot was calculated, where These are the control parameters for the average movement speed of tourists and the real-time average decibel value of the scenic area, respectively.

2. The 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 event and performance data recording module. The weather acquisition module is used to acquire the current weather forecast data of the scenic area, and the event and performance data recording module is used to statistically record data related to the events and performances held in the scenic area.

3. The service management system based on big data according to claim 2, characterized in that: The visitor enthusiasm analysis module includes a big data analysis and prediction module and a live-site analysis module. The big data analysis and prediction module is used to predict the visitor enthusiasm of the scenic area based on historical big data. The live-site analysis module is used to analyze the real-time visitor situation in the scenic area and determine the real-time visitor enthusiasm.

4. A service management system based on big data according to claim 3, characterized in that: The service management module includes a reservation registration setting module and an on-site management control module. The reservation registration setting module is used to set the upper limit of the scenic area's visitor flow based on the analysis results. The on-site management control module is used to further manage and control the visitor flow based on the current situation, on the basis of setting the upper limit of the scenic area's visitor 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 processing submodule and a data matching submodule. The data processing submodule is used to process the scenic area and related data collected and obtained by the big data statistics module. The data matching submodule is used to match the processed data with a historical big data database to obtain the best predicted visitor flow limit result under the current scenic area and visitor 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, an image person 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 area. The image person tracking submodule is used to identify the image in the image and to frame and track the people in the image. The decibel analysis submodule is used to parse 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 1, characterized in that: Step S5 further includes: Step S51: Match values ​​based on historical big data, output predicted tourist enthusiasm, and the system converts and sets the number of people registering for reservations at the scenic spot based on the predicted data; Step S52: Further obtain the visitor enthusiasm index value of the scenic spot. When the visitor enthusiasm index value Q is higher than the default preset value n%, the current upper limit value of the number of people registering reservations in the scenic spot is lowered to (1-n%) of the original value through the on-site management control module.

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