WO2022217684A1 - 一种景区饱和度预测方法和服务器 - Google Patents
一种景区饱和度预测方法和服务器 Download PDFInfo
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- WO2022217684A1 WO2022217684A1 PCT/CN2021/094004 CN2021094004W WO2022217684A1 WO 2022217684 A1 WO2022217684 A1 WO 2022217684A1 CN 2021094004 W CN2021094004 W CN 2021094004W WO 2022217684 A1 WO2022217684 A1 WO 2022217684A1
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/14—Travel agencies
Definitions
- the present application relates to the technical field of scenic spot data processing, and in particular, to a method and server for predicting the saturation of scenic spots.
- the existing scenic spot management system cannot provide users with the flow of people in the scenic spot. There will be congestion in the scenic spot during the peak flow of people in the scenic spot. If tourists enter the scenic spot during the peak period, it will not only affect the tourist experience of the scenic spot, but also delay the tourists. Therefore, how to provide tourists with the crowd saturation of the scenic spot, so as to facilitate the tourists to make a reasonable planning of the scenic spot tour time, is a technical problem to be solved urgently by those skilled in the art.
- the present application provides a method and a server for predicting the saturation of a scenic spot, which are used to solve the technical problem that the existing scenic spot management system cannot provide users with the saturation of people flow in the scenic spot, which brings great inconvenience to tourists.
- the first aspect of the present application provides a method for predicting the saturation of a scenic spot, including:
- the second scenic spot saturation is sent to the user in the form of a list.
- the obtaining the real-time number of people in the preset scenic spot includes:
- the real-time number of people in the preset scenic spot is calculated according to the preset positioning device worn by the tourists in the preset scenic spot.
- the saturation of the first scenic spot is written into the blockchain through the blockchain network.
- the obtaining the number of people in the historical scenic spots in each time period of the day includes:
- the calculation formula of the saturation of the second scenic spot is:
- the saturation of the first scenic spot in the current period plus the difference between the saturation of the first scenic spot in the current historical period and the saturation of the first scenic spot in the previous historical period.
- a second aspect of the present application provides a scenic spot saturation prediction server server, including:
- the response module is used to obtain the real-time number of people in the preset scenic spot in response to the user request;
- a first saturation push module configured to calculate the current first scenic spot saturation of the preset scenic spot according to the real-time number of people and the saturation threshold of the preset scenic spot, and send the first scenic spot saturation to the user;
- the second saturation prediction module is used to obtain the number of people in the historical scenic spot in each time period of the day, and predict the saturation of the second scenic spot in each time period after the current time of the preset scenic spot on the current day according to the saturation of the first scenic spot and the number of people in the historical scenic spot Spend;
- the second saturation push module is configured to send the second scenic spot saturation to the user in the form of a list.
- the data writing module is used to write the saturation of the first scenic spot into the blockchain through the blockchain network.
- the embodiments of the present application have the following advantages:
- the present application provides a method for predicting the saturation of a scenic spot, including: in response to a user request, obtaining the real-time number of people in a preset scenic spot; calculating the current first scenic spot saturation of the preset scenic spot according to the real-time number of people and the saturation threshold of the preset scenic spot, Send the saturation of the first scenic spot to the user; obtain the number of people in the historical scenic spot in each period of the day, and predict the saturation of the second scenic spot in the preset scenic spot of the day at each time period after the current time according to the saturation of the first scenic spot and the number of people in the historical scenic spot;
- the scenic saturation is sent to the user in the form of a list.
- the method for predicting the saturation of a scenic spot can respond to a user request, calculate the saturation of the first scenic spot of the preset scenic spot according to the real-time number of people in the preset scenic spot, and predict the preset scenic spot on the current day according to the saturation of the first scenic spot and historical period data
- the saturation of the second scenic spot in each time period provides tourist users with the saturation prediction of the preset scenic spots, which can effectively prevent the tourist users from blindly entering the preset scenic spots as planned because they do not know the crowd saturation of the preset scenic spots, thus affecting tourism.
- the problem of the user's tourist experience in the scenic spot solves the technical problem that the existing scenic spot management system cannot provide the user with the crowd saturation of the scenic spot, which brings great inconvenience to the tourists.
- FIG. 1 is a schematic flowchart of a method for predicting the saturation of a scenic spot provided in the embodiment of the present application
- FIG. 2 is another schematic flowchart of a method for predicting the saturation of a scenic spot provided in the embodiment of the present application
- FIG. 3 is another schematic flowchart of a method for predicting the saturation of a scenic spot provided in the embodiment of the present application
- FIG. 4 is a schematic structural diagram of a scenic spot saturation prediction server provided in an embodiment of the present application.
- the present application provides an embodiment of a method for predicting the saturation of a scenic spot, including:
- Step 101 Acquire the real-time number of people in the preset scenic spot in response to a user request.
- the user can send the relevant information of obtaining the preset scenic spot to the server through the mobile network device before entering the preset scenic spot, that is, before the target goes to the scenic spot.
- the way that the user sends a request to the server through the mobile network device can be actively requested in the preset application software of the mobile network device, or it can be in the user's mobile network device.
- the preset application software After opening the preset application software, the preset application software automatically requests.
- the server responds to the user request and calculates the real-time number of people in the preset scenic spot.
- each tourist needs to wear a preset positioning device when entering the preset scenic spot. Therefore, the server can calculate the number of tourists according to the positioning device in the preset scenic spot. The real-time number of people in the preset scenic spot is displayed. When the tourists walk out of the preset scenic spot, the preset positioning device worn by the tourists is recovered at the exit of the preset scenic spot, so as to facilitate recycling and save costs.
- Step 102 Calculate the current first scenic spot saturation of the preset scenic spot according to the real-time number of people and the saturation threshold of the preset scenic spot, and send the first scenic spot saturation to the user.
- the server can calculate the current first scenic spot saturation of the preset scenic spot in combination with the saturation threshold of the preset scenic spot, and send the first scenic spot saturation to the sender of the request.
- the current time is 11:00
- the real-time number of people in the preset scenic spot is 1,000
- the saturation threshold of the preset scenic spot is 1,500 people
- the saturation of the first scenic spot at 11:00 is: 1000/1500.
- Step 103 Obtain the number of people in the historical scenic spot in each time period of the day, and predict the saturation level of the second scenic spot in each time period after the current time in the preset scenic spot on the day according to the saturation of the first scenic spot and the number of people in the historical scenic spot.
- the server needs to predict the saturation of the preset scenic spots in the time period after the current time, so as to provide the user with the predicted situation of the preset scenic spots and facilitate the user to make travel planning.
- the daily number of ticket purchasers of the preset scenic spots can be obtained on the ticketing platform, and the number of tourists staying in the preset scenic spots in each historical period can also be obtained.
- the server can obtain the number of historical scenic spots in each period of the day, such as The current day is March 10, 2020, then the server can obtain the historical period corresponding to each period from March 10, 2020 00:00-24:00, such as March 9, 2020 10:00-11:00 period
- the number of historical scenic spots is 800 person-times
- the number of historical scenic spots during the period of 11:00-12:00 is 1100 person-times. Then, according to the number of people in the historical scenic spots in these historical periods, combined with the saturation of the first scenic spot, the saturation of the second scenic spot at each time period after the current time can be predicted.
- the calculation of the saturation of the second scenic spot It may be the saturation of the first scenic spot in the current period plus the difference between the saturation of the first scenic spot in the current historical period and the saturation of the first scenic spot in the previous historical period. For example, if the current time is 11:00, the real-time number of people in the preset scenic spot is 1000, and the saturation threshold of the preset scenic spot is 1500, then the saturation of the first scenic spot at 11:00 is 1000/1500. The number of people in the historical scenic spot during the period of 10:00-11:00 is 800 people, then the saturation of the first scenic spot in the historical period of 10:00-11:00 is 800/1500, and the saturation of the first scenic spot in the historical period of 11:00-12:00 is 800/1500. The saturation of the first scenic spot in the time period is 1100/1500. Therefore, the saturation of the second scenic spot of the preset scenic spot in the 11:00-12:00 time period of the day is 1000/1500 (1100/1500-800/1500).
- Step 104 Send the saturation of the second scenic spot to the user in the form of a list.
- the server when the server predicts and obtains the saturation of the second scenic spot in each time period of the preset scenic spot, it needs to send the saturation of the second scenic spot to the user and display it to the user.
- the list is sent to the user in the way of “Time Period Item” and “Second Scenic Spot Saturation Item”. Each time period in the “Time Period Item” corresponds to the saturation level of each second scenic spot in the “Second Scenic Spot Saturation Item”. After receiving the list, the user can make more reasonable travel planning.
- the user plans to go to the scenic spot B during the period of 11:00-12:00, if the saturation of the second scenic spot displayed in the list does not exceed 1, it indicates that the scenic spot B is in a state of under-saturated traffic and there is no congestion, the user can enter the preset scenic spot as planned. If the saturation of the second scenic spot displayed in the list exceeds 1, it means that scenic spot B is in a state of traffic saturation and there is congestion In this case, entering Scenic Area B may affect the sightseeing experience of the scenic spot.
- the method for predicting the saturation of a scenic spot can respond to a user request, calculate the saturation of a first scenic spot of a preset scenic spot according to the real-time number of people in the preset scenic spot, and predict the saturation of the first scenic spot and historical period data
- the saturation of the second scenic spot for each time period of the preset scenic spot on the same day provides tourist users with the saturation prediction of the preset scenic spot, which can effectively prevent tourist users from blindly entering the preset scenic spot as planned because they do not know the crowd saturation of the preset scenic spot. , thereby affecting the tourist user's experience in the scenic spot, solving the technical problem that the existing scenic spot management system cannot provide users with the crowd saturation of the scenic spot, which brings great inconvenience to the tourists.
- FIG. 2 another embodiment of a method for predicting the saturation of a scenic spot is provided in this application, including:
- Step 201 Acquire the real-time number of people in the preset scenic spot in response to a user request.
- step 201 in this embodiment of the present application is the same as step 101 in Embodiment 1, and details are not repeated here.
- Step 202 Calculate the current first scenic spot saturation of the preset scenic spot according to the real-time number of people and the saturation threshold of the preset scenic spot, send the first scenic spot saturation to the user, and write the first scenic spot saturation into the area through the blockchain network. in the blockchain.
- step 202 of this embodiment of the present application after obtaining the saturation of the first scenic spot, writes the saturation of the first scenic spot into the blockchain through the blockchain network,
- the blockchain is a shared database, and the data or information stored in it has the characteristics of "unforgeable”, “full traces”, “traceable”, “open and transparent”, and "collective maintenance". Therefore, the saturation of the first scenic spot is written as Entering the blockchain can ensure the reliability of the data.
- Step 203 Obtain the number of people in the historical scenic spot from the blockchain, and predict the saturation level of the second scenic spot in each time period after the current time in the preset scenic spot on the current day according to the saturation of the first scenic spot and the number of people in the historical scenic spot.
- Step 204 Send the saturation of the second scenic spot to the user in the form of a list.
- step 203 and step 204 in this embodiment of the present application are the same as step 103 and step 104 in embodiment 1, and details are not repeated here.
- Step 205 Highlight the saturation of the second scenic spot greater than or equal to 1 and the corresponding time period in the list.
- the saturation of the second scenic spot greater than or equal to 1 in the list and the corresponding time period are highlighted, which helps to remind the user that the preset scenic spots in these time periods are prone to saturation. Open the saturation period so as not to cause a bad experience.
- FIG. 3 another embodiment of a method for predicting the saturation of a scenic spot is provided in this application, including:
- Step 301 in response to a user request, obtain the real-time number of people in a preset scenic spot.
- Step 302 Calculate the current first scenic spot saturation of the preset scenic spot according to the real-time number of people and the saturation threshold of the preset scenic spot, send the first scenic spot saturation to the user, and write the first scenic spot saturation into the area through the blockchain network. in the blockchain.
- Step 303 Obtain the number of people in the historical scenic spot from the blockchain, and predict the saturation of the second scenic spot in each time period after the current time in the preset scenic spot on the current day according to the saturation of the first scenic spot and the number of people in the historical scenic spot.
- Step 304 Send the saturation of the second scenic spot to the user in the form of a list.
- Step 305 Highlight the saturation of the second scenic spot greater than or equal to 1 and the corresponding time period in the list.
- Step 306 Acquire all the optional scenic spots within the preset distance of the preset scenic spots, and display the location of the optional scenic spots and the scenic spot saturation list of the optional scenic spots to the user.
- step 306 may be added to obtain all optional scenic spots within the preset distance of the preset scenic spots, and display the positions of the optional scenic spots and the location of the optional scenic spots to the user.
- the list of scenic spots saturation of optional scenic spots can avoid the problem of not providing users with optional scenic spot preview solutions when the preset scenic spots are saturated, which will affect the user's tour experience. If the preset scenic spots are predicted to be saturated within a certain period of time , the user considers that the tour experience of the preset scenic spots will decrease, and can choose the scenic spots to visit among all the optional scenic spots within the preset distance of the preset scenic spots.
- the server can provide the user with a variety of scenic spot tour options, which further improves the user experience. tour experience and tourism service experience.
- step 306 it may further include:
- Step 307 Calculate the distances between all the optional scenic spots and the preset scenic spots, and sort and label all the optional scenic spots according to the distance from near to far.
- each optional scenic spot is marked, which is beneficial for users to select scenic spots nearby and avoid users blindly selecting scenic spots with far distances. Bring about physical fatigue problems and cause problems that affect the user's browsing experience.
- FIG. 4 an embodiment of a scenic spot saturation prediction server is provided in this application, including:
- the response module is used to obtain the real-time number of people in the preset scenic spot in response to the user request.
- the first saturation push module is configured to calculate the current first scenic spot saturation of the preset scenic spot according to the real-time number of people and the saturation threshold of the preset scenic spot, and send the first scenic spot saturation to the user.
- the second saturation prediction module is used to obtain the number of people in the historical scenic spot in each time period of the day, and predict the saturation of the second scenic spot in each time period after the current time of the preset scenic spot on the current day according to the saturation of the first scenic spot and the number of people in the historical scenic spot Spend.
- the second saturation push module is configured to send the second scenic spot saturation to the user in the form of a list.
- the data writing module is used to write the saturation of the first scenic spot into the blockchain through the blockchain network.
- the user can send the relevant information of obtaining the preset scenic spot to the server through the mobile network device before entering the preset scenic spot, that is, before the target goes to the scenic spot.
- the way that the user sends a request to the server through the mobile network device can be actively requested in the preset application software of the mobile network device, or it can be in the user's mobile network device.
- the preset application software After opening the preset application software, the preset application software automatically requests.
- the server responds to the user request and calculates the real-time number of people in the preset scenic spot.
- each tourist needs to wear a preset positioning device when entering the preset scenic spot. Therefore, the server can calculate the number of tourists according to the positioning device in the preset scenic spot. The real-time number of people in the preset scenic spot is displayed.
- the server can calculate the current first scenic spot saturation of the preset scenic spot in combination with the saturation threshold of the preset scenic spot, and send the first scenic spot saturation to the mobile network device that sent the request, for example , the current time is 11:00, the real-time number of people in the preset scenic spot is 1,000, and the saturation threshold of the preset scenic spot is 1,500 people, then the saturation of the first scenic spot in the preset scenic spot at 11:00 is 1000/1500.
- the saturation of the first scenic spot is written into the blockchain through the blockchain network.
- the blockchain is a shared database, and the data or information stored in it is "unforgeable” and “full-process” Features such as “remaining traces”, “traceability”, “openness and transparency”, and “collective maintenance”, therefore, writing the saturation of the first scenic spot into the blockchain can ensure the reliability of the data.
- the server needs to predict the saturation of the preset scenic spot in the time period after the current time, so as to provide the user with the predicted situation of the preset scenic spot and facilitate the user to make travel planning.
- the daily number of ticket purchasers of the preset scenic spots can be obtained on the ticketing platform, and the number of tourists staying in the preset scenic spots in each historical period can also be obtained.
- the server can obtain the number of historical scenic spots in each period of the day, such as The current day is March 10, 2020, then the server can obtain the historical period corresponding to each period from March 10, 2020 00:00-24:00, such as March 9, 2020 10:00-11:00 period
- the number of historical scenic spots is 800 person-times
- the number of historical scenic spots during the period of 11:00-12:00 is 1100 person-times. Then, according to the number of people in the historical scenic spots in these historical periods, combined with the saturation of the first scenic spot, the saturation of the second scenic spot at each time period after the current time can be predicted.
- the calculation of the saturation of the second scenic spot It may be the saturation of the first scenic spot in the current period plus the difference between the saturation of the first scenic spot in the current historical period and the saturation of the first scenic spot in the previous historical period. For example, if the current time is 11:00, the real-time number of people in the preset scenic spot is 1000, and the saturation threshold of the preset scenic spot is 1500, then the saturation of the first scenic spot at 11:00 is 1000/1500. The number of people in the historical scenic spot during the period of 10:00-11:00 is 800, then the saturation of the first scenic spot in the historical period of 10:00-11:00 is 800/1500, and the historical saturation of the preset scenic spot is 800/1500 in the historical period of 11:00-12:00. The saturation of the first scenic spot in the time period is 1100/1500. Therefore, the saturation of the second scenic spot of the preset scenic spot in the period of 11:00-12:00 of the day is 1000/1500 (1100/1500-800/1500).
- the server predicts and obtains the saturation of the second scenic spot in each time period of the preset scenic spot, it needs to send the saturation of the second scenic spot to the user and display it to the user.
- the saturation of the second scenic spot is sent to the user in the form of a list , the list includes "Time Period Item” and "Second Scenic Spot Saturation Item", and each time period in the "Time Period Item” corresponds to the saturation level of each second scenic spot in the "Second Scenic Spot Saturation Item".
- the user can make more reasonable travel planning.
- the user plans to go to the scenic spot B during the period of 11:00-12:00, if the saturation of the second scenic spot displayed in the list does not exceed 1, it indicates that the scenic spot B is in a state of under-saturated traffic and there is no congestion, the user can enter the preset scenic spot as planned. If the saturation of the second scenic spot displayed in the list exceeds 1, it means that scenic spot B is in a state of traffic saturation and there is congestion In this case, entering Scenic Area B may affect the sightseeing experience of the scenic spot.
- the device for predicting the saturation of a scenic spot can respond to a user request, calculate the saturation of a first scenic spot of a preset scenic spot according to the real-time number of people in the preset scenic spot, and predict the saturation of the first scenic spot and historical period data
- the saturation of the second scenic spot for each time period of the preset scenic spot on the same day provides tourist users with the saturation prediction of the preset scenic spot, which can effectively prevent tourist users from blindly entering the preset scenic spot as planned because they do not know the crowd saturation of the preset scenic spot. , so as to affect the tourist user's experience in the scenic spot, and solve the technical problem that the existing scenic spot management system cannot provide users with the crowd saturation of the scenic spot, which brings great inconvenience to the tourists.
- the disclosed system, server and method may be implemented in other manners.
- the server embodiments described above are only illustrative.
- the division of the units is only a logical function division. In actual implementation, there may be other division methods.
- multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented.
- the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of servers or units, and may be in electrical, mechanical or other forms.
- the units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
- each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
- the above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.
- the integrated unit if implemented in the form of a software functional unit and sold or used as an independent product, may be stored in a computer-readable storage medium.
- the technical solutions of the present application can be embodied in the form of software products in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, and the computer software products are stored in a storage medium , including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.
- a computer device which may be a personal computer, a server, or a network device, etc.
- the aforementioned storage media include: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory) Memory, English abbreviation: RAM), magnetic disk or optical disk and other media that can store program code.
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Abstract
本申请公开了一种景区饱和度预测方法和服务器，能够响应用户请求，根据预置景区的实时人数计算预置景区的第一景区饱和度，并根据第一景区饱和度和历史时段数据预测当日预置景区各时段的第二景区饱和度，为旅游用户提供了预置景区的饱和度预测情况，能够有效避免旅游用户因不清楚预置景区的人流饱和情况而盲目按计划进入预置景区，从而影响旅游用户的景区游览体验的问题，解决了现有的景区管理系统不能为用户提供景区的人流饱和情况，给游客带来了极大的不便的技术问题。
Description
本申请涉及景区数据处理技术领域，尤其涉及一种景区饱和度预测方法和服务器。
互联网技术的快速发展，给传统的旅游业带来了重大的冲击，也为旅游业提供了极大的便利性。现有的基于互联网技术的旅游业，订票、售票、出票等业务都能够在往上进行操作，无需再到景区售票点购买门票，节省了购票时间，也避免了游客到景区售票点之后却出现门票已售完的局面。
但是现有的景区管理系统并不能为用户提供景区的人流情况，在景区人流高峰期会出现景区拥堵，若游客在高峰期进入景区，则不但会影响到游客的景区游览体验，还会耽误游客的景区游览进程，给游客带来了极大的不便，因此，如何为游客提供景区的人流饱和情况，以便于游客对景区游览时间进行合理的规划，是本领域技术人员亟待解决的技术问题。
本申请提供了一种景区饱和度预测方法和服务器，用于解决现有的景区管理系统不能为用户提供景区的人流饱和情况，给游客带来了极大的不便的技术问题。
有鉴于此，本申请第一方面提供了一种景区饱和度预测方法，包括：
响应于用户请求，获取预置景区的实时人数；
根据所述实时人数和所述预置景区的饱和阈值计算所述预置景区当前的第一景区饱和度，将所述第一景区饱和度发送给用户；
获取当日各时段的历史景区人数，根据所述第一景区饱和度和所述历史景区人数预测当日所述预置景区在当前时间之后各时段的第二景区饱和度；
将所述第二景区饱和度以列表的方式发送给用户。
可选地，所述获取预置景区的实时人数，包括：
根据预置景区内游客佩戴的预置定位装置计算所述预置景区内的实时人数。
可选地，还包括：
通过区块链网络将所述第一景区饱和度写入区块链中。
可选地，所述获取当日各时段的历史景区人数，包括：
从所述区块链中获取所述历史景区人数。
可选地，所述第二景区饱和度的计算公式为：
当前时段的所述第一景区饱和度加上历史当前时段的所述第一景区饱和度与历史前一时段的所述第一景区饱和度之差。
可选地，还包括：
将所述列表中大于或等于1的所述第二景区饱和度和对应的时间段进行高亮显示。
可选地，还包括：
获取所述预置景区预置距离范围内的所有可选景区，并向用户显示所述可选景区的位置以及所述可选景区的景区饱和度列表。
可选地，还包括：
计算所述所有可选景区分别与所述预置景区的距离，将所述所有可选景区按所述距离由近到远进行排序标号。
本申请第二方面提供了一种景区饱和度预测服务器服务器，包括：
响应模块，用于响应于用户请求，获取预置景区的实时人数；
第一饱和度推送模块，用于根据所述实时人数和所述预置景区的饱和阈值计算所述预置景区当前的第一景区饱和度，将所述第一景区饱和度发送给用户；
第二饱和度预测模块，用于获取当日各时段的历史景区人数，根据所述第一景区饱和度和所述历史景区人数预测当日所述预置景区在当前时间之后各时段的第二景区饱和度；
第二饱和度推送模块，用于将所述第二景区饱和度以列表的方式发送给用户。
可选地，还包括：
数据写入模块，用于通过区块链网络将所述第一景区饱和度写入区块链中。
从以上技术方案可以看出，本申请实施例具有以下优点：
本申请中提供了一种景区饱和度预测方法，包括：响应于用户请求，获取预置景区的实时人数；根据实时人数和预置景区的饱和阈值计算预置景区当前的第一景区饱和度，将第一景区饱和度发送给用户；获取当日各时段的历史景区人数，根据第一景区饱和度和历史景区人数预测当日预置景区在当前时间之后各时段的第二景区饱和度；将第二景区饱和度以列表的方式发送给用户。本申请中提供的景区饱和度预测方法，能够响应用户请求，根据预置景区的实时人数计算预置景区的第一景区饱和度，并根据第一景区饱和度和历史时段数据预测当日预置景区各时段的第二景区饱和度，为旅游用户提供了预置景区的饱和度预测情况，能够有效避免旅游用户因不清楚预置景区的人流饱和情况而盲目按计划进入预置景区，从而影响旅游用户的景区游览体验的问题，解决了现有的景区管理系统不能为用户提供景区的人流饱和情况，给游客带来了极大的不便的技术问题的技术问题。
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请中记载的一些实施例，对于本领域普通技术人员来讲，还可以根据这些附图获得其他的附图。
图1为本申请实施例中提供的一种景区饱和度预测方法的一个流程示意图；
图2为本申请实施例中提供的一种景区饱和度预测方法的另一个流程示意图；
图3为本申请实施例中提供的一种景区饱和度预测方法的再一个流程示意图；
图4为本申请实施例中提供的一种景区饱和度预测服务器的结构示意图。
为了使本技术领域的人员更好地理解本申请方案，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
实施例1
为了便于理解，请参阅图1，本申请提供了一种景区饱和度预测方法的一个实施例，包括：
步骤101、响应于用户请求，获取预置景区的实时人数。
需要说明的是，在用户外出旅游时，往往都会事先进行路线规划，比如在哪个时间到达景区A，在景区A逗留多久，然后在哪个时间再前往下一个景区B，但是在实际的旅游过程中，用户在计划时间到达某个景区的话，有可能会因为该景区人数饱和，过多的人数逗留在同一个景区中，容易造成拥堵，影响用户旅游的时间计划，也会影响景区拍照的效果，进而影响到用户的景区旅游体验，因此，为解决以上问题，本申请实施例中，用户可以在进入预置景区，即目标前往景区之前，通过移动网络设备向服务器发送获取预置景区的相关信息的请求，包括预置景区的饱和情况，应理解，用户通过移动网络设备向服务器发送请求的方式可以在通过移动网络设备的预置应用软件中主动请求，也可以是在用户在移动网络设备中打开预置应用软件之后，预置应用软件自动请求。服务器在接收到用户请求之后，对用户请求进行响应，计算预置景区的实时人数。在一个实施例中，为方便对预置景区内的游客人数进行准确计算，在每个游客进入预置景区时，需佩戴预置定位装置，因此，服务器可以根据预置景区内的定位装置计算出预置景区内的实时人数，在游客走出预置景区时，在预置景区出口处回收游客佩戴的预置定位装置，以便于循环使用，节约成本。
步骤102、根据实时人数和预置景区的饱和阈值计算预置景区当前的第一景区饱和度，将第一景区饱和度发送给用户。
需要说明的是，在计算得到预置景区的实时人数之后，服务器可以结合预置景区的饱和阈值计算出预置景区当前的第一景区饱和度，并将第一景区饱和度发送给发送请求的移动网络设备，例如，当前时间为11:00，预置景区的实时人数为1000人次，该预置景区的饱和阈值为1500人次，那么该预置景区在11:00的第一景区饱和度为1000/1500。
步骤103、获取当日各时段的历史景区人数，根据第一景区饱和度和历史景区人数预测当日预置景区在当前时间之后各时段的第二景区饱和度。
需要说明的是，在用户发送请求后，服务器需要预测当前时间之后的时间段的预置景区饱和度，以便于向用户提供预置景区的预测情况，方便用户做好旅游规划。预置景区每天的购票人数是可以在售票平台上获得的，每个历史时段逗留在预置景区内的游客人数也是可以获得的，因此，服务器可以获取得到当日各时段的历史景区人数，比如当日为2020年3月10日，那么服务器可以获取到2020年3月10日00:00-24:00每个时段对应的历史时段，比如2020年3月9日10:00-11:00时段的历史景区人数为800人次，11:00-12:00时段的历史景区人数为1100人次。那么根据这些历史时段的历史景区人数，结合第一景区饱和度，可以预测出当日预置景区在当前时间之后各时段的第二景区饱和度，在一个实施例中，第二景区饱和度的计算可以是当前时段的第一景区饱和度加上历史当前时段的第一景区饱和度与历史前一时段的第一景区饱和度之差。比如，当前时间为11:00，预置景区的实时人数为1000人次，该预置景区的饱和阈值为1500人次，那么该预置景区在11:00的第一景区饱和度为1000/1500，10:00-11:00时段的历史景区人数为800人次，那么预置景区在10:00-11:00历史时段的第一景区饱和度为800/1500，在11:00-12:00历史时段的第一景区饱和度为1100/1500，因此，预置景区在当日11:00-12:00时段的第二景区饱和度为1000/1500（1100/1500-800/1500）。
步骤104、将第二景区饱和度以列表的方式发送给用户。
需要说明的是，服务器在预测得到预置景区各时段的第二景区饱和度时候，需要向用户发送第二景区饱和度并显示给用户，本申请实施例中，将第二景区饱和度以列表的方式发送给用户，该列表包括“时段项目”和“第二景区饱和度项目”，“时段项目”中各时段与“第二景区饱和度项目”中各第二景区饱和度一一对应。用户在接收到该列表以后，可以进行更加合理的旅游规划，例如，用户计划在11:00-12:00时段前往景区B，若列表中显示的第二景区饱和度不超过1，则表明景区B处于人流未饱和的状态，不存在拥堵情况，则用户可以按计划进入预置景区，若列表中显示的第二景区饱和度超过了1，在则表明景区B处于人流饱和的状态，存在拥堵情况，进入景区B将可能会影响景区游览体验。
本申请实施例中提供的一种景区饱和度预测方法，能够响应用户请求，根据预置景区的实时人数计算预置景区的第一景区饱和度，并根据第一景区饱和度和历史时段数据预测当日预置景区各时段的第二景区饱和度，为旅游用户提供了预置景区的饱和度预测情况，能够有效避免旅游用户因不清楚预置景区的人流饱和情况而盲目按计划进入预置景区，从而影响旅游用户的景区游览体验的问题，解决了现有的景区管理系统不能为用户提供景区的人流饱和情况，给游客带来了极大的不便的技术问题。
实施例2
作为对实施例1的进一步改进，请参阅图2，本申请中提供了一种景区饱和度预测方法的另一个实施例，包括：
步骤201、响应于用户请求，获取预置景区的实时人数。
需要说明的是，本申请实施例中的步骤201与实施例1中的步骤101一致，在此不再进行赘述。
步骤202、根据实时人数和预置景区的饱和阈值计算预置景区当前的第一景区饱和度，将第一景区饱和度发送给用户，并通过区块链网络将第一景区饱和度写入区块链中。
需要说明的是，本申请实施例的步骤202在实施例1的步骤102的基础上，在得到第一景区饱和度之后，通过区块链网络将第一景区饱和度写入区块链中，区块链是一个共享数据库，存储于其中的数据或信息，具有“不可伪造”“全程留痕”“可以追溯”“公开透明”“集体维护”等特征，因此，将第一景区饱和度写入区块链中能够保证数据的可靠性。
步骤203、从区块链中获取历史景区人数，根据第一景区饱和度和历史景区人数预测当日预置景区在当前时间之后各时段的第二景区饱和度。
步骤204、将第二景区饱和度以列表的方式发送给用户。
需要说明的是，本申请实施例中的步骤203和步骤204与实施例1中的步骤103和步骤104一致，在此不再进行赘述。
步骤205、将列表中大于或等于1的第二景区饱和度和对应的时间段进行高亮显示。
需要说明的是，本申请实施例中将列表中大于或等于1的第二景区饱和度和对应的时间段进行高亮显示，有助于提醒用户这些时间段预置景区易产生饱和，注意避开饱和时段，以免造成不好的体验。
实施例3
作为对实施例2的进一步改进，请参阅图3，本申请中提供了一种景区饱和度预测方法的另一个实施例，包括：
步骤301、响应于用户请求，获取预置景区的实时人数。
步骤302、根据实时人数和预置景区的饱和阈值计算预置景区当前的第一景区饱和度，将第一景区饱和度发送给用户，并通过区块链网络将第一景区饱和度写入区块链中。
步骤303、从区块链中获取历史景区人数，根据第一景区饱和度和历史景区人数预测当日预置景区在当前时间之后各时段的第二景区饱和度。
步骤304、将第二景区饱和度以列表的方式发送给用户。
步骤305、将列表中大于或等于1的第二景区饱和度和对应的时间段进行高亮显示。
步骤306、获取预置景区预置距离范围内的所有可选景区，并向用户显示可选景区的位置以及可选景区的景区饱和度列表。
需要说明的是，本申请实施例中，在实施例2的基础上，还可以增加步骤306，获取预置景区预置距离范围内的所有可选景区，并向用户显示可选景区的位置以及可选景区的景区饱和度列表，能够避免在预置景区饱和的情况下，没有向用户提供可选的景区预览方案，而影响用户游览体验的问题，若预置景区在某时段内预测达到饱和，用户考虑到预置景区的游览体验度会下降，可以在预置景区预置距离范围内的所有可选景区中选择景区进行游览，当然，若预置景区在某时段内预测未达到饱和，用户想改变游览景点，也可以从预置景区预置距离范围内的所有可选景区中选择景区进行游览，因此，本申请实施例中服务器可以为用户提供多种景区游览选择，进一步提高了用户的游览体验和旅游服务体验。
在一个实施例中，在步骤306之后，还可以包括：
步骤307、计算所有可选景区分别与预置景区的距离，将所有可选景区按距离由近到远进行排序标号。
需要说明的是，在获得所有可选景区之后，根据所有可选景区距离预置景区的距离远近，对各个可选景区进行标识，有利于用户就近选择景区，避免用户盲目选择距离远的景区，带来身体疲累问题而导致影响用户游览体验的问题。
实施例4
为了便于理解，请参阅图4，本申请中提供了一种景区饱和度预测服务器的实施例，包括：
响应模块，用于响应于用户请求，获取预置景区的实时人数。
第一饱和度推送模块，用于根据所述实时人数和预置景区的饱和阈值计算预置景区当前的第一景区饱和度，将第一景区饱和度发送给用户。
第二饱和度预测模块，用于获取当日各时段的历史景区人数，根据所述第一景区饱和度和所述历史景区人数预测当日所述预置景区在当前时间之后各时段的第二景区饱和度。
第二饱和度推送模块，用于将所述第二景区饱和度以列表的方式发送给用户。
进一步地，还包括：
数据写入模块，用于通过区块链网络将所述第一景区饱和度写入区块链中。
需要说明的是，在用户外出旅游时，往往都会事先进行路线规划，比如在哪个时间到达景区A，在景区A逗留多久，然后在哪个时间再前往下一个景区B，但是在实际的旅游过程中，用户在计划时间到达某个景区的话，有可能会因为该景区人数饱和，过多的人数逗留在同一个景区中，容易造成拥堵，影响用户旅游的时间计划，也会影响景区拍照的效果，进而影响到用户的景区旅游体验，因此，为解决以上问题，本申请实施例中，用户可以在进入预置景区，即目标前往景区之前，通过移动网络设备向服务器发送获取预置景区的相关信息的请求，包括预置景区的饱和情况，应理解，用户通过移动网络设备向服务器发送请求的方式可以在通过移动网络设备的预置应用软件中主动请求，也可以是在用户在移动网络设备中打开预置应用软件之后，预置应用软件自动请求。服务器在接收到用户请求之后，对用户请求进行响应，计算预置景区的实时人数。在一个实施例中，为方便对预置景区内的游客人数进行准确计算，在每个游客进入预置景区时，需佩戴预置定位装置，因此，服务器可以根据预置景区内的定位装置计算出预置景区内的实时人数。
在计算得到预置景区的实时人数之后，服务器可以结合预置景区的饱和阈值计算出预置景区当前的第一景区饱和度，并将第一景区饱和度发送给发送请求的移动网络设备，例如，当前时间为11:00，预置景区的实时人数为1000人次，该预置景区的饱和阈值为1500人次，那么该预置景区在11:00的第一景区饱和度为1000/1500。在得到第一景区饱和度之后，通过区块链网络将第一景区饱和度写入区块链中，区块链是一个共享数据库，存储于其中的数据或信息，具有“不可伪造”“全程留痕”“可以追溯”“公开透明”“集体维护”等特征，因此，将第一景区饱和度写入区块链中能够保证数据的可靠性。
在用户发送请求后，服务器需要预测当前时间之后的时间段的预置景区饱和度，以便于向用户提供预置景区的预测情况，方便用户做好旅游规划。预置景区每天的购票人数是可以在售票平台上获得的，每个历史时段逗留在预置景区内的游客人数也是可以获得的，因此，服务器可以获取得到当日各时段的历史景区人数，比如当日为2020年3月10日，那么服务器可以获取到2020年3月10日00:00-24:00每个时段对应的历史时段，比如2020年3月9日10:00-11:00时段的历史景区人数为800人次，11:00-12:00时段的历史景区人数为1100人次。那么根据这些历史时段的历史景区人数，结合第一景区饱和度，可以预测出当日预置景区在当前时间之后各时段的第二景区饱和度，在一个实施例中，第二景区饱和度的计算可以是当前时段的第一景区饱和度加上历史当前时段的第一景区饱和度与历史前一时段的第一景区饱和度之差。比如，当前时间为11:00，预置景区的实时人数为1000人次，该预置景区的饱和阈值为1500人次，那么该预置景区在11:00的第一景区饱和度为1000/1500，10:00-11:00时段的历史景区人数为800人次，那么预置景区在10:00-11:00历史时段的第一景区饱和度为800/1500，在11:00-12:00历史时段的第一景区饱和度为1100/1500，因此，预置景区在当日11:00-12:00时段的第二景区饱和度为1000/1500（1100/1500-800/1500）。
服务器在预测得到预置景区各时段的第二景区饱和度时候，需要向用户发送第二景区饱和度并显示给用户，本申请实施例中，将第二景区饱和度以列表的方式发送给用户，该列表包括“时段项目”和“第二景区饱和度项目”，“时段项目”中各时段与“第二景区饱和度项目”中各第二景区饱和度一一对应。用户在接收到该列表以后，可以进行更加合理的旅游规划，例如，用户计划在11:00-12:00时段前往景区B，若列表中显示的第二景区饱和度不超过1，则表明景区B处于人流未饱和的状态，不存在拥堵情况，则用户可以按计划进入预置景区，若列表中显示的第二景区饱和度超过了1，在则表明景区B处于人流饱和的状态，存在拥堵情况，进入景区B将可能会影响景区游览体验。
本申请实施例中提供的一种景区饱和度预测装置，能够响应用户请求，根据预置景区的实时人数计算预置景区的第一景区饱和度，并根据第一景区饱和度和历史时段数据预测当日预置景区各时段的第二景区饱和度，为旅游用户提供了预置景区的饱和度预测情况，能够有效避免旅游用户因不清楚预置景区的人流饱和情况而盲目按计划进入预置景区，从而影响旅游用户的景区游览体验的问题，解决了现有的景区管理系统不能为用户提供景区的人流饱和情况，给游客带来了极大的不便的技术问题的技术问题。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统，服务器和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
本申请的说明书及上述附图中的术语“第一”、“第二”、“第三”、“第四”等（如果存在）是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本申请的实施例例如能够以除了在这里图示或描述的那些以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统，服务器和方法，可以通过其它的方式实现。例如，以上所描述的服务器实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，服务器或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备（可以是个人计算机，服务器，或者网络设备等）执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器（英文全称：Read-Only Memory，英文缩写：ROM）、随机存取存储器（英文全称：Random Access
Memory，英文缩写：RAM）、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，以上实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围。
Claims (10)
- 一种景区饱和度预测方法，其特征在于，包括：响应于用户请求，获取预置景区的实时人数；根据所述实时人数和所述预置景区的饱和阈值计算所述预置景区当前的第一景区饱和度，将所述第一景区饱和度发送给用户；获取当日各时段的历史景区人数，根据所述第一景区饱和度和所述历史景区人数预测当日所述预置景区在当前时间之后各时段的第二景区饱和度；将所述第二景区饱和度以列表的方式发送给用户。
- 根据权利要求1所述的景区饱和度预测方法，其特征在于，所述获取预置景区的实时人数，包括：根据预置景区内游客佩戴的预置定位装置计算所述预置景区内的实时人数。
- 根据权利要求1所述的景区饱和度预测方法，其特征在于，还包括：通过区块链网络将所述第一景区饱和度写入区块链中。
- 根据权利要求3所述的景区饱和度预测方法，其特征在于，所述获取当日各时段的历史景区人数，包括：从所述区块链中获取所述历史景区人数。
- 根据权利要求1所述的景区饱和度预测方法，其特征在于，所述第二景区饱和度的计算公式为：当前时段的所述第一景区饱和度加上历史当前时段的所述第一景区饱和度与历史前一时段的所述第一景区饱和度之差。
- 根据权利要求1所述的景区饱和度预测方法，其特征在于，还包括：将所述列表中大于或等于1的所述第二景区饱和度和对应的时间段进行高亮显示。
- 根据权利要求1所述的景区饱和度预测方法，其特征在于，还包括：获取所述预置景区预置距离范围内的所有可选景区，并向用户显示所述可选景区的位置以及所述可选景区的景区饱和度列表。
- 根据权利要求7所述的景区饱和度预测方法，其特征在于，还包括：计算所述所有可选景区分别与所述预置景区的距离，将所述所有可选景区按所述距离由近到远进行排序标号。
- 一种景区饱和度预测服务器，其特征在于，包括：响应模块，用于响应于用户请求，获取预置景区的实时人数；第一饱和度推送模块，用于根据所述实时人数和所述预置景区的饱和阈值计算所述预置景区当前的第一景区饱和度，将所述第一景区饱和度发送给用户；第二饱和度预测模块，用于获取当日各时段的历史景区人数，根据所述第一景区饱和度和所述历史景区人数预测当日所述预置景区在当前时间之后各时段的第二景区饱和度；第二饱和度推送模块，用于将所述第二景区饱和度以列表的方式发送给用户。
- 根据权利要求9所述的景区饱和度预测服务器，其特征在于，还包括：数据写入模块，用于通过区块链网络将所述第一景区饱和度写入区块链中。
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140278688A1 (en) * | 2013-03-15 | 2014-09-18 | Disney Enterprises, Inc. | Guest movement and behavior prediction within a venue |
| CN104899650A (zh) * | 2015-05-26 | 2015-09-09 | 成都中科大旗软件有限公司 | 基于多源数据分析对旅游景区客流量进行预测的方法 |
| CN106355289A (zh) * | 2016-09-20 | 2017-01-25 | 杭州东信北邮信息技术有限公司 | 一种基于位置服务的景区客流量预测方法 |
| CN110837908A (zh) * | 2018-08-17 | 2020-02-25 | 姜云兰 | 一种旅游景区实时人数智能预测系统 |
| CN111126715A (zh) * | 2020-01-03 | 2020-05-08 | 成都中科大旗软件股份有限公司 | 景区客流量管控系统 |
| CN112580877A (zh) * | 2020-12-22 | 2021-03-30 | 北京东方风景智慧科技有限公司 | 景区内综合管理系统 |
-
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- 2021-05-17 WO PCT/CN2021/094004 patent/WO2022217684A1/zh active Application Filing
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140278688A1 (en) * | 2013-03-15 | 2014-09-18 | Disney Enterprises, Inc. | Guest movement and behavior prediction within a venue |
| CN104899650A (zh) * | 2015-05-26 | 2015-09-09 | 成都中科大旗软件有限公司 | 基于多源数据分析对旅游景区客流量进行预测的方法 |
| CN106355289A (zh) * | 2016-09-20 | 2017-01-25 | 杭州东信北邮信息技术有限公司 | 一种基于位置服务的景区客流量预测方法 |
| CN110837908A (zh) * | 2018-08-17 | 2020-02-25 | 姜云兰 | 一种旅游景区实时人数智能预测系统 |
| CN111126715A (zh) * | 2020-01-03 | 2020-05-08 | 成都中科大旗软件股份有限公司 | 景区客流量管控系统 |
| CN112580877A (zh) * | 2020-12-22 | 2021-03-30 | 北京东方风景智慧科技有限公司 | 景区内综合管理系统 |
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