Health-care tourism risk management method

Abstract

The invention relates to the technical field of health care tourism, in particular to a health care tourism risk management method. The method comprises the following steps: S1, collecting historical risk data of a playing place to form a historical risk data set; s2, dividing the historical risk data set into a training set, a verification set and a test set, and constructing a risk prediction model; s3, predicting whether a risk exists or not through the trained risk prediction model according to the playing time and season of the tourist; and S4, carrying out risk management and control in the process of assisting the tourist in playing through the cooperation of the intelligent bracelet worn on the tourist who plays and the data processing module. According to the health-care tourism risk management method provided by the invention, historical data and real-time data are combined, whether risks exist or not is predicted by the risk prediction model, and meanwhile, the positions, body health and the like of tourists are monitored in real time through smart bracelets worn by the tourists; and later diseases caused by untimely treatment due to sudden diseases of tourists can be prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of health-care tourism, and in particular to a health-care tourism risk management method. Background Art

[0002] As an emerging form of tourism, health tourism combines health care and tourism leisure, and is increasingly favored by consumers. However, health tourism activities often involve a variety of risks, such as health risks (such as disease transmission, food poisoning), environmental risks (such as extreme weather, environmental pollution) and safety risks (such as falls, traffic accidents). If these risks are not effectively managed, they may have a serious impact on the health and safety of tourists, and may even lead to legal liability and financial losses;

[0003] Existing tourism risk management methods cannot effectively monitor the tourists' play locations and the tourists' own locations when they are applied. It is easy to fail to find the tourists themselves when a risk accident occurs, and the location range of the tourists cannot be confirmed.

[0004] To this end, a health tourism risk management method is designed to provide another technical solution to the above technical problems. Summary of the invention

[0005] Based on this, it is necessary to provide a health tourism risk management method to address the above technical problems, so as to solve the technical problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A health tourism risk management method, the steps are as follows:

[0008] S1: Collect historical risk data of tourist attractions to form a historical risk data set;

[0009] S2: Divide the historical risk dataset into training set, validation set and test set, and build a risk prediction model;

[0010] S3: Based on the time and season of tourists’ travel, the trained risk prediction model is used to predict whether there is risk;

[0011] S4: Assist tourists in risk management during their tour by using smart bracelets worn by tourists and data processing modules.

[0012] As a preferred implementation of the health tourism risk management method provided by the present invention, in the step S1, historical risk data of tourist attractions are collected, including but not limited to natural disasters, disease transmission, and facility aging risk data, and are classified and discharged in the form of time + risk type + tourist activity type to form a historical risk data set.

[0013] As a preferred implementation of the health tourism risk management method provided by the present invention, the time in the historical risk data set is converted into the corresponding season, week or specific holiday.

[0014] As a preferred implementation of the health tourism risk management method provided by the present invention, the historical risk data set is divided into a training set, a validation set and a test set according to a ratio of 6:2:2.

[0015] As a preferred implementation of the health tourism risk management method provided by the present invention, in the step S2, a risk prediction model is constructed, and the steps are as follows:

[0016] A. Build a risk prediction model and train it using the training set;

[0017] B. After training, verify whether the risk prediction model is accurate through the validation set;

[0018] C. If the verification is correct, go to step D; if the verification is wrong, go to step E;

[0019] D. Verify again through the test set;

[0020] E. Re-collect historical risk data and redivide it into training set, validation set and test set, and repeat step A.

[0021] As a preferred implementation of the health tourism risk management method provided by the present invention, in the step S4, the risk control of tourists during their travels is assisted by the cooperation of the smart bracelet worn by the tourists and the data processing module, and the steps are as follows:

[0022] The smart bracelet is used to locate the tourists and provide information. When the tourists are monitored to stay in the same place for a long time, the smart bracelet or the tour guide can confirm whether the tourists are safe.

[0023] If the tourist is healthy at this time, the location monitoring function of the smart bracelet will be restarted. If the tourist is unwell, the nearest medical treatment point will be recommended to the tourist.

[0024] As a preferred implementation of the health tourism risk management method provided by the present invention, the relevant information is displayed to the doctor through the tour guide based on the physical health data of the tourists to prevent unclear data caused by tourists falling into a coma.

[0025] As a preferred implementation of the health tourism risk management method provided by the present invention, during the tourists' play, the smart bracelet is used to check in and identify at different play locations to prevent the tourists' unclear location due to poor signals at the play location;

[0026] When the risk prediction model predicts that there is a risk in the near future, the smart bracelet will vibrate to warn tourists of the risk, allowing them to follow the navigation of the smart bracelet to enter the nearest avoidance location. At the same time, the location of the avoidance location can be indicated to tourists through voice, broadcast, alarm sound, signboards, etc. at the tourist location;

[0027] After the disaster, all tourists will be gathered together using smart bracelets and undergo safety checks through physical examinations and blood sample tests to ensure their health.

[0028] As a preferred implementation of the health tourism risk management method provided by the present invention, when the smart bracelet detects the health of tourists, it is determined whether the signal between the smart bracelet and the data processing module is interrupted by regularly sending a heartbeat signal and a position signal to the data processing module;

[0029] When it is determined that the signal between the smart bracelet and the data processing module is interrupted, the smart bracelet will store the latest data, and at the same time, the tour guide's device will prompt that the corresponding tourist's location is lost, and the tour guide will query and confirm the tourist's location.

[0030] As a preferred implementation of the health tourism risk management method provided by the present invention, if the data processing module regularly receives the heartbeat signal and location signal of the tourist transmitted back by the smart bracelet, it is determined that the signal between the smart bracelet and the data processing module is not interrupted;

[0031] If the data processing module does not regularly receive the heartbeat signal and location signal of the tourist sent back by the smart bracelet, it is determined that the signal between the smart bracelet and the data processing module is interrupted;

[0032] The data processing module attempts to reconnect with the signal of the corresponding smart bracelet. If the connection is successful, the smart bracelet works normally and retransmits the data during the offline period after the signal connection is successful.

[0033] It can be seen without a doubt that the above-mentioned technical solution of the present application can definitely solve the technical problem to be solved by the present application.

[0034] At the same time, through the above technical solutions, the present invention has at least the following beneficial effects:

[0035] The present invention provides a health tourism risk management method, which combines historical data with real-time data to allow a risk prediction model to predict whether there is a risk. At the same time, the location and physical health of tourists are monitored in real time through smart bracelets worn by tourists, so as to prevent the consequences of untimely medical treatment caused by sudden illness of tourists.

[0036] The risk prediction model constructed by convolutional neural network (CNN) facilitates the assessment of the risks of places visited by tourists based on the collected data, thereby reducing the risks of tourists' visits in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 It is a schematic diagram of the risk management process of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0040] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings.

[0041] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features and technical solutions in the embodiments may be combined with each other.

[0042] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0043] Reference Figure 1 , a health tourism risk management method, the steps are as follows:

[0044] Collect historical risk data of tourist destinations, including but not limited to natural disasters, disease transmission, aging facilities and other risk data. Specifically, crawler software can be used to collect information on relevant risk events, tourists' comments, feedback and risk warning information on tourism platforms, and analyze insurance companies' claims data at relevant locations in statistical data released by official agencies such as the Centers for Disease Control, Meteorological Bureau, and Tourism Bureau, news websites, and social media to identify common risks, and classify and discharge them according to time + risk type + tourist activity type to form a historical risk data set;

[0045] Convert the time in the historical risk data set into the corresponding season, week or specific holiday;

[0046] The historical risk dataset is divided into training set, validation set and test set according to 6:2:2;

[0047] A risk prediction model is constructed by a convolutional neural network (CNN), and is trained by a training set. After the training is completed, the risk prediction model is verified by a validation set to see if it is accurate. If the validation set verifies that the risk prediction model parameters are correct, the model is verified again by a test set. If the validation set verifies that the risk prediction model parameters are incorrect, historical risk data is collected again to form a new historical risk data set. At the same time, the risk prediction model is constructed by a training set in the new historical risk data set and retrained. The steps are repeated until the test parameters are correct.

[0048] According to the time and season of tourists' travel, the trained risk prediction model is used to predict whether there is a risk. If there is a risk, the travel time is changed to reduce the risk of tourists' travel. If there is no risk, the travel is normal. At the same time, the official information such as local weather forecast can be combined to confirm whether there are natural risks such as heavy rain and heavy snow half a month before the departure of the travel. At the same time, during the tourists' travel, data is continuously collected, and the risk prediction model is used to predict whether there is a risk based on the combination of historical data and real-time data;

[0049] According to the time and season of tourists' travel, various travel routes are planned for tourists, and corresponding transportation methods and hotel recommendations are automatically generated according to different travel routes; the entry of travel expenses can also be increased, and travel routes, transportation methods, and hotel accommodations can be automatically re-recommended according to the expenses;

[0050] The smart bracelets worn by tourists and the data processing module are used to assist tourists in risk management during their travels;

[0051] The data processing module in this embodiment is used to remotely receive data from the smart bracelet, and at the same time enable the back-end personnel to predict whether there is a risk based on the risk prediction model, and control the smart bracelet through the data processing module to prompt the corresponding tourists.

[0052] The health information of tourists is monitored in real time through smart bracelets. At the same time, the location of tourists can be located and information prompts can be given through smart bracelets. When the monitored tourists stay in the same place for a long time (for example, an alarm is set to be prompted if they stay in the same place for 5 minutes without moving, and the back-end data viewing personnel are warned), the smart bracelet or the tour guide can confirm whether the tourists are safe. Specifically, the information can be transmitted to the smart bracelet or to the tour guide to confirm whether the tourists are safe at this time;

[0053] If the tourist is healthy at this time, the location monitoring function of the smart bracelet will be restarted. If the tourist is unwell, the nearest medical point will be recommended to the tourist and the best route will be planned. At the same time, the relevant information can be shown to the doctor through the tour guide based on the tourist's health data to prevent unclear data caused by the tourist's coma;

[0054] During the tourists' visit, they can use smart bracelets to check in and identify at different places to prevent tourists from being unclear about their location due to poor signals at the places to visit. At the same time, when the risk prediction model predicts that there will be risks in the near future, the smart bracelets will vibrate to warn tourists of the risks, allowing them to enter the nearest avoidance location according to the navigation of the smart bracelets. At the same time, the avoidance location can be indicated to tourists through voice, broadcast, alarm sound, signboards, etc. at the places to visit.

[0055] When the smart bracelet detects the health of tourists, it determines whether the signal between the smart bracelet and the data processing module is interrupted by whether the heartbeat signal and the position signal are sent to the data processing module regularly. If the data processing module regularly receives the heartbeat signal and the position signal of the tourists sent back by the smart bracelet, it is determined that the signal between the smart bracelet and the data processing module is not interrupted. If the data processing module does not regularly receive the heartbeat signal and the position signal of the tourists sent back by the smart bracelet, it is determined that the signal between the smart bracelet and the data processing module is interrupted.

[0056] When it is determined that the signal between the smart bracelet and the data processing module is interrupted, the smart bracelet is allowed to store the latest data, including but not limited to the location signal, the identification signal of the playing location check-in, the body data signal, etc., and then the data processing module is allowed to try to reconnect with the signal of the corresponding smart bracelet. If the connection is successful, the smart bracelet is allowed to work normally, and after the signal connection is successful, the data during the offline period is re-transmitted. If the connection fails, the guide's device prompts the corresponding tourist that the location is lost;

[0057] When the signal is interrupted, since the data processing module does not receive the heartbeat signal and location signal of the tourist, the tour guide searches for the tourist according to the location signal sent back by the tourist's smart bracelet to confirm the tourist's safety. The search method includes but is not limited to telephone communication, manual search, etc.

[0058] After the disaster, all tourists will be gathered together using smart bracelets and undergo safety checks through physical examinations and blood sample tests to ensure their health.

[0059] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A health tourism risk management method, characterized in that: Here are the steps: S1: Collect historical risk data of tourist attractions to form a historical risk data set; S2: Divide the historical risk dataset into training set, validation set and test set, and build a risk prediction model; S3: Based on the time and season of tourists’ travel, the trained risk prediction model is used to predict whether there is risk; S4: Assist tourists in risk management during their tour by using smart bracelets worn by tourists and data processing modules.

2. A health tourism risk management method according to claim 1, characterized in that: In the step S1, historical risk data of tourist attractions are collected, including but not limited to natural disasters, disease transmission, and facility aging risk data, and are classified and discharged in the form of time + risk type + tourist activity type to form a historical risk data set.

3. A health tourism risk management method according to claim 2, characterized in that: Convert the time in the historical risk data set into the corresponding season, week or specific holiday.

4. A health tourism risk management method according to claim 1, characterized in that: The historical risk dataset is divided into training set, validation set and test set according to the ratio of 6:2:

2.

5. A health tourism risk management method according to claim 1, characterized in that: In step S2, a risk prediction model is constructed, and the steps are as follows: A. Build a risk prediction model and train it using the training set; B. After training, verify whether the risk prediction model is accurate through the validation set; C. If the verification is correct, go to step D; if the verification is wrong, go to step E; D. Verify again through the test set; E. Re-collect historical risk data and redivide it into training set, validation set and test set, and repeat step A.

6. A health tourism risk management method according to claim 1, characterized in that: In the step S4, the risk control of tourists during their travels is assisted by the cooperation of the smart bracelets worn by the tourists and the data processing module, and the steps are as follows: The smart bracelet is used to locate the tourists and provide information. When the tourists are monitored to stay in the same place for a long time, the smart bracelet or the tour guide can confirm whether the tourists are safe. If the tourist is healthy at this time, the location monitoring function of the smart bracelet will be restarted. If the tourist is unwell, the nearest medical treatment point will be recommended to the tourist.

7. A health tourism risk management method according to claim 6, characterized in that: The tour guide will show the doctor relevant information based on the tourists' health data to prevent unclear data caused by tourists falling into a coma.

8. A health tourism risk management method according to claim 6, characterized in that: During the tourists' visit, they can check in and identify themselves at different places through smart bracelets to prevent tourists from being unclear about their location due to poor signals at the places where they visit. When the risk prediction model predicts that there is a risk in the near future, the smart bracelet will vibrate to warn tourists of the risk, allowing them to enter the nearest avoidance location according to the navigation of the smart bracelet.

9. A health tourism risk management method according to claim 6, characterized in that: When the smart bracelet detects the health of tourists, it determines whether the signal between the smart bracelet and the data processing module is interrupted by regularly sending heartbeat signals and location signals to the data processing module; When it is determined that the signal between the smart bracelet and the data processing module is interrupted, the smart bracelet will store the latest data, and at the same time, the tour guide's device will prompt that the corresponding tourist's location is lost, and the tour guide will query and confirm the tourist's location.

10. A health tourism risk management method according to claim 9, characterized in that: If the data processing module regularly receives the heartbeat signal and location signal of the tourist sent back by the smart bracelet, it is determined that there is no interruption in the signal between the smart bracelet and the data processing module; If the data processing module does not regularly receive the heartbeat signal and location signal of the tourist sent back by the smart bracelet, it is determined that the signal between the smart bracelet and the data processing module is interrupted; The data processing module attempts to reconnect with the signal of the corresponding smart bracelet. If the connection is successful, the smart bracelet works normally and retransmits the data during the offline period after the signal connection is successful.

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