Method and device for monitoring contact behavior and infection risk of users diagnosed with infectious diseases

Abstract

The present invention provides a method and device for monitoring the contact behavior and infection risk of users diagnosed with infectious diseases. The tracking key of the current mobile terminal in each working cycle and the time information of the corresponding recording time period are encrypted and used as an anonymous identifier to mark the mobile terminal in a specific time period. The anonymous identifier is broadcast via Bluetooth and other mobile terminals within a set range around are scanned. By downloading the collection of tracking keys of the diagnosed users broadcast by the server, it is calculated and compared whether there is a record that is consistent with the anonymous identifier of other terminal devices recorded locally. If so, it is determined that the user of the current mobile terminal has come into contact with the diagnosed user. And the infectious disease infection probability is directly obtained from the server. By directly using the Bluetooth module and data for monitoring and processing, the monitoring of the contact behavior between a large number of monitoring objects and diagnosed users under the condition of large traffic volume is realized, and the monitoring frequency and accuracy can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of infectious disease prevention and control, and particularly to a method and device for monitoring the contact behavior and infection risk of confirmed infectious disease users. Background Art

[0002] In the aspects of infectious disease prevention and control and infection risk monitoring, the existing technologies mainly rely on manual epidemiological investigations and screenings, which are inefficient and inaccurate. Respiratory infectious diseases can cause infections within a specific spatial range, posing great difficulties to epidemiological investigations and risk assessment work. Especially at present, with extremely complex traffic and a huge number of people in circulation and a large number of monitoring objects, the requirements for the monitoring frequency and accuracy of respiratory infectious disease infection risks are getting higher and higher. Therefore, there is an urgent need for a new method for monitoring infectious disease infection risks to meet more complex monitoring requirements. Summary of the Invention

[0003] In view of this, the embodiments of the present invention provide a method and device for monitoring the contact behavior and infection risk of confirmed infectious disease users to eliminate or improve one or more defects existing in the prior art and solve the problem that it is difficult to effectively monitor a huge number of monitoring objects under the conditions of large traffic volume, high monitoring frequency, and high accuracy requirements.

[0004] The technical solution of the present invention is as follows:

[0005] On the one hand, the present invention provides a method for monitoring the contact behavior of confirmed infectious disease users, including:

[0006] Randomly generating a tracking key uniquely corresponding to the current mobile terminal in each working cycle;

[0007] Dividing each working cycle into multiple recording time periods, encrypting the tracking key of the current mobile terminal and the time information of the current recording time period by using a preset encryption algorithm to obtain an anonymous identifier of the current mobile terminal in the current time period, and periodically broadcasting it to the surroundings;

[0008] Enabling the Bluetooth of the current mobile terminal to perform periodic scanning to obtain the anonymous identifiers of other mobile terminals within a set range around in each recording time period, and storing the anonymous identifier of the current mobile terminal in each recording time period, the time information of each recording time period, and the anonymous identifiers of the scanned other mobile terminals locally;

[0009] Downloading from the server multiple tracking keys generated by one or more confirmed infectious disease user mobile terminals during the risk period to form a tracking key collection;

[0010] Encrypt all the tracking keys in the preset tracking key set and each recording time period within the risk period by using the preset encryption algorithm to obtain a plurality of confirmed user anonymous identifiers;

[0011] Compare all the confirmed user anonymous identifiers with all the anonymous identifiers recorded locally by the current mobile terminal. If there is a matching record, it is determined that the user of the current mobile terminal has come into contact with the confirmed user;

[0012] And / or, when the user of the current mobile terminal is confirmed to be infected, upload all the tracking keys within the risk period to the server.

[0013] In some embodiments, turn on the Bluetooth of the current mobile terminal for periodic scanning to obtain the anonymous identifiers of other mobile terminals within a set range around each recording time period, including:

[0014] Obtain the signal strength of the Bluetooth of the scanned other mobile terminals through two methods: Bluetooth device listening and Bluetooth device scan callback. Calculate the distance between the current mobile terminal and the other mobile terminal according to the signal strength. The calculation formula is:

[0015] d = 10^((abs(RSSI)-(A)) / (10×n));

[0016] Wherein, d represents the distance between the current mobile terminal and the other mobile terminal, in meters; RSSI represents the signal strength of Bluetooth, in dBm; A represents the standard signal strength of Bluetooth when separated by 1 meter, in dBm; n represents the environmental attenuation factor, with a range of 2 to 4.

[0017] In some embodiments, the method further includes:

[0018] Send an infection risk prediction request message to the server. The infection risk prediction request message at least includes the anonymous identifiers of the current mobile terminal in each recording time period, the time information of each recording time period, and the anonymous identifiers of the scanned other mobile terminals recorded locally by the current mobile terminal within a set time period;

[0019] Receive the infection risk level information of the current mobile terminal returned by the server. When the infection risk level information indicates that the infection risk reaches a preset level, give an alarm prompt; wherein, the infection risk level information is obtained based on the infection probability of respiratory infectious diseases predicted by the Wells - Riley model, and the infection probability of respiratory infectious diseases is calculated and divided into levels 0 - 10 according to the infection risk from low to high.

[0020] In some embodiments, the method further includes: reporting daily health information to the server to form a log for query by the current mobile terminal.

[0021] On the other hand, the present invention also provides an infectious disease infection risk monitoring method, including:

[0022] Receiving a plurality of tracking keys generated by a mobile terminal of a confirmed user during a risk period, and broadcasting them for mobile terminals of ordinary users to whom they are broadcast to determine whether each ordinary user has come into contact with the confirmed user according to the above-mentioned infectious disease confirmed user contact behavior monitoring method;

[0023] And receiving infection risk prediction request information sent by mobile terminals of one or more ordinary users, anonymous identifiers of other mobile terminals within a set surrounding range periodically scanned during the risk period, and corresponding recording time periods; the anonymous identifiers and corresponding recording time periods are obtained according to the above-mentioned infectious disease confirmed user contact behavior monitoring method;

[0024] Obtaining contact objects and contact times of each ordinary user according to the anonymous identifiers of other mobile terminals collected by the mobile terminals of each ordinary user during the risk time period and the corresponding recording time periods;

[0025] Using the Wells-Riley model to calculate a first probability that an ordinary user is infected by a designated contact object according to the contact objects and contact times of each ordinary user, obtaining a second probability that the designated contact object is already infected, multiplying the first probabilities of multiple designated contact objects by the second probability and then performing probability superposition to obtain the infectious disease infection probability of the corresponding ordinary user, and determining the infection risk level.

[0026] In some embodiments, multiplying the first probabilities of multiple designated contact objects by the second probability and then performing probability superposition to obtain the infectious disease infection probability of the corresponding ordinary user, the calculation formula is:

[0027]

[0028] wherein, R0 represents the infection probability of the ordinary user himself, R i represents the infection probability of the i-th contact user himself, P i represents the probability that the i-th contact user infects the ordinary user, and n represents the number of contact users.

[0029] In some embodiments, the infectious disease infection risk monitoring method further includes:

[0030] In the case where the confirmed user does not actively report the tracking key, receiving the identity information of the confirmed user released by a preset official platform, and sending a tracking key request message to each confirmed user;

[0031] Receive multiple tracking keys within the risk period returned by the mobile terminal of the confirmed user, and broadcast them for the mobile terminals of the ordinary users to whom they are broadcast to determine whether each ordinary user has come into contact with the confirmed user according to the above-mentioned method for monitoring the contact behavior of infectious disease confirmed users.

[0032] On the other hand, the present invention also provides a mobile terminal device, including:

[0033] A Bluetooth scanning module, which is used to randomly generate a tracking key uniquely corresponding to the current mobile terminal within each working cycle; divide each working cycle into multiple recording time periods, encrypt the tracking key of the current mobile terminal and the time information of the current recording time period by using a preset encryption algorithm to obtain an anonymous identifier of the current mobile terminal in the current time period, and perform periodic broadcasting to the surroundings; perform periodic scanning to obtain the anonymous identifiers of other mobile terminals within a set range around in each recording time period, and store the anonymous identifiers of the current mobile terminal in each recording time period, the time information of each recording time period, and the scanned anonymous identifiers of the other mobile terminals locally;

[0034] A Bluetooth scanning information local matching module, which is used to download multiple tracking keys generated by one or more mobile terminals of infected confirmed users within the risk period from the server to form a tracking key set; use the preset encryption algorithm to encrypt all the tracking keys in the tracking key set and each recording time period within the risk period to obtain multiple anonymous identifiers of confirmed users; compare all the anonymous identifiers of confirmed users with all the anonymous identifiers recorded locally by the current mobile terminal, and if there are consistent records, it is determined that the user of the current mobile terminal has come into contact with the confirmed user.

[0035] In some embodiments, it further includes:

[0036] An infection risk prediction module, which is used to upload all the tracking keys within the risk period to the server when the user of the current mobile terminal is confirmed to be infected; and send an infection risk prediction request message to the server to query the infection risk level information.

[0037] A user health information module, which is used to report daily health information to the server for query by the current mobile terminal.

[0038] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the steps of the above method are implemented.

[0039] The beneficial effects of the present invention are at least:

[0040] In the method and device for monitoring the contact behavior and infection risk of users diagnosed with infectious diseases, the tracking key of the current mobile terminal in each working cycle and the time information of the corresponding recording time period are encrypted and used as an anonymous identifier to mark the mobile terminal in a specific time period. The anonymous identifier is broadcast via Bluetooth and other mobile terminals within a set range around are scanned. By downloading the collection of tracking keys of users diagnosed by the server, it is calculated and compared whether there is a record that is the same as the anonymous identifier of other terminal devices recorded locally. If so, it is determined that the user of the current mobile terminal has come into contact with the user diagnosed with the disease. With the help of the Bluetooth module and data for monitoring and processing, the monitoring of the contact behavior between a large number of monitoring objects and users diagnosed with the disease under the condition of a large traffic volume is realized, and the monitoring frequency and accuracy can be effectively improved.

[0041] Furthermore, the server calculates the contact objects and contact times of each ordinary user based on the anonymous identifiers uploaded by each ordinary user and the corresponding recording time periods, and calculates the infectious disease infection probability based on the Wells-Riley model, which can effectively evaluate the infection risk of users.

[0042] The additional advantages, objectives, and features of the present invention will be partially described below and will become partially apparent to those of ordinary skill in the art after studying the following text, or can be learned from the practice of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the written description, claims, and drawings.

[0043] Those skilled in the art will understand that the objectives and advantages that can be achieved by the present invention are not limited to the above specific descriptions, and the above and other objectives that the present invention can achieve will be more clearly understood according to the following detailed description. Description of the Drawings

[0044] The drawings described herein are used to provide a further understanding of the present invention, form a part of this application, and do not limit the present invention. In the drawings:

[0045] Figure 1 It is a schematic flowchart of the method for monitoring the contact behavior of users diagnosed with infectious diseases according to an embodiment of the present invention;

[0046] Figure 2 It is a schematic flowchart of the method for monitoring the infectious disease infection risk according to an embodiment of the present invention;

[0047] Figure 3 It is a schematic diagram of the system architecture for monitoring the contact behavior of users diagnosed with infectious diseases and the infectious disease infection risk provided by an embodiment of the present invention;

[0048] Figure 4Schematic diagram of the structure of a mobile terminal device according to an embodiment of the present invention;

[0049] Figure 5 Schematic diagram of the structure of the server backend module according to an embodiment of the present invention;

[0050] Figure 6 Schematic diagram of the structure of the PC - segment web page model according to an embodiment of the present invention;

[0051] Figure 7 Schematic diagram of the risk relationship of infection between users in an embodiment of the present invention. Detailed implementation manners

[0052] To make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with the implementation manners and the accompanying drawings. Here, the illustrative implementation manners of the present invention and their descriptions are used to explain the present invention, but do not limit the present invention.

[0053] Here, it should also be noted that in order to avoid obscuring the present invention due to unnecessary details, only the structures and / or processing steps closely related to the solution of the present invention are shown in the drawings, while other details less related to the present invention are omitted.

[0054] It should be emphasized that the term "including / comprising" when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0055] Here, it should also be noted that if not otherwise specified, the term "connection" in this article can refer not only to direct connection, but also to indirect connection with an intermediate.

[0056] For the prevention and control of infectious diseases and the monitoring of infection risks, especially for airborne respiratory infectious diseases, direct or indirect contact in the same space is the main factor for the occurrence of infection. In traditional technologies, the assessment and analysis of the infection risk of personnel are all completed through manual epidemiological investigations and screenings. The information relied on is mainly provided by the confirmed personnel themselves, and the whole process is completed with the help of manpower, resulting in low efficiency and high error rates.

[0057] The present invention provides a method for monitoring the contact behavior of users diagnosed with infectious diseases, as Figure 1 shown, including steps S101 - S107:

[0058] It should be emphasized here that the steps S101 - S107 in this embodiment are not intended to limit the sequence of the steps. Instead, it should be understood that in some specific scenarios, the steps can be executed in a swapped order or in parallel.

[0059] Step S101: Randomly generate a tracking key uniquely corresponding to the current mobile terminal in each working cycle.

[0060] Step S102: Divide each working cycle into multiple recording time periods, and use a preset encryption algorithm to encrypt the tracking key of the current mobile terminal and the time information of the current recording time period to obtain the anonymous identifier of the current mobile terminal in the current time period, and perform periodic broadcasting to the surroundings.

[0061] Step S103: Turn on the Bluetooth of the current mobile terminal for periodic scanning to obtain the anonymous identifiers of other mobile terminals within a set range around in each recording time period, and store the anonymous identifiers of the current mobile terminal in each recording time period, the time information of each recording time period, and the anonymous identifiers of the other mobile terminals scanned in local.

[0062] Step S104: Download from the server multiple tracking keys generated by one or more confirmed user mobile terminals infected during the risk period to form a tracking key collection.

[0063] Step S105: Use a preset encryption algorithm to encrypt all the tracking keys in the tracking key collection and each recording time period during the risk period to obtain multiple confirmed user anonymous identifiers.

[0064] Step S106: Compare all the confirmed user anonymous identifiers with all the anonymous identifiers recorded locally by the current mobile terminal. If there is a matching record, it is determined that the user of the current mobile terminal has come into contact with the confirmed user.

[0065] And / or, Step S107: When the user of the current mobile terminal is confirmed to be infected, upload all the tracking keys during the risk period to the server.

[0066] In Step S101, the working cycle can be set according to the actual monitoring requirements. In each working cycle, the tracking key adopted by the current mobile terminal remains unchanged. The working cycle can adopt a duration of one day, one week, or other periodically set time lengths. The tracking key corresponds one-to-one with the corresponding mobile terminal and can be an electronic signature generated according to a set rule. The tracking key is updated every time a working cycle passes.

[0067] In step S102, dividing a working cycle into multiple finer-grained recording time periods is actually to better mark the contact time between different mobile terminals. Exemplarily, the recording time periods can be divided into 5 minutes, 10 minutes, etc. Exemplarily, the preset encryption algorithm can adopt a hash algorithm, DSA (Digital Signature Algorithm) digital signature algorithm, AES (Advanced Encryption Standard) encryption algorithm, etc. The time information here can be time data containing year, month, day, hour, minute, and second information, or the time information can be configured according to a dedicated timing standard. By encrypting the tracking key and time information to obtain an anonymous identifier, it is possible to mark both the identity of the mobile device and the information of the recording time period to which it belongs at the corresponding moment. The current mobile terminal periodically broadcasts the anonymous identifier outward to mark its location.

[0068] In step S103, periodically scan the anonymous identifiers sent by other mobile terminals present in the vicinity through Bluetooth and record them. Among them, the set range can be set according to the contact risk distance during the spread of infectious diseases, such as 1m, 10m, or 15m, etc. Storing the anonymous identifier of the current mobile terminal in each recording time period, the time information of each recording time period, and the anonymous identifiers of other scanned mobile terminals locally actually marks the objects that the current mobile terminal has come into contact with.

[0069] In some embodiments, in step S103, turn on the Bluetooth of the current mobile terminal for periodic scanning to obtain the anonymous identifiers of other mobile terminals within the set range in each recording time period, including:

[0070] Obtain the signal strength of the Bluetooth of other scanned mobile terminals through two methods: Bluetooth device listening and Bluetooth device scan callback, and calculate the distance between the current mobile terminal and other mobile terminals according to the signal strength. The calculation formula is:

[0071] d = 10^((abs(RSSI)-(A)) / (10×n)); (1)

[0072] Where, d represents the distance between the current mobile terminal and other mobile terminals, with the unit of m; RSSI represents the signal strength of Bluetooth, with the unit of dbm; A represents the standard signal strength of Bluetooth when separated by 1 meter, with the unit of dBm; n represents the environmental attenuation factor, and the range is 2 - 4.

[0073] In step S104, the current mobile terminal can query and download the corresponding tracking key from the server based on the information of the existing confirmed users published by the server. The current mobile terminal can also query at set time intervals or specified periods, or a user can initiate the query actively.

[0074] The risk period is set according to the transmission characteristics of infectious diseases. For example, the risk period is adaptively adjusted according to the length of the incubation period. Exemplarily, for an infectious virus with an incubation period of 14 days, the risk period can be set to 14 days before diagnosis.

[0075] The risk period of a confirmed user will include multiple working cycles, that is, multiple tracking keys. The tracking keys of one or more confirmed users within the risk period together constitute a tracking key set.

[0076] In steps S105 and S106, after obtaining the tracking key set, the current mobile terminal encrypts each tracking key in it with each recorded time period within the risk period to obtain multiple anonymous identifiers of confirmed users, and compares them with the anonymous identifiers of all other mobile terminals contacted within the risk period recorded locally. If there is a matching record, it can be determined that the current mobile terminal has come into contact with a confirmed user.

[0077] In step S107, if the user of the current mobile terminal is confirmed to be infected, the user can actively upload all the tracking keys within the risk period for the server to broadcast and publish. In some other embodiments, the server can also obtain information of confirmed persons through other official platforms or channels and request the tracking keys within the risk period from the mobile terminals of the corresponding confirmed persons.

[0078] In some embodiments, the method further includes steps S108 and S109:

[0079] Step S108: Send an infection risk prediction request message to the server. The infection risk prediction request message at least includes the anonymous identifiers of the current mobile terminal at each recorded time period recorded locally by the current mobile terminal within the set time period, the time information of each recorded time period, and the anonymous identifiers of other mobile terminals scanned.

[0080] Step S109: Receive the infection risk level information of the current mobile terminal returned by the server. When the infection risk level information indicates that the infection risk reaches a preset level, an alarm prompt is given; wherein, the infection risk level information is obtained based on the infection probability of respiratory infectious diseases predicted by the Wells-Riley model, and according to the calculated infection probability of respiratory infectious diseases, the levels 0-10 are divided according to the infection risk from low to high.

[0081] In step S108, the current mobile terminal can send a request to the server to evaluate its own infection risk. Specifically, in order to achieve accurate risk assessment, it is necessary to first upload the anonymous identifiers of other surrounding mobile terminals searched within the risk period, that is, the information of other mobile terminals contacted within the risk period.

[0082] In step S109, the server analyzes the infection risk among users according to the social network influence propagation model and feeds it back to the current mobile terminal.

[0083] In some embodiments, the method further includes step S110: reporting daily health information to the server to form a log for query by the current mobile terminal.

[0084] On the other hand, the present invention also provides an infectious disease infection risk monitoring method, as Figure 2 shown, including steps S201 to S204:

[0085] Step S201: Receive multiple tracking keys generated by the mobile terminal of the confirmed user during the risk period and broadcast them for the mobile terminals of the ordinary users to which they are broadcast to determine whether each ordinary user has come into contact with the confirmed user according to the infectious disease confirmed user contact behavior monitoring method in the above steps S101 to 107.

[0086] Step S202: Receive the infection risk prediction request information sent by the mobile terminals of one or more ordinary users, the anonymous identifiers of other mobile terminals within a set range around them obtained by periodic scanning during the risk period, and the corresponding recording time periods; the anonymous identifiers and the corresponding recording time periods are obtained according to the infectious disease confirmed user contact behavior monitoring method in the above steps S101 to 107.

[0087] Step S203: Obtain the contact objects and contact times of each ordinary user according to the anonymous identifiers of other mobile terminals collected by the mobile terminals of each ordinary user during the risk time period and the corresponding recording time periods.

[0088] Step S204: Use the Wells-Riley model to calculate the first probability of an ordinary user being infected by a designated contact object according to the contact objects and contact times of each ordinary user, obtain the second probability of the designated contact object being infected, multiply the first probabilities of multiple designated contact objects by the second probability and then perform probability superposition to obtain the infectious disease infection probability of the corresponding ordinary user, and determine the infection risk level.

[0089] In step S201, the server receives multiple tracking keys during the risk period reported by the confirmed user and broadcasts them to multiple mobile terminals within the service range for each mobile terminal to independently query whether it has come into contact with the confirmed user according to steps S101 to S107.

[0090] In steps S202 to S204, after the server receives the anonymous identifiers of other mobile terminals within a set range around the mobile terminals of ordinary users obtained by periodic scanning during the risk period and the corresponding recording time periods, the server calculates the infection risk.

[0091] Since the infection probability of respiratory infectious diseases is affected by many factors and many of the influencing parameters are difficult to accurately determine, it is currently difficult to predict the infection probability of respiratory infectious diseases mechanistically. In this embodiment, an algorithm for calculating the infection probability between users is referenced from the widely used Wells-Riley model.

[0092] The Wells-Riley model is as follows:

[0093] P = C / S = 1 - e (-IqpT) / Q ; (2)

[0094] In formula (2), P is the infection probability; C is the number of infected people; S is the total number of susceptible people; I is the number of infected people contacted; q is the number of pathogens exhaled by a patient; p is the breathing volume of a person, m3 / h; T is the exposure time, h; Q is the ventilation volume of the room, m3 / h.

[0095] The P calculated here is the probability that a contact object can infect an ordinary user. On this basis, multiplying by the infection probability R of the contact user can obtain the infection probability of a single contact object to an ordinary user. When there are multiple contact objects, the probability calculation formula is used for accumulation, and the calculation formula is as follows:

[0096]

[0097] Among them, R0 represents the infection probability of an ordinary user himself, R i represents the infection probability of the i-th contact user himself, P i represents the probability of the i-th contact user infecting an ordinary user, and n represents the number of contact users. R i ×P i is the infection probability obtained after an ordinary user contacts the contact user i. (1 - R i ×P i ) is the non-infection probability obtained after an ordinary user contacts the contact user i. is the non-infection probability obtained after an ordinary user contacts multiple contact users. is the infection probability obtained after an ordinary user contacts multiple contact users.

[0098] In some embodiments, before step S202, that is, before receiving the infection risk prediction request information sent by the mobile terminals of one or more ordinary users, the anonymous identifiers of other mobile terminals within the set range around them obtained by periodic scanning during the risk period, and the corresponding recording time period, it further includes: obtaining the login account information of the mobile terminals of each ordinary user and performing identity verification.

[0099] In some embodiments, the infectious disease infection risk monitoring method further includes step S205 and step S206:

[0100] Step S205: In the case that the diagnosed user does not actively report the tracking key, receive the identity information of the diagnosed user released by the preset official platform, and send a tracking key request message to each diagnosed user.

[0101] Step S206: Receive multiple tracking keys within the risk period returned by the mobile terminal of the diagnosed user, and broadcast them for the mobile terminals of the ordinary users to which the broadcast is made to judge whether each ordinary user has contact with the diagnosed user according to the infectious disease diagnosed user contact behavior monitoring method described in the above steps S101 to S107.

[0102] On the other hand, the present invention also provides a mobile terminal device, which at least includes a Bluetooth scanning module and a Bluetooth scanning information local matching module:

[0103] The Bluetooth scanning module is used to randomly generate a tracking key uniquely corresponding to the current mobile terminal in each working cycle; divide each working cycle into multiple recording time periods, encrypt the tracking key of the current mobile terminal and the time information of the current recording time period by using a preset encryption algorithm to obtain an anonymous identifier of the current mobile terminal in the current time period, and perform periodic broadcasting to the surroundings; perform periodic scanning to obtain the anonymous identifiers of other mobile terminals within a set range around in each recording time period, and store the anonymous identifiers of the current mobile terminal in each recording time period, the time information of each recording time period, and the scanned anonymous identifiers of other mobile terminals locally.

[0104] The Bluetooth scanning information local matching module is used to download multiple tracking keys generated by one or more mobile terminals of infected diagnosed users within the risk period from the server to form a tracking key set; encrypt all the tracking keys in the tracking key set and each recording time period within the risk period by using a preset encryption algorithm to obtain multiple anonymous identifiers of the diagnosed users; compare all the anonymous identifiers of the diagnosed users with all the anonymous identifiers recorded locally by the current mobile terminal, and if there is a consistent record, it is determined that the user of the current mobile terminal has contact with the diagnosed user.

[0105] In some embodiments, the mobile terminal device further includes an infection risk prediction module and a user health information module.

[0106] The infection risk prediction module is used to upload all the tracking keys within the risk period to the server in the case that the user of the current mobile terminal is diagnosed as infected. And send an infection risk prediction request message to the server to query the infection risk level information.

[0107] The user health information module is used to report daily health information to the server for query by the front mobile terminal.

[0108] Regarding the implementation of the functions of each part of the mobile terminal device, reference can be made to the foregoing text.

[0109] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the above method are implemented.

[0110] The present invention will be described below in conjunction with a specific embodiment:

[0111] This embodiment adopts information technology means and mainly provides the following two services for users based on mobile phone Bluetooth scan information.

[0112] First, it is determined whether the user has been in close contact with an infectious disease patient. The user uses the mobile application, and through mobile phone Bluetooth scan technology, exchanges and records encrypted Bluetooth information with other users. Each user has a daily tracking key randomly generated every day; the confirmed user can upload his own key to the system server; all users can download the key of the confirmed user from the server to the local mobile terminal, and calculate and match the downloaded key with the locally recorded Bluetooth connection information; if there is a successfully matched record, it proves that the user has been in close contact with the confirmed user, and then the system will notify the user.

[0113] Second, for infection risk prediction, this embodiment designs an algorithm on the server side to predict the infection risk based on Bluetooth information, enabling the system to also provide users with a Bluetooth infection risk prediction service. The user can upload the Bluetooth scan records of his mobile application to the server and request to calculate his own infection risk; the server calculates the infection risk level of the user based on all the stored user Bluetooth scan records and then notifies the user.

[0114] By using this embodiment, the user can know whether he has been in contact with an infected person without disclosing his personal privacy; the user can also predict his own infection risk by reporting his own information. In addition, this embodiment can also provide an information query service for epidemic prevention experts, hoping to use the Bluetooth information data of the system to help relevant departments promote the epidemic prevention and control work.

[0115] This embodiment mainly relies on Bluetooth technology to implement. For example, Android 4.3 introduced support for low-power Bluetooth. Currently, mainstream Android systems are more equipped with classic Bluetooth or dual-mode Bluetooth. In Android development, the permission to use the mobile phone's Bluetooth can be declared in the configuration file, so that the application can control the mobile phone's Bluetooth and obtain the information of the mobile phone's Bluetooth by obtaining the Bluetooth adapter. Bluetooth has five states: standby state, broadcast state, scanning state, connection initiation state, and connection state. This system controls the mobile phone's Bluetooth, uses the broadcast state of Bluetooth to broadcast specific information to surrounding devices, and uses the scanning state of Bluetooth to obtain the specific information broadcast by surrounding Bluetooth devices, without establishing a Bluetooth connection, so as to achieve information exchange between users.

[0116] On the Android platform, the system can obtain the RSSI of the scanned Bluetooth device through two methods: Bluetooth device monitoring and Bluetooth device scan callback. RSSI stands for Received Signal Strength Indicator, which represents the received signal strength, and the unit is dBm. RSSI can reflect the attenuation degree of the signal, usually a negative value. In the ideal case without attenuation, the value of RSSI is 0dBm. The weaker the signal, the smaller the value of RSSI.

[0117] Obtain the signal strength of the Bluetooth of other mobile terminals scanned through two methods: Bluetooth device monitoring and Bluetooth device scan callback, and calculate the distance between the current mobile terminal and other mobile terminals according to the signal strength. The calculation formula is:

[0118] d = 10^((abs(RSSI)-(A)) / (10×n)); (1)

[0119] Among them, d represents the distance between the current mobile terminal and other mobile terminals, and the unit is m; RSSI represents the signal strength of Bluetooth, and the unit is dbm; A represents the standard signal strength of Bluetooth when separated by 1 meter, and the unit is dBm; n represents the environmental attenuation factor, and the range is 2-4.

[0120] Specific to the actual situation, because of different environments and different corresponding parameters of the signal source device, the corresponding parameters in the formula are also different.

[0121] If you want to obtain a more accurate distance result, you should conduct experiments on each parameter in the formula for calibration for the device. However, from the perspective of the requirements and implementation of this project, it is not realistic to do so. Therefore, in this embodiment, the method of assigning common empirical values to parameters A and n is adopted, so that a rough and available distance data can be calculated through the formula.

[0122] This embodiment aims to implement an infectious disease infection risk monitoring system based on Bluetooth connection information. By combining the advantages of the centralized solution that relies entirely on server processing and the decentralized solution that only depends on mobile devices for processing, this embodiment provides more flexible and diverse services for users with different needs.

[0123] This embodiment can provide services for ordinary users based on Bluetooth scan information. Ordinary users can log in to use this system, or they can use it anonymously without logging in. For anonymous users (i.e., users who have not logged in), the system provides services such as Bluetooth scanning on the mobile device locally, downloading broadcast keys, and local matching; for registered users, the system can additionally provide the service of infection risk prediction.

[0124] In addition to the basic Bluetooth scan record matching function on the mobile device, on the server side, the system can calculate the infection risk of ordinary users according to the Bluetooth scan information uploaded by ordinary users and the keys of confirmed users stored in the server, and notify the users of the predicted results according to the designed algorithm model. Compared with the function design of the "Communication Travel Card" software in the prior art, it only notifies a user when the user has contact records with confirmed patients or suspected patients. However, the spread of the epidemic is a network-like chain spread. Confirmed patients will gradually spread the infection risk in the personnel contact network, resulting in a certain infection risk for everyone in the network. This embodiment can provide the function of predicting their own infection risk for ordinary users based on Bluetooth scan information on the server side. This embodiment also requires reviewers to review the information reported by users. This embodiment can also provide support services related to epidemic prevention for epidemic prevention experts, such as querying the reported data of users.

[0125] This embodiment includes a mobile application software, a PC web page, a server backend, and a data storage part.

[0126] This embodiment follows the principle of high cohesion and low coupling, and divides this system into five-layer structures: presentation logic layer, control logic layer, business logic layer, data access layer, and data persistence layer according to the logical functions of each part. The system architecture is as follows Figure 3 as shown.

[0127] According to the requirements analysis of various users and the physical structure design of the system, this embodiment mainly sets three functional modules: mobile terminal functional module, server backend functional module, and PC web page functional module.

[0128] Among them, the functional module structure diagram of the mobile terminal is as Figure 4 shown.

[0129] In the Bluetooth scanning module, the mobile application first checks whether there is a daily tracking key for today locally. If not, the system generates a random and unique daily tracking key for the user and stores it in the local database. A user's daily tracking key is changed every day.

[0130] Then, the system checks whether there is an anonymous identifier table for today locally. If not, it generates an anonymous identifier table for the user based on the daily tracking key for today. The generation process is as follows: Taking 10 minutes as a recording time period, 24 hours a day can be divided into 144 time periods. The user's daily tracking key and the information of 144 time periods are used to generate 144 anonymous identifiers through an encryption algorithm, forming an anonymous identifier table. The application will query the corresponding anonymous identifier from the anonymous identifier table according to the time period and use this anonymous identifier as the mobile phone Bluetooth name. That is to say, the mobile phone Bluetooth name (i.e., the anonymous identifier) is changed every ten minutes.

[0131] After the user activates the Bluetooth periodic scanning function, the application will control the mobile phone Bluetooth to perform periodic scanning, process the data of each Bluetooth scanning information, and store the processed data (including the anonymous identifier of the target machine, date, etc.) in the local database. The data structure of the Bluetooth scanning information table is shown in Table 1. After the user logs in or is diagnosed, they can upload the daily tracking key stored locally to the server.

[0132] Table 1 Bluetooth information table

[0133] Field Name Type Length Explanation id int 11 Record Number userid int 11 User Number my_identifier varchar 45 Local Anonymous Identifier target_identifier varchar 45 Target Machine Anonymous Identifier distance float 4 Scanning Distance duration int 11 Scanning Duration date date 3 Date

[0134] In the local matching module of Bluetooth scanning information, the mobile application first downloads the set of the latest broadcast keys (i.e., the daily tracking keys of the diagnosed users within 14 days) from the server.

[0135] Using the same method as generating anonymous identifiers from the above daily tracking key, the broadcast key and the time information of the recording time period are used to generate the corresponding anonymous identifiers through an encryption algorithm and match them with the target anonymous identifiers in the locally saved Bluetooth scanning records. If there is a successfully matched record, it means that the user has been in close contact with a diagnosed user. After all the broadcast keys are matched, the result of the local matching will be notified to the user.

[0136] In the infection risk prediction module, the mobile application first submits the user's recent Bluetooth scanning information to the server. The data structure of the Bluetooth scanning information table is shown in Table 1. Then it requests the server to query the user's Bluetooth infection risk. After the server calculates the user's latest Bluetooth infection risk, the application obtains the result and notifies the user.

[0137] In the user health information module, users can report their health information to the server. For example, confirmed users can report their confirmed information. After the administrator reviews the health information reported by the users, the server changes the health status of the users. Users can view their health information reporting records.

[0138] This embodiment also provides a server backend module, the structure of which is as Figure 5 shown:

[0139] In the function interface module, the server backend program provides function interfaces for various services to the mobile application and the PC web page. For example, it provides services such as user account login, reporting user health information, and downloading broadcast keys for the mobile application, and services such as querying user information and reviewing user health information for the PC web page.

[0140] In the Bluetooth infection risk prediction module, the server backend module receives the request of a certain user on the mobile side, calculates and matches according to the recent Bluetooth scan information submitted by the user and the daily tracking keys of all users stored in the server database, obtains the infection probability and infection risk level of the user according to the calculation result, and then feeds back the infection risk level to the mobile application.

[0141] This embodiment also provides a PC web page module, the structure of which is as Figure 6 shown:

[0142] The review module directly faces the reviewers and provides functions for reviewers to review the health information reported by users and view the review records. After the reviewer logs in to the reviewer account on the PC web page, they can view the list of health information marked as "pending review" reported by users on the review page, select a piece of health information for review operations. After the review operation, this piece of health information is marked as "passed" or "not passed". Reviewers can view the list of reviewed user health information on the review record page.

[0143] The user information query module directly faces the epidemic prevention experts and provides functions for epidemic prevention experts to view some information of users in the system to help epidemic prevention experts analyze the infection situation of the user group and the spread of infectious diseases. After the epidemic prevention experts log in to the epidemic prevention expert account on the PC web page, they can view some information of all users in the system, such as health status, infection risk level, etc. Select a user, and they can also view the detailed information uploaded by this user, such as itinerary information, etc.

[0144] Furthermore, the server backend module provides an assessment of the infection risk.

[0145] The server database stores the IDs of ordinary users and their infection risk levels. The infection risks of ordinary users are divided into levels 0 to 10 from high to low. Level 0 is the default infection risk level for ordinary users, and level 10 is the infection risk level for confirmed users. The "Social Network Influence Propagation Model" can be referred to analyze the infection risk relationships between users. Based on the Bluetooth scan information uploaded by users stored in the server, the system can obtain the contact information between users.

[0146] The contact relationship between users can be abstracted into a graph structure. A user is a node, and a contact relationship between users is an edge.

[0147] Although the contact relationship is a two-way contact, according to the infection logic, the current infection risk of a user should only be affected by users with a higher infection risk level that the user has come into contact with and the user's own previous infection risk, and not be affected by users with a lower infection risk level. Moreover, the intensity of contact between a user and other users is different, and the influence received from other users should also be different. Therefore, weights should be added to the contact relationship between users to represent the strength of influence. Specifically in this project, the strength of this influence can be represented by the infection probability of a user with a higher infection risk level on another user with a lower infection risk level. Therefore, the relationship formed between users can be described as a weighted directed graph. The infection risk relationship between users is as Figure 7 shown.

[0148] In Figure 7 , each node represents a user, the number inside the node represents the infection risk level of the user, each directed edge represents a possible infection relationship of a user to another user, and the weight of the edge is the infection probability.

[0149] Because the infection probability of respiratory infectious diseases is affected by many factors, and many of the influencing parameters are difficult to accurately determine, it is currently difficult to predict the infection probability of respiratory infectious diseases mechanistically. This embodiment refers to the widely used Wells-Riley model to design an algorithm for calculating the infection probability between users.

[0150] The Wells-Riley model is as the formula:

[0151] P = C / S = 1 - e (-IqpT) / Q ; (2)

[0152] In formula (2), P is the infection probability; C is the number of infected people; S is the total number of susceptible people; I is the number of infected people contacted; q is the number of pathogens exhaled by a patient; p is the breathing volume of a person, m3 / h; T is the exposure time, h; Q is the ventilation volume of the room, m3 / h.

[0153] According to Formula 2 and specific settings of parameters, an algorithm for calculating the infection probability between two users can be designed. Based on this algorithm, the system can calculate the infection probability of a single contact between two users by using the Bluetooth scan information of the users as parameters.

[0154] In the server, each user has an "infection probability calculated based on Bluetooth scan information", abbreviated as "infection probability", which is a percentage value ranging from 0 to 1; each user also has an "infection risk level calculated based on Bluetooth scan information", abbreviated as "infection risk level", with a level ranging from 0 to 10. Level 0 is the default infection risk level for ordinary users, and level 10 is the infection risk level for confirmed users. The infection risk level is obtained by rounding the infection probability multiplied by 10. The infection probability is an intermediate value of the algorithm and is not shown to users. Users can only obtain the infection risk level.

[0155] In the algorithm, an "ordinary user" refers to the user who initiates a request for the Bluetooth infection risk prediction service, and a "contact user" refers to a user with a higher infection risk level than an ordinary user, including confirmed users. Through the previous research on the infection relationship between users and the algorithm for calculating the infection probability of a single contact between two users, this embodiment proposes an algorithm for calculating the infection risk of a user.

[0156] For each ordinary user, the process of calculating their infection risk is as follows:

[0157] 1. Obtain the information of the ordinary user, including:

[0158] (1) The current infection risk level of the ordinary user.

[0159] (2) The Bluetooth scan information of the ordinary user within 14 days.

[0160] 2. Obtain the list of contact users who have contact with the ordinary user.

[0161] 3. Create a list of hash maps to store the infection probability of each contact user to the ordinary user.

[0162] 4. Traverse each item in the contact user list (i.e., a contact user):

[0163] (1) Obtain the infection probability R of the contact user.

[0164] (2) Obtain the daily tracking key of the contact user within 14 days.

[0165] (3) Divide the daily tracking key of the contact user and the Bluetooth scan information of the ordinary user into 14 days, and perform a round of matching every day, that is, 14 rounds of matching.

[0166] (4) According to the results of the 14 rounds of matching, the infection probability P of this contact user to the ordinary user can be calculated.

[0167] Store R*P into the hash map list.

[0168] 5. Calculate the overall infection probability of contact users to ordinary users based on the infection probability of each contact user in the hash map list to ordinary users using the probability calculation formula, and then obtain the infection risk level of ordinary users.

[0169] Among them, the probability calculation formula is:

[0170]

[0171] In formula 3, R0 represents the infection probability of an ordinary user himself / herself, R j represents the infection probability of contact user i himself / herself, P i represents the infection probability of contact user i to an ordinary user. R i ×P i is the infection probability obtained after an ordinary user contacts contact user i. (1 - R i ×P i ) is the non-infection probability obtained after an ordinary user contacts contact user i. is the non-infection probability obtained after an ordinary user contacts multiple contact users. 1 - is the infection probability obtained after an ordinary user contacts multiple contact users.

[0172] To ensure that the infection risk can only spread from users with a higher level to users with a lower level, the server backend needs to ensure that this algorithm first calculates for users with a higher infection risk level. Therefore, when a user requests to obtain the Bluetooth infection risk level, the server backend will first calculate and update the data of the Bluetooth infection risk levels of all users (sorted by infection risk level from high to low) in sequence. After all calculations are completed, the Bluetooth infection risk level of this user will be queried and fed back to this user.

[0173] For the contact behavior and infection risk monitoring method and device of the confirmed infectious disease user, encrypt the tracking key of the current mobile terminal in each working cycle and the time information of the corresponding recording time period, use it as an anonymous identifier to mark the mobile terminal in a specific time period, broadcast it via Bluetooth and scan the anonymous identifiers of other mobile terminals within a set range around. By downloading the collection of tracking keys of confirmed users broadcast by the server, calculate and compare whether there is a record that is consistent with the anonymous identifier of other terminal devices recorded locally. If so, it is determined that the user of the current mobile terminal has contacted the confirmed user. With the help of the Bluetooth module and data for monitoring and processing, it realizes the monitoring of the contact behavior of a large number of monitoring objects with confirmed users under the condition of a large traffic volume, and can effectively improve the monitoring frequency and accuracy.

[0174] Furthermore, the server calculates the contact objects and contact times of each ordinary user based on the anonymous identifiers uploaded by each ordinary user and the corresponding recording time periods, and calculates the infectious disease infection probability based on the Wells-Riley model, which can effectively evaluate the infection risk of users.

[0175] Those of ordinary skill in the art should understand that the various exemplary components, systems, and methods described in connection with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Specifically, whether to execute in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, and so on. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium or transmitted via a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical discs, hard disks, fiber optic media, radio frequency (RF) links, and so on. The code segment can be downloaded via a computer network such as the Internet or an intranet.

[0176] It should also be noted that the exemplary embodiments mentioned in the present invention describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps can be executed in the order mentioned in the embodiments, can be different from the order in the embodiments, or several steps can be executed simultaneously.

[0177] In the present invention, the features described and / or illustrated for one embodiment can be used in the same way or in a similar way in one or more other embodiments, and / or combined with the features of other embodiments or replace the features of other embodiments.

[0178] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, various changes and modifications can be made to the embodiments of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring the contact behavior of users diagnosed with infectious diseases, characterized in that, Including: Randomly generate a tracking key uniquely corresponding to the current mobile terminal within each working cycle; Divide each working cycle into multiple recording time periods, and use a preset encryption algorithm to encrypt the tracking key of the current mobile terminal and the time information of the current recording time period to obtain an anonymous identifier of the current mobile terminal in the current time period, and perform periodic broadcasting to the surroundings; Turn on the Bluetooth of the current mobile terminal for periodic scanning to obtain the anonymous identifiers of other mobile terminals within a set range around in each recording time period, and store the anonymous identifiers of the current mobile terminal in each recording time period, the time information of each recording time period, and the anonymous identifiers of the scanned other mobile terminals locally; Download from the server multiple tracking keys generated by one or more infected confirmed user mobile terminals during the risk period to form a tracking key set; Use the preset encryption algorithm to encrypt all the tracking keys in the tracking key set and each recording time period during the risk period to obtain multiple confirmed user anonymous identifiers; Compare all the confirmed user anonymous identifiers with all the anonymous identifiers recorded locally by the current mobile terminal. If there are consistent records, it is determined that the user of the current mobile terminal has come into contact with the confirmed user; And / or, when the user of the current mobile terminal is confirmed to be infected, upload all the tracking keys during the risk period to the server; Among them, turning on the Bluetooth of the current mobile terminal for periodic scanning to obtain the anonymous identifiers of other mobile terminals within a set range around in each recording time period includes: Obtain the signal strength of the Bluetooth of the scanned other mobile terminals through two methods: Bluetooth device listening and Bluetooth device scan callback, and calculate the distance between the current mobile terminal and the other mobile terminal according to the signal strength. The calculation formula is: d = 10^((abs(RSSI)-(A)) / (10×n)); Where, d represents the distance between the current mobile terminal and the other mobile terminal, in meters; RSSI represents the signal strength of Bluetooth, in dBm; A represents the standard signal strength of Bluetooth when separated by 1 meter, in dBm; n represents the environmental attenuation factor, with a range of 2 to 4; The method further includes: Send an infection risk prediction request message to the server. The infection risk prediction request message at least includes the anonymous identifiers of the current mobile terminal in each recording time period, the time information of each recording time period, and the anonymous identifiers of the scanned other mobile terminals recorded locally by the current mobile terminal within a set time period; Receive the infection risk level information of the current mobile terminal returned by the server, and perform an alarm prompt when the infection risk level information indicates that the infection risk reaches a preset level; among them, the infection risk level information is divided into levels 0 to 10 according to the infection risk from low to high; The method further includes: reporting daily health information to the server to form a log for the current mobile terminal to query.

2. A method for monitoring the risk of infectious disease infection, characterized in that, Including: Receive multiple tracking keys generated by the mobile terminal of a confirmed user during a risk period, and broadcast them for the mobile terminals of the ordinary users to whom they are broadcast to determine whether each ordinary user has come into contact with the confirmed user according to the method for monitoring the contact behavior of an infectious disease confirmed user as claimed in claim 1; In addition, receive infection risk prediction request information sent by the mobile terminals of one or more ordinary users, the anonymous identifiers of other mobile terminals within a preset range scanned periodically during the risk period, and the corresponding recording time periods; the anonymous identifiers and the corresponding recording time periods are obtained according to the method for monitoring the contact behavior of an infectious disease confirmed user as claimed in claim 1; Obtain the contact objects and contact times of each ordinary user according to the anonymous identifiers of other mobile terminals collected by the mobile terminals of each ordinary user during the risk period and the corresponding recording time periods; Use the Wells-Riley model to calculate the first probability that an ordinary user is infected by a designated contact object according to the contact objects and contact times of each ordinary user, obtain the second probability that the designated contact object is already infected, multiply the first probabilities of multiple designated contact objects by the second probability and then perform probability superposition to obtain the infectious disease infection probability of the corresponding ordinary user, and determine the infection risk level.

3. The method for monitoring the risk of infectious disease infection according to claim 2, characterized in that, Multiply the first probabilities of multiple designated contact objects by the second probability and then perform probability superposition to obtain the infectious disease infection probability of the corresponding ordinary user, and the calculation formula is: Among them, R0 represents the infection probability of an ordinary user himself, and R i represents the infection probability of the i-th contact user himself, and P i represents the probability that the i-th contact user infects an ordinary user, and n represents the number of contact users.

4. The method for monitoring the risk of infectious disease infection according to claim 2, characterized in that, The method further includes: In the case where the confirmed user does not actively report the tracking key, receive the identity information of the confirmed user released by a preset official platform, and send a tracking key request information to each confirmed user; Receive multiple tracking keys within the risk period returned by the mobile terminal of the confirmed user, and broadcast them for the mobile terminals of the ordinary users to whom they are broadcast to determine whether each ordinary user has come into contact with the confirmed user according to the method for monitoring the contact behavior of an infectious disease confirmed user as claimed in claim 1.

5. A mobile terminal device, characterized in that, Include: A Bluetooth scanning module, which is used to randomly generate a tracking key uniquely corresponding to the current mobile terminal in each working cycle; divide each working cycle into multiple recording time periods, encrypt the tracking key of the current mobile terminal and the time information of the current recording time period by using a preset encryption algorithm to obtain the anonymous identifier of the current mobile terminal in the current time period, and perform periodic broadcast to the surroundings; perform periodic scanning to obtain the anonymous identifiers of other mobile terminals within a preset range around each recording time period, and store the anonymous identifier of the current mobile terminal in each recording time period, the time information of each recording time period, and the scanned anonymous identifiers of the other mobile terminals locally; A local matching module for Bluetooth scanning information, which is used to download multiple tracking keys generated by the mobile terminals of one or more infected confirmed users during the risk period from the server to form a tracking key collection; Encrypt all the tracking keys in the said preset tracking key set and each recording time period within the risk period by using the said preset encryption algorithm to obtain a plurality of confirmed user anonymous identifiers; compare all the confirmed user anonymous identifiers with all the anonymous identifiers recorded locally by the said current mobile terminal. If there is a consistent record, it is determined that the user of the said current mobile terminal has come into contact with the confirmed user. Among them, periodic scanning to obtain the anonymous identifiers of other mobile terminals within a set range around each recording time period includes: Obtain the signal strength of the Bluetooth of the scanned said other mobile terminals through two methods: Bluetooth device listening and Bluetooth device scan callback. Calculate the distance between the current mobile terminal and the said other mobile terminals according to the said signal strength. The calculation formula is: d = 10^((abs(RSSI)-(A)) / (10×n)); Wherein, d represents the distance between the said current mobile terminal and the said other mobile terminals, with the unit of m; RSSI represents the signal strength of Bluetooth, with the unit of dbm; A represents the standard signal strength of Bluetooth when separated by 1 meter, with the unit of dBm; n represents the environmental attenuation factor, and the range is 2 to 4. An infection risk prediction module, which is used to upload all the tracking keys within the risk period to the said server when the user of the said current mobile terminal is confirmed to be infected; and send an infection risk prediction request message to the server to query the infection risk level information; send an infection risk prediction request message to the server, and the said infection risk prediction request message at least includes the anonymous identifiers of the said current mobile terminal in each recording time period, the time information of each recording time period, and the anonymous identifiers of the scanned said other mobile terminals recorded locally by the said current mobile terminal within a set time period. Receive the infection risk level information of the said current mobile terminal returned by the server, and give an alarm prompt when the said infection risk level information indicates that the infection risk reaches the preset level; wherein, the said infection risk level information is divided into levels 0 to 10 according to the infection risk from low to high. A user health information module, which is used to report the daily health information to the said server and form a log for the said current mobile terminal to query.

6. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the said processor executes the said program, it realizes the steps of the method according to any one of claims 1 to 4.

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