WO2022135197A1

WO2022135197A1 - Epidemic prediction method and electronic device

Info

Publication number: WO2022135197A1
Application number: PCT/CN2021/137280
Authority: WO
Inventors: 李君劲; 吴莲; 陈勇
Original assignee: 华为技术有限公司
Priority date: 2020-12-22
Filing date: 2021-12-11
Publication date: 2022-06-30
Also published as: CN114724727A

Abstract

Embodiments of the present application relate to the field of electronic technology, and provide an epidemic prediction method and an electronic device. The method mainly comprises: a user-side electronic device determining whether a user is a close contact person of a confirmed case on the basis of the trajectory of the confirmed case and the historical trajectory of the user, and then monitoring physiological signs of the user, and reporting monitoring data and the user's health risk score, so that a server can predict the development trend of an epidemic in a specific area, thereby reminding the user of the epidemic situation. By means of the method, users can be provided with health risk analysis and epidemic development trend on time and conveniently, such that users can learn their own infection risks, and people can reasonably plan travel and arrange daily life.

Description

A method and electronic device for epidemic prediction

This application claims the priority of the Chinese patent application with the application number 202011532380.5 and the application title "An Epidemic Prediction Method and Electronic Device" filed with the State Intellectual Property Office on December 22, 2020, the entire contents of which are incorporated by reference in in this application.

technical field

The present application relates to the field of electronic technology, and in particular, to a method and electronic device for epidemic prediction.

Background technique

The outbreak of the epidemic will bring serious harm to society and individuals. At present, people cannot understand the multi-directional epidemic development information in real time based on electronic equipment, and the epidemic prevention and control mainly relies on human screening. On the one hand, this may increase the risk of related staff being infected during the screening process; on the other hand, it cannot enable users to obtain timely and comprehensive regional epidemic development trends. If users can get information on the development of the epidemic at any time and understand their own infection risks, it will be convenient for people to rationally plan travel and arrange daily life, etc., which is conducive to epidemic prevention and control.

SUMMARY OF THE INVENTION

In the method for epidemic prediction provided by the embodiment of the present application, the user's multi-dimensional information is collected through the electronic device of the user terminal and reported to the server, so that the server can predict the epidemic development trend of the relevant area, and the user can timely and conveniently understand himself. health risks and regional epidemic trends.

In the first aspect, a method for epidemic prediction is provided. The method is applied to an electronic device on the user side. The method mainly includes: the electronic device obtains, from a server, the trajectory information and the infectious disease of a person diagnosed with one or more infectious diseases. feature information, and then use the user's historical trajectory information and the confirmed person's trajectory information stored in the electronic device to determine whether the user has had close contact with the confirmed person. The electronic device will monitor the physiological signs of the user, and calculate the health risk score of the user based on the monitoring results of the physiological signs, and report the health risk score of the physiological signs to the server, so that the server will be based on the health risk score. Predict the trend of epidemic risk in a specific area.

Wherein, the electronic device can obtain the trajectory information of the diagnosed person from the server, and the trajectory information includes location information and time information corresponding to the location information, such as the location corresponding to the confirmed person at a certain moment.

In an implementation manner, the electronic device may locate the user, acquire and store the user's trajectory information (eg, GPS information).

In an implementation manner, the electronic device may also acquire characteristic information of infectious diseases from the server, and the characteristic information may include: transmission route, transmission distance, incubation period, and physiological signs related to infectious diseases, etc. of infectious diseases.

It should be understood that the physiological signs related to infectious diseases mentioned in this application may refer to the physiological signs that are affected when a user is infected with an infectious disease virus. If blood oxygen drops, the physiological signs related to the infectious disease can be body temperature and blood oxygen saturation; or, after viral infection of some infectious diseases, the heart rate, body temperature, and blood oxygen saturation of the infected person will change. The physiological signs related to the infectious disease may be heart rate, body temperature, and blood oxygen saturation.

In an implementation manner, the electronic device may monitor the physiological signs of the user after obtaining authorization from the user.

In an implementation manner, the electronic device may report the user's health risk score after obtaining the user's authorization.

With reference to the first aspect, in some implementations of the first aspect, the electronic device may include a display screen. When the electronic device determines that the user is a close contact based on the user's historical movement trajectory and the movement trajectory of the diagnosed person, the electronic device may The first information is displayed to the user through the display screen, and the first information can be used to query the user whether it is determined to be a close contact of the confirmed person.

It should be understood that in practical applications, only the positions determined based on the trajectory information are close, which cannot accurately reflect whether the user has close contact with the confirmed person. Close contact with confirmed persons can improve the accuracy of close contact judgment.

In an implementation manner, if the user confirms that he has been in close contact with the confirmed person, the user can input first confirmation information into the electronic device, indicating that the user is a close contact of the confirmed person.

With reference to the first aspect, in some implementations of the first aspect, after the electronic device determines that the user is a close contact (for example, the user inputs the first confirmation information to the electronic device), the electronic device may further display second information, which is The second information is used to request the user to monitor physiological signs; after that, after the electronic device obtains authorization from the user, for example, the electronic device receives the second confirmation information input by the user, and the second confirmation information is used to indicate that the user agrees to the electronic device to perform physiological monitoring. For physical sign monitoring, the electronic device may monitor the physiological signs of the user, or the electronic device may instruct other associated devices to monitor the physiological signs of the user.

Optionally, the physiological signs monitored by the electronic device include physiological signs related to infectious diseases.

According to the above method, by monitoring the physiological signs of close contacts, the electronic device can focus on monitoring the physiological signs of users who are at high risk of being infected, and timely give early warnings to the health risks of users, so that users can take corresponding measures in a timely manner, such as timely Seek medical attention to avoid further damage to the user's health.

With reference to the first aspect, in some implementations of the first aspect, when the user's health score indicates that the user has a health risk, the electronic device may also display first prompt information, where the first prompt information is used to indicate to the user There are health risks.

According to the above method, by prompting the user of the health risk, it is convenient for the user to take corresponding measures in time, such as seeking medical treatment in time, so as to avoid further damage to the user's health.

With reference to the first aspect, in some implementations of the first aspect, the electronic device may further display third information, where the third information is used to request the user to report the monitoring data of the physiological signs; if the user agrees to report his physiological signs monitoring data, the user can input the third confirmation information to the electronic device, the third confirmation information is used to indicate the consent to report the data of the physiological sign monitoring; after receiving the third confirmation information, the electronic device can report it to the server according to the user's instruction User's physiological signs monitoring data.

With reference to the first aspect, in some implementations of the first aspect, the electronic device may receive first indication information sent by the server, where the first indication information is used to indicate the epidemic risk level of the first area and/or the risk level of the first area. Total number of people at risk; based on the first indication information, the electronic device can display fourth information, and the fourth information can prompt the user of the epidemic risk level of the first area and/or the total number of people at risk in the first area.

In an implementation manner, the server predicts the epidemic situation in the first area based on the data reported by the electronic device, including the total number of persons at risk (including the number of confirmed persons and suspected persons) in the first area, and then the server may The risk level of the first area is sent to the electronic device, and the electronic device displays it to the user.

According to the above method, by prompting the user of the epidemic risk level and/or the total number of people at risk in a specific area, it is convenient for the user to understand the epidemic risk area, reasonably adjust the travel route, and reduce the risk of being infected by an infectious disease.

With reference to the first aspect, in some implementations of the first aspect, the electronic device calculates the user's health risk score according to the user's physiological sign information and infectious disease characteristic information, which may include: at least one first physiological sign associated with the infectious disease, where the first physiological sign is a feature affected by the infectious disease; after that, obtain monitoring data of at least one first physiological sign information of the user; and then according to the at least one physiological sign The monitoring data is weighted and calculated by at least one first preset weight corresponding to each other, so as to obtain the health risk score of the user.

According to the method for epidemic prediction provided by the embodiment of the present application, after determining that the user is a close contact of the epidemic based on the trajectory information of the user and the confirmed patient, with the authorization of the user, physical sign detection is carried out to realize automatic alarm on the risk of the epidemic. , and allows the server to predict the development of the epidemic according to the reported data, reducing human participation, thereby reducing the number of infected people and improving the efficiency of epidemic prevention and control.

With reference to the first aspect, in some implementations of the first aspect, the electronic device may further display the monitoring data of the first physiological sign corresponding to different time points to the user; and/or, display the monitoring data of the first physiological sign Monitor data trends over time.

In combination with the first aspect, in some implementations of the first aspect, the electronic device may mark the epidemic risk level of the region at the position corresponding to the first region on the map page according to the epidemic risk level of the first region predicted by the server. Optionally, the total risk population corresponding to the area may also be marked on the map page. So that the user can intuitively obtain the epidemic risk level of the first area or the total number of people at risk in the first area from the map page displayed by the electronic device.

With reference to the first aspect, in some implementations of the first aspect, the characteristic information of the infectious disease includes the transmission distance of the infectious disease, and determining the infectious disease according to the trajectory information of the user and the trajectory information of the confirmed person. The user is a close contact of the confirmed person, including: determining the contact distance between the user and the confirmed person according to the user's trajectory information and the confirmed person's trajectory information; when the contact distance is smaller than the confirmed person When the transmission distance is determined, and the contact time between the user and the confirmed person is greater than the first threshold, it is determined that the user is a close contact of the confirmed person.

With reference to the first aspect, in some implementations of the first aspect, the trajectory information further includes a location identifier, and the method further includes: when it is determined according to the location identifier that the user and the diagnosed person are in the first When the space is confined, it is determined that the user is a close contact of the confirmed person.

With reference to the first aspect, in some implementations of the first aspect, the location identifier includes at least one of the following: train number and/or compartment number, room identifier, or elevator identifier; the first enclosed space includes at least one of the following Type: train car, room, or elevator car.

With reference to the first aspect, in some implementations of the first aspect, the characteristic information of the infectious disease includes at least one of the following: the transmission route of the infectious disease virus, the transmission distance of the infectious disease virus, the infectious disease virus Incubation period of the disease, physiological signs associated with the infectious disease.

With reference to the first aspect, in some implementations of the first aspect, the physiological signs include at least one of the following: body temperature, heart rate, RRI during one heartbeat, electrocardiogram (ECG), blood oxygen saturation Spo2, and sleep characteristics.

In a second aspect, a method for epidemic prediction is provided, which is applied to a server. The method mainly includes: the server sends trajectory information of a person diagnosed with an infectious disease and feature information of the infectious disease to an electronic device, wherein the trajectory information of the confirmed person includes: Location information and time information corresponding to the location information; after that, the server can receive the user's health risk score sent by the electronic device, and the user here can be a person who is self-confirmed on the electronic device side as a close contact of the confirmed person; Predict the epidemic risk of the first area according to the user's health risk score, the first area is the area where the user has stayed in a preset historical time period (such as within a week), and the epidemic risk in this area includes confirmed and suspected infectious diseases. total number of people at risk.

According to the epidemic prediction method provided by the embodiment of the present application, after determining that the user is a close contact of the epidemic based on the trajectory information of the user and the confirmed patient, the user is monitored for physiological signs, so as to automatically alert the epidemic risk, and make the server Based on the reported data, it is possible to predict the development of regional epidemic trends, so that users can reasonably plan travel routes based on the predicted epidemic development trends and reduce the risk of infection. This method can improve the efficiency of epidemic prevention and control while reducing human participation and the number of infected people.

With reference to the second aspect, in some implementations of the second aspect, the server may preset multiple risk levels, each risk level has a corresponding health risk score interval, and the server may report the user's health risk score according to the electronic device. The health risk score interval to which the user belongs is determined, and the risk level corresponding to the user is determined, wherein each of the risk levels has a corresponding second preset weight; according to the number of newly-increased risk numbers corresponding to the multiple risk levels, multiple risk levels are determined. The corresponding diffusion coefficients respectively; the diffusion coefficients of multiple risk levels and the corresponding second preset weights are weighted and calculated to obtain the revised diffusion coefficient; according to the revised diffusion coefficient and the number of people at risk in the first area, the first area is predicted of the total number of people at risk.

Among them, the number of people at risk includes those who have been diagnosed and those who are suspected of being infected.

According to the above method, the diffusion coefficients of different risk levels are first calculated, and then the diffusion coefficients of each risk level and the second preset weights corresponding to each risk level are weighted and calculated to obtain the revised diffusion coefficient, which can be used to predict the number of new risk people. It is associated with the risk level to achieve a more accurate prediction of the epidemic risk.

With reference to the second aspect, in some implementations of the second aspect, the method further includes: determining the epidemic risk level of the first area according to the total number of people at risk; sending a first indication to the electronic device information, where the first indication information is used to indicate the epidemic risk level of the first area and/or the total number of people at risk in the first area.

In conjunction with the second aspect, in some implementations of the second aspect, the total risk population includes:

Wherein, t is time, N _r (t) is the total number of people at risk at time point t, rs _sm is the average confirmation rate of the user confirming that he is a close contact through the electronic device, and rs _sp is the Corrected diffusion coefficient, N _con is the current new risk person obtained by other means, and N _b is the new risk person determined according to the data sent by the electronic device.

In a third aspect, an electronic device is provided, including: a receiving module configured to obtain trajectory information of a person diagnosed with an infectious disease and feature information of the infectious disease, where the trajectory information includes location information and time corresponding to the location information information; according to the trajectory information of the user and the trajectory information of the confirmed person, when it is detected that the user is a close contact of the confirmed person, the monitoring data of the physiological signs of the user is obtained; the processing module is used for according to The monitoring data and the characteristic information of the infectious disease determine the health risk score of the user; the sending module is used to report the health risk score to the server, and the health risk score is used to predict the epidemic risk in the first area , the first area is an area where the user has stayed in a preset historical time period, and the epidemic risk includes the total number of at-risk persons of the infectious disease confirmed persons and suspected persons.

With reference to the third aspect, in some implementations of the third aspect, the electronic device further includes a display module configured to display first information, where the first information is used to query the user whether he is the close contact person , the first information includes location information and/or time information of the close contact.

With reference to the third aspect, in some implementations of the third aspect, the display module is further configured to display second information, where the second information is used to request the user for physiological sign monitoring; the receiving module is further configured to Second confirmation information input by a user is received, and the second confirmation information user indicates that the user agrees to the electronic device to perform the physiological sign monitoring.

With reference to the third aspect, in some implementations of the third aspect, the display module is further configured to display third information, where the third information is used to request the user to report the monitoring data of the physiological sign; the receiving module is further configured to The third confirmation information is used to receive the third confirmation information input by the user, and the third confirmation information is used to indicate that the user agrees to report the data of the physiological sign monitoring.

With reference to the third aspect, in some implementations of the third aspect, the receiving module is further configured to receive first indication information sent by the server, where the first indication information is used to indicate an epidemic risk in the first area level and/or the total number of people at risk in the first area; the display module is further configured to display fourth information, where the fourth information is used to prompt the user of the epidemic risk level and/or of the first area or the total at-risk population of the first region.

With reference to the third aspect, in some implementations of the third aspect, the processing module is further configured to acquire at least one first physiological sign according to the infectious disease characteristic information, where the first physiological sign is related to the infectious disease obtain the monitoring data of the at least one first physiological sign of the user; weight the monitoring data of the at least one physiological sign according to at least one first preset weight to obtain the health risk score of the user, Wherein, the at least one first preset weight is in a one-to-one correspondence with the at least one physiological sign.

With reference to the third aspect, in some implementations of the third aspect, the display module is further configured to display monitoring data of the first physiological sign corresponding to different time points; and/or, display the first physiological sign Monitor data trends over time.

With reference to the third aspect, in some implementations of the third aspect, the display module is further configured to display a map page, where the epidemic risk level of the first area is marked on the map page.

With reference to the third aspect, in some implementations of the third aspect, the processing module is further configured to determine the contact distance between the user and the confirmed person according to the user's trajectory information and the confirmed person's trajectory information; When the contact distance is less than the transmission distance, and the contact time between the user and the confirmed person is greater than a first threshold, it is determined that the user is a close contact of the confirmed person.

With reference to the third aspect, in some implementations of the third aspect, the processing module is further configured to, when it is determined according to the location identifier that the user and the confirmed person are in the first confined space at the same time, determine that the user is close contacts of the confirmed person.

With reference to the third aspect, in some implementations of the third aspect, the location identification includes at least one of the following: train number and/or compartment number, room identification or elevator identification; the first enclosed space includes at least one of the following Type: train car, room, or elevator car.

With reference to the third aspect, in some implementations of the third aspect, the transmission route of the infectious disease virus, the transmission distance of the infectious disease virus, the incubation period of the infectious disease, and the physiological signs related to the infectious disease .

With reference to the third aspect, in some implementations of the third aspect, the physiological signs include at least one of the following: body temperature, heart rate, RRI during one heartbeat, blood oxygen saturation Spo2, and sleep characteristics.

In a fourth aspect, a server is provided, comprising: a sending module for sending trajectory information of a person diagnosed with an infectious disease and feature information of the infectious disease to an electronic device, the trajectory information including location information and time information; a receiving module , for receiving the user health risk score sent by the electronic device, the user is a person who is confirmed to be a close contact of the confirmed person; a processing module, used for predicting the epidemic situation in the first area according to the health risk score Risk, the first area is an area where the user has stayed in a preset historical time period, and the epidemic risk includes the total number of at-risk persons of the infectious disease confirmed and suspected persons.

With reference to the fourth aspect, in some implementations of the fourth aspect, the processing module is further configured to determine the risk level corresponding to the user according to the health risk score interval to which the user's health risk score belongs, and each The risk level has a corresponding second preset weight; according to the number of newly-increased risk numbers corresponding to the multiple risk levels, the diffusion coefficients corresponding to the multiple risk levels are determined respectively; the diffusion coefficients of the multiple risk levels are divided The coefficient and the corresponding second preset weight are weighted to obtain a modified diffusion coefficient; according to the modified diffusion coefficient and the existing number of people at risk in the first area, the total number of people at risk in the first area is predicted.

With reference to the fourth aspect, in some implementations of the fourth aspect, the processing module is further configured to determine the epidemic risk level of the first area according to the total number of people at risk; the sending module is configured to send the electronic The device sends first indication information, where the first indication information is used to indicate the epidemic risk level of the first area and/or the total number of people at risk in the first area.

In combination with the fourth aspect, in some implementations of the fourth aspect, the processing module is further configured to calculate the total number of risk persons:

In a fifth aspect, an electronic device is provided, comprising a communication interface, a processor, a memory, and a computer program stored in the memory and executable on the processor, when the processor is executing the computer program At the time, the electronic device is made to implement the method for epidemic prediction according to any one of the implementation manners of the first aspect above.

In a sixth aspect, there is provided a server comprising a communication interface, a processor, a memory, and a computer program stored in the memory and executable on the processor, when the processor executes the computer program , so that the server implements the method for epidemic prediction according to any implementation manner of the second aspect above.

In a seventh aspect, a computer-readable storage medium is provided, comprising computer instructions, which, when the computer instructions are executed on a computer, cause the computer to execute the implementation of any one of the first aspect or the second aspect above. Methods of epidemic prediction.

In an eighth aspect, a computer product is provided, characterized in that, when the computer product is run on a computer, the computer is caused to execute the epidemic prediction according to any implementation manner of the first aspect or the second aspect. method.

Description of drawings

FIG. 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application.

FIG. 2 is a schematic diagram of a system architecture for epidemic prediction provided by an embodiment of the present application.

FIG. 3 is a schematic diagram of a user graphical interface in an epidemic prediction process provided by an embodiment of the present application.

FIG. 4 is a schematic diagram of a user graphical interface in another epidemic prediction process provided by an embodiment of the present application.

FIG. 5 is a schematic diagram of displaying a monitoring result of a physiological sign of a user according to an embodiment of the present application.

FIG. 6 is a schematic diagram of another system architecture for epidemic prediction provided by an embodiment of the present application.

FIG. 7 is a schematic diagram of a graphical user interface in an epidemic prediction process provided by an embodiment of the present application.

FIG. 8 is a schematic diagram of an epidemic risk display provided by an embodiment of the present application.

FIG. 9 is a schematic flowchart of a method for epidemic prediction provided by an embodiment of the present application.

FIG. 10 is a schematic flowchart of another method for epidemic prediction provided by an embodiment of the present application.

FIG. 11 is a schematic diagram of distribution of persons at risk of an epidemic according to an embodiment of the present application.

FIG. 12 is a schematic diagram of a change trend of a predicted total number of people at risk according to an embodiment of the present application.

FIG. 13 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

FIG. 14 is a schematic structural diagram of a server provided by an embodiment of the present application.

Detailed ways

The embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application. The terms used in the implementation part of the embodiments of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application. In the description of the embodiments of the present application, unless otherwise specified, "/" means or means, for example, A/B can mean A or B; "and/or" in this document is only an association that describes an associated object Relation, it means that there can be three kinds of relations, for example, A and/or B can mean that A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, "plurality" refers to two or more than two.

Hereinafter, the terms "first" and "second" are only used for descriptive purposes, and should not be understood as indicating or implying relative importance and implicitly indicating the number of the indicated technical features. Thus, a reference to a "first", "second" feature may expressly or implicitly include one or more of that feature.

During the epidemic, in order to control the spread of the epidemic, it is necessary to track the movement trajectories of migrants. The current personnel tracking methods mainly include: (1) When personnel enter public places such as shopping malls and shopping centers, the venue requires scanning codes and reporting to the back-end server for personnel mobility tracking; (2) When personnel take public transportation, pass The ticket information is traceable to the mobile personnel. If an infectious person is found in a public place, the CDC, hospitals and other government agencies and social public institutions will conduct social investigations to analyze the close contacts of the epidemic based on the trajectory information of the confirmed patients and users; then observe all close contacts within the relevant time period Physiological characteristics of the epidemic, analyze the risk level of the epidemic in the region, and evaluate the impact of related risks.

Generally speaking, the trajectory information is obtained based on the positioning information of the user's electronic device, such as GPS information of a mobile phone or a wearable device. However, since the positioning information is limited in accuracy and cannot reflect various complex scenarios that may appear in reality, a large judgment error will occur in practical applications. For example, if the GPS information shows that the distance between the user and the confirmed patient is relatively close during a certain period of time, this situation may in practice be: (1) The user and the confirmed patient are in the same building, but on different floors, For example, the diagnosed patient is located on a high floor, and the user only stays in the underground garage for a period of time; or, (2) when the user and the diagnosed patient are driving on the road, due to red lights and other reasons, they park at a similar location at the same time. In the aforementioned situation, the risk of the user being infected is low, and if it is directly determined that the users in the similar situation are all patients, a large error will occur. In addition, the current screening of infected patients is mainly done by manpower, and limited manpower cannot screen all suspected patients in a timely and effective manner, and it will also increase the risk of virus infection by testing personnel in the process.

The general public has limited information on the actions of confirmed patients, and cannot judge by themselves whether they have had close contact with confirmed patients; even if close contact can be confirmed, due to the lack of understanding of the characteristics of the new virus, they cannot accurately judge whether it is based on their own physical signs. Infection with the virus, determine the risk of disease. Therefore, it is necessary to provide a method that can facilitate users to inquire about their own risk of disease, so that users can actively understand their own risks and facilitate early response measures.

In response to the above problems and needs, the embodiments of the present application provide a method for epidemic prediction, which can determine the user's risk through multi-dimensional information, which can accurately monitor the epidemic risk, and facilitate the user to query information such as their own infection risk and epidemic development trend, so that the Users can adjust their daily travel based on their own conditions and the development of the epidemic, and it is also convenient for relevant departments to take more targeted measures for epidemic prevention and control.

The methods for epidemic prediction provided in the embodiments of the present application can be applied to electronic devices such as mobile phones, tablet computers, wearable devices, and in-vehicle devices. The embodiment of the present application does not limit the specific type of the electronic device.

Exemplarily, FIG. 1 shows a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) 130, a charge management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, Mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone jack 170D, sensor module 180, buttons 190, motor 191, indicator 192, camera 193, display screen 194, and user Identity module (subscriber identification module, SIM) card interface 195 and so on. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, and an environmental sensor Light sensor 180L, bone conduction sensor 180M, etc.

It can be understood that the structures illustrated in the embodiments of the present application do not constitute a specific limitation on the electronic device 100 . In other embodiments of the present application, the electronic device 100 may include more or less components than shown, or combine some components, or separate some components, or arrange different components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

The processor 110 may include one or more processing units, for example, the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing, GPU), an image signal processor ( image signal processor (ISP), audio processor/digital processor (the audio processor), controller, memory, video codec, audio codec, digital signal processor (DSP), baseband processor and/or neural-network processing unit (NPU), etc. Wherein, different processing units may be independent devices, or may be integrated in one or more processors.

The controller may be the nerve center and command center of the electronic device 100 . The controller can generate an operation control signal according to the instruction operation code and timing signal, and complete the control of fetching and executing instructions.

A memory may also be provided in the processor 110 for storing instructions and data. In some embodiments, the memory in processor 110 is cache memory. This memory may hold instructions or data that have just been used or recycled by the processor 110 . If the processor 110 needs to use the instruction or data again, it can be called directly from the memory. Repeated accesses are avoided, and the waiting events of the processor 110 are reduced, thereby improving the efficiency of the system.

For example, in the present application, a firmware program (firmware) is stored in the memory, so that the controller or the processor can implement the audio processing method of the present application through an interface or a protocol.

In some embodiments, the processor 110 may include one or more interfaces. The interface may include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, a universal asynchronous transceiver (universal asynchronous transmitter) receiver/transmitter, UART) interface, mobile industry processor interface (MIPI), general-purpose input/output (GPIO), user identification module interface, and/or universal serial bus interface, etc. .

The I2C interface is a bidirectional synchronous serial bus that includes a serial data line (SDA) and a serial clock line (SCL). In some embodiments, the processor 110 may contain multiple sets of I2C buses. The processor 110 can be respectively coupled to the touch sensor 180K, the microphone 170C, the camera 193 and the like through different I2C bus interfaces. For example, the processor 110 may be coupled to the touch sensor 180K through an I2C interface, and the processor 110 communicates with the touch sensor 180K through an I2C bus interface to implement the touch function of the electronic device 100 .

The I2S interface can be used for audio data transmission. In some embodiments, the processor 110 may contain multiple sets of I2S buses. The processor 110 may be coupled with the audio module 170 through an I2S bus to implement communication between the processor 110 and the audio module 170 . In some embodiments, the audio module 170 may receive audio signals through an I2S interface to implement the function of recording audio.

The PCM interface can also be used for audio communications, sampling, quantizing and encoding analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface. In some embodiments, the audio module 170 can also transmit audio signals to the wireless communication module 160 through the PCM interface, so as to realize the function of answering calls through the Bluetooth headset; Audio data collected by the microphone.

The UART interface is a universal serial data bus used for asynchronous communication. The bus may be a bidirectional communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, a UART interface is typically used to connect the processor 110 with the wireless communication module 160 . For example, the processor 110 communicates with the Bluetooth module in the wireless communication module 160 through the UART interface to implement the Bluetooth function. In some embodiments, the audio module 170 can receive the audio signal transmitted by the Bluetooth module through the UART interface, so as to realize the function of recording audio through the wireless microphone in the Bluetooth headset.

The MIPI interface can be used to connect the processor 110 with peripheral devices such as the display screen 194 and the camera 193 . MIPI interfaces include camera serial interface (CSI), display serial interface (DSI), etc. In some embodiments, the processor 110 communicates with the camera 193 through a CSI interface, so as to realize the photographing function of the electronic device 100 . The processor 110 communicates with the display screen 194 through the DSI interface to implement the display function of the electronic device 100 .

The GPIO interface can be configured by software. GPIO can be configured as a control signal or as a data signal. In some embodiments, the GPIO interface may be used to connect the processor 110 with the camera 193, the display screen 194, the wireless communication module 160, the audio module 170, the sensor module 180, and the like. The GPIO interface can also be configured as I2C interface, I2S interface, UART interface, MIPI interface, etc.

The USB interface 130 is an interface that conforms to the USB standard specification, and may specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, and the like. The USB interface 130 can be used to connect a charger to charge the electronic device 100, and can also be used to transmit data between the electronic device 100 and peripheral devices. It can also be used to connect headphones to play audio through the headphones. The interface can also be used to connect other electronic devices, such as AR devices.

It should be understood that the interface connection relationship between the modules illustrated in the embodiments of the present application is only a schematic illustration, and does not constitute a structural limitation of the electronic device 100 . In other embodiments of the present application, the electronic device 100 may also adopt different interface connection manners in the foregoing embodiments, or a combination of multiple interface connection manners.

Internal memory 121 may be used to store computer executable program code, which includes instructions. The processor 110 executes various functional applications and data processing of the electronic device 100 by executing the instructions stored in the internal memory 121. The internal memory 121 may include a storage program area and a storage data area. The storage program area can store an operating system, an application program required for at least one function (such as an audio playback function, an image playback function, etc.), and the like. The storage data area may store data (such as audio data, phone book, etc.) created during the use of the electronic device 100 and the like. In addition, the internal memory 121 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, universal flash storage (UFS), and the like.

The power management module 141 is used for connecting the battery 142 , the charging management module 140 and the processor 110 . The power management module 141 receives input from the battery 142 and/or the charging management module 140 and supplies power to the processor 110 , the internal memory 121 , the external memory, the display screen 194 , the camera 193 , and the wireless communication module 160 . The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle times, battery health status (leakage, impedance). In some other embodiments, the power management module 141 may also be provided in the processor 110 . In other embodiments, the power management module 141 and the charging management module 140 may also be provided in the same device.

Display screen 194 is used to display images, videos, and the like. Display screen 194 includes a display panel. The display panel can use liquid crystal display (LCD), organic light-emitting diode (OLED), active-matrix organic light-emitting diode (AMOLED), flexible light-emitting diode Diode (flex light-emitting diode, FLED), quantum dot light emitting diode (quantum dot light emitting diodes, QLED), etc.

The wireless communication function of the electronic device 100 may be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modulation and demodulation processor, the baseband processor, and the like. The wireless communication module 160 may provide a wireless communication solution including 2G/3G/4G/5G etc. applied on the electronic device 100 .

The electronic device 100 may implement audio functions through an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, an application processor, and the like. For example, audio playback, recording, etc. The keys 190 of the electronic device 100 may include a power-on key, a volume key, and the like. The key 190 may be a mechanical key or a touch key. Motor 191 can generate vibrating cues. The motor 191 can be used for vibrating alerts for incoming calls, and can also be used for touch vibration feedback. The indicator 192 can be an indicator light, which can be used to indicate the charging status, the change of power, and also can be used to indicate messages, missed calls, notifications, and the like. The SIM card interface 195 is used to connect a SIM card.

In addition, the electronic device 100 also includes various sensors, such as a pressure sensor 180A, for sensing pressure signals, which can be converted into electrical signals. The gyro sensor 180B may be used to determine the motion attitude of the electronic device 100 . The air pressure sensor 180C is used to measure air pressure. The magnetic sensor 180D includes a Hall sensor. The acceleration sensor 180E can detect the magnitude of the acceleration of the electronic device 100 in one direction (generally three axes). Distance sensor 180F for measuring distance. The electronic device 100 can measure the distance through infrared or laser. Proximity light sensor 180G may include, for example, light emitting diodes (LEDs) and light detectors, such as photodiodes. The ambient light sensor 180L is used to sense ambient light brightness. The electronic device 100 can adaptively adjust the brightness of the display screen 160 according to the perceived ambient light brightness. The fingerprint sensor 180H is used to collect fingerprints. The electronic device 100 can use the collected fingerprint characteristics to realize fingerprint unlocking, accessing application locks, taking pictures with fingerprints, answering incoming calls with fingerprints, and the like. Touch sensor 180K, also called "touch panel". The touch sensor 180K may be disposed on the display screen 194 , and the touch sensor 180K and the display screen 194 form a touch screen, also called a “touch screen”. The touch sensor 180K is used to detect a touch operation on or near it. The touch sensor 180K may pass the detected touch operation to the application processor to determine the type of touch event.

The embodiment of the present application further includes a temperature sensor 180J and a bone conduction sensor 180M. The temperature sensor 180J is used to detect the temperature. For example, the temperature sensor 180J may be a non-contact infrared temperature sensor that can measure the temperature of an object using infrared rays. It should be understood that the types and numbers of infrared temperature sensors are not limited in the embodiments of the present application. The temperature sensor 180J can receive the human body temperature signal to realize the temperature detection function. In some embodiments, the electronic device 100 uses the temperature detected by the temperature sensor 180J to execute a temperature processing strategy.

The bone conduction sensor 180M can acquire vibration signals. In some embodiments, the bone conduction sensor 180M can acquire the vibration signal of the vibrating bone mass of the human voice. The bone conduction sensor 180M can also contact the pulse of the human body and receive the blood pressure beating signal. In some embodiments, the bone conduction sensor 180M can also be disposed in the earphone, combined with the bone conduction earphone. The audio module can parse out the voice signal based on the vibration signal of the voice part vibrating bone mass obtained by the bone conduction sensor 180M, so as to realize the voice function.

In some implementation manners, the application processor may analyze the heart rate information based on the blood pressure beat signal obtained by the bone conduction sensor 180M, so as to realize the heart rate detection function and the sleep monitoring function.

In addition, the processor 110 of the electronic device 100 can also perform signal extraction, signal enhancement, and algorithm training by optimizing the hardware optical path and algorithm, integrating multiple light sources, and utilizing the characteristics of different reflection and absorption rates of oxyhemoglobin and deoxyhemoglobin to specific light. , abnormal suppression and other measures to measure the pulse oxygen saturation.

In some embodiments, the method for epidemic prediction provided by the embodiments of the present application may be executed by a single device at the user end, for example, through a smart watch or smart bracelet at the user end; or may be executed by multiple devices (such as a mobile phone and a smart bracelet) The method for epidemic prediction provided by the embodiment of the present application is performed by multi-device cooperation.

Exemplarily, first, taking the electronic device as a smart watch as an example, a scenario when the client is executed by a single device is introduced with reference to FIG. 2 . FIG. 2 shows a schematic diagram of the system architecture when the client is a single device. The system architecture includes a server and electronic devices on the client side, and there may be multiple clients on the system architecture (eg, user 1, user 2, and user 3).

In some embodiments, system maintenance personnel can obtain the track information of confirmed persons (hereinafter referred to as track information) and virus feature information of infectious diseases (hereinafter referred to as feature information) published by government agencies such as CDCs, hospitals, etc., The acquired trajectory information is converted into geographic information required by the system (the GPS information is taken as an example below) and the corresponding time information is input to the server. After the server receives the feature information and trajectory information, it stores the information in the regional sample database. Wherein, when inputting the trajectory information, the system maintenance personnel may also input additional information in the trajectory information, such as train number, carriage number, house number, and the like.

Exemplarily, the system maintainer can add the trajectory information of a patient diagnosed with a virus and the characteristic information of the virus according to the latest published epidemic information, or perform the tracking information of the confirmed patient or the characteristic information of the virus already stored in the database. renew. For example, when a new type of virus appears, the server can receive and store the characteristic information of the new type of virus and the trajectory information of the confirmed person corresponding to the virus; or, when the characteristic information of a certain virus is updated, the server can receive and update the information. The characteristic information of the virus; or, when it is found that a virus has a newly diagnosed person, the server receives the trajectory information of the new person and adds it to the trajectory information of the confirmed person corresponding to the virus.

In some embodiments, the server may send the updated trajectory information and virus feature information to an electronic device on the client side, such as a smart watch. The electronic device may store the acquired trajectory information and virus characteristic information in the internal memory 121 .

Exemplarily, FIG. 3 shows a schematic diagram of a graphical user interface (graphical user interface, GUI) in an epidemic prediction process provided by an embodiment of the present application. For ease of understanding, a smart watch is taken as an example here, and the interaction process between the electronic device and the user is introduced in combination with the system architecture shown in FIG. 2 .

As shown in (a) of Figure 3, the user can click on the "View XX infectious disease confirmed personnel's action route" option on the main interface of the smart watch, and the main interface can also include Bluetooth icons, battery information and time information, etc.; After detecting the user's query operation, the smart watch can display a search interface for the movement trajectory of the diagnosed person as shown in (b) in Figure 3, which includes: a search box for infectious disease types, a search box for location/region, and a search box for time etc., the user can enter the corresponding content in the search box, and then click the search icon; alternatively, the user can also click the drop-down box control, and the electronic device can respond to the user's click operation, and can display as shown in (c) in Figure 3 option, when the user clicks the drop-down control corresponding to the disease type search box, the S infectious disease, X infectious disease, and Y infectious disease can be displayed in the drop-down list. In addition, the user can also drag the slider on the right side of the drop-down option area to query more The type of infectious disease in XX; the user enters or selects the type of infectious disease, location/area, and date, such as the user enters S infectious disease, XX city, XX district, after June 18, 2020, as shown in (d) in Figure 3 , the user clicks the search icon, indicating that the user inquires about the trajectory of the confirmed infectious disease patients in XX District, XX City on June 18, 2020; the user's search operation is detected, and the smart watch can display as shown in (e) in Figure 3 The map page displayed is marked with the trajectory and route of the confirmed S infectious disease person in the area and time period queried by the user.

It should be understood that, in the embodiment of the present application, the specific steps for the user to query the action route of the diagnosed person may also include other various steps, which are not limited to the steps listed above, which are not limited in the present application.

In some embodiments, after receiving the track information and feature information, the smart watch determines whether the user is a close contact of the confirmed person based on the user's historical track information stored locally, and reminds the user of the risk of close contact. For example, when it is detected that the user has close contact with the confirmed person, as shown in (a) in Figure 4, the smart watch interface can display risk prompt information, such as "You start at 2020-06-18, 13:00. , in the A shopping mall, you may be in close contact with the confirmed person of S infectious disease for 1 hour, please confirm whether there is a possibility of close contact”; when it is detected that the user clicks the “Confirm” option, as shown in (b) in Figure 4, The smart watch interface can display the prompt information of physiological signs monitoring, such as "whether to agree to monitor the physiological signs related to S infectious disease"; when it is detected that the user clicks the "confirm" icon, it means that the user agrees to the electronic device to monitor the physiological signs. , the smart watch can turn on the physiological sign monitoring function.

It should be understood that, in actual application, only the positions judged based on the trajectory information are close, and it cannot accurately reflect whether the user and the confirmed person are in close contact with each other. Therefore, the above prompt information shown in (a) in Figure 4 , which can prompt the user to recall whether they were in close contact with the confirmed person at the indicated time and place, thereby improving the accuracy of close contact judgment.

In some embodiments, the physiological signs monitored by the smart watch include physiological signs related to the S infectious disease. For example, after the virus infection of the S infectious disease, the body temperature of the infected person will rise and the blood oxygen will drop. Physiological signs can be body temperature and blood oxygen saturation; or, for example, after viral infection of S infectious disease, the heart rate, body temperature, and blood oxygen saturation of the infected person will change, then the monitored physiological signs can include heart rate, Body temperature, blood oxygen saturation. Optionally, the detected physiological signs may also include other various types, which are not limited here.

In some embodiments, after the smart watch detects that the user clicks the "confirm" icon, the electronic device monitors the physiological signs of the user; after that, as shown in (c) of FIG. 4 , the smart watch can further display the physiological signs of the user The prompt information of the information report, such as "Do you agree to report your physiological sign data for risk analysis"; when it is detected that the user clicks the "Confirm" operation, it means that the user agrees to the smart watch to report the monitoring data of his physiological signs to the server, Then, the smart watch can report the measured physiological sign data of the user to the server.

In some embodiments, the smart watch can also calculate the user's health risk score based on certain criteria according to the acquired physiological sign monitoring data, where the health risk score is used to indicate the probability of the user contracting an infectious disease. Exemplarily, the smart watch can continuously monitor changes in the user's physiological sign data for multiple consecutive days, and then draw a change trend graph of the health risk score and/or a change trend graph of the physiological sign monitoring results. The smart watch can alert the user of health risks based on the monitoring results of various physiological signs and/or health risk scores, as shown in (d) in Figure 4, when the monitoring results of the user's physiological signs indicate that the user's physiological signs meet the When infected with the characteristics of infectious diseases, or when the health risk score is higher than the preset high risk score, the smart watch interface can display, for example, "It is detected that you are a high-risk group of infectious diseases, please seek medical treatment in time".

In some embodiments, after the smart watch calculates the user's health risk score according to the monitored physiological sign data, as shown in (e) of Figure 4, the smart watch can further display prompt information reported by the user's health risk score, such as " Whether you agree to report your health risk score for risk analysis”; when it is detected that the user clicks the “Confirm” icon, it means that the user agrees to the smart watch to report its health risk score, and the smart watch will report the user’s health risk score to the server. .

It should be understood that, in this embodiment of the present application, the electronic device may periodically detect whether the user is a close contact of a confirmed person and report user data (physiological sign measurement result or health risk score). For example, the electronic device detects whether the user is a close contact based on the updated trajectory information of the confirmed person every 30 minutes; every 1 hour, it reports the user's physiological sign monitoring data, etc. Wherein, the period for detecting whether the user is a close contact by the electronic device may be the same as or different from the period for reporting user data, which is not limited here.

In addition, the detection process of the user's physiological signs by the electronic device can be carried out in real time and continuously. In other words, after obtaining the user's authorization (for example, the user clicks the confirmation icon in (b) of FIG. 4 ), the electronic device turns on the physiological sign. The sign detection function can continuously monitor the user's physiological signs in the background to obtain accurate sign data of the user at different times.

According to the data reported by the user and the existing risk personnel, the server predicts the development trend of the regional epidemic situation based on a certain calculation model, such as predicting the total number of risk personnel in certain regions. Exemplarily, the server may, according to the predicted epidemic development trend, update the epidemic situation of certain areas where the user stayed in the preset historical time period, and send the risk level of these areas to the user's electronic device.

In some embodiments, as shown in (f) of FIG. 4 , the user can query the monitoring results of various physiological signs on the monitoring result query interface of the smart watch, and each physiological sign includes, for example: heart rate, R-wave interval (R-R interval, RRI) heart rate, blood oxygen, sleep characteristics, electrocardiogram (ECG), etc. When the user clicks on the icons marked with the names of different physiological signs, the smart watch can display the monitoring results of the corresponding physiological signs. For example, as shown in (f) in Figure 4, the user clicks on the heart rate option, and the smart watch responds to the user's click operation and displays (g) in Figure 4, presenting the monitoring results and status (heart rate) of the heart rate to the user. , 65, normal state).

There may be other forms of query and display of monitoring results. For example, in other embodiments, the monitoring result query interface may include query options. When the user clicks on the query options, the smart watch can simultaneously display multiple detected items. Results of physiological signs (similar to that shown in (a) in Figure 5). This application does not limit this.

FIG. 5 shows a schematic diagram of a display interface of a physiological sign measurement result provided by an embodiment of the present application.

In some embodiments, when the smart watch detects the user's operation of inputting a physical sign monitoring result query, it can also display the interface shown in (a) in FIG. 5 , which can be a physiological sign monitoring result display interface, including multiple Physiological sign measurement results and the changing trend of physiological signs data. The physiological signs are, for example, blood oxygen, heart rate, and sleep characteristics. The change trend graph of this interface is used to indicate the change trend of blood oxygen (as shown in the uppermost curve in (a) of Figure 5) and the change trend of heart rate (as shown in the lowermost curve of (a) in Figure 5) ) and sleep features (as shown by the dotted filled area in Fig. 5(a)). It can be seen from (a) in Figure 5 that at 01:15, the user's blood oxygen is 98%, the heart rate is 65, and the user is in an awake state at this time.

Alternatively, in some embodiments, if the user clicks “OK” in the interface shown in (d) or (e) of FIG. 4 , the smart watch may also display the user's health risk score. As shown in (b) of FIG. 5 , the smart watch displays the health risk score of the user calculated according to certain criteria. Exemplarily, the display interface includes health risk scores corresponding to different dates.

Optionally, as shown in (c) of FIG. 5 , the smart watch can also mark the status corresponding to the health risk score corresponding to different dates on the calendar page, and display it to the user. Among them, when the user's risk score on a certain date is in the preset normal state score range, the lower part of the date is marked as normal, indicating that the user's health is in a normal state; when the user's health risk score on a certain date is in the preset normal state score range When the risk status (such as low risk, medium risk, high risk, etc.) is in the score range, the corresponding risk status (such as low risk, medium risk, high risk, etc.) is marked below the date, indicating that the user’s current health has health risks. In addition, if the user has a health risk, the calendar interface can also display the risk level (60% as shown in (c) in Figure 5); it can also display a suggestion option in the risk state, and the user can click on the suggestion option to obtain appropriate health advice.

It should be understood that in practical applications, many users' smart watches are connected to other devices, and multiple devices can work together. Users can view information more clearly on the large screen. Alternatively, after other devices are connected to the smart watch, the smart watch can integrate the monitoring data of the user's physiological signs to analyze the user's health risk score.

The following describes a method for a user terminal to cooperate with multiple devices to complete the epidemic prediction provided by the embodiments of the present application with reference to the accompanying drawings.

In some embodiments, the method for epidemic prediction provided by the embodiments of the present application may also be implemented through the system architecture shown in FIG. 6 . In the system architecture shown in FIG. 6 , multiple electronic devices at the user end may be a combination, and the electronic devices in the combination have different divisions of labor. Taking the combination of electronic devices including a mobile phone and a smart watch as an example, the mobile phone and the smart watch can establish a wireless connection, such as a Bluetooth connection, and the mobile phone and the smart watch can work together. Exemplarily, the division of labor of each electronic device in the electronic device combination shown in FIG. 6 may include but is not limited to the following situations:

Scenario 1: The mobile phone uses its communication function and computing power to interact with the server and the user, and analyzes whether the user is a close contact with the epidemic, as well as the user's health risk score; while the smartwatch mainly uses physiological signs The monitoring function is used to detect the physiological signs of the user under the instruction of the mobile phone, and send the detected data to the mobile phone for the mobile phone to calculate the health risk score, or for the user to view.

Scenario 2: The smart watch is used to monitor the user's physiological sign data, process the physiological sign data, and interact with the server and the user; while the mobile phone uses its display function to receive the physiological sign monitoring results of the smart watch and display various items to the user. Results of physiological signs, health risk scores, reminder messages, etc.

It should be understood that the division of labor of each electronic device in the electronic device combination can be set in various ways based on the functions supported by the electronic device itself, which is not limited in this application. Moreover, the system architecture shown in FIG. 6 is described by taking the combination of the user equipment as a mobile phone and a smart watch as an example, but the present application is not limited to this. A combination of smart watches, etc.

For ease of understanding, the following takes Scenario 1 as an example to introduce the epidemic prediction process under the system architecture. In this case, the server can send the trajectory information of the confirmed person and the characteristic information of the infectious disease to the mobile phone, and the mobile phone can determine whether the user is a close contact of the confirmed person based on the stored historical trajectory information of the user; when it is determined that the user is a close contact When contacting a person, the mobile phone can prompt the user that there is a risk of close contact; if the mobile phone obtains the user's confirmation of the close contact operation and the user authorizes the monitoring of physiological signs, the mobile phone can instruct the smart watch worn by the user to monitor the physiological signs; According to the instructions of the mobile phone, the smart watch turns on the physiological sign monitoring function, and sends the obtained physical sign monitoring results to the mobile phone, and the mobile phone calculates the health risk score and reports the relevant data.

Exemplarily, with reference to the system architecture shown in FIG. 6 , FIG. 7 shows a schematic diagram of a graphical user interface in an epidemic prediction process provided by an embodiment of the present application. For ease of understanding, the combination of a mobile phone and a smart watch is still used as an example for description.

7(a) shows the interface in the unlocking mode of the mobile phone, the screen display interface of the mobile phone displays the current interface content 701, and the interface content 701 may be the main interface of the mobile phone. The interface content 701 displays a variety of applications (application, App), such as clock, calendar, gallery, memo, file management, email, music, calculator, Huawei video, weather, browser, smart life, settings, voice recorder , an app store, and apps for camera, contacts, phone, messaging, and fitness. It should be understood that the interface content 701 may further include other more application programs, which is not limited in this embodiment of the present application.

As shown in (a) of FIG. 7, the user clicks the icon of the sports health application; in response to the user's click operation, the mobile phone opens the sports health application and displays the health interface 702, as shown in (b) of FIG. 7 , the health interface 702 may include a plurality of display sub-areas, including a step counting display sub-area 10, a vital sign data query sub-area 20, and an epidemic risk query sub-area 30 and the like. The step count display sub-area 10 may include data such as the number of steps of the day, the distance walked, and the calories consumed by the user; the vital sign data query sub-area 20 may include a number of physiological sign measurement results query options, including heart rate, blood oxygen saturation, etc. In addition, it also includes healthy life options, which are used to enable users to query information such as health advice.

When the mobile phone detects that the user clicks the epidemic risk query icon, the mobile phone can display the interface as shown in (c) in Figure 7, which is the query interface for the trajectory information of the diagnosed person and the infectious disease characteristic information, including the movement trajectory query of the confirmed person icon, the infectious disease characteristic information query icon.

Among them, as shown in (c) in Figure 7, when the user clicks the icon to view the action route of the infectious disease confirmed person, the mobile phone can display the interface shown in (d) in Figure 7, including the type of infectious disease, In the input area of location/area and time (area 703), the user can input the name of the infectious disease, as well as the location and time of the query; Select the option you want to view.

When the mobile phone detects the query information entered by the user, for example, the user enters or selects S infectious disease in the infectious disease type search box, enters or selects XX district in XX city in the location/region search box, and enters or selects 2020-6 in the time search box After -18, click the search icon (704); in response to the search operation input by the user, as shown in (e) of Figure 7, the mobile phone can display the S infectious disease confirmed person 2020-6-18 queried by the user in a certain The movement track of XX District of XX City, such as the mobile phone interface, can display a map page, and the movement path of the diagnosed person, as well as the information of the stop place and the corresponding stay time are marked on the map page.

In some embodiments, when the mobile phone detects that the user clicks to query the characteristic information of infectious diseases on the interface as shown in (c) of FIG. 7 , the mobile phone can display the characteristic information of various infectious diseases, including: incubation period, Transmission distance, disease symptoms, etc.

In some embodiments, when the mobile phone determines that the user is a close contact based on the acquired trajectory information and the stored historical trajectory information of the user, a prompt message is displayed, for example, prompting the user "at XX time, XX place is close to the confirmed person If you have been in contact for X hours, there is a risk of close contact”; the user judges whether the close contact has actually occurred based on the historical path he has passed, and excludes the misjudgment of close contact only because of the geographical proximity. If there is close contact, you can Click "Confirm", otherwise, click "Cancel".

It should be understood that in practical applications, only the positions determined based on the trajectory information are close, and it cannot accurately reflect whether the user has real close contact with the confirmed person. Therefore, the prompt information can prompt the user to recall whether he was close to the confirmed person at the indicated time and place. Contact, improve the accuracy of close contact judgment.

Among them, when the user clicks "Confirm", it indicates that the user confirms that there is close contact, and there may be a greater risk of infection. When the mobile phone detects that the user clicks the "Confirm" icon, it means that the user confirms that there has been close contact with the confirmed person, and in response to the confirmation operation, the mobile phone can display the prompt information of physiological sign monitoring to the user, such as "Do you agree to your When it is detected that the user clicks "confirm", the mobile phone can instruct the wearable device connected to it to monitor the physiological signs of the user.

After receiving the instructions of the mobile phone, the smart watch monitors the physiological signs of the user. Specifically, the smart watch can send the monitoring data to the mobile phone during the monitoring process; or, after the monitoring is completed, the smart watch can send the monitoring data to the mobile phone.

Exemplarily, at the beginning of physiological sign monitoring or during the monitoring process, the mobile phone can also display prompt information, such as prompting the user "whether to agree to report your physiological sign information for risk analysis"; when it is detected that the user clicks "confirm" , indicating that the user agrees to the mobile phone to report the monitoring data of its physiological signs to the server, then the mobile phone can report the physiological signs data measured by itself to the server.

In some embodiments, the mobile phone may further calculate the user's health risk score based on certain criteria according to the acquired sign information, where the health risk score is used to indicate the probability that the user is infected with a virus. Based on the monitoring results of various physiological signs and/or health risk scores, the smart watch can provide health risk reminders to users, such as displaying a reminder message that "you are detected as a high-risk group of S infectious diseases, please seek medical attention in time".

In some embodiments, after the mobile phone calculates the user's health risk score according to the measured physiological sign data, the mobile phone may further display prompt information for reporting the user's health risk score, such as "Do you agree to report your health risk score for risk analysis?" ; When it is detected that the user clicks the "Confirm" operation, it means that the user agrees to the mobile phone to report its health risk score to the server, and the mobile phone can report the user's health risk score to the server.

In some embodiments, the user can query the monitoring results of various physiological signs on the detection and query interface of the mobile phone, and each physiological sign includes, for example, heart rate, RRI heart rate, blood oxygen, sleep characteristics, ECG, and the like. When the user clicks on the icons marked with the names of different physiological signs, the mobile phone can display the monitoring results of the corresponding physiological signs. Alternatively, when the user clicks to query the monitoring results of physical signs, the mobile phone can simultaneously display the monitoring results of multiple monitored physiological signs.

For a schematic diagram of an interface for displaying the monitoring results of the physiological signs displayed by the mobile phone, see FIG. 5 , which will not be repeated here.

In some possible embodiments, the electronic device for reporting user data may also receive data sent by multiple devices. For example, in a family scenario, if the smart watches of multiple family members are connected to one electronic device , multiple devices can send the separately measured physical data of different users to the electronic device, the electronic device calculates the health risk scores of different users, and reports the health risk scores, so that multiple health risk scores can be reported.

It should be understood that when a person is infected with an infectious disease, other people who have more daily contact with the person, such as family members, colleagues, etc., will have an increased risk of being infected. Therefore, when a close contact with a confirmed person is detected After that, users can be further prompted to report the health risk scores of multiple people to improve the efficiency of epidemic prediction.

In some embodiments, the server may predict the development trend of the epidemic in the area reported by the user based on the reported data of the user's electronic device, including the total number of people at risk in the area, the epidemic risk level, and the like. The server sends the generated information to the electronic device, and the electronic device can further display the total number of people at risk and/or the epidemic risk level in the area to the user.

Exemplarily, as shown in FIG. 8 , it is a schematic diagram of an example of a risk map provided in this embodiment of the present application. Among them, the electronic device can mark the epidemic risk level of different regions on the map page based on the obtained epidemic risk level of each region, such as "XX high-risk area of infectious disease epidemic" and "XX infectious disease epidemic medium-risk area" for users. Viewing, it is convenient for users to plan travel routes and avoid areas with high epidemic risk levels.

The external implementation of the epidemic prediction process provided by the embodiments of the present application has been introduced above with reference to the accompanying drawings and specific scenarios. In order to better understand the epidemic prediction method provided by the present application, the specific implementation process and algorithm are described below from the internal implementation level.

Exemplarily, FIG. 9 shows an epidemic prediction method provided by an embodiment of the present application. Can be applied to electronic equipment. The method includes the following steps:

S901: Acquire trajectory information of a person diagnosed with an infectious disease and feature information of the infectious disease, where the trajectory information includes location information and time information corresponding to the location information.

Wherein, the electronic device can obtain the track information of the diagnosed person from the server, and the track information includes location information and time information, such as the position corresponding to the diagnosed person at a certain moment. The electronic device may also obtain characteristic information of the infectious disease from the server, and the characteristic information may include the transmission route, transmission distance, incubation period, and physiological signs related to the infectious disease, etc. of the infectious disease virus.

S902 , when it is detected that the user is a close contact of the confirmed person according to the trajectory information of the user and the trajectory information of the confirmed person, monitor the physiological signs of the user, and obtain monitoring data of the physiological signs.

Wherein, in the embodiment of the present application, the trajectory information of the diagnosed person and the historical trajectory information of the user may both be GPS information.

In some embodiments, the user's trajectory information may be the user's trajectory information within a preset historical time period, for example, the trajectory information within a week.

In some embodiments, the electronic device may obtain the contact distance between the user and the confirmed person according to the location information and time information in the historical track information of the user and the location information and time information of the confirmed person, where the contact distance here may be Refers to the distance between the user's location and the location of the confirmed person at the same time. When the contact distance between the user and the confirmed person is less than the transmission distance of the infectious disease virus (that is, close contact), the risk of the user being infected with the virus is greater, and the time of close contact between the user and the confirmed person can be further judged; when the user and the confirmed person are in close contact When the duration of close contact of the confirmed person is greater than the first threshold, it is determined that the user is a close contact of the confirmed person.

In other words, the electronic device can obtain the trajectory information of the diagnosed person based on step S901, and determine whether the user's historical trajectory information stored on the electronic device appears in the preset interval of the confirmed person's route, and the time in the preset interval is greater than the first threshold. , if so, the user is considered to be a close contact of the confirmed person.

It should be understood that if the user has only a very brief (less than the first threshold) close contact with the confirmed person, the risk of the user being infected is low, and the user is not regarded as a close contact of the confirmed person at this time; When a user has close contact with a confirmed person, and the duration of the close contact is greater than or equal to the first threshold, the user is considered to be a close contact of the confirmed person, and physical signs monitoring and data reporting will be carried out later.

Specifically, the electronic device can also score the close contact risk according to the contact distance between the user and the confirmed person. The calculation formula of the close contact risk score is shown in formula (1-1):

Among them, ds is the close contact risk score;

d is the contact distance between the user and the confirmed person;

_dmax is the maximum transmission distance of the infectious virus.

In some embodiments, if the electronic device determines, based on the additional information in the trajectory information of the confirmed person, that the user and the confirmed person used to be in the same confined space, for example, the same confined space as a train car, room, elevator car, etc., it may not be Then consider the length of time that they are in the same confined space, and directly determine that the user is a close contact of the confirmed person.

In some embodiments, after it is determined that the user is a close contact of the confirmed person, the electronic device may display first information (the close contact risk prompt information shown in (a) of FIG. 4 ), the first information The information is used to inquire whether the user is a close contact of the confirmed person, and the first information includes location information and/or time information of the close contact between the user and the confirmed person. Optionally, when the electronic device prompts the user that there is a risk of close contact, the risk level of the close contact may be determined according to ds, and the risk level of the close contact may be prompted to the user.

The electronic device receives the first confirmation information input by the user (as shown in (a) of FIG. 4 , the user clicks the confirmation option), and the first confirmation information may be used to indicate that the user is a close contact person.

Exemplarily, the electronic device may display second information (such as the physiological sign monitoring prompt information shown in (b) of FIG. 4 ), where the second information is used to request the user to perform physiological sign monitoring. When the electronic device receives the second confirmation information input by the user (the user clicks the confirmation option as shown in (b) in FIG. 4 ), the user's physiological sign monitoring can be started, and the second confirmation information can be used to indicate that the user agrees Electronic devices monitor physiological signs.

Optionally, when the electronic device itself supports the function of monitoring physiological signs, the electronic device can monitor the physiological signs of the user; The user performs physiological sign monitoring.

S903, obtain the health risk score of the user according to the monitoring data and the characteristic information of the infectious disease.

In some embodiments, the electronic device may acquire weights corresponding to different physiological signs, and the weights may be preset weights according to a characteristic model of an infectious disease.

In some embodiments, the electronic device may acquire at least one first physiological sign according to the feature information of the infectious disease, where the first physiological sign is a feature associated with the infectious disease; after that, the electronic device may acquire at least one first physiological sign of the user for the at least one first physiological sign. Monitoring the physical sign information, and obtaining the monitoring data of the first physiological sign; then, the electronic device can perform weighted calculation on the monitoring data of the at least one first physiological sign according to the preset weight corresponding to the at least one first physiological sign, and obtain The user's health risk score.

Exemplarily, taking the physiological signs of the user's heart rate, RRI, Spo2, and body temperature obtained by the smart watch as an example, the calculation formula of the health score can be formula (1-2):

S _health =hr _weight ×heart rate change value+RRI _weight ×RRI heart rate change value+Spo2 _weight ×blood oxygen change value+T _weight ×body temperature change value (1-2)

Wherein, S _health is a health risk score; hr _weight is a preset weight corresponding to heart rate; RRI _weight is a preset weight corresponding to heart rate; T _weight is a preset weight corresponding to body temperature. It should be understood that the change value of each physiological sign may be a change value between the monitored physiological sign monitoring data and the preset normal data of the physiological sign.

Optionally, the weight of the physiological sign that is more related to the infectious disease can be set to be larger, and the weight of the physiological sign that is less related to the infectious disease can be set to be smaller. This application does not limit this.

In some embodiments, the electronic device may display the first prompt information to the user according to the user's health score to prompt the user that there is a health risk. For example, if the electronic device determines that the user's health score is not within the normal range, the electronic device may display risk prompt information as shown in (d) of FIG. 4 .

In some embodiments, the electronic device may also display third information to the user (as shown in (e) in FIG. 4 ), and request the user to report the user's health risk score; The electronic device receives the third confirmation information input by the user (the user clicks the confirmation option as shown in (e) in Figure 4), that is, when the user agrees to report his health risk score, the electronic device can report the user's health risk score to the server. .

In some embodiments, the electronic device may display the first physiological sign information of the user at different time points; and/or, display the changing trend of the first physiological sign information over time (as shown in FIG. 5( a) shown in the figure).

In some embodiments, the electronic device may display the health risk scores of the user at different time points (as shown in (b) of FIG. 5 ); or, mark the health risks corresponding to different dates on the calendar interface level (as shown in (c) in Figure 5).

S904, report a health risk score to the server, where the health risk score is used to predict the epidemic risk in the first area, where the first area is the area where the user stayed in the preset historical time period, and the epidemic risk includes infectious disease confirmed personnel and suspected personnel total number of people at risk.

In some embodiments, after the electronic device obtains the authorization of the user (as shown in (e) of FIG. 4 ), the electronic device can report the health risk score to the server; the server can predict the health risk score according to the health risk score Outbreak risk in the first region.

The first area may be an area where the user has stayed within a preset historical time, and the preset historical time may be flexibly set in advance, such as a week, etc., which is not limited in this embodiment of the present application. In addition, the area where the user stays in the embodiment of the present application may refer to the area where the user stays longer than a second threshold, wherein the second threshold can be flexibly set according to actual conditions such as the transmission ability of infectious diseases and viruses, which is not done here. limited.

In some embodiments, the server may send the predicted epidemic risk of the first area to the electronic device, and the electronic device may prompt the user of the epidemic risk level of the first area. For example, the electronic device may display on the map page of the electronic device that the first area is an epidemic risk area (as shown in FIG. 8 ), and the map page may be marked with the epidemic risk level of the first area.

It should be understood that by prompting the user of the epidemic risk level of a specific area, it is convenient for the user to understand the epidemic risk area, reasonably adjust the travel route, and reduce the risk of being infected by an infectious disease.

In some embodiments, the server can use the risk analysis model of the infected population to predict the development trend of the epidemic situation in the first area, such as the prediction of the total number of people at risk (including the number of confirmed and suspected people), and based on the predicted total number of people at risk Assess the risk level of the area. Specifically, the server can combine historical risk populations, newly diagnosed populations, and risk populations reported for data analysis into seed risks; use the growth rate of the number of confirmed and suspected individuals (increased risk populations) as a diffusion coefficient to predict the total risk number of people. In addition, the calibration factor for the diffusion coefficient and the total number of people at risk can be calibrated with the user's confirmation rate.

FIG. 10 shows a schematic flowchart of epidemic risk prediction on the server side provided by an embodiment of the present application. The process shown in FIG. 10 may be executed by the server, and may specifically include the following steps:

S1001 , determining the diffusion coefficient of the infectious disease in the first region according to the health risk score.

The first area may be an area where the user has stayed within a preset historical time period. The diffusion coefficient of the infectious disease in the first region refers to the growth rate of the number of people at risk in that region. Among them, the number of people who are at increased risk is the number of users who have confirmed close contact with confirmed people through electronic devices.

It should be understood that in some existing risk population prediction processes, the growth rate of the risk population is directly used as a diffusion coefficient to predict the possible total risk population in the future. However, since different users may have different risk levels, if all users who are confirmed to be close contacts at the terminal are directly regarded as risk personnel, it will lead to a large error. Therefore, in the embodiment of the present application, the user is divided into risk levels by reporting the health risk score of the user, and the diffusion coefficient (growth rate) of each risk level is determined. Corrected to obtain the corrected total diffusion coefficient associated with the epidemic level.

For ease of understanding, the schematic diagram of the distribution of risk populations in the first region shown in FIG. 11 is taken as an example for a more intuitive description. In Fig. ₁₁ , _X3 is _a confirmed person, X1, X2, _X4 _- X6 are close contacts of _X3 , specifically, the close contact parameters _of X1 and _X3 (including the contact distance The contact time) is X _{(1, 3)} , the close contact parameter of X ₂ and X ₃ is X _{(2, 3)} , the close contact parameter of X ₄ and X ₃ is X _{(4, 3)} , X ₅ The close contact parameter with X ₃ is X _{(3, 5)} , and the close contact parameter between X ₆ and X ₃ is X _{(3, 6)} ; and different people have different health risk scores, such as X ₁ to X ₆ The health risk scores of are corresponding to h ₁ to h ₆ . Then, when the server calculates the diffusion coefficient according to the close contacts, it needs to combine the risk levels corresponding to the health risk scores of different close contacts (such as h ₁ to h ₆ ) to calculate the diffusion coefficient of each risk level and the weight of the risk level. Perform weighted calculations to obtain a more accurate diffusion coefficient, and then more accurately predict the total number of people at risk of future epidemics.

Specifically, after receiving the user's health risk score reported by the electronic device, the server determines the risk level corresponding to the user according to the score range to which the health risk score belongs, for example, when the user's health risk score belongs to the score range corresponding to the high risk level , it is determined that the user belongs to the high risk level; when the user's health risk score belongs to the score range corresponding to the medium risk level, it is determined that the user belongs to the medium risk level; when the user's health risk score belongs to the score or score range corresponding to the low risk level , it is determined that the user is at low risk; when the user's health risk score belongs to the score or score range corresponding to the risk-free level, it is determined that the user is at no risk.

In some embodiments, the server may preset weights corresponding to different risk levels. For example, as shown in Table 1, the weight corresponding to the high risk level is 100%, the weight corresponding to the medium risk level is 50%, and the weight corresponding to the low risk level is 100%. The weight is 20%, and the weight corresponding to the risk-free level is 0.

Table 1

风险等级Risk level	权重Weights
高风险 high risk	100％100%
中风险medium risk	50％50%
低风险 low risk	20％20%
无风险no risk	00

Then the corrected diffusion coefficient _rs can be calculated according to formula (1-3):

Corrected r _sp = r _{high risk} × 100% + r _medium × 50% + r _low × 20% + r _none × 0% (1-3)

Among them, r _{high risk} is the diffusion coefficient corresponding to high risk level; r _{medium risk} is the diffusion coefficient corresponding to medium risk level; r _{low risk} is the diffusion coefficient corresponding to low risk level; r _{no risk} is the diffusion coefficient corresponding to no risk level.

Wherein, the diffusion coefficient of each risk level can be calculated according to the new number of people at the risk level and the existing number of risk people at the risk level.

S1002, according to the diffusion coefficient and the number of confirmed and suspected persons in the first area, predict the total number of people at risk in the first area.

The server uses the risk analysis model of the infected population to predict the total number of people at risk of the epidemic in the first region.

The total number of people at risk in the first area can be calculated based on the confirmed people and risk groups and the revised diffusion coefficient. Among them, the formula for calculating the total number of people at risk is shown in (1-4). At a certain time point t, the total number of people at risk Nr(t) in the first area is composed of multiple personnel, including the current number of new risks Ncon(t) obtained by other means, and the new risks of electronic equipment risk analysis. The number of people N _b (t), the modified diffusion coefficient rs _sp of the first region, and the average electronic device confirmation rate _rsm of the first region. Among them, other methods in the current number of newly-increased risk persons obtained by other methods refer to other methods other than the method of reporting the health risk score through an electronic device.

In addition, the server can predict the number of new at-risk persons in the first area according to the diffusion coefficient, and draw a trend graph of the total number of at-risk persons (as shown in Figure 12).

In some embodiments, the server may send the predicted total number of people at risk to the electronic device, and the electronic device may display it to the user to prompt the development trend of the epidemic. The server can also send the change trend graph of the total number of people at risk to the electronic device, and the electronic device can display it to the user, so that the user can obtain the development trend of the epidemic more intuitively.

S1003: Determine the epidemic risk level of the first area according to the predicted total number of people at risk.

The server may determine the epidemic risk level of the first area according to the predicted total number of people at risk. For example, when the total number of people at risk is less than or equal to 10, the epidemic risk level of the corresponding first area is low risk; when the total number of people at risk is greater than 10 and less than or equal to 100, the corresponding epidemic risk level of the first area is medium Risk level; when the total number of people at risk is greater than 100, the corresponding epidemic risk level of the first area is a high risk level.

In some embodiments, the server can send the epidemic risk level to the electronic device; the electronic device can mark its risk level in the first area of the map page according to the epidemic risk level (as shown in FIG. 8 ), so that the user can more intuitively Get the epidemic situation in different regions.

Exemplarily, as shown in FIG. 13 , it is a schematic structural diagram of an electronic device provided in an embodiment of the present application. The electronic device 1300 includes a receiving module 1301 , a monitoring module 1302 , a processing module 1303 and a sending module 1304 .

Among them, the receiving module 1301 can be used to obtain the trajectory information of the person diagnosed with the infectious disease and the characteristic information of the infectious disease, where the trajectory information includes location information and time information.

The monitoring module 1302 can be used to monitor the physiological signs of the user when it is detected that the user is a close contact of the confirmed person according to the trajectory information of the user and the trajectory information of the confirmed person, and obtain all the information. monitoring data of the above-mentioned physiological signs.

The processing module 1303 may be configured to obtain the health risk score of the user according to the monitoring data and the characteristic information of the infectious disease.

The sending module 1304 can be used to report the health risk score to the server, and the health risk score is used for the server to predict the epidemic risk of the first area, where the first area is where the user stays in a preset historical time period The epidemic risk includes the total number of at-risk persons of the infectious disease confirmed and suspected persons.

In some embodiments, the electronic device 1300 may further include a display module, which may be configured to display first information, where the first information is used to query the user whether it is the close contact person, and the first information includes the Location and/or time information of close encounters.

The receiving module 1301 may also be configured to receive first confirmation information input by the user, where the first confirmation information is used to indicate that the user is the close contact person.

In some embodiments, the display module may be further configured to display second information, where the second information is used to request the user to perform physiological sign monitoring.

The receiving module 1301 may also be configured to receive second confirmation information input by a user, where the second confirmation information user indicates that the user agrees to the electronic device to monitor the physiological signs.

In some embodiments, the display module may be further configured to display first prompt information according to the health risk score, where the first prompt information is used to indicate to the user that there is a health risk.

In some embodiments, the display module may also be configured to display third information, where the third information is used to request the user to report the data of the monitoring of physiological signs.

The receiving module 1301 may also be configured to receive third confirmation information input by the user, where the third confirmation information is used to indicate that the user agrees to report the data of physiological sign monitoring.

In some embodiments, the display module may be further configured to display fourth information, where the fourth information is used to prompt the user of the epidemic risk level and/or the total number of persons at risk in the first area.

In some embodiments, the processing module may be further configured to acquire at least one first physiological sign according to the infectious disease characteristic information, where the first physiological sign is a characteristic related to an infectious disease; acquire the at least one first physiological sign of the user Monitoring data of physical sign information; weight the monitoring data according to the preset weight, and calculate the health risk score of the user.

In some embodiments, the display module may also be configured to display monitoring data of the first physiological sign corresponding to different time points; and/or, display a change trend of the monitoring data of the first physiological sign over time.

In some embodiments, the display module may also be configured to display a map page, where the epidemic risk level of the first area is marked on the map page.

In some embodiments, the processing module 1303 can also be used to determine the contact distance between the user and the confirmed person according to the user's trajectory information and the confirmed person's trajectory information; when the contact distance is less than the transmission distance, and the contact time between the user and the confirmed person is greater than At the first threshold, it is determined that the user is a close contact of the confirmed person.

In some embodiments, the processing module 1303 may also be configured to determine that the user is a close contact of the confirmed person when it is determined according to the location identifier that the user and the confirmed person are in the first confined space at the same time.

The location may include at least one of the following: train number and/or compartment number, room identification or elevator identification; the first enclosed space includes at least one of the following: a train compartment, a room, or an elevator car.

In addition, the characteristic information of the infectious disease in the embodiment of the present application includes at least one of the following: the transmission route of the infectious disease virus, the transmission distance of the infectious disease virus, the incubation period of the infectious disease, and the physiological signs related to the infectious disease. Physiological signs include at least one of the following: body temperature, heart rate, RRI during one heartbeat, blood oxygen saturation Spo2, and sleep characteristics.

Exemplarily, as shown in FIG. 14 , it is a schematic structural diagram of a server provided by an embodiment of the present application. The server 1400 includes a sending module 1401 , a receiving module 1402 and a processing module 1403 .

In some embodiments, the sending module 1401 may be configured to send trajectory information of a person diagnosed with an infectious disease and feature information of an infectious disease to the electronic device, where the trajectory information includes location information and time information.

The receiving module 1402 may be configured to receive the health risk score of the user sent by the electronic device, and the user is a person who is confirmed to be a close contact of the confirmed person.

The processing module 1403 can be used to predict the epidemic risk of the first area according to the health risk score, the first area is the area where the user has stayed in the preset historical time period, and the epidemic risk includes the total number of infectious disease confirmed personnel and suspected personnel. Number of people at risk.

In some embodiments, the processing module 1403 can also be used to determine the diffusion coefficient of the infectious disease in the first area according to the risk score; predict the total number of people at risk in the first area according to the diffusion coefficient and the existing number of people at risk and the diffusion coefficient in the first area. Number of people at risk; determine the epidemic risk level of the first area according to the total number of people on the sub-line.

In some embodiments, the processing module 1403 may be further configured to determine diffusion coefficients corresponding to multiple risk levels, where the diffusion coefficients are used to indicate the growth rate of the number of people corresponding to the risk levels, and the risk levels are used to indicate is the probability of a person diagnosed with the infectious disease; according to the preset weights corresponding to the plurality of diffusion coefficients, the plurality of diffusion coefficients are weighted to determine the diffusion coefficient of the infectious disease in the first region.

In some embodiments, the processing module 1403 can also be used to calculate the total number of people at risk according to the following formula:

Among them, t is time, N _r (t) is the total number of people at risk at time point t, rs _sm is the average confirmation rate of users in the first area confirming that they are close contacts, and rs _sp is the first The diffusion coefficient of infectious diseases in a region, N _con is the current newly diagnosed person known from the outside, and N _b is the newly added risk number determined according to the data sent by the electronic device.

Embodiments of the present application also provide a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the computer or processor is run on a computer or a processor, the computer or the processor is made to execute any one of the above methods. or multiple steps.

Embodiments of the present application also provide a computer program product including instructions. The computer program product, when run on a computer or processor, causes the computer or processor to perform one or more steps of any of the above methods.

In the above-mentioned embodiments, it may be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented in software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general purpose computer, special purpose computer, computer network, or other programmable device. The computer instructions may be stored in or transmitted over a computer-readable storage medium. The computer instructions can be sent from one website site, computer, server, or data center to another website site, computer, server or data center for transmission. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, a data center, or the like that includes an integration of one or more available media. The usable media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVDs), or semiconductor media (eg, solid state disks (SSDs)), and the like.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented. The process can be completed by instructing the relevant hardware by a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed , which may include the processes of the foregoing method embodiments. The aforementioned storage medium includes: ROM or random storage memory RAM, magnetic disk or optical disk and other mediums that can store program codes.

The above are only specific implementations of the embodiments of the present application, but the protection scope of the embodiments of the present application is not limited thereto, and any changes or substitutions within the technical scope disclosed in the embodiments of the present application shall be covered by this within the protection scope of the application examples. Therefore, the protection scope of the embodiments of the present application should be subject to the protection scope of the claims.

Claims

A method for epidemic prediction, characterized in that, applied to electronic equipment, comprising:

Obtaining trajectory information of a person diagnosed with an infectious disease and feature information of the infectious disease, where the trajectory information includes location information and time information corresponding to the location information;

According to the trajectory information of the user and the trajectory information of the confirmed person, when it is detected that the user is a close contact of the confirmed person, acquiring monitoring data of the physiological signs of the user;

Determine the health risk score of the user according to the monitoring data and the characteristic information of the infectious disease;

The health risk score is reported to the server, and the health risk score is used to predict the epidemic risk in the first area, where the first area is the area where the user stayed in the preset historical time period, and the epidemic risk includes all The total number of people at risk of confirmed and suspected infectious diseases.
The method according to claim 1, wherein, when it is detected that the user is a close contact of the confirmed person according to the trajectory information of the user and the trajectory information of the confirmed person, the method Also includes:

Displaying first information, where the first information is used to inquire whether the user is the close contact person, the first information includes location information and/or time information of the close contact.
The method according to claim 1 or 2, wherein the method further comprises:

displaying second information, where the second information is used to request the user for physiological sign monitoring;

Second confirmation information input by a user is received, where the second confirmation information is used to indicate that the user agrees to the electronic device to perform the physiological sign monitoring.
The method according to claim 3, wherein the method further comprises:

displaying third information, where the third information is used to request the user to report the monitoring data of the physiological sign;

Third confirmation information input by the user is received, where the third confirmation information is used to indicate agreement to report the data of the physiological sign monitoring.
The method according to any one of claims 1-4, wherein the method further comprises:

receiving first indication information sent by the server, where the first indication information is used to indicate the epidemic risk level of the first area and/or the total number of people at risk in the first area;

Fourth information is displayed, where the fourth information is used to prompt the user of the epidemic risk level of the first area and/or the total number of people at risk in the first area.
The method according to any one of claims 1-5, wherein the calculating the user's health risk score according to the physiological sign information and the characteristic information of the infectious disease further comprises:

Obtain at least one first physiological sign according to the characteristic information of the infectious disease, where the first physiological sign is a characteristic related to the infectious disease;

acquiring monitoring data of the at least one first physiological sign of the user, each of the first physiological signs having a corresponding first preset weight;

Weighted calculation is performed according to the monitoring data of the at least one first physiological sign and the corresponding first preset weight, so as to obtain the health risk score of the user.
The method according to any one of claims 1-6, wherein the method further comprises:

Display the monitoring data of the first physiological sign corresponding to different time points; and/or,

The trend of the monitoring data of the first physiological sign over time is displayed.
The method according to any one of claims 1-7, wherein the method further comprises:

A map page is displayed, and the map page is marked with the epidemic risk level of the first area.
The method according to any one of claims 1 to 8, wherein the characteristic information of the infectious disease includes a transmission distance of the infectious disease, and the trajectory information according to the user and the trajectory information of the diagnosed person , determine that the user is a close contact of the confirmed person, including:

Determine the contact distance between the user and the confirmed person according to the trajectory information of the user and the confirmed person;

When the contact distance is less than the transmission distance, and the contact time between the user and the confirmed person is greater than a first threshold, it is determined that the user is a close contact of the confirmed person.
The method according to any one of claims 1-9, wherein the trajectory information of the diagnosed person and the user's trajectory information further includes a location identifier, and the method further includes:

When it is determined according to the location identifier that the user and the confirmed person are in the first confined space at the same time, it is determined that the user is a close contact of the confirmed person.
The method according to claim 10, wherein the location identifier comprises at least one of the following:

Train number and/or car number, room identification or elevator identification;

The first closed space includes at least one of the following:

A train car, room, or elevator car.
The method according to any one of claims 1-11, wherein the characteristic information of the infectious disease comprises at least one of the following:

The transmission route of the infectious disease virus, the transmission distance of the infectious disease virus, the incubation period of the infectious disease, and the physiological signs related to the infectious disease.
The method according to any one of claims 1-12, wherein the physiological signs include at least one of the following:

Body temperature, heart rate, RRI during one heartbeat, electrocardiogram, blood oxygen saturation Spo2, sleep characteristics.
A method for epidemic prediction, characterized in that, applied to a server, comprising:

Sending the trajectory information of the person diagnosed with the infectious disease and the characteristic information of the infectious disease to the electronic device, where the trajectory information includes location information and time information;

receiving a health risk score of a user sent by the electronic device, where the user is a person confirmed to be a close contact of the confirmed person;

Predict the epidemic risk of a first area according to the health risk score, where the first area is an area where the user has stayed in a preset historical time period, and the epidemic risk includes the total number of confirmed and suspected infectious disease personnel. Number of people at risk.
The method according to claim 14, wherein the server is preset with a plurality of risk levels, each of the risk levels has a corresponding health risk score interval, and the method further comprises:

Determine the risk level corresponding to the user according to the health risk score interval to which the user's health risk score belongs, and each of the risk levels has a corresponding second preset weight;

According to the number of newly-increased risk numbers corresponding to the multiple risk levels, respectively, determining the diffusion coefficients corresponding to the multiple risk levels;

Perform weighted calculation on the diffusion coefficients of the multiple risk levels and the corresponding second preset weights to obtain a revised diffusion coefficient;

The total risk population in the first region is predicted according to the modified diffusion coefficient and the existing risk population in the first region.
The method according to claim 14 or 15, wherein the method further comprises:

Determine the epidemic risk level of the first area according to the total number of people at risk;

Send first indication information to the electronic device, where the first indication information is used to indicate the epidemic risk level of the first area and/or the total number of people at risk in the first area.
The method according to any one of claims 14 to 16, wherein the total risk population comprises:

Where, t is time, N r (t) is the total number of people at risk at time point t, rs sm is the average confirmation rate of the user confirming that he is a close contact through the electronic device, and rs sp is the corrected diffusion coefficient, N con is the current new risk personnel obtained by other means, and N b is the newly increased risk personnel determined according to the data sent by the electronic device.
An electronic device, characterized by comprising a communication interface, a processor, a memory, and a computer program stored in the memory and executable on the processor, when the processor executes the computer program, The electronic device is made to implement the method for epidemic prediction according to any one of claims 1 to 13.
A server, characterized in that it includes a communication interface, a processor, a memory, and a computer program stored in the memory and executable on the processor, when the processor executes the computer program, causing The server implements the method for epidemic prediction according to any one of claims 14 to 17.
A computer-readable storage medium, characterized by comprising computer instructions, which, when the computer instructions are executed on a computer, cause the computer to execute the method for epidemic prediction according to any one of claims 1 to 17.
A computer product, characterized in that, when the computer product runs on a computer, the computer is made to execute the method for epidemic prediction according to any one of claims 1 to 17.