WO2022142685A1

WO2022142685A1 - Infection probability prediction method and apparatus for infectious disease, storage medium and electronic device

Info

Publication number: WO2022142685A1
Application number: PCT/CN2021/127548
Authority: WO
Inventors: 赖昆
Original assignee: 医渡云（北京）技术有限公司
Priority date: 2020-12-29
Filing date: 2021-10-29
Publication date: 2022-07-07
Also published as: CN112712902A; CN112712902B

Abstract

An infection probability prediction method and apparatus for an infectious disease, a storage medium, and an electronic device, which relate to the technical field of big data processing. The method comprises: obtaining a target device identification of an infectious disease patient, and obtaining a device identification to be predicted that has an associated relationship with the target device identification from within a preset database according to the target device identification and a preset time period (S110); according to the number of associations between the target device identification and the device identification to be predicted, determining a contact duration between the infectious disease patient and a subject to be predicted corresponding to the device identification to be predicted (S120); according to a current signal strength between a first wireless communication apparatus corresponding to the target device identification and a second wireless communication apparatus corresponding to the device identification to be predicted, determining a contact distance between the infectious disease patient and the subject (S130); and according to the contact duration and the contact distance, determining the probability of infection of the infectious disease patient to the subject (S140). The described method improves the accuracy of the probability of infection.

Description

Infection probability prediction method and device, storage medium and electronic device of infectious disease

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority of the Chinese patent application filed on December 29, 2020 with the application number 202011591936.8 and titled “Method and Device for Predicting Infection Probability of Infectious Diseases, Storage Medium, and Electronic Equipment”, all of which are in the Chinese patent application. The contents are incorporated herein by reference in their entirety.

technical field

The embodiments of the present disclosure relate to the technical field of big data processing, and in particular, to a method for predicting the probability of infection of an infectious disease, an apparatus for predicting the probability of infection of an infectious disease, a computer-readable storage medium, and an electronic device.

Background technique

In the study of epidemic transmission, analyzing the spatiotemporal connection between people is a very important analysis method. Therefore, early detection of the virus carrier and timely isolation of it can prevent him from spreading to others, thereby effectively preventing the large-scale spread of the epidemic.

In the context of the epidemic, due to the need for traceability and control of the epidemic, it is necessary to trace the close contacts of confirmed patients. In the prior art, most of them are tracked by means of paper records, camera records, etc. according to the time and place of the incident through the recollections of the diagnosed patients. Probability of contagion to other groups of people who may have been in contact with confirmed patients were analyzed.

SUMMARY OF THE INVENTION

According to one aspect of the present disclosure, there is provided a method for predicting the probability of contagion of an infectious disease, comprising:

Acquiring the target device identification of the infectious disease patient, and according to the target device identification and a preset time period, acquiring the to-be-predicted device identification that is associated with the target device identification from a preset database;

Determine, according to the number of associations between the target device identifier and the to-be-predicted device identifier, the contact duration between the infectious disease patient and the to-be-predicted object corresponding to the to-be-predicted device identifier;

Determine the contact between the infectious disease patient and the object to be predicted according to the current signal strength between the first wireless communication device corresponding to the target device identification and the second wireless communication device corresponding to the device identification to be predicted distance;

According to the contact duration and contact distance, the infection probability of the infectious disease patient to the object to be predicted is determined.

According to one aspect of the present disclosure, there is provided an infectious disease probability prediction device, comprising:

an acquisition module, configured to acquire the target device identification of the infectious disease patient, and obtain the to-be-predicted device identification associated with the target device identification from a preset database according to the target device identification and a preset time period;

The first calculation module is used to determine the contact time between the infectious disease patient and the to-be-predicted object corresponding to the to-be-predicted device identifier according to the number of associations between the target device identifier and the to-be-predicted device identifier ;

The second calculation module is configured to determine the relationship between the infectious disease patient and the The contact distance between the objects to be predicted;

An infection probability prediction module, configured to determine the infection probability of the infectious disease patient to the to-be-predicted object according to the contact duration and contact distance.

According to one aspect of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements any one of the above-mentioned methods for predicting the infection probability of an infectious disease.

According to one aspect of the present disclosure, there is provided an electronic device, comprising:

processor; and

a memory for storing executable instructions for the processor;

Wherein, the processor is configured to execute any one of the above-mentioned methods for predicting the probability of contagion of an infectious disease by executing the executable instructions.

It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description serve to explain the principles of the disclosure. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and for those of ordinary skill in the art, other drawings can also be obtained from these drawings without creative effort. In the attached image:

FIG. 1 schematically shows a flowchart of a method for predicting the probability of contagion of an infectious disease according to an exemplary embodiment of the present disclosure.

FIG. 2 schematically shows a block diagram of an infectious disease probability prediction system according to an exemplary embodiment of the present disclosure.

FIG. 3 schematically shows a method of obtaining, according to an exemplary embodiment of the present disclosure, an identifier of a device to be predicted that has an associated relationship with the identifier of the target device from a preset database according to the identifier of the target device and a preset time period. Method flow diagram.

FIG. 4 schematically shows a method for determining the infectious disease patient and the to-be-predicted corresponding to the to-be-predicted device identifier according to the number of associations between the target device identifier and the to-be-predicted device identifier according to an exemplary embodiment of the present disclosure. A flow chart of the method for the duration of contact between objects.

FIG. 5 schematically shows a flowchart of another method for predicting the probability of contagion of an infectious disease according to an exemplary embodiment of the present disclosure.

FIG. 6 schematically shows a block diagram of an apparatus for predicting the probability of contagion of an infectious disease according to an exemplary embodiment of the present disclosure.

FIG. 7 schematically shows an electronic device for implementing the above-mentioned method for predicting the probability of contagion of an infectious disease according to an exemplary embodiment of the present disclosure.

Detailed ways

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments, however, can be embodied in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated descriptions will be omitted. Some of the block diagrams shown in the figures are functional entities that do not necessarily necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and/or processor devices and/or microcontroller devices.

This exemplary embodiment first provides a method for predicting the probability of contagion of an infectious disease, and the method can run on a server, a server cluster, or a cloud server, etc.; of course, those skilled in the art can also run the method of the present disclosure on other platforms as required , which is not specially limited in this exemplary embodiment. Referring to Figure 1, the method for predicting the probability of contagion of an infectious disease may include the following steps:

Step S110. Obtain the target device identification of the infectious disease patient, and obtain the to-be-predicted device identification that is associated with the target device identification from a preset database according to the target device identification and a preset time period;

Step S120. Determine the contact duration between the infectious disease patient and the object to be predicted corresponding to the device identifier to be predicted according to the number of associations between the target device identifier and the device identifier to be predicted;

Step S130. According to the current signal strength between the first wireless communication device corresponding to the target device identification and the second wireless communication device corresponding to the device identification to be predicted, determine the relationship between the infectious disease patient and the object to be predicted. contact distance between

Step S140. According to the contact duration and contact distance, determine the infection probability of the infectious disease patient to the object to be predicted.

In the above-mentioned method for predicting the probability of contagion of infectious diseases, on the one hand, by obtaining the target device identification of the infectious disease patient, and according to the target device identification and the preset time period, obtain from the preset database the identification of the target device associated with the target device identification. The identification of the device to be predicted; further, other groups of people who may have released the diagnosed patient can be obtained according to the identification of the device to be predicted, which solves the problem of tracking efficiency caused by the inability to timely confirm other groups of people who may have released the confirmed patient in the prior art. On the other hand, it solves the problem of low coverage in the prior art due to the inability to accurately find all other groups of people who may have been in contact with confirmed patients; The number of associations with the identification of the equipment to be predicted, to determine the contact time between the infectious disease patient and the object to be predicted corresponding to the identification of the equipment to be predicted; and according to the first wireless communication device corresponding to the identification of the target equipment corresponds to the identification of the equipment to be predicted The current signal strength between the second wireless communication devices is determined to determine the contact distance between the infectious disease patient and the object to be predicted; finally, according to the contact duration and contact distance, the infection probability of the infectious disease patient to the predicted object is determined, which solves the problem of existing Due to the inability to scientifically analyze the infection probability of other groups of people who may have been in contact with the confirmed patient in the technology, the accuracy of the infection probability is lower, and the accuracy of the infection probability is improved.

Hereinafter, each step involved in the method for predicting the probability of contagion of an infectious disease according to an exemplary embodiment of the present disclosure will be explained and described in detail with reference to the accompanying drawings.

First, the proper terms involved in the exemplary embodiments of the present disclosure are explained and explained.

BLE (Bluetooth Low Energy): Bluetooth Low Energy.

Service UUID (Universally Unique Identifier): Universal Unique Identifier, which is a service distinguishing code used in Bluetooth broadcasting, with a length of 16 bits, generally expressed as 4 hexadecimal characters, such as "FE35".

Close Contact: Abbreviation for close contact. There may be different definitions for close contact in different scenarios. For example, a person who has a history of contact with a confirmed patient within 2 meters and for 15 minutes or more during life, work and other activities during the infection period. close contacts, or close contacts.

API (Application Programming Interface): Application programming interface.

RSSI (Received Signal Strength Indication): Received Signal Strength Indication. This value is generally used in Bluetooth development to approximate the distance between devices.

ElasticSearch: A search engine based on the Lucene library. It provides a distributed, multi-tenant-enabled full-text search engine with an HTTP web interface and schemaless JSON documents.

Next, the purpose of the invention of the exemplary embodiments of the present disclosure is explained and illustrated.

The implementation of the exemplary embodiments of the present disclosure relies on the low-power bluetooth module that comes with the mobile phone. Currently, most smart phones on the market have built-in low-power bluetooth modules, which can fully support the implementation of this solution. Specifically, the example embodiment of the present disclosure uses the low-power Bluetooth module built in the mobile phone to automatically and closely track the crowd, and has the characteristics of automatic tracking without perception, high coverage, and almost no labor cost input. Health regulators can quickly locate relevant contacts.

Further, the infection probability prediction system of the infectious disease involved in the exemplary embodiment of the present disclosure will be explained and explained.

Specifically, as shown in FIG. 2 , the infectious disease probability prediction system may include a terminal device 210 and a server 220 , and the terminal device 210 is connected to the server 220 via a network. The server includes a Bluetooth close connection library (preset database) 221 , a service information library 222 and an analysis system 223 .

Specifically, the terminal device 210 is provided with an application program (App), which is responsible for collecting and reporting Bluetooth close connection data, the database is responsible for receiving and storing the Bluetooth close connection data uploaded by the terminal device, and the analysis system is responsible for data query and analysis, and output infection probability. The following are explained one by one:

First of all, for the terminal device, the App set in the terminal device is the core part of collecting and reporting Bluetooth close data. At present, the mainstream smartphone iOS and Android systems on the market provide a complete Bluetooth Low Energy (BLE) API. in:

BLE API can include broadcast side and scan side. In the close tracking scenario, the mobile phone acts as both the broadcast terminal and the scanning terminal. The user 230 needs to install an App on the terminal device to manage the Bluetooth close tracking function, including starting and stopping the Bluetooth close tracking function, authorizing the Bluetooth function, and reminding the occurrence of close contact events.

Further, in order to realize Bluetooth close tracking, it is necessary to set a unique Bluetooth ID (hereinafter referred to as BID) for each mobile phone (which can also be understood as an App account installed on each mobile phone). Because the Bluetooth MAC address of a personal mobile phone belongs to the private data of the device, mainstream mobile operating systems, such as iOS and Android, generally do not provide APIs to directly obtain the Bluetooth MAC address of the mobile phone (the one that can be obtained is also a dynamically changing virtual address) , the mobile phone Bluetooth MAC address is not suitable for use as a unique BID. Based on this, the BID can be generated through an algorithm using the user's information. For example, jointly use the user's ID number, name, birthday and random number to perform SHA-256 calculation and cropping. Of course, other algorithms that can support hash value calculation can also be used to calculate the user's ID number, name, The birthday and random number are calculated, and then the calculated hash value is cut to generate a BID, which is not limited in this example.

After each mobile phone is assigned a unique BID, the BID is broadcasted by BLE on the broadcast side. When broadcasting, a specific Service UUID is specified, which is used to indicate that the BID is included in the broadcast. In addition to carrying the BID in the broadcast data, it is also possible to rewrite the Bluetooth broadcast name in the broadcast name with a specific naming rule, and write the BID into it (for example, rewrite the broadcast Bluetooth name to BID=1234567890123456).

At the same time as the broadcast, the mobile phone starts BLE scanning, and brings in a specific Service UUID for filtering (filtering out broadcasts that do not contain BID information). BLE scanning is generally very fast. In 1-2 seconds, hundreds of devices that are broadcasting around can be scanned, and the Bluetooth name used by the device broadcast can be obtained. If the advertised Bluetooth name conforms to a specific naming rule (eg "BID="+sixteen characters), the sixteen characters are directly used as the target BID for close recording. If the bluetooth name does not conform to the naming rules, connect the device with bluetooth, and after the connection is successful, the target BID can be read from the broadcast data. When reading the BID value, the RSSI value of the strength of the Bluetooth signal can also be obtained at the same time, and the RSSI value can be converted into an estimated distance between devices through an empirical formula, thereby estimating the distance between two people.

Further, after collecting certain close data (BID, RSSI, etc.), the terminal device can upload the data to the database for storage. It should be added that, because the current smart phones on the market generally have a power-saving design, after the mobile app enters the background, the program operation will be suspended, resulting in the scanning cannot be performed normally. When developing an app, additional processing should be done in the background mode to ensure that the scanning process and uploading process can still run normally after the app returns to the background.

Secondly, for the database, it is mainly responsible for the reception and storage of Bluetooth close data. There are no restrictions on the communication protocols of the App in the server and terminal devices, and can be based on various protocols, but the data must be encrypted and transmitted during the transmission process (the specific encryption algorithm can be asymmetric encryption, or other encryption algorithms can be used. This does not make special restrictions) to prevent privacy data leakage. In addition, the Bluetooth data needs to be separately stored in a preset database (ie, the Bluetooth close connection library), which is used to distinguish different authorizations from ordinary service data, which is stored in the service information database. In addition, since the BID that Bluetooth contact relies on is calculated through an algorithm, its business meaning cannot be directly obtained from the BID itself. Therefore, it is impossible to obtain the contact information of a specific person based on the BID alone, which also guarantees the user's privacy data. will not leak.

At the same time, it should be added that the Bluetooth close connection app is generally promoted in a region or even a country, and in order to ensure the accuracy rate, it will generally be forced to be promoted by the government health department, and the installation rate and usage rate will be very high. Under normal circumstances, a large amount of Bluetooth connection information is generated every day, and the concurrent request processing capability and data storage capability of the server need to be carefully designed. Therefore, the database needs to be deployed on the cloud platform to support dynamic expansion, and a simulated stress test should be done before going online. Data storage needs to be divided into query data for online use and raw data. The query data is used by the analysis system for query and analysis, and the raw data is permanently retained on the big data platform for backup.

Finally, for the analysis system, it can implement the method for predicting the probability of contagion of infectious diseases described in the exemplary embodiment of the present disclosure. At the same time, the analysis system is real-time interactive, requiring fast response of data analysis results, which can be built based on the search framework of ElasticSearch.

Hereinafter, steps S110 to S140 will be explained and described with reference to FIG. 2 .

In step S110, the target device identifier of the infectious disease patient is acquired, and according to the target device identifier and a preset time period, the device identifier to be predicted that is associated with the target device identifier is acquired from a preset database.

First, the target device identification is explained and explained. Based on the content recorded above, it can be known that the target device identifier is obtained by the terminal device in the following manner: first, perform a hash operation on the attribute information of the infectious disease patient to obtain a hash value; The hash value is trimmed to obtain a hash value with a preset length, and the hash value with the preset length is used as the target device identifier. Specifically, first, the attribute information (ID number, name, birthday, etc., of course, mobile phone number and other information) of infectious disease patients (users) can be hashed to obtain the hash value. The hash value can be obtained through the SHA-256 algorithm or other algorithms. This example does not impose special restrictions on this; it should be added that in the calculation process of the hash value, the attribute information can also be obtained based on the attribute information. Add a random number. Then, trim the hash value to obtain a hash value with a preset length; the specific trimming rule is: trimming from the middle part of the hash value, removing the head and tail of the hash value, and then obtaining a hash value with a predetermined length. Set the length of the hash value. Through this method, the security of the target device identification can be further improved, thereby improving the security of the data of infectious disease patients.

It should be further explained here that the device identifiers of all users can be calculated by the above method. This is just for the convenience of description, so it is defined as the target device identifier, and there is no other restriction.

Further, in this exemplary embodiment, after a certain user is diagnosed as a patient with an infectious disease, the target device identification of the infectious disease patient can be obtained, and according to the target device identification and a preset time period, from the preset The to-be-predicted device identification that has an associated relationship with the device identification is obtained from the database of . Specifically, referring to FIG. 3 , according to the target device identifier and the preset time period, acquiring the device identifier to be predicted that has an associated relationship with the device identifier from the preset database may include steps S310 to S330 . in:

In step S310, a retrieval condition is generated according to the target device identifier and a preset time period, and a time interval corresponding to the retrieval condition is determined from the preset database;

In step S320, a target index tree corresponding to the retrieval condition is constructed according to the time interval;

In step S330, the target index tree is searched layer by layer, so as to obtain an index satisfying the retrieval condition from the leaf node of the target index tree, and an identifier of a device to be predicted associated with the index.

Hereinafter, steps S310 to S330 will be explained and described. Specifically, due to the huge amount of data uploaded by the App of the terminal device, it is difficult for the traditional database to perform fast query. Therefore, through a distributed database (such as HBase), massive data can be stored in the form of key-value on a distributed cluster with multiple backups and security, that is, the above-mentioned preset database.

Specifically, first, the retrieval condition is generated according to the target device identifier and a preset time period, wherein the preset time period can be determined according to the time of diagnosis of the infectious disease patient; for example, it can be two weeks before the time of diagnosis, or It can be other time, which is not limited in this example. Secondly, when the retrieval condition is obtained, the time interval corresponding to the retrieval condition can be determined from the above-mentioned preset database; further, after the time interval is obtained, each day can be used as a leaf node based on the time interval. Build a target index tree corresponding to the retrieval conditions; finally, perform a layer-by-layer search (day-by-day search) on the target index tree, and then obtain an index (target device identifier) that satisfies the retrieval condition from the leaf node of the target index tree, and the index corresponding to the index The associated device ID to be predicted. Through this method, the search efficiency can be improved, thereby improving the prediction efficiency of the infection probability.

In step S120, the contact duration between the infectious disease patient and the object to be predicted corresponding to the device identification to be predicted is determined according to the number of associations between the target device identification and the device identification to be predicted.

In this exemplary embodiment, referring to FIG. 4 , according to the number of associations between the target device identifier and the to-be-predicted device identifier, the relationship between the infectious disease patient and the to-be-predicted object corresponding to the to-be-predicted device identifier is determined. The contact duration between the two may include steps S410-S420. in:

In step S410, calculate the number of associations according to the number of times that the identifier of the device to be predicted and the identifier of the target device appear simultaneously in the leaf node;

In step S420, calculate the contact between the infectious disease patient and the to-be-predicted object corresponding to the to-be-predicted device identifier according to the time interval between the association times and the plurality of data included in the leaf node duration.

Hereinafter, steps S410 to S420 will be explained and described. For example, assuming that the device identifier to be predicted and the target device identifier appear ten times in the same leaf node at the same time, the number of associations is 10; and, assuming that the time interval is 1 min, then the relationship between the infectious disease patient and the object to be predicted is 10. The contact time is 10min. Of course, the time interval here can also be selected according to actual needs, such as 30s or 90s, etc., which is not limited in this example.

What needs to be added here is that in order to obtain the above-mentioned association times, the corresponding data needs to be obtained first, and the data can be obtained in the following ways:

First, acquire the name information of one or more broadcasting devices scanned by the first wireless communication device; secondly, when judging that the name information conforms to the preset naming rule, take the broadcasting device conforming to the preset naming rule as the first two wireless communication devices; then, extract the device identification to be predicted from the name information corresponding to the second wireless communication device; finally, according to the target device identification, the device identification to be predicted, the first wireless communication device The data is generated from the signal strength between the communication device and the second wireless communication device and the current location of the first terminal device.

For example, the terminal device starts BLE scanning, and obtains the scanned service UUID of one or more broadcast devices (Bluetooth devices); then, it is judged whether the Service UUID complies with the preset naming rules, that is, whether it includes BID information, if not. Include, then filter out; if included, then use it as the second wireless communication device; then, extract BID (sixteen characters) from the Service UUID as the device identification to be predicted; Finally, according to the target device identification, the device identification to be predicted , signal strength, and current location to generate data. What needs to be added here is that if the Bluetooth name does not conform to the naming rules, the device is connected to Bluetooth, and after the connection is successful, the identification of the device to be predicted can be read from the broadcast data.

Further, in order to further improve the tracking efficiency, the method for predicting the infection probability of the infectious disease further includes: calculating the infection path of the infectious disease patient according to each of the current positions. That is to say, the infection path of the infectious disease patient can be calculated according to the current position included in the data continuously uploaded by the App of the terminal device of the infectious disease patient, and then the population who may have been in contact with the infectious disease patient can be tracked according to the infection path, Avoid tracking failures due to lack of bluetooth data.

In step S130, according to the current signal strength between the first wireless communication device corresponding to the target device identification and the second wireless communication device corresponding to the to-be-predicted device identification, determine the infectious disease patient and the to-be-predicted device identification Contact distance between objects.

Specifically, it may include: according to the current signal strength between the first wireless communication device corresponding to the target device identification and the second wireless communication device corresponding to the to-be-predicted device identification, the first wireless communication device and the second wireless communication device The contact distance between the infectious disease patient and the object to be predicted is determined based on the standard signal strength of the wireless communication device at a preset distance and a preset environmental attenuation factor. The specific calculation method can refer to the following formula (1):

d=10∧[(ABS(RSSI)-A)/(10*n)]; Formula (1)

Among them, d is the contact distance, RSSI is the signal strength value, A is the absolute value of RSSI when the distance between the first wireless communication device (Bluetooth) and the second wireless device (Bluetooth) is 1 meter, and n is the environmental attenuation factor.

In step S140, according to the contact duration and contact distance, the infection probability of the infectious disease patient to the object to be predicted is determined.

In this exemplary embodiment, the infection probability can be determined after the above-mentioned contact duration and contact distance are obtained. For example, a distance of less than 2 meters and a duration of 15 minutes or more is a high-risk close connection, that is, the probability of infection is high. The specific calculation rules between the infection probability and the contact time and contact distance can be calculated according to the specific infectious disease, and there are no special restrictions on this here. Further, when the probability of infection is obtained, relevant personnel can take corresponding measures according to the probability of infection to the object to be predicted. For example, for the object to be predicted with a high probability of infection, medical isolation can be used; The objects to be predicted can be isolated by ordinary isolation, which can avoid the problem of excessive infection and waste of medical resources.

Hereinafter, the method for predicting the probability of contagion of an infectious disease according to the exemplary embodiment of the present disclosure will be further explained and described in conjunction with 5. Referring to Figure 5, the method for predicting the probability of contagion of an infectious disease may include the following steps:

Step S510, according to the target Bluetooth ID where the infectious disease patient is located and the latent date of the infectious disease, obtain the to-be-predicted Bluetooth ID associated with the target Bluetooth ID from the Bluetooth proximity library;

Step S520, according to the association times between the target bluetooth identifier and the to-be-predicted bluetooth identifier, calculate the contact duration between the infectious disease patient and the to-be-predicted object corresponding to the to-be-predicted bluetooth identifier;

Step S530, according to the current signal strength between the first Bluetooth corresponding to the target Bluetooth identification and the second Bluetooth corresponding to the Bluetooth identification to be predicted, calculate the contact distance between the infectious disease patient and the object to be predicted;

Step S540, according to the contact duration and contact distance, calculate the infection probability that the infectious disease patient treats the predicted object;

Step S550, take corresponding measures according to the size of the infection probability for the object to be predicted. For example, for the object to be predicted with a high probability of infection, medical isolation can be used; for the object to be predicted with a small probability of infection, ordinary isolation can be used. .

The method for predicting the probability of contagion of an infectious disease provided by the exemplary embodiment of the present disclosure can efficiently locate the object to be predicted, and save a lot of time for manual investigation. The method can be applied not only to the prediction of the probability of infection of infectious diseases, but also to the final scenarios involving close contact.

The present disclosure also provides an infection probability prediction device for infectious diseases. Referring to FIG. 6 , the apparatus for predicting the probability of infection of an infectious disease may include an acquisition module 610 , a first determination module 620 , a second determination module 630 , and a prediction module 640 for the probability of infection. in:

The obtaining module 610 is used to obtain the target device identification of the infectious disease patient, and according to the target device identification and a preset time period, obtain the device identification to be predicted that has an associated relationship with the device identification from a preset database;

The first determining module 620 is configured to determine the contact time between the infectious disease patient and the object to be predicted corresponding to the device identification to be predicted according to the number of associations between the target device identification and the device identification to be predicted. ;

The second determination module 630 is configured to determine, according to the current signal strength between the first wireless communication device corresponding to the target device identification and the second wireless communication device corresponding to the device identification to be predicted, the The contact distance between the objects to be predicted;

The infection probability prediction module 640 is configured to determine the infection probability of the infectious disease patient to the object to be predicted according to the contact duration and contact distance.

In an exemplary embodiment of the present disclosure, according to the target device identifier and a preset time period, acquiring from a preset database the device identifier to be predicted that has an associated relationship with the target device identifier, including:

generating a retrieval condition according to the target device identifier and a preset time period, and determining a time interval corresponding to the retrieval condition from the preset database;

According to the time interval, construct the target index tree corresponding to the retrieval condition;

The target index tree is searched layer by layer to obtain an index that satisfies the retrieval condition and an identifier of a device to be predicted associated with the index from a leaf node of the target index tree.

In an exemplary embodiment of the present disclosure, the relationship between the infectious disease patient and the to-be-predicted object corresponding to the to-be-predicted device identification is determined according to the number of associations between the target device identification and the to-be-predicted device identification. length of contact, including:

Calculate the number of associations according to the number of times the identifier of the device to be predicted and the identifier of the target device appear simultaneously in the leaf node;

According to the time interval between the association times and the plurality of data included in the leaf node, the contact time between the infectious disease patient and the object to be predicted corresponding to the identifier of the device to be predicted is calculated.

In an exemplary embodiment of the present disclosure, the data is obtained by the terminal device of the infectious disease patient in the following manner:

acquiring name information of one or more broadcasting devices scanned by the first wireless communication device;

When judging that the name information complies with a preset naming rule, a broadcast device that complies with the preset naming rule is used as the second wireless communication device;

extracting the identifier of the device to be predicted from the name information corresponding to the second wireless communication device;

The data is generated according to the target device identification, the to-be-predicted device identification, the signal strength between the first wireless communication device and the second wireless communication device, and the current location of the first terminal device.

In an exemplary embodiment of the present disclosure, the apparatus for predicting the probability of contagion of infectious diseases further includes:

The infection path calculation module can be configured to calculate the infection path of the infectious disease patient according to each of the current positions.

In an exemplary embodiment of the present disclosure, the infection is determined according to the current signal strength between the first wireless communication device corresponding to the target device identification and the second wireless communication device corresponding to the to-be-predicted device identification. The contact distance between the patient and the object to be predicted, including:

According to the current signal strength between the first wireless communication device corresponding to the target device identification and the second wireless communication device corresponding to the to-be-predicted device identification, the first wireless communication device and the second wireless communication device are predicted at an interval. The contact distance between the infectious disease patient and the object to be predicted is determined by setting the standard signal strength at the distance and the preset environmental attenuation factor.

In an exemplary embodiment of the present disclosure, the target device identifier is obtained by the terminal device of the infectious disease patient in the following manner:

Perform a hash operation on the attribute information of the infectious disease patient to obtain a hash value;

The hash value is trimmed by using a preset trimming rule to obtain a hash value with a preset length, and the hash value with a preset length is used as the target device identifier.

The specific details of each module in the above-mentioned infectious disease probability prediction apparatus have been described in detail in the corresponding infectious disease probability prediction method, and therefore will not be repeated here.

It should be noted that although several modules or units of the apparatus for action performance are mentioned in the above detailed description, this division is not mandatory. Indeed, according to embodiments of the present disclosure, the features and functions of two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above may be further divided into multiple modules or units to be embodied.

Additionally, although the various steps of the methods of the present disclosure are depicted in the figures in a particular order, this does not require or imply that the steps must be performed in the particular order or that all illustrated steps must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step for execution, and/or one step may be decomposed into multiple steps for execution, and the like.

In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

As will be appreciated by one skilled in the art, various aspects of the present disclosure may be implemented as a system, method or program product. Therefore, various aspects of the present disclosure can be embodied in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, which may be collectively referred to herein as implementations "circuit", "module" or "system".

An electronic device 700 according to this embodiment of the present disclosure is described below with reference to FIG. 7 . The electronic device 700 shown in FIG. 7 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present disclosure.

As shown in FIG. 7, electronic device 700 takes the form of a general-purpose computing device. Components of the electronic device 700 may include, but are not limited to, the above-mentioned at least one processing unit 710 , the above-mentioned at least one storage unit 720 , a bus 730 connecting different system components (including the storage unit 720 and the processing unit 710 ), and a display unit 740 .

Wherein, the storage unit stores program codes, and the program codes can be executed by the processing unit 710, so that the processing unit 710 executes various exemplary methods according to the present disclosure described in the above-mentioned “Exemplary Methods” section of this specification. Implementation steps. For example, the processing unit 710 may perform step S110 as shown in FIG. 1 : obtain the target device identification of the infectious disease patient, and obtain the identification of the target device from the preset database according to the target device identification and the preset time period. The device identifier has an associated device identifier to be predicted; Step S120: According to the number of associations between the target device identifier and the device identifier to be predicted, determine the infectious disease patient and the identifier corresponding to the device to be predicted. the contact duration between the objects to be predicted; Step S130: According to the current signal strength between the first wireless communication device corresponding to the target device identification and the second wireless communication device corresponding to the device identification to be predicted, determine the Contact distance between the infectious disease patient and the object to be predicted; Step S140: Determine the infection probability of the infectious disease patient to the object to be predicted according to the contact duration and the contact distance.

The storage unit 720 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 7201 and/or a cache storage unit 7202 , and may further include a read only storage unit (ROM) 7203 .

The storage unit 720 may also include a program/utility 7204 having a set (at least one) of program modules 7205 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, An implementation of a network environment may be included in each or some combination of these examples.

The bus 730 may be representative of one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local area using any of a variety of bus structures bus.

The electronic device 700 may also communicate with one or more external devices 800 (eg, keyboards, pointing devices, Bluetooth devices, etc.), with one or more devices that enable a user to interact with the electronic device 700, and/or with Any device (eg, router, modem, etc.) that enables the electronic device 700 to communicate with one or more other computing devices. Such communication may take place through input/output (I/O) interface 750 . Also, the electronic device 700 may communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN), and/or a public network such as the Internet) through a network adapter 760 . As shown, network adapter 760 communicates with other modules of electronic device 700 via bus 730 . It should be understood that, although not shown, other hardware and/or software modules may be used in conjunction with electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives and data backup storage systems.

From the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein may be implemented by software, or may be implemented by software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure may be embodied in the form of software products, and the software products may be stored in a non-volatile storage medium (which may be CD-ROM, U disk, mobile hard disk, etc.) or on the network , including several instructions to cause a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to an embodiment of the present disclosure.

In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium on which a program product capable of implementing the above-described method of the present specification is stored. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product comprising program code for causing the program product to run on a terminal device when the program product is run on a terminal device. The terminal device performs the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned "Example Method" section of this specification.

A program product for implementing the above method according to an embodiment of the present disclosure may adopt a portable compact disc read only memory (CD-ROM) and include program codes, and may run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples (non-exhaustive list) of readable storage media include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

A computer readable signal medium may include a propagated data signal in baseband or as part of a carrier wave with readable program code embodied thereon. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A readable signal medium can also be any readable medium, other than a readable storage medium, that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural Programming Language - such as the "C" language or similar programming language. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server execute on. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (eg, using an Internet service provider business via an Internet connection).

In addition, the above-mentioned figures are merely schematic illustrations of the processes included in the methods according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not indicate or limit the chronological order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, in multiple modules.

Other embodiments of the present disclosure will readily suggest themselves to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or techniques in the technical field not invented by the present disclosure . The specification and examples are to be regarded as exemplary only, with the true scope and spirit of the disclosure being indicated by the claims.

Claims

An infectious disease probability prediction method, comprising:

Acquiring the target device identification of the infectious disease patient, and according to the target device identification and a preset time period, acquiring the to-be-predicted device identification that is associated with the target device identification from a preset database;

Determine, according to the number of associations between the target device identifier and the to-be-predicted device identifier, the contact duration between the infectious disease patient and the to-be-predicted object corresponding to the to-be-predicted device identifier;

Determine the contact between the infectious disease patient and the object to be predicted according to the current signal strength between the first wireless communication device corresponding to the target device identification and the second wireless communication device corresponding to the device identification to be predicted distance;

According to the contact duration and contact distance, the infection probability of the infectious disease patient to the object to be predicted is determined.
The method for predicting the probability of contagion of an infectious disease according to claim 1, wherein, according to the target device identifier and a preset time period, the device to be predicted that is associated with the target device identifier is obtained from a preset database identification, including:

generating a retrieval condition according to the target device identifier and a preset time period, and determining a time interval corresponding to the retrieval condition from the preset database;

According to the time interval, construct the target index tree corresponding to the retrieval condition;

The target index tree is searched layer by layer to obtain an index that satisfies the retrieval condition and an identifier of a device to be predicted associated with the index from a leaf node of the target index tree.
The method for predicting the probability of contagion of an infectious disease according to claim 2, wherein, according to the number of associations between the target device identification and the device identification to be predicted, the infectious disease patient and the device identification to be predicted corresponding to the identification are determined. Contact duration between objects to be predicted, including:

Calculate the number of associations according to the number of times the identifier of the device to be predicted and the identifier of the target device appear simultaneously in the leaf node;

According to the time interval between the association times and the plurality of data included in the leaf node, the contact time between the infectious disease patient and the object to be predicted corresponding to the identifier of the device to be predicted is calculated.
The method for predicting the probability of infection of an infectious disease according to claim 3, wherein the data is obtained by the equipment terminal of the patient with the infectious disease in the following manner:

acquiring name information of one or more broadcasting devices scanned by the first wireless communication device;

When judging that the name information complies with a preset naming rule, a broadcast device that complies with the preset naming rule is used as the second wireless communication device;

extracting the identifier of the device to be predicted from the name information corresponding to the second wireless communication device;

The data is generated according to the target device identification, the to-be-predicted device identification, the signal strength between the first wireless communication device and the second wireless communication device, and the current location of the first terminal device.
The method for predicting the probability of contagion of an infectious disease according to claim 4, wherein the method for predicting the probability of contagion of an infectious disease further comprises:

Based on each of the current positions, an infection route of the infectious disease patient is calculated.
The method for predicting the probability of contagion of infectious diseases according to claim 1, wherein according to the current signal strength between the first wireless communication device corresponding to the target device identification and the second wireless communication device corresponding to the to-be-predicted device identification , determine the contact distance between the infectious disease patient and the object to be predicted, including:

According to the current signal strength between the first wireless communication device corresponding to the target device identification and the second wireless communication device corresponding to the device identification to be predicted, the first wireless communication device and the second wireless communication device are predicted at an interval. The standard signal strength at the time of setting the distance and the preset environmental attenuation factor are used to determine the contact distance between the infectious disease patient and the object to be predicted.
The method for predicting the probability of contagion of an infectious disease according to any one of claims 1-6, wherein the target device identifier is obtained by the terminal device of the patient with the infectious disease in the following manner:

Perform a hash operation on the attribute information of the infectious disease patient to obtain a hash value;

The hash value is trimmed by using a preset trimming rule to obtain a hash value with a preset length, and the hash value with a preset length is used as the target device identifier.
An infectious disease probability prediction device, comprising:

an acquisition module, configured to acquire the target device identification of the infectious disease patient, and obtain the to-be-predicted device identification associated with the target device identification from a preset database according to the target device identification and a preset time period;

A first determination module, configured to determine the contact duration between the infectious disease patient and the object to be predicted corresponding to the device identification to be predicted according to the number of associations between the identification of the target device and the identification of the device to be predicted ;

The second determination module is configured to determine the relationship between the infectious disease patient and the The contact distance between the objects to be predicted;

An infection probability prediction module, configured to determine the infection probability of the infectious disease patient to the to-be-predicted object according to the contact duration and contact distance.
A computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements the method for predicting the infection probability of an infectious disease according to any one of claims 1-7.
An electronic device comprising:

processor; and

a memory for storing executable instructions for the processor;

Wherein, the processor is configured to execute the method for predicting the probability of contagion of an infectious disease according to any one of claims 1-7 by executing the executable instructions.