Method, device and storage medium for identifying users of interest during disaster situations

Abstract

The present disclosure relates to a method, device and storage medium for identifying users of interest during a disaster. The method for identifying users of interest during a disaster includes: obtaining location information of a place of interest during a disaster; determining a wireless cell covering the place of interest based on the obtained location information; selecting candidate users of interest from users served by the wireless cell based on prior knowledge; determining users of interest from the candidate users of interest using an unsupervised clustering algorithm, wherein the unsupervised clustering algorithm performs longitude and latitude clustering based on call detail record (CDR) data of the users of interest within a recent period of time using a DBSCAN unsupervised clustering algorithm; and submitting a list of the users of interest to a mobile service operator so that the mobile service operator can provide guaranteed services for the users of interest.

Description

Technical Field

[0001] The present disclosure generally relates to the field of information technology, and more particularly to a method, device, and medium for identifying users of interest during a disaster. Background Art

[0002] According to the 2019 Statistical Bulletin of the Communications Industry issued by the Ministry of Industry and Information Technology, the total number of mobile phone users in my country exceeds 1.6 billion, and the national mobile phone user penetration rate reaches 114.4 units per 100 people. These data show that China's mobile phone penetration rate is very high.

[0003] During disasters, the usage rate of mobile phones is often higher than normal, which results in the inability to successfully call key personnel, such as rescue workers, etc. In some disaster areas, the signal strength of mobile phones will be affected, which will also greatly reduce the success rate of call connection.

[0004] The prior art does not yet provide a technical solution for ensuring the quality of mobile communication services for key personnel during a disaster. Summary of the invention

[0005] A brief overview of the disclosure is given below in order to provide a basic understanding of some aspects of the disclosure. However, it should be understood that this overview is not an exhaustive overview of the disclosure. It is not intended to identify the key or important parts of the disclosure, nor is it intended to limit the scope of the disclosure. Its purpose is simply to give some concepts of the disclosure in a simplified form as a prelude to a more detailed description given later.

[0006] According to one aspect of the present disclosure, a method for identifying users of interest during a disaster is provided, comprising: obtaining location information of a place of interest during a disaster; determining a wireless cell covering the place of interest based on the obtained location information; screening candidate users of interest from users served by the wireless cell based on prior knowledge; determining users of interest from the candidate users of interest using an unsupervised clustering algorithm, wherein the unsupervised clustering algorithm performs longitude and latitude clustering based on call detail record (CDR) data of the users of interest within a recent period of time using the DBSCAN unsupervised clustering algorithm; and submitting a list of the users of interest to a mobile service operator so that the mobile service operator can provide guaranteed services to the users of interest.

[0007] According to another aspect of the present disclosure, a device for identifying users of interest during a disaster is provided, comprising: a memory having instructions stored thereon; and a processor configured to execute the instructions stored on the memory to perform the method according to the above aspects of the present disclosure.

[0008] According to another aspect of the present disclosure, a computer-readable storage medium is provided, which includes computer-executable instructions. When the computer-executable instructions are executed by one or more processors, the one or more processors execute the method according to the above aspects of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings, which constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0010] The present disclosure may be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0011] Figure 1 A flowchart of a process 100 for identifying users of interest during a disaster situation according to one embodiment of the present invention is shown;

[0012] Figure 2 An exemplary process of the method of the present invention in the case of an epidemic situation is shown;

[0013] Figure 3 An exemplary configuration of a computing device that can implement an embodiment according to the present disclosure is shown. DETAILED DESCRIPTION

[0014] The following detailed description is made with reference to the accompanying drawings, and the following detailed description is provided to assist in a comprehensive understanding of the various exemplary embodiments of the present disclosure. The following description includes various details to assist in understanding, but these details are considered to be examples only and not to limit the present disclosure, which is defined by the appended claims and their equivalents. The words and phrases used in the following description are only used to enable a clear and consistent understanding of the present disclosure. In addition, for the sake of clarity and brevity, descriptions of well-known structures, functions, and configurations may be omitted. Those of ordinary skill in the art will recognize that various changes and modifications may be made to the examples described herein without departing from the spirit and scope of the present disclosure.

[0015] The inventors have found that the prior art does not utilize mobile communications and other public information to automatically identify key mobile communication users, such as medical staff, ambulance drivers, etc., during the epidemic and provide them with guaranteed services.

[0016] In addition, the inventors recognize that DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a typical density clustering algorithm. DBSCAN generally assumes that the category can be determined by the compactness of the sample distribution. Samples of the same category are closely connected, that is, there must be samples of the same category not far away from any sample of the category. By classifying closely connected samples into one category, a cluster category is obtained. By classifying all groups of closely connected samples into different categories, the final results of all cluster categories are obtained. In addition, DBSCAN has been implemented in Python programming.

[0017] In view of this, the inventor provides the present invention to pre-select users through public information user behavior tags, and then perform unsupervised clustering of the pre-selected users' geographic locations to form a medical staff user behavior pattern and discover a method for a medical staff list.

[0018] Figure 1 A flow chart of a process 100 for identifying users of interest during a disaster situation is shown according to one embodiment of the present invention.

[0019] At step 101, the process 100 begins.

[0020] In step 103, location information of the places of interest during the disaster is obtained.

[0021] The places of concern during a disaster are, for example, some specific locations in the area where the epidemic occurs, such as houses, schools, hospitals, etc. This information can be obtained from the Internet, self-media, and current news.

[0022] The position information includes longitude and latitude. Of course, other positioning information can be used as needed.

[0023] In step 105, based on the acquired location information, a wireless cell covering the point of interest is determined.

[0024] For example, determining the wireless cell covering the location of interest may include: matching the longitude and latitude of the mobile base station with the longitude and latitude of the location of interest, calculating the distance between the mobile base station and the location of interest, and determining the wireless cell based on the coverage distance of wireless networks in different frequency bands and combined with the azimuth information of the wireless cell.

[0025] In step 107, candidate users of interest are screened out from the users served by the wireless cell based on prior knowledge.

[0026] The prior knowledge may include: user activity areas, user daily active time, user weekly active time, and specific areas related to disasters. This information may also be obtained from the Internet, self-media, and current news.

[0027] In step 109, an unsupervised clustering algorithm is used to determine the user of interest from the candidate users of interest, wherein the unsupervised clustering algorithm performs longitude and latitude clustering based on the call detail record (CDR) data of the user of interest in a recent period of time using the DBSCAN unsupervised clustering algorithm.

[0028] For example, using an unsupervised clustering algorithm to determine the follow users from the candidate follow users may include:

[0029] 1) Use the following formula to calculate the distance between two points of interest as the aggregation radius parameter:

[0030] d=R*arcos[cos(Y1)*cos(Y2)*cos(X1-X2)+sin(Y1)*sin(Y2)]

[0031] Among them, X1, X2 are longitude, Y1, Y2 are latitude;

[0032] 2) Perform geographic location clustering using DBSCAN based on the following formula to obtain the user's clustering area:

[0033] db = DBSCAN (eps = "aggregation radius", min_samples = "aggregation points", metric = "metric"); and

[0034] 3) Determine the concerned user based on the analysis of the obtained clustering areas.

[0035] In step 111, the list of the concerned users is submitted to the mobile service operator so that the mobile service operator can provide guaranteed services for the concerned users.

[0036] The list may be a list of concerned users' names, phone numbers, etc. The guaranteed service may include one or more of no downtime, package traffic, fee reduction, and priority handling of complaints.

[0037] When the epidemic broke out, medical staff from all over the country went to the epidemic site to provide support. Without increasing the workload of medical staff, it is necessary to filter out a list of medical staff through user behavior, and provide important communication guarantees such as non-stop service and service level improvement for medical staff. In order to find a list of medical staff based on the user behavior patterns of medical staff during the epidemic without increasing manual work and to focus on protecting these personnel, the reference system according to the embodiment of the present invention can be used. Figure 1The described technical solution is used to identify medical staff in special scenarios based on user behavior tags.

[0038] The following uses the epidemic situation as an example. Figure 2 An embodiment of the present invention is described in detail. Figure 2 The exemplary processing process of the method of the present invention in the case of an epidemic situation is shown. It is worth noting that the following description includes a large number of unnecessary details, and those skilled in the art can choose these details as needed.

[0039] In blocks 201 and 203, prior knowledge is established, such as establishing a key hospital database.

[0040] Obtain a list of designated hospitals, specific model hospitals in emergency situations, makeshift hospitals, and quarantine points from the website of the National Health Commission, and use the public map API to obtain the longitude and latitude of these hospitals.

[0041] In blocks 205 and 207, prior knowledge is established, such as identifying key mobile network base station information.

[0042] Match the longitude and latitude of the mobile base station with the longitude and latitude of the hospital, calculate the distance between the longitude and latitude of the base station and the hospital and isolation point, and identify the wireless cells covering these hospitals based on the coverage distance of wireless networks in different frequency bands and information such as the cell azimuth.

[0043] At 209 , prior knowledge building is performed, such as user feature pre-selection.

[0044] Obtain user behavior of general medical personnel through news, operator medical and health channel service managers, etc., and use available prior experience to filter data.

[0045] For general hospitals and quarantine points, the duty cycle is usually 5 days / week, and each duty time is about 5-12 hours. However, for hospitals with specific modes in emergency situations, the duty cycle is usually 5 days / week, and each duty time is about 5-24 hours. Build a user model of the activity area and the duration of continuous activity in the area. With this prior knowledge, patients who are resident in hospitals\quarantine points or people who need to be isolated can be screened out.

[0046] Using the current smartphone continuous heartbeat packet mechanism, users will continue to generate service call records in the operator's CDR data even if they are not using their phones. The observation of CDR data represents the observation of user behavior. The operator's full CDR data is classified and users who meet the conditions are pre-selected. The pre-selection model can compress the calculation amount from tens of millions of users' billions of data to tens of millions of users' tens of thousands of data, reducing the calculation amount of the next step of unsupervised clustering operation.

[0047] In block 211 , an unsupervised clustering algorithm is used to further improve the accuracy of target users for pre-selected users.

[0048] The user list selected by the prior model is reverse matched with the full CDR data to obtain the full call records of the user for half a month. The call record data is clustered by longitude and latitude using the DBSCAN unsupervised clustering algorithm. The spherical distance is used to calculate the geographical distance between different locations as the aggregation radius parameter (d = R*arcos[cos(Y1)*cos(Y2)*cos(X1-X2)+sin(Y1)*sin(Y2)], where X1, X2 are longitudes and Y1, Y2 are latitudes). Python DBSCAN is used for geographical location clustering (db = DBSCAN(eps = "aggregation radius", min_samples = "aggregation points", metric = '"metric method"')) to obtain the main activity area of ​​the user. By analyzing whether the clustering area is the workplace (hospital, quarantine point) and the residence (residential area / hotel), it is determined whether the user is a medical staff and a user model is established.

[0049] At block 213, a list of medical personnel is determined.

[0050] By mining the operator's CDR bills and matching the user feature model, we found a list of medical and health personnel and submitted it to the BOSS system to implement key protection. First, the medical and health personnel identified can work without downtime and worry about using communication tools; second, the list of medical and health personnel is exported to the complaint pre-processing center to provide one-to-one service for any problems they encounter when using the network, and quickly solve the problems.

[0051] Compared with the prior art, this technical solution has the following advantages and technical effects: the present invention realizes the automatic identification and statistics function of medical users during disasters, reduces the important communication guarantee for these medical personnel, and improves the user perception of medical personnel.

[0052] Figure 3 An exemplary configuration of a computing device 1200 capable of implementing embodiments according to the present disclosure is shown.

[0053] The computing device 1200 is an example of a hardware device to which the above aspects of the present disclosure can be applied. The computing device 1200 can be any machine configured to perform processing and / or computing. The computing device 1200 can be, but is not limited to, a workstation, a server, a desktop computer, a laptop computer, a tablet computer, a personal data assistant (PDA), a smart phone, a vehicle-mounted computer, or a combination thereof.

[0054] like Figure 3As shown, the computing device 1200 may include one or more elements that can be connected or communicated with the bus 1202 via one or more interfaces. The bus 2102 may include, but is not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus, etc. The computing device 1200 may include, for example, one or more processors 1204, one or more input devices 1206, and one or more output devices 1208. The one or more processors 1204 may be any kind of processor, and may include, but is not limited to, one or more general-purpose processors or special-purpose processors (such as special-purpose processing chips). The processor 1202 may be configured to implement a technical solution for identifying a user of interest during a disaster. The input device 1206 may be any type of input device capable of inputting information to the computing device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a microphone, and / or a remote controller. Output device 1208 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video / audio output terminals, vibrators, and / or printers.

[0055] The computing device 1200 may also include or be connected to a non-transitory storage device 1214, which may be any non-transitory storage device that can implement data storage, and may include, but is not limited to, a disk drive, an optical storage device, a solid-state memory, a floppy disk, a flexible disk, a hard disk, a tape, or any other magnetic medium, a compressed disk or any other optical medium, a cache memory, and / or any other storage chip or module, and / or any other medium from which a computer can read data, instructions, and / or code. The computing device 1200 may also include a random access memory (RAM) 1210 and a read-only memory (ROM) 1212. The ROM 1212 may store programs, utilities, or processes to be executed in a non-volatile manner. The RAM 1210 may provide volatile data storage and store instructions related to the operation of the computing device 1200. The computing device 1200 may also include a network / bus interface 1216 coupled to a data link 1218. The network / bus interface 1216 may be any type of device or system capable of enabling communication with an external device and / or a network, and may include but is not limited to a modem, a network card, an infrared communication device, a wireless communication device, and / or a chipset (such as a Bluetooth TM equipment, 802.11 equipment, WiFi equipment, WiMax equipment, cellular communication facilities, etc.).

[0056] The present disclosure may be implemented as any combination of an apparatus, a system, an integrated circuit, and a computer program on a non-transitory computer-readable medium. One or more processors may be implemented as an integrated circuit (IC), an application specific integrated circuit (ASIC), or a large scale integrated circuit (LSI), a system LSI, a super LSI, or a super LSI component that performs some or all of the functions described in the present disclosure.

[0057] The present disclosure includes the use of software, applications, computer programs, or algorithms. The software, applications, computer programs, or algorithms may be stored on a non-transitory computer-readable medium to enable a computer such as one or more processors to perform the above steps and the steps described in the accompanying drawings. For example, one or more memories store the software or algorithm in executable instructions, and one or more processors may associate a set of instructions to execute the software or algorithm to provide various functions according to the embodiments described in the present disclosure.

[0058] Software and computer programs (which may also be referred to as programs, software applications, applications, components or code) include machine instructions for a programmable processor and may be implemented in a high-level procedural language, an object-oriented programming language, a functional programming language, a logic programming language, or an assembly language or a machine language. The term "computer-readable medium" refers to any computer program product, apparatus or device for providing machine instructions or data to a programmable data processor, such as magnetic disks, optical disks, solid-state storage devices, memories and programmable logic devices (PLDs), including computer-readable media that receive machine instructions as computer-readable signals.

[0059] For example, computer readable media may include dynamic random access memory (DRAM), random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), compact disk read-only memory (CD-ROM) or other optical disk storage devices, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to carry or store the desired computer readable program code in the form of instructions or data structures and can be accessed by a general or special purpose computer or a general or special purpose processor. As used herein, disk or disc includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically by laser. Combinations of the above are also included in the scope of computer readable media.

[0060] The above description provides examples without limiting the scope, applicability or configuration set forth in the claims. The functions and arrangements of the elements discussed may be changed without departing from the spirit and scope of the present disclosure. Various embodiments may appropriately omit, replace or add various processes or components. For example, features described with respect to certain embodiments may be combined in other embodiments.

[0061] Similarly, while operations are depicted in a particular order in the drawings, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed to achieve desired results. In certain circumstances, multitasking and parallel processing can be advantageous.

Claims

1. A method for identifying users of interest during a disaster, comprising: Obtain location information of places of interest during a disaster; Determining a wireless cell covering the location of interest based on the acquired location information; Filtering candidate users of interest from users served by the wireless cell based on prior knowledge; Determine the concerned user from the candidate concerned users by using an unsupervised clustering algorithm, wherein the unsupervised clustering algorithm performs longitude and latitude clustering based on the call detail record (CDR) data of the candidate concerned users in a recent period of time by using the DBSCAN unsupervised clustering algorithm; as well as Submit the list of the concerned users to the mobile service operator so that the mobile service operator can provide guaranteed services for the concerned users.

2. The method according to claim 1, wherein: The location information includes longitude and latitude, and wherein determining a wireless cell covering the location of interest includes: Match the latitude and longitude of the mobile base station with the latitude and longitude of the location of interest. Calculating the distance between the mobile base station and the location of interest, and The wireless cell is determined according to the coverage distance of wireless networks in different frequency bands and in combination with the azimuth information of the wireless cell.

3. The method according to claim 1, wherein: The prior knowledge includes: user activity areas, user daily active time, user weekly active time, and specific areas related to disasters.

4. The method according to claim 1, wherein: Using an unsupervised clustering algorithm to determine the follow users from the candidate follow users includes: Use the following formula to calculate the distance between two locations that the user has visited as the radius parameter of the aggregation: d=R*arcos[cos(Y1)*cos(Y2)*cos(X1-X2)+sin(Y1)*sin(Y2)] Among them, X1, X2 are longitude, Y1, Y2 are latitude; Use DBSCAN to perform geographic location clustering to obtain the user's cluster area based on the following formula: db = DBSCAN (eps = "aggregation radius", min_samples = "aggregation points", metric = "metric"); and The user of interest is determined based on an analysis of the obtained clustering areas.

5. The method according to claim 1, wherein: The guaranteed services include one or more of no downtime, packaged traffic, fee reduction, and priority handling of complaints.

6. A device for identifying users of interest during a disaster, comprising: a memory having instructions stored therein; as well as A processor configured to execute instructions stored on the memory to perform the method according to any one of claims 1 to 5.

7. A computer-readable storage medium comprising computer-executable instructions, which, when executed by one or more processors, cause the one or more processors to perform the method according to any one of claims 1 to 5.

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