Method for obtaining user movement information and related device

Abstract

This disclosure provides a method and apparatus for acquiring user mobility information, a computer-readable storage medium, and an electronic device, belonging to the fields of computer and communication technology. The method includes: selecting an analysis area within a first range; acquiring base station cells whose coverage overlaps with the analysis area; acquiring signaling data of the overlapping base station cells within a first time period; acquiring user dwell information for each area within the analysis area based on the signaling data within the first time period; and acquiring user mobility information between different areas within the analysis area based on the dwell information. This method can achieve the acquisition of user mobility information.

Description

Technical Field

[0001] This disclosure relates to the fields of computer and communication technology, and more specifically, to methods and apparatus for acquiring user mobile information, computer-readable storage media, and electronic devices. Background Technology

[0002] With the improvement of people's living standards, the per capita mobile phone ownership is extremely high. Users generate a large amount of data on the network side when they are mobile or using services. When users switch networks, relevant data is generated through signaling interactions. This data can be used to determine the time of the user's service activity and the location of the base station cell.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] This disclosure provides a method and apparatus for acquiring user mobile information, a computer-readable storage medium, and an electronic device, which can acquire user mobile information and improve user experience.

[0005] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0006] According to one aspect of this disclosure, a method for obtaining user mobile information is provided, comprising:

[0007] Select the analysis region within the first range;

[0008] Obtain base station cells whose coverage overlaps with the analysis area;

[0009] Obtain signaling data for the first time period of the overlapping base station cells;

[0010] Based on the signaling data within the first time period, obtain the user's residency information in each region of the analysis area;

[0011] Based on the residency information, obtain the user's movement information between different areas in the analysis area.

[0012] In one embodiment, selecting the analysis region within the first range includes:

[0013] Within the first range, the area that the user must pass through is selected as the analysis area.

[0014] In one embodiment, acquiring base station cells whose coverage overlaps with the analysis area includes:

[0015] The gridded coverage of base station cells in the region where the analysis area is located is obtained based on the measurement report with AGPS.

[0016] Based on the gridded coverage of the base station cells in the region where the analysis area is located and the analysis area, obtain the base station cells that overlap with the analysis area.

[0017] In one embodiment, the method further includes:

[0018] Users with the aforementioned residency information are cleaned to remove interfering users from the analysis area.

[0019] In one embodiment, cleaning up users with the residency information to remove interfering users from the analysis area includes:

[0020] Remove interfering users whose resident information exists only in one of the analysis areas.

[0021] In one embodiment, the method further includes:

[0022] Obtain measurement reports, including AGPS, within the second range for the user;

[0023] Based on the user's AGPS measurement report and artificial intelligence prediction device, the latitude and longitude of the user's measurement report without AGPS is predicted.

[0024] In one embodiment, the method further includes:

[0025] The user's movement trajectory within the second range is obtained based on the actual AGPS and predicted latitude and longitude from the user's measurement report.

[0026] According to one aspect of this disclosure, a device for acquiring user mobile information is provided, comprising:

[0027] Select the module and configure it to select the analysis region within a first range;

[0028] The first acquisition module is configured to acquire base station cells whose coverage overlaps with the analysis area;

[0029] The second acquisition module is configured to acquire signaling data of the overlapping base station cells within a first time period;

[0030] The third acquisition module is configured to acquire user dwell information in each region of the analysis area based on signaling data within the first time period.

[0031] The fourth acquisition module is configured to acquire the user's movement information between different areas in the analysis area based on the residency information.

[0032] According to one aspect of this disclosure, an electronic device is provided, comprising:

[0033] One or more processors;

[0034] A storage device configured to store one or more programs, which, when executed by one or more processors, cause the one or more processors to perform the method as described in any of the above embodiments.

[0035] According to one aspect of this disclosure, a computer-readable storage medium is provided that stores a computer program, which, when executed by a processor, implements the method as described in any of the above embodiments.

[0036] The method for obtaining user mobile information disclosed herein is capable of obtaining user mobile information.

[0037] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0038] The following figures illustrate certain illustrative embodiments of the invention, wherein the same reference numerals denote the same elements. These described embodiments are exemplary embodiments of this disclosure and are not intended to limit it in any way.

[0039] Figure 1 A schematic diagram of an exemplary system architecture for a method of obtaining user mobility information to which embodiments of the present disclosure can be applied is shown;

[0040] Figure 2 A schematic diagram of the structure of a computer system suitable for implementing embodiments of the present disclosure is shown;

[0041] Figure 3 A flowchart illustrating a method for obtaining user mobility information according to an embodiment of the present disclosure is shown schematically.

[0042] Figure 4 A basic flowchart of an embodiment of the present disclosure is shown;

[0043] Figure 5 A block diagram of a user mobility information acquisition device according to an embodiment of the present disclosure is shown schematically.

[0044] Figure 6 A block diagram schematically illustrates an apparatus for acquiring user movement information according to another embodiment of the present disclosure;

[0045] Figure 7A block diagram of an apparatus for acquiring user movement information according to another embodiment of the present disclosure is shown schematically. Detailed Implementation

[0046] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many 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 more thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0047] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., may be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring aspects of this disclosure.

[0048] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0049] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0050] Figure 1 A schematic diagram of an exemplary system architecture 100 for which methods for obtaining user mobility information can be applied according to embodiments of the present disclosure is shown.

[0051] like Figure 1 As shown, system architecture 100 may include one or more of terminals 101, 102, and 103, a network 104, and a server 105. Network 104 is the medium used to provide a communication link between terminals 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0052] It should be understood that Figure 1The number of terminals, networks, and servers shown is merely illustrative. Depending on implementation needs, there can be any number of terminals, networks, and servers. For example, server 105 could be a server cluster consisting of multiple servers.

[0053] Staff or users can use terminals 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Terminals 101, 102, and 103 can be various electronic devices with displays, including but not limited to smartphones, tablets, portable and desktop computers, digital cinema projectors, etc.

[0054] Server 105 can be a server providing various services. For example, when a staff member sends a command to server 105 to obtain user mobility information via terminal 103 (or terminal 101 or 102), server 105 can select an analysis area within a first range; obtain base station cells whose coverage overlaps with the analysis area; obtain signaling data of the overlapping base station cells within a first time period; obtain user dwell information in each area of ​​the analysis area based on the signaling data within the first time period; and obtain user mobility information between different areas of the analysis area based on the dwell information. Server 105 can display the user's mobility information on terminal 103 or other terminals, allowing staff members to view the user's mobility information based on the content displayed on the terminal.

[0055] Figure 2 A schematic diagram of the structure of a computer system suitable for implementing the embodiments of the present disclosure is shown.

[0056] It should be noted that, Figure 2 The computer system 200 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0057] like Figure 2 As shown, the computer system 200 includes a central processing unit (CPU) 201, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 202 or programs loaded from storage section 208 into random access memory (RAM) 203. The RAM 203 also stores various programs and data required for system operation. The CPU 201, ROM 202, and RAM 203 are interconnected via a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0058] The following components are connected to I / O interface 205: an input section 206 including a keyboard, mouse, etc.; an output section 207 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 208 including a hard disk, etc.; and a communication section 209 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 209 performs communication processing via a network such as the Internet. A drive 210 is also connected to I / O interface 205 as needed. Removable media 211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 210 as needed so that computer programs read from them can be installed into storage section 208 as needed.

[0059] In particular, according to embodiments of this disclosure, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 209, and / or installed from removable medium 211. When the computer program is executed by central processing unit (CPU) 201, it performs various functions defined in the methods and / or apparatus of this application.

[0060] It should be noted that the computer-readable storage medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, 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 device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF (Radio Frequency), etc., or any suitable combination thereof.

[0061] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods, apparatus, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions selected in the blocks may occur in a different order than those shown in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0062] The modules and / or units and / or sub-units described in the embodiments of this disclosure can be implemented in software or hardware, and the described modules and / or units and / or sub-units can also be located in a processor. The names of these modules and / or units and / or sub-units do not, in some cases, constitute a limitation on the module and / or unit and / or sub-unit itself.

[0063] On the other hand, this application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 3 Each step.

[0064] In related technologies, machine learning methods and deep learning methods can be used to acquire user mobile information, and different methods have different applicable scopes.

[0065] Figure 3 A flowchart illustrating a method for obtaining user mobility information according to an embodiment of the present disclosure is shown. The method steps of this embodiment can be executed by a terminal or a server, or by interaction between a terminal and a server, or by a server cluster; for example, they can be executed by the methods described above. Figure 1 The server 105 in the middle executes, but this disclosure is not limited thereto.

[0066] In step S310, an analysis region is selected within the first range.

[0067] In this step, the terminal or server selects an analysis area within a first range.

[0068] In one embodiment, the first scope can be a nationwide, provincial, or municipal area. The analysis area can be areas that passengers must pass through, such as departure halls, arrival halls, and waiting halls at airports and high-speed rail stations.

[0069] In one embodiment, selecting the analysis region within the first range includes:

[0070] Within the first range, the area that the user must pass through is selected as the analysis area.

[0071] In step S320, base station cells whose coverage overlaps with the analysis area are obtained.

[0072] In this step, the terminal or server acquires base station cells whose coverage overlaps with the analysis area.

[0073] In one embodiment, acquiring base station cells whose coverage overlaps with the analysis area includes:

[0074] The gridded coverage of base station cells in the region where the analysis area is located is obtained based on the measurement report (MR) with AGPS (Assisted Global Positioning System).

[0075] Based on the gridded coverage of the base station cells in the region where the analysis area is located and the analysis area, obtain the base station cells that overlap with the analysis area.

[0076] The region where the analysis area is located can be a suitable area that includes each analysis area, such as the street or community where each analysis area is located.

[0077] In step S330, signaling data of the overlapping base station cells within a first time period is obtained.

[0078] In this step, the terminal or server obtains signaling data for the first time period of the overlapping base station cells.

[0079] In one embodiment, the data may include signaling data (4G: S1MME (interface between base station eNodeB and mobility management entity MME), 5G: N1N2 (N1 is the interface between the core network and the mobile phone, and N2 is the interface between the core network and the base station)) and MR data.

[0080] In step S340, the user's dwell information in each region of the analysis area is obtained based on the signaling data within the first time period.

[0081] In this step, the terminal or server obtains the user's residence information in each region of the analysis area based on the signaling data within the first time period.

[0082] In one embodiment, the residency information includes, for example, start time and residency time.

[0083] In step S350, the user's movement information between different areas in the analysis area is obtained based on the dwell information.

[0084] In this step, the terminal or server obtains the user's movement information between different areas in the analysis area based on the residency information.

[0085] In one embodiment, the mobility information is, for example, a user's mobility information from Beijing Airport to Shanghai Airport on a certain day.

[0086] In one embodiment, the method further includes: cleaning up users with the residency information to remove interfering users from the analysis area.

[0087] In one embodiment, cleaning up users with the residency information to remove interfering users from the analysis area includes:

[0088] Remove interfering users whose resident information exists only in one of the analysis areas.

[0089] In one embodiment, the method further includes:

[0090] Obtain measurement reports, including AGPS, within the second range for the user;

[0091] Based on the user's AGPS measurement report and artificial intelligence prediction device, the latitude and longitude of the user's measurement report without AGPS is predicted.

[0092] The second scope includes, for example, the city, community, or street where the user lives.

[0093] In one embodiment, the method further includes:

[0094] The user's movement trajectory within the second range is obtained based on the actual AGPS and predicted latitude and longitude from the user's measurement report.

[0095] The method of this disclosure will be explained in detail below with specific examples:

[0096] When users use APP (Application) services through mobile terminals, the mobile communication network periodically generates MR (Measurement Report) to assess the quality of the wireless environment. The MR includes information such as TA (Timing Advance), RSRP (Reference Signal Receiving Power), PCI (Physical Cell Identification), and FCN (Frequency Channel Number). About 2% of the MR records contain AGPS positioning information.

[0097] Currently, by utilizing big data and AI (Artificial Intelligence) technologies, the positioning accuracy of mobile networks based on MR can reach 50-100 meters (using AGPS latitude and longitude and features such as TA and RSRP in MR to build a fingerprint (feature) database, and using AI to predict the latitude and longitude of MR records).

[0098] MR data: From its generation to its collection and processing on the big data platform, the current latency is about one hour.

[0099] Signaling data: 4G signaling data (S1MME) is output in 15-minute increments with a collection delay of about 10 minutes; 5G signaling data (N1N2) is output in hourly increments with a collection delay of about 30 minutes.

[0100] Airports and high-speed railways, as transportation hubs, are among the most important scenarios for emergency service support. Statistical analysis of passenger flow in airports and high-speed railways is one of the main research topics for critical security.

[0101] Obtaining passenger data directly is difficult (due to privacy concerns, multiple ticketing channels, numerous airlines, and dynamic rescheduling), making it challenging to track passenger origins and destinations, which poses difficulties for passenger flow statistics and analysis in critical security scenarios.

[0102] This disclosure utilizes user mobile phone signaling data to identify the origin and destination of passengers at major transportation hubs such as airports and high-speed rail stations, enabling multi-dimensional statistical analysis of passenger flow at these hubs. This avoids the drawbacks of traditional methods that rely on mobile phone number location to determine migration direction, supporting enterprises in providing efficient and accurate emergency service support.

[0103] Figure 4 A basic flowchart of an embodiment of the present disclosure is shown.

[0104] This disclosure only extracts signaling data from cells whose coverage area overlaps with the defined hub area. Based on the sequential occurrence of signaling data from user mobile phones in different areas while fully considering regional overlap, it determines the transportation hubs the user has passed through and their travel direction, achieving accurate analysis of passenger flow migration at transportation hubs with minimal data. The main process is as follows:

[0105] (1) Selection and Configuration of Transportation Hub Areas: Select the area to be analyzed (polygon) on the map and define the area scene name. Configure the area type (airport, high-speed rail, etc.), province, city, area ID (Identity Document, unique code), center latitude and longitude, etc. The area may only include departure halls, arrival halls, waiting halls, and other places that passengers must pass through. If there are multiple airports, high-speed rail stations, etc. in the same city, select and define them separately. Different areas can overlap.

[0106] (2) MR latitude and longitude prediction and backfilling: A fingerprint database is constructed using records with AGPS information in massive MR data and features such as TA and RSRP in the records. Based on AI algorithm, the latitude and longitude of other records without AGPS are predicted and backfilled into the MR records.

[0107] (3) Cell coverage calculation: Rasterize the AGPS records in MR (at least one week of data), connect the coverage grids of each base station cell, and generate the coverage outline polygon (i.e., latitude and longitude point set) and cell coverage area of ​​each cell.

[0108] (4) Regional cell list and data collection: Extract cells that intersect with the configured regional range from the cell coverage data of the corresponding province and city, and the data collection department collects data according to the required cell list (currently, the S1MME data collection frequency is 15 minutes).

[0109] (5) List of region and user signaling data association: The data and signaling data of each region are associated (through cell ID) to obtain the list of user signaling data in each region (region ID, region type, encrypted MDN (MDN is an abbreviation for MSISDN, mobile user number: MSISDN: Mobile Subscriber International ISDN Number), signaling occurrence time, cell ID, etc.).

[0110] (6) Data processing: Data is collected within a time range of period (T, e.g., day) + extension period (X, e.g., 6 hours), and then sorted by user and timestamp; for each user, records with adjacent times in the same area are aggregated to obtain aggregated data (user, area ID, type, start time, stay duration, etc.); interference users in the aggregated data are removed (i.e., users who only appear in one area during the statistical period or have time overlap in overlapping areas but no other destination and the two areas are close to each other); the remaining data is used to generate a user area migration data list (MDN, A, B, start time of each segment, stay duration, interval distance) according to user migration intervals. If there are overlapping areas between the start and end points, the start / end point is determined by comprehensively evaluating factors such as distance, time, speed, area type, and stay duration (the other one is discarded).

[0111] (7) Statistical Analysis: User migration statistics (origin and destination) can be performed by region, province, city, etc., to trace user trajectories, or MR precise positioning data can be used to further analyze user travel trajectories. Based on travel history information, it is possible to distinguish between high-speed rail / airplane employees and ordinary passengers, passenger travel modes, stay duration, travel frequency, etc. (user profiling).

[0112] This disclosure extracts only cell signaling data that overlaps with the coverage area and the custom hub area. Based on the order in which signaling data from the user's mobile phone appears in different areas, and taking into full account the factors of regional overlap, it determines the transportation hubs and travel directions that the user passes through, and achieves accurate analysis of passenger flow migration at transportation hubs with as little data as possible.

[0113] The solution disclosed herein may mainly include the following modules:

[0114] (1) Transportation hub area selection and configuration module:

[0115] Select the core area of ​​the transportation hub (a place that passengers must pass through) on the map and configure the relevant parameters. The "Regional Information Configuration Table" mainly includes: Region ID (unique number, which can be in the form of area code plus serial number), Region Name, Region Type (airport, high-speed rail, etc.), Province and City of the Region and Province and City Code, Latitude and Longitude of the Region Center Point, Region Outline (a polygon in WKT format, i.e., the set of latitude and longitude points of the polygon), and Cell ID Set (cells whose coverage area overlaps with the outline of the selected region are filled in this field, in the format: Base Station ID…CellID@Base Station ID…CellID…. There can be shared cells between regions in the same city, that is, two regions overlap, and there are base station cells that can cover both regions at the same time, i.e., overlapping cells). The main data is as follows (only the key fields are listed, other fields are omitted, the same below):

[0116] Regional Information Table (Sample)

[0117]

[0118]

[0119] (2) MR latitude and longitude prediction and backfilling module + cell coverage calculation module:

[0120] A fingerprint database is constructed using records with AGPS information in the MR data and features such as TA and RSRP in the records. Based on AI algorithms, the latitude and longitude of other MR records without AGPS information are predicted and backfilled into the MR. The AGPS records in the MR are rasterized (data from at least the most recent week), and the coverage raster of each base station cell is connected to generate the coverage outline polygon of each cell.

[0121] (3) List of regional communities and data collection:

[0122] From the cell coverage data of the corresponding province and city, extract the cells that intersect with the configured area. The data collection department collects relevant signaling data (including 4G and 5G signaling data (S1MME and N1N2) and MR data) according to the required cell list (province, city, cell ID (base station ID + cell ID (CELLID))). The signaling data content includes at least the following information: data date (YYMMDDHH), user number, service occurrence timestamp, cell ID, user number, user's home province, etc.

[0123] Signaling call detail record (sample)

[0124] date Community ID Timestamp User Number Province of origin of the number 20210718 333222_1 2021 / 7 / 18 13:44:20 MDN1 (encryption) Guangdong 20210718 333222_2 2021 / 7 / 18 14:14:20 MDN1 (encryption) 20210718 333222_3 2021 / 7 / 18 14:24:20 MDN1 (encryption) 20210718 555555_1 2021 / 7 / 18 14:34:20 MDN1 (encryption) 20210718 555555_2 2021 / 7 / 18 14:35:20 MDN1 (encryption) ... MDNn

[0125] (4) List of region and user signaling data associations:

[0126] Each region's data is left-associated with the signaling data (the associated field is the cell ID, and the region information configuration table is the left table), resulting in a list of regions associated with user signaling data (region ID, region type, region name, encrypted user number, service occurrence timestamp in signaling, cell ID, data date, etc.), generating a "Region and User Signaling Data Association List Table".

[0127] Example of a list of regions and user signaling data associations.

[0128] user Timestamp Area Name Region ID Region Type Community ID Other details omitted. MDN1 2021 / 7 / 18 13:44:20 Guangzhou South Railway Station 2001 high-speed rail MDN1 2021 / 7 / 18 14:14:20 Guangzhou South Railway Station 2001 high-speed rail MDN1 2021 / 7 / 18 14:24:20 Guangzhou South Railway Station 2001 high-speed rail MDN1 2021 / 7 / 18 14:33:20 Guangzhou North Railway Station 2002 high-speed rail MDN1 2021 / 7 / 18 14:35:20 Guangzhou North Railway Station 2002 Airport

[0129] (5) Data processing:

[0130] The data for that time range is extracted from the "Regional and User Signaling Data Association List" using a statistical period (T, e.g., days) + extension period (X, e.g., 6 hours) pattern for statistical analysis. The extracted data is then sorted by user and timestamp, as shown in the following example:

[0131] Example of a list of regions and user signaling data associations.

[0132]

[0133] For each user, records of adjacent times within the same area are aggregated to obtain "User Travel Trajectory Statistics". Key information includes: date, user number, area ID, area type, start and end times (earliest and latest timestamps in the area), duration of stay (latest timestamp - earliest timestamp, unit: minutes / second), number of records, etc. An example is shown below:

[0134] User travel trajectory statistics

[0135]

[0136] Note: "Interval" is an abbreviation for the area between regions.

[0137] Remove interfering users from the aggregated data (staff members and passersby within the region), i.e., delete users who appear only in one region during the statistical period, or users who appear in two adjacent regions of different types with overlapping data (the two regions are close in distance but different in type, such as a high-speed rail station and an airport), and whose appearance times overlap but have no other records in distant regions. See MDN2 in the table above. The rule for determining proximity is: the distance between the center points of two regions is less than 3 kilometers; anything exceeding 3 kilometers is considered distant. The 3-kilometer threshold is a configurable parameter that can be adjusted according to actual conditions (only a few cities nationwide have high-speed rail stations and airports bordering each other, belonging to this type of overlapping region; in such cases, the center points of each region need to be determined based on the actual region selection area before calculating the distance between overlapping regions).

[0138] Generate a user area migration data list (MDN, departure station A, arrival station B, start time of each segment, duration between stations, distance between stations, mode of transportation, etc.) based on the remaining data according to user migration intervals. Only user data that has migrated within period T is stored. Period X is used to determine whether users have moved within period T. If a user belongs to period X but not period T, they are not saved, and these users are saved in the next period T.

[0139] Special case handling:

[0140] (1) AAB model: If there is an overlapping area at the starting point and no overlapping area at the ending point, then the record with the same type as the starting area is directly matched using the type of the ending area (the record with a different type of area is discarded and does not need to be matched) to generate the user migration record.

[0141] (2) ABB model: If there is an overlapping area at the destination but no overlapping area at the starting point, then the starting point area type is directly matched with the destination area type, the other record with a different area type is discarded, and a migration record is generated.

[0142] (4) AABB model: If there is an overlap between the starting and ending areas, the starting / ending area is determined by comprehensively evaluating factors such as inter-station distance, inter-station duration (the last timestamp of leaving the previous station minus the earliest timestamp of entering the next station, in minutes or seconds), and inter-station speed (inter-station distance / inter-station duration, in kilometers / hours) (discarding the other one). For example, if the inter-station distance exceeds 300 kilometers and the inter-station speed exceeds 400 kilometers / hour, both area types can be regarded as airports; otherwise, they are high-speed rail. Specific parameter thresholds can be configured and adjusted according to the actual situation.

[0143] (5) ABBC model: This model has no overlapping areas at the starting point, overlapping areas in the middle, and no overlapping areas at the ending point. The starting point and ending point have different area types, while the middle point has two area types. In this case, the area type from the starting point to the middle point is used, and the area type from the middle point to the ending point is used. Transfer records between different area types at the middle nodes are added, and the mode of transportation is filled in as "transfer". In this case, three migration records need to be saved, namely AB, BB, and BC.

[0144] The ABAB (BABA) model involves users continuously switching between overlapping areas (A and B belong to the same city, and the distance between areas is less than a threshold). If no subsequent area records are available, the user is considered to be either a worker or a passerby in that overlapping area. If subsequent area records are available, the process can be handled according to the four models mentioned earlier. Records of switching between areas are compressed into two records, one for A and one for B, and then processed according to the subsequent trajectory using models 1-4.

[0145] The generated list of user migration records is shown below (main fields are extracted).

[0146] User migration record list (sample)

[0147]

[0148] (6) Statistical analysis:

[0149] User migration statistics (origin and destination) can be performed by region name, province, city, mode of transportation, etc., to trace user trajectories, or further analyze user travel trajectories using MR precise positioning data. Based on user travel history information, it is possible to distinguish between high-speed rail / airplane crew members and ordinary passengers, passenger travel modes, stay duration, travel frequency, etc. (user profiling).

[0150] A graph database can be used to store user travel trajectories. Nodes include: region name (high-speed rail / airport, attributes include: province, city, area code, etc.) and user number (attributes include: age, gender, province, city, etc.). Directed edges are constructed based on user destinations (edge ​​attributes include time, mode of transportation, stay duration, etc.), and undirected edges are constructed based on high-speed rail lines. This facilitates graph retrieval, tracing user trajectories, and statistical analysis of nodes.

[0151] The main advantages and benefits of this disclosure are:

[0152] Based on the customizable area selection of cells, this method offers strong operability and controllable data scale: Based on the current number of airports and high-speed rail stations (200+ high-speed rail stations, 700+ bullet train stations, and 200+ airports), this method selects cells that can actually cover the selected area (where passengers must pass through). Then, it collects data according to the cell list and updates the cell list regularly based on the addition or change of the area, making the scale of collected and processed data controllable and easy to operate (currently, the group's data center gathers XDR+MR data from all provinces in China and can customize data according to the cell list, with an XDR sampling frequency of 15-minute granularity).

[0153] Accurately identify the origin and destination of passengers in a region: Combining the characteristics of "closed" transportation between regions by high-speed rail and airplanes, the method disclosed in this paper can effectively eliminate interference data, accurately identify the origin and destination of passengers in each region, and further distinguish between ordinary passengers and crew members by combining historical data.

[0154] This publicly available method, based on user mobile phone signaling data, leverages the point-to-point closed-loop transportation characteristics of high-speed rail and airports—where passengers who board the train / plane will inevitably appear at the next station, with no passengers being dropped off midway. This allows for easy elimination of regional staff and transient passengers, enabling precise identification of passenger origins and destinations at major transportation hubs such as airports and high-speed rail stations. It facilitates multi-dimensional statistical analysis of passenger flow at these hubs, as well as user profiling and trajectory tracing. The publicly available method utilizes a controllable data range and scale (over 200 high-speed rail stations nationwide, up to 40 airports, and over 10,000 base stations, equivalent to the scale of a local network), making it highly operable.

[0155] The main technical points are as follows:

[0156] (1) Method for identifying the origin and destination of passengers in the region: Make full use of the characteristics of high-speed rail and air transport, long-distance point-to-point transportation, no passengers getting on or off in the middle (unlike car transport), and accurately determine the origin and destination of passengers by integrating the signaling information of various transportation hubs and according to the time sequence.

[0157] (2) Regional passerby removal method: Based on signaling cell-level data collection, the output frequency is high and fast (and the source end does not need secondary processing, unlike MR data which requires MDN backfilling and latitude and longitude prediction, etc.). Furthermore, due to the point-to-point transportation characteristics, regional passersby will not appear in other areas, which facilitates the removal of interference data.

[0158] (3) Overlapping area arrival and departure identification mechanism: By comprehensively evaluating and analyzing factors such as the type of area the passenger passes through, the distance between sections, and the duration of the section, interference factors are eliminated and the compatibility of the method is enhanced.

[0159] (4) Output data facilitates multidimensional statistical analysis: Saves the arrival and departure information of each user segment, which facilitates multidimensional statistical analysis of passenger migration and regional passenger flow according to region, user, province, city, region type, etc., and can trace user trajectory, etc.

[0160] Figure 5 The diagram schematically illustrates a block diagram of a user mobility information acquisition device according to an embodiment of the present disclosure. The user mobility information acquisition device 500 provided in this embodiment can be located on a server, or partially on a terminal and partially on the server; for example, it can be located on... Figure 1 The server 105 (Secure Computing Platform) is mentioned, but this disclosure is not limited thereto.

[0161] The device 500 for acquiring user mobile information provided in this embodiment may include a selection module 510, a first acquisition module 520, a second acquisition module 530, a third acquisition module 530, and a fourth acquisition module 540.

[0162] The selection module is configured to select an analysis region within a first range.

[0163] The first acquisition module is configured to acquire base station cells whose coverage overlaps with the analysis area;

[0164] The second acquisition module is configured to acquire signaling data of the overlapping base station cells within a first time period;

[0165] The third acquisition module is configured to acquire user dwell information in each region of the analysis area based on signaling data within the first time period.

[0166] The fourth acquisition module is configured to acquire the user's movement information between different areas in the analysis area based on the residency information.

[0167] According to embodiments of this disclosure, the user mobile information acquisition device 700 described above can be used to implement... Figure 3 The method for obtaining user mobility information is described in the implementation method. Furthermore, for any omissions in the description of the apparatus, please refer to the description of the method and system; these will not be repeated here.

[0168] Figure 6 A block diagram of an apparatus 600 for acquiring user movement information according to another embodiment of the present disclosure is shown schematically.

[0169] like Figure 6 As shown, except Figure 5In addition to the selection module 510, the first acquisition module 520, the second acquisition module 530, the third acquisition module 530, and the fourth acquisition module 540 described in the embodiments, the device 600 for acquiring user mobility information also includes a display module 610.

[0170] Specifically, after the fourth acquisition module acquires the user's movement information between different areas in the analysis area, the display module 610 displays the user's movement information between different areas in the analysis area to the customer or staff.

[0171] In the device 600 for acquiring user movement information, the user's movement information can be displayed through the display module 610.

[0172] Figure 7 A block diagram of an apparatus 700 for acquiring user movement information according to another embodiment of the present disclosure is shown schematically.

[0173] like Figure 7 As shown, except Figure 5 In addition to the selection module 510, the first acquisition module 520, the second acquisition module 530, the third acquisition module 530, and the fourth acquisition module 540 described in the embodiments, the device 700 for acquiring user mobility information also includes a storage module 710.

[0174] Specifically, the storage module 710 is used to store data during the processing of acquiring user mobile information for easy retrieval and reference later.

[0175] It is understood that the selection module 510, the first acquisition module 520, the second acquisition module 530, the third acquisition module 530, the fourth acquisition module 540, the display module 610, and the storage module 710 can be implemented in a single module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in a single module. According to embodiments of this disclosure, at least one of the selection module 510, the first acquisition module 520, the second acquisition module 530, the third acquisition module 530, the fourth acquisition module 540, the display module 610, and the storage module 710 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or can be implemented in hardware or firmware in any other reasonable manner of integrating or packaging the circuitry, or in a suitable combination of software, hardware, and firmware implementations. Alternatively, at least one of the following modules can be implemented, at least partially, as a computer program module: the selection module 510, the first acquisition module 520, the second acquisition module 530, the third acquisition module 530, the fourth acquisition module 540, the display module 610, and the storage module 710. When the program is run by a computer, it can perform the functions of the corresponding module.

[0176] Since each module of the user mobility information acquisition apparatus of the exemplary embodiments of this disclosure can be used to implement the above... Figure 3 The steps of the example implementation of the method for obtaining user mobility information described herein are as follows: therefore, for details not disclosed in the device implementation of this disclosure, please refer to the implementation of the method for obtaining user mobility information described above.

[0177] The specific implementation of each module, unit, and subunit in the device for acquiring user mobility information provided in this embodiment can refer to the content of the method for acquiring user mobility information described above, and will not be repeated here.

[0178] It should be noted that although several modules, units, and sub-units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules, units, and sub-units described above can be embodied in a single module, unit, or sub-unit. Conversely, the features and functions of a single module, unit, and sub-unit described above can be further divided into multiple modules, units, and sub-units.

[0179] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this disclosure.

[0180] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0181] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for obtaining user mobile information, characterized in that, include: Select the analysis region within the first range; Obtain base station cells whose coverage overlaps with the analysis area; Obtain signaling data for the first time period of the overlapping base station cells; Based on the signaling data within the first time period, obtain the user's residency information in each region of the analysis area; Based on the residency information, obtain the user's movement information between different areas in the analysis area.

2. The method according to claim 1, characterized in that, The analysis region selected within the first range includes: Within the first range, the area that the user must pass through is selected as the analysis area.

3. The method according to claim 1, characterized in that, The base station cells whose coverage overlaps with the analysis area include: The gridded coverage of base station cells in the region where the analysis area is located is obtained based on the measurement report with AGPS. Based on the gridded coverage of the base station cells in the region where the analysis area is located and the analysis area, obtain the base station cells that overlap with the analysis area.

4. The method according to claim 1, characterized in that, Also includes: Users with the aforementioned residency information are cleaned to remove interfering users from the analysis area.

5. The method according to claim 4, characterized in that, Cleaning users with the aforementioned residency information to remove interfering users from the analysis area includes: Remove interfering users whose resident information exists only in one of the analysis areas.

6. The method according to claim 1, characterized in that, Also includes: Obtain measurement reports, including AGPS, within the second range for the user; Based on the user's AGPS measurement report and artificial intelligence prediction device, the latitude and longitude of the user's measurement report without AGPS is predicted.

7. The method according to claim 6, characterized in that, Also includes: The user's movement trajectory within the second range is obtained based on the actual AGPS and predicted latitude and longitude from the user's measurement report.

8. A device for acquiring user mobile information, characterized in that, include: Select the module and configure it to select the analysis region within a first range; The first acquisition module is configured to acquire base station cells whose coverage overlaps with the analysis area; The second acquisition module is configured to acquire signaling data of the overlapping base station cells within a first time period; The third acquisition module is configured to acquire user dwell information in each region of the analysis area based on signaling data within the first time period. The fourth acquisition module is configured to acquire the user's movement information between different areas in the analysis area based on the residency information.

9. An electronic device, characterized in that, include: One or more processors; A storage device configured to store one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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