A method, device and equipment for determining a cell camping user migration type

Abstract

The application discloses a cell residence user migration type determination method, device and equipment, the method comprises the following steps: obtaining network data of a user residence cell; obtaining a migration degree value of a user residence area according to the network data; determining the migration type of the user residence area according to the migration degree value. Through the above method, the change degree of the network service cell of the user can be intuitively reflected, and whether the user has migrated can be effectively identified; whether the user is passively migrated due to network failure can be accurately judged; the network hidden failure can be accurately identified, effective data support can be provided for network maintenance personnel to check cell failure quickly, and the network optimization efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of wireless access network technology, and specifically to a method, apparatus, and device for determining the migration type of a resident user in a cell. Background Technology

[0002] With increasingly higher demands for network quality from wireless network users, quickly locating faulty base station cells and promptly carrying out repairs to reduce user network quality complaints is a pressing issue. Current network monitoring primarily relies on backend alarms. For cells with hidden faults, such as antenna problems or passive component failures, network optimization personnel need to conduct layer-by-layer testing and troubleshooting on-site. Analysis of user migration due to network faults typically involves determining the user's permanent base station cell based on base station information from the user's mobile voice call or internet traffic records, or base station information from the network-side mobility management entity signaling, and further, based on the configuration information of the correspondence between base stations and residential cells. Summary of the Invention

[0003] In view of the above problems, embodiments of the present invention are proposed to provide a method, apparatus and device for determining the migration type of a resident user in a cell to overcome or at least partially solve the above problems.

[0004] According to one aspect of the present invention, a method for determining the migration type of a cell-based user is provided, comprising:

[0005] Obtain network data of the user's registered community;

[0006] Based on the network data, the migration level value of the user's residence area is obtained;

[0007] Based on the migration level value, the migration type of the user's residence area is determined.

[0008] According to another aspect of the present invention, an apparatus for determining the migration type of a resident user in a cell is provided, comprising:

[0009] The acquisition module is used to acquire network data of the user's registered cell.

[0010] The processing module is used to obtain the migration degree value of the user's residence area based on the network data; and to determine the migration type of the user's residence area based on the migration degree value.

[0011] According to another aspect of the present invention, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0012] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the method for determining the migration type of the cell-resident user described above.

[0013] According to another aspect of the present invention, a computer storage medium is provided, the storage medium storing at least one executable instruction, the executable instruction causing a processor to perform an operation corresponding to the method for determining the migration type of a cell resident user described above.

[0014] According to the solution provided in the above embodiments of the present invention, the method for determining the migration type of a user residing in a cell can obtain network data of the user's cell; obtain the migration degree value of the user's area based on the network data; and determine the migration type of the user's area based on the migration degree value. This solves the problems of being unable to accurately locate a faulty base station cell, requiring further on-site investigation and consuming significant manpower and resources; the limited scope, low efficiency, and high cost of troubleshooting latent faults; and the need for manual maintenance of the configuration information of the correspondence between base stations and cells, leading to frequent network optimization adjustments and a high probability of errors in the correspondence. It also addresses the problem of inaccurate analysis of user migration behavior in base station cells, achieving a direct reflection of the degree of change in the user's network service cell, effectively identifying whether a user has migrated; accurately determining whether a user's passive migration is due to a network fault; accurately identifying latent network faults; and providing effective data support for network maintenance personnel to quickly verify cell faults, thus improving network optimization efficiency.

[0015] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific implementation methods of the embodiments of the present invention are described below. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0017] Figure 1 A flowchart of a method for determining the migration type of a resident user in a cell according to an embodiment of the present invention is shown;

[0018] Figure 2 A flowchart of a method for determining the migration type of a cell-based user according to a specific embodiment 2 of the present invention is shown;

[0019] Figure 3This diagram illustrates the structure of the device for determining the migration type of a resident user in a cell according to an embodiment of the present invention.

[0020] Figure 4 A schematic diagram of the structure of a computing device provided in an embodiment of the present invention is shown. Detailed Implementation

[0021] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0022] Figure 1 A flowchart illustrating a method for determining the migration type of a cell-based user according to an embodiment of the present invention is shown. Figure 1 As shown, the method includes the following steps:

[0023] Step 11: Obtain network data of the user's residential community;

[0024] Step 12: Based on the network data, obtain the migration level value of the user's residence area;

[0025] Step 13: Determine the migration type of the user's residence area based on the migration degree value.

[0026] In this embodiment, the migration degree value of the cell in the user's camping area is obtained by calculation and other methods from the network data of the user's camping cell, and the migration type of the user's camping area is determined based on the migration degree value; wherein, the user's camping cell is the cell occupied by the user at a certain moment; the network data includes XDR signaling data, user service data and MRO / MDT data, where MRO refers to materials and services for maintenance, repair and operation of equipment, and MDT refers to mobile data terminal;

[0027] It should be noted that users generally reside in only one cell. Therefore, a user's cell can be categorized into two scenarios: Scenario 1, where the user is in the same location at different times but resides in a different cell at different times; Scenario 2, where the user is in a different location at different times but resides in the same cell at different times. A user's cell at a single moment cannot accurately represent their permanent cell. Since users with relatively stable spatial location information generally have relatively stable user residence areas, this application analyzes the mobility of user residence cells to form a user residence area from the set of cell sets of user residence cells over a period of time.

[0028] By processing the migration level value of the user's residence area, the migration type of the user's residence area is obtained, which can intuitively reflect the degree of change of the user's network service cell, effectively identify whether the user has migrated; accurately determine whether the user has been passively migrated due to network failure; accurately identify hidden network faults, and quickly provide effective data support for network maintenance personnel to check cell faults, thereby improving the efficiency of network optimization.

[0029] In an optional embodiment of the present invention, step 12 includes:

[0030] Step 121: Obtain a user residence area feature table based on the network data;

[0031] Step 122: Obtain the migration degree value of the user's residence area based on the user residence area feature table.

[0032] The user residence area feature table includes: user identifier, cell number corresponding to the user identifier, and at least two feature values ​​corresponding to the cell number.

[0033] This embodiment obtains a user residence area feature table based on network data, and then obtains the migration degree value of the user residence area. Preferably, the user residence area feature table includes user identifiers in two different time periods, the cell number corresponding to the user identifier, and at least two feature values ​​corresponding to the cell number. By processing the feature value information in the two different time periods, the migration degree value of the user residence area can be obtained.

[0034] In an optional embodiment of the present invention, step 121 includes:

[0035] Step 1211: Normalize the feature values ​​in the user residence area feature table to obtain normalized values;

[0036] Step 1212: Obtain the migration degree value of the user's residence area based on the normalized value.

[0037] In this embodiment, the feature values ​​in the user residency area feature table are normalized. Preferably, this can be achieved using the formula... The values ​​of i = 1, 2, ..., n are normalized to obtain normalized values, where n is a positive integer and x... ij The normalized value represents the number of processes of the i-th user in the j-th cell within a time period, where n is the number of users and m is the number of cells. Based on the normalized value, the migration degree of the user's residence area is obtained.

[0038] Step 1212 specifically includes:

[0039] Step 12121: Obtain the distance between the first normalized value of the first eigenvalue and the second normalized value of the second eigenvalue;

[0040] Step 12122: Obtain the migration degree value of the user's residence area based on the distance.

[0041] In this embodiment, the first feature value is the feature value within the first time period, and the second feature value is the feature value within the second time period. There is a corresponding relationship between the first time period and the second time period. By calculating the distance between the first normalized value of the first feature value and the second normalized value of the second feature value, the migration degree value of the user's residence area is obtained. This migration degree value is used to represent the degree of migration of the user in the residence area between different cells.

[0042] It should be noted that the distance between the first normalized value and the second normalized value is preferably calculated using the Euclidean distance method, but this application is not limited to this, and the distance can also be the Harman distance or the absolute distance, etc.

[0043] It can be done through the formula: The distance between the first normalized value and the second normalized value is calculated; where E is the distance between the first normalized value and the second normalized value, and s 1j s is the first normalized value of the number of processes in the j-th cell within the first time period. 2j Let be the second normalized value of the number of processes in the j-th cell during the second time period. The range of the first and second normalized values ​​is [0,1]. Therefore, the range of the migration degree value is...

[0044] In a specific embodiment 1, taking XDR signaling data as an example, the user identifier, cell number, number of processes in time period 1, and number of processes in time period 2 are statistically obtained for each user in two time periods, as shown in Table 1:

[0045]

[0046]

[0047] Table 1

[0048] Table 1 shows the user with user ID MS1. The cells within the user's camping area are cells A to H, where A to H are cell numbers used to distinguish different cells within the user's camping area. Cell A has 1000 processes in time period 1 and 300 processes in time period 2; Cell B has 405 processes in time period 1 and 1500 processes in time period 2; Cell C has 0 processes in time period 1 and 143 processes in time period 2; Cell D has 25 processes in time period 1 and 30 processes in time period 2; Cell E has 10 processes in time period 1 and 59 processes in time period 2; Cell F has 4 processes in time period 1 and 0 processes in time period 2; Cell G has 1 process in time period 1 and 1 process in time period 2; Cell H has 1 process in time period 1 and 0 processes in time period 2.

[0049] Through formula The normalization processes for time period 1 and time period 2 are performed on i = 1, 2, ..., n respectively. The normalized values ​​are shown in Table 2.

[0050]

[0051] Table 2

[0052] Table 2 clearly shows that the user identified as MS1 changed their primary cell usage area from cell A in time period 1 to cell B in time period 2. While it can be determined that a migration of the user's home area occurred in both time periods, the user shared the same home cell, making it impossible to quantify the degree of migration. Therefore, the distance between the normalized values ​​of time period 1 and time period 2 is used to quantify the degree of migration, using the formula:

[0053] The distance between the normalized value of time period 1 and the normalized value of time period 2 is calculated. Specifically:

[0054]

[0055] Among them, E MS1 E represents the distance between the normalized value of user MS1 in time period 1 and the normalized value in time period 2. MS1 =0.544 is a migration level value that quantifies the degree of migration of users' residence areas;

[0056] Table 2 also shows that during the migration of user MS1's registered cell, there were instances of the same registered cell, but there were also instances where the user did not have the same registered cell between time period 1 and time period 2, as shown in Table 3:

[0057]

[0058] Table 3

[0059] Table 3 shows the number of processes and the normalized value of the number of processes for user MS2 within the user's home area during time periods 1 and 2. The user's home cell within this area includes cells with cell numbers A to L. As shown in Table 3, user MS2's home cells do not overlap between time periods 1 and 2, suggesting a preliminary probability of 100% home area migration. Using the formula:

[0060] The calculated migration rate of user MS2 within the user's residence area is 0.577, which is significantly inconsistent with the initial assessment that the probability of user migration within the residence area is 100%. Therefore, the data needs to be processed accordingly. By merging the cells that do not co-reside in time period 1 and time period 2, the results are shown in Table 4.

[0061]

[0062] Table 4

[0063] Table 4 shows the user residency area feature table obtained after merging cells without common residency in the user residency area feature table; the migration degree value of user MS2 is calculated as follows:

[0064]

[0065] The range of migration degree values ​​is as follows: therefore The data shows that the migration level of user MS2 within the user's residence area is the maximum migration level, indicating that user MS2 has undergone absolute migration within the user's residence area, which is consistent with the actual situation.

[0066] In an optional embodiment of the present invention, the migration type in step 13 includes: user relocation migration or network failure migration.

[0067] In this embodiment, preferably, the migration characteristics of cell users can be classified using a machine learning algorithm based on the migration degree value obtained in step 12, identifying the user migration type as user relocation migration or network failure migration. Failure migration is characterized by a high percentage of users migrating to neighboring cells and a low user migration degree value E; user relocation is characterized by a low percentage of users migrating to neighboring cells and a high user migration degree value E. It should be noted that here, neighboring cells refer to cells in the user's residence area where the distance between each other cell and the main cell meets a preset distance, and the main cell is the cell where the user primarily resides within the user's residence area.

[0068] Figure 2 A flowchart illustrating a method for determining the migration type of a cell-based user according to a specific embodiment 2 of the present invention is shown. Figure 2 As shown in a specific embodiment 2, by collecting user XDR signaling data, user service data, and MRO / MDT data, a user residence area feature table is constructed. The migration degree value E for each period is calculated by combining the 24-hour data of users whose primary residence is cell A during the 8th to 14th day period with the data from the 1st to 7th day period. The migration characteristics of users in the primary cell are analyzed to identify whether the users are moving away or migrating due to network failure. Moving away indicates that a user moves from their original residence cell for personal reasons, such as moving from home, company, or leaving school for holidays. Network failure migration indicates that the network service cell changes due to a network failure or network adjustment, causing the user's primary residence cell to migrate, even though the user may not have actually moved. The specific steps are as follows:

[0069] (1) Using the user residence area feature table, find the user set U with cell A as the primary cell during the period from day 8 to day 14, and count the number of users in user set U. u ;

[0070] (2) Calculate the migration degree value E for each user in user set U during the 24-hour period from day 8 to day 14, and calculate the minimum migration degree value E. min The average migration level E of each user in user set U avg and median value E mid ;

[0071] (3) Count the number of users in user set U whose primary cell A changes. At the same time, count the number of migrated cells for users in user set U who were in primary cell A during the period from day 1 to day 7, and for users in user set U who changed from primary cell A to other cells in the user's residence area during the period from day 8 to day 14. c ;

[0072] (4) Calculate the distance from the main cell A to each cell in the cell set C. The preferred distance can be calculated using the latitude and longitude of the cell. Set cells with a distance of less than 500 meters as neighboring cells of cell A, and then count the number of users who migrated from cell A to neighboring cells. un and the number of communities cn ;

[0073] (5) The number of users through user set U u The minimum migration degree value E for each user in the user set U. min Average value E avg and median value E mid The number of users who migrated from main cell A to other cells (count) c The number of users who migrated from main cell A to neighboring cells (count) un and the number of communities cn Using machine learning algorithms, the migration characteristics of users in a community are classified to identify whether a user is moving out or migrating due to network failure.

[0074] In an optional embodiment of the present invention, the method for determining the migration type of a resident user in a cell further includes:

[0075] Step 14: Locate network faults based on the migration type of the user's residency area.

[0076] In this embodiment, by judging the user's migration type, preferably by combining data information such as historical network adjustment data, network alarm data, and network fault data, the FP-growth association algorithm is used to calculate the correlation between various network adjustments, network faults, and various alarms and network faults. The frequent itemset Z of network faults that occur during network adjustments or alarms is mined. After identifying users in the cell as migrating due to network faults, the current network adjustment records and network alarm records are collected, and then the frequent itemset Z of network faults is matched to further classify network faults into network adjustments, explicit faults, and implicit faults, thereby realizing the location of network fault cells.

[0077] The solution of this invention obtains network data of the user's residential cell; obtains a migration degree value of the user's residential area based on the network data; and determines the migration type of the user's residential area based on the migration degree value. This enables a direct reflection of the degree of change in the user's network service cell, effectively identifying whether the user has migrated; accurately determining whether the user's migration is due to a network failure; accurately identifying hidden network faults; and quickly providing effective data support for network maintenance personnel to check cell faults, thereby improving network optimization efficiency.

[0078] Figure 3A schematic diagram of the structure of the device for determining the migration type of a resident user in a cell according to an embodiment of the present invention is shown. Figure 3 As shown, the device 30 includes:

[0079] Module 31 is used to acquire network data of the user's registered cell.

[0080] The processing module 32 is used to obtain the migration degree value of the user's residence area based on the network data; and to determine the migration type of the user's residence area based on the migration degree value.

[0081] Optionally, based on the network data, the migration level value of the user's residence area is obtained, including:

[0082] Based on the network data, a user residence area feature table is obtained;

[0083] Based on the user residence area feature table, the migration degree value of the user residence area is obtained.

[0084] Optionally, the user residency area feature table includes:

[0085] User identifier, the cell number corresponding to the user identifier, and at least two feature values ​​corresponding to the cell number.

[0086] Optionally, based on the user residency area feature table, the migration degree value of the user residency area is obtained, including:

[0087] The feature values ​​in the user residency area feature table are normalized to obtain normalized values;

[0088] Based on the normalized value, the migration degree value of the user's residence area is obtained.

[0089] Optionally, based on the normalized value, the migration degree value of the user's residence area is obtained, including:

[0090] Obtain the distance between the first normalized value of the first eigenvalue and the second normalized value of the second eigenvalue;

[0091] The degree of user residence area migration is obtained based on the distance.

[0092] Optionally, the migration type includes: user relocation migration or network failure migration.

[0093] Optionally, the processing module 32 is also used to locate network faults based on the migration type of the user's residence area.

[0094] It should be noted that this device is the same as the method for determining the migration type of users residing in the aforementioned cell. All implementation methods in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effect.

[0095] This invention provides a non-volatile computer storage medium storing at least one executable instruction that can execute the method for determining the migration type of a cell-resident user in any of the above method embodiments.

[0096] Figure 4 The diagram shows a structural schematic of a computing device provided in an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0097] like Figure 4 As shown, the computing device may include a processor, a communications interface, memory, and a communications bus.

[0098] The processor, communication interface, and memory communicate with each other via a communication bus. The communication interface is used to communicate with other network elements, such as clients or other servers. The processor executes programs, specifically the steps described in the embodiment of the method for determining the migration type of cell-based users for computing devices.

[0099] Specifically, the program may include program code, which includes computer operation instructions.

[0100] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0101] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0102] Specifically, the program can be used to cause the processor to execute the method for determining the migration type of the cell-resident user in any of the above method embodiments. The specific implementation of each step in the program can be found in the corresponding descriptions of the steps and units in the above embodiments of the method for determining the migration type of the cell-resident user, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0103] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the embodiments of the present invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the embodiments of the present invention.

[0104] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0105] Similarly, it should be understood that, in order to streamline the embodiments of the invention and aid in understanding one or more of the various inventive aspects, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0106] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0107] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0108] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The embodiments of the present invention can also be implemented as device or apparatus programs (e.g., computer programs and computer program products) for performing part or all of the methods described herein. Such programs implementing the embodiments of the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0109] It should be noted that the above embodiments are illustrative of the present invention and not restrictive of the invention, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the present invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for determining the migration type of a resident user in a residential community, characterized in that, include: Obtain network data of the user's registered community; Based on the network data, a user residence area feature table is obtained; Based on the user residence area feature table, obtain the migration degree value of the user residence area; Based on the migration level value, determine the migration type of the user's residence area; The migration types include: user relocation migration or network failure migration; The step of obtaining the migration degree value of the user's residence area based on the user residence area feature table includes: The feature values ​​in the user residency area feature table are normalized to obtain normalized values; Obtain the distance between the first normalized value of the first feature value and the second normalized value of the second feature value; wherein, the first feature value is the feature value within the first time period, the second feature value is the feature value within the second time period, and there is a corresponding relationship between the first time period and the second time period; Based on the distance, the migration level value of the user's residence area is obtained.

2. The method for determining the migration type of a resident user in a residential community according to claim 1, characterized in that, The user residency area feature table includes: User identifier, the cell number corresponding to the user identifier, and at least two feature values ​​corresponding to the cell number.

3. The method for determining the migration type of a resident user in a residential community according to claim 1, characterized in that, Also includes: Based on the migration type of the user's residence area, network fault location is performed.

4. A device for determining the migration type of a resident user in a residential community, characterized in that, include: The acquisition module is used to acquire network data of the user's registered cell. The processing module is used to obtain a user residence area feature table based on the network data; and to obtain a migration degree value of the user residence area based on the user residence area feature table. Based on the migration level value, the migration type of the user's residence area is determined; the migration type includes: user relocation migration or network failure migration; The processing module is further used for: The feature values ​​in the user residency area feature table are normalized to obtain normalized values; Obtain the distance between the first normalized value of the first feature value and the second normalized value of the second feature value; wherein, the first feature value is the feature value within the first time period, the second feature value is the feature value within the second time period, and there is a corresponding relationship between the first time period and the second time period; Based on the distance, the migration level value of the user's residence area is obtained.

5. A computing device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the method for determining the migration type of a cell resident user as described in any one of claims 1-3.

6. A computer storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the method for determining the migration type of a cell resident user as described in any one of claims 1-3.

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