User identification method and device, electronic device and medium

Abstract

The present disclosure relates to a user identification method and device, electronic equipment and medium, and relates to the field of computer technology. The method comprises: obtaining target cells that overlap with the airport area according to the coverage of each cell; extracting target user trajectory data that intersects with the airport area from user trajectory data according to the cell type of the target cell; identifying the user type of the target user trajectory data according to the indicator information of the target user trajectory data and the parameter information of the airport area. The method can reflect the real network coverage of the region, avoid the miscalculation, omission or over-calculation problems that occur in the related technology of identification through base station coarse positioning and mobile phone number attribution, reduce the amount of calculation for analyzing users in a large range of cells, and improve the accuracy of user identification.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a user identification method and device, electronic equipment and medium. Background Art

[0002] As a transportation hub, the airport is one of the important scenarios for emergency services. The statistical analysis of passenger flow at the airport is a major research topic. Due to user privacy, multiple ticketing channels, multiple airlines, and dynamic ticket changes, it is difficult to directly obtain passenger data. In addition to passengers, there are also a large number of airport send-off personnel, airport staff, and people passing through the airport, which also makes passenger flow statistical analysis difficult.

[0003] In the related technology, airport passengers are identified through rough positioning of base stations and the location of mobile phone numbers, and then passenger flow statistics are analyzed. However, this method is prone to miscalculation, omission or over-counting, and cannot accurately identify passengers or classify airport passenger flow, which affects the results of passenger flow statistics analysis.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The present disclosure provides a user identification method and device, electronic device and medium, which can avoid the miscalculation, omission or over-calculation problems that occur in the related art of identification by means of base station rough positioning and mobile phone number attribution, and improve the accuracy of user identification. The technical solution of the present disclosure is as follows:

[0006] According to one aspect of an embodiment of the present disclosure, a user identification method is provided, including: acquiring a target cell overlapping with an airport area according to the coverage of each cell; extracting target user trajectory data having an intersection with the airport area from user trajectory data according to the cell type of the target cell; and identifying the user type of the target user trajectory data according to indicator information of the target user trajectory data and parameter information of the airport area.

[0007] In one embodiment of the present disclosure, the method also includes: generating the user trajectory data according to the measurement report and S1MME signaling; and if the user trajectory data is obtained according to the assisted global positioning system AGPS positioning information in the measurement report, marking the user trajectory data as a first positioning type; if the user trajectory data is obtained based on the AGPS positioning information in the measurement report and predicted based on a machine learning algorithm, marking the user trajectory data as a second positioning type; if the user trajectory data is obtained according to the longitude and latitude information of the main service cell in the measurement report, marking the user trajectory data as a third positioning type; if the user trajectory data is obtained using the base station longitude and latitude information in the S1MME signaling, marking the user trajectory data as a fourth positioning type.

[0008] In one embodiment of the present disclosure, the method also includes: if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is 100%, marking the target cell as the first type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is greater than or equal to a first preset ratio, marking the target cell as the second type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is greater than or equal to a second preset ratio and less than the first preset ratio, marking the target cell as the third type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is less than the second preset ratio, marking the target cell as the fourth type.

[0009] In one embodiment of the present disclosure, according to the cell type of the target cell, target user trajectory data having an intersection with the airport area is extracted from the user trajectory data, including: if the cell type of the target cell is the first type or the second type, user trajectory data of the first trajectory type is extracted from the user trajectory data, wherein the user trajectory data of the first trajectory type includes user trajectory data of the first positioning type, the second positioning type, the third positioning type and the fourth positioning type in the target cell; if the cell type of the target cell is the third type or the fourth type, user trajectory data of the second trajectory type is extracted from the user trajectory data, wherein the user trajectory data of the second trajectory type includes user trajectory data of the first positioning type and the second positioning type in the target cell, and the user trajectory data of the second trajectory type is within the airport area; if the cell type of the target cell is the third type, user trajectory data of the third trajectory type is extracted from the user trajectory data, wherein the user trajectory data of the third trajectory type includes user trajectory data of the third positioning type in the target cell, and the user trajectory data of the third trajectory type is within the airport area.

[0010] In one embodiment of the present disclosure, the method further includes: acquiring a target user according to the user trajectory data of the first trajectory type, the user trajectory data of the second trajectory type, and the user trajectory data of the third trajectory type.

[0011] In one embodiment of the present disclosure, according to the cell type of the target cell, target user trajectory data that intersects with the airport area is extracted from the user trajectory data, including: extracting user trajectory data of a fourth trajectory type from the user trajectory data, wherein the user trajectory data of the fourth trajectory type is trajectory data of the target user outside the airport area.

[0012] In one embodiment of the present disclosure, the user type of the target user trajectory data is identified based on the indicator information of the target user trajectory data and the parameter information of the airport area, including: obtaining the indicator information of the target user trajectory data, the user trajectory data information of the previous cycle of the target user trajectory data, and the user trajectory data information of the next cycle of the target user trajectory data; according to the acquired indicator information, the parameter information of the airport area, the user trajectory data information of the previous cycle, and the user trajectory data information of the next cycle, the target user trajectory data is analyzed to identify the user type of the target user trajectory data.

[0013] In one embodiment of the present disclosure, the indicator information of the target user trajectory data includes: first time, second time, first distance, second distance, third distance, dwell time, trajectory type, number of repeated occurrences, distance and number of cells passed; wherein, the first time is the earliest time of the user trajectory data, the second time is the latest time of the user trajectory data, the first distance is the farthest distance in the user trajectory data before entering the airport area, the second distance is the farthest distance in the user trajectory data during entering the airport area, and the third distance is the farthest distance in the user trajectory data after leaving the airport area.

[0014] In one embodiment of the present disclosure, the parameter information of the airport area includes: a round-trip time threshold for different locations, a maximum distance between the airport runway and the area center, a permanent residence days threshold, and a working time threshold.

[0015] In one embodiment of the present disclosure, the user types include: arriving passengers, departing passengers, transit passengers, out-of-town passengers, local passengers, staff, people who frequently travel to and from the airport, pick-up and drop-off personnel, and passers-by.

[0016] According to another aspect of an embodiment of the present disclosure, a user identification device is provided, including: an acquisition module, used to acquire a target cell overlapping with an airport area according to the coverage of each cell; an extraction module, used to extract target user trajectory data having an intersection with the airport area from user trajectory data according to the cell type of the target cell; and an identification module, used to identify the user type of the target user trajectory data according to indicator information of the target user trajectory data and parameter information of the airport area.

[0017] In one embodiment of the present disclosure, the acquisition module is also used to: generate the user trajectory data according to the measurement report and S1MME signaling. And, the device also includes a marking module, which is used to: if the user trajectory data is obtained according to the AGPS positioning information in the measurement report, then mark the user trajectory data as the first positioning type; if the user trajectory data is obtained based on the AGPS positioning information in the measurement report and predicted based on the machine learning algorithm, then mark the user trajectory data as the second positioning type; if the user trajectory data is obtained according to the longitude and latitude information of the main service cell in the measurement report, then mark the user trajectory data as the third positioning type; if the user trajectory data is obtained using the base station longitude and latitude information in the S1MME signaling, then mark the user trajectory data as the fourth positioning type.

[0018] In one embodiment of the present disclosure, the marking module is also used for: if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is 100%, marking the target cell as the first type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is greater than or equal to a first preset ratio, marking the target cell as the second type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is greater than or equal to a second preset ratio and less than the first preset ratio, marking the target cell as the third type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is less than the second preset ratio, marking the target cell as the fourth type.

[0019] In one embodiment of the present disclosure, the extraction module is further used to: if the cell type of the target cell is the first type or the second type, extract user trajectory data of the first trajectory type from the user trajectory data, wherein the user trajectory data of the first trajectory type includes user trajectory data of the first positioning type, the second positioning type, the third positioning type and the fourth positioning type in the target cell; if the cell type of the target cell is the third type or the fourth type, extract user trajectory data of the second trajectory type from the user trajectory data, wherein the user trajectory data of the second trajectory type includes user trajectory data of the first positioning type and the second positioning type in the target cell, and the user trajectory data of the second trajectory type is within the airport area; if the cell type of the target cell is the third type, extract user trajectory data of the third trajectory type from the user trajectory data, wherein the user trajectory data of the third trajectory type includes user trajectory data of the third positioning type in the target cell, and the user trajectory data of the third trajectory type is within the airport area.

[0020] In one embodiment of the present disclosure, the extraction module is further used to: acquire the target user according to the user trajectory data of the first trajectory type, the user trajectory data of the second trajectory type, and the user trajectory data of the third trajectory type.

[0021] In one embodiment of the present disclosure, the extraction module is further used to extract user trajectory data of a fourth trajectory type from the user trajectory data, wherein the user trajectory data of the fourth trajectory type is trajectory data of the target user outside the airport area.

[0022] In one embodiment of the present disclosure, the identification module is also used to: obtain index information of the target user trajectory data, user trajectory data information of the previous cycle of the target user trajectory data, and user trajectory data information of the next cycle of the target user trajectory data; analyze the target user trajectory data according to the acquired index information, parameter information of the airport area, user trajectory data information of the previous cycle and user trajectory data information of the next cycle, and identify the user type of the target user trajectory data.

[0023] In one embodiment of the present disclosure, the indicator information of the target user trajectory data includes: first time, second time, first distance, second distance, third distance, dwell time, trajectory type, number of repeated occurrences, distance and number of cells passed; wherein, the first time is the earliest time of the user trajectory data, the second time is the latest time of the user trajectory data, the first distance is the farthest distance in the user trajectory data before entering the airport area, the second distance is the farthest distance in the user trajectory data during entering the airport area, and the third distance is the farthest distance in the user trajectory data after leaving the airport area.

[0024] In one embodiment of the present disclosure, the parameter information of the airport area includes: a round-trip time threshold for different locations, a maximum distance between the airport runway and the area center, a permanent residence days threshold, and a working time threshold.

[0025] In one embodiment of the present disclosure, the user types include: arriving passengers, departing passengers, transit passengers, out-of-town passengers, local passengers, staff, people who frequently travel to and from the airport, pick-up and drop-off personnel, and passers-by.

[0026] According to another aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the above-mentioned user identification method.

[0027] According to another aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the above-mentioned user identification method.

[0028] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects: it is possible to analyze the coverage of each cell and obtain the target cell that overlaps with the airport area, and then extract the user trajectory that intersects with the airport area according to the cell type of the target cell, and then analyze these trajectories using the indicator information contained in the trajectory and the parameter information of the airport area to identify the corresponding user type, which can reflect the actual network coverage of the area and avoid the miscalculation, omission or over-calculation problems that occur in the related technology of identification through base station coarse positioning and mobile phone number attribution. In addition, the user trajectory is extracted according to the cell type of the target cell to meet the analysis of users in the target cell, which can reduce the amount of calculation for analyzing users in a large area of ​​cells and improve the accuracy of user identification.

[0029] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0031] Figure 1 is a flow chart showing a user identification method according to an exemplary embodiment;

[0032] Figure 2 is a schematic diagram of an airport area and a target cell according to an exemplary embodiment;

[0033] Figure 3 is a flow chart of a user identification method according to another exemplary embodiment;

[0034] Figure 4 is a block diagram of a user identification device according to an exemplary embodiment;

[0035] Figure 5 The figure is a structural block diagram of an electronic device for user identification according to an exemplary embodiment. DETAILED DESCRIPTION

[0036] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0038] It should be noted that the user information involved in this disclosure, including but not limited to user device information, user personal information, etc., is information authorized by the user or fully authorized by all parties.

[0039] The method provided in the embodiments of the present disclosure can be executed by any type of electronic device, such as a server or a terminal device, or the interaction between a server and a terminal device. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and this application does not limit this.

[0040] Among them, the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0041] The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to these.

[0042] Figure 1 FIG. 1 is a flow chart of a user identification method according to an exemplary embodiment. Figure 1 As shown, the user identification method includes the following steps.

[0043] Step S110: acquiring target cells that overlap with the airport area according to the coverage of each cell.

[0044] Step S120: extracting target user trajectory data that intersects with the airport area from the user trajectory data according to the cell type of the target cell.

[0045] Step S130 , identifying the user type of the target user trajectory data according to the indicator information of the target user trajectory data and the parameter information of the airport area.

[0046] In the user identification method provided by the embodiment of the present disclosure, the coverage of each cell can be analyzed to obtain the target cell that overlaps with the airport area, and then the user trajectory that intersects with the airport area is extracted according to the cell type of the target cell. Then, the trajectory is analyzed using the indicator information contained in the trajectory and the parameter information of the airport area to identify the corresponding user type, which can reflect the actual network coverage of the area and avoid the miscalculation, omission or over-calculation problems that occur in the related technology of identification through base station coarse positioning and mobile phone number attribution. In addition, the user trajectory is extracted according to the cell type of the target cell to meet the analysis of users in the target cell, which can reduce the calculation amount for analyzing users in a large area of ​​cells and improve the accuracy of user identification.

[0047] In step S110, the coverage of each cell can be analyzed, and the cell with overlapping parts with the airport area can be obtained, and the obtained cell is determined to be the target cell. Among them, the coverage of each cell can be obtained according to the measurement report (MeasurementReport, MR) with Assisted Global Positioning System (Assisted Global Positioning System, AGPS) positioning information. When a user uses Internet services through a mobile terminal, MR that can be used to evaluate the quality of the wireless environment is periodically generated. MR can include information such as timing advance (Timing advance, TA), reference signal receiving power (Reference Signal Receiving Power, RSRP), physical cell identification (Physical Cell Identification, PCI), and absolute radio frequency channel number (E-UTRA Absolute RadioFrequency Channel Number, EARFCN). In addition, about 2% of MRs carry AGPS positioning information, and the coverage of each cell can be determined using MRs with AGPS positioning information. The specific implementation is: rasterizing the AGPS positioning information in the MRs, and connecting the grids covered by each cell together according to the rasterization results to obtain the coverage of each cell.

[0048] It should be noted that there are one or more target cells, and the degree of overlap between each target cell and the airport area is different. If a target cell overlaps more with the airport area, it means that the closer the target cell is to the center of the airport area, the more accurate the result will be if the user behavior trajectory in the target cell is used to analyze the user type. Therefore, after obtaining the target cell, the cell type can be marked, and the target user trajectory data can be extracted based on the cell type later.

[0049] In an exemplary embodiment, the user identification method may also include: if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is 100%, marking the target cell as the first type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is greater than or equal to a first preset ratio, marking the target cell as the second type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is greater than or equal to a second preset ratio and less than the first preset ratio, marking the target cell as the third type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is less than the second preset ratio, marking the target cell as the fourth type.

[0050] It can be seen that the cell type may include the first type, the second type, the third type and the fourth type. The cell type of the target cell may be determined by the coverage rate of the target cell, wherein the coverage rate of the target cell is the ratio of the overlapping part of the coverage range of the target cell and the airport area to the coverage range of the target cell. In addition, the first preset ratio is greater than the second preset ratio, such as the first preset ratio is 80% and the second preset ratio is 50%, then it can be obtained that: (1) if the target cell is of the first type, it means that the coverage rate of the target cell is 100%, that is, the coverage range of the target cell is within the airport area; (2) if the target cell is of the second type, it means that the coverage rate of the target cell is greater than or equal to 80%; (3) if the target cell is of the third type, it means that the coverage rate of the target cell is greater than or equal to 50% and less than 80%; (4) if the target cell is of the fourth type, it means that the coverage rate of the target cell is less than 50%.

[0051] Figure 2 FIG. 1 is a schematic diagram showing an airport area and a target cell according to an exemplary embodiment. Figure 2 , the cells that overlap with the airport area are cells A, B, C, D and E. The coverage of cells A and B is within the airport area, and their cell types are both of the first type, the cell type of cell C is the second type, the cell type of cell D is the third type, and the cell type of cell E is the fourth type.

[0052] Of course, the first type, the second type, the third type and the fourth type are used to distinguish the coverage of the cell, and can also be expressed in other forms. For example, if the coverage of the target cell is 100%, its cell type is marked as C0; if the coverage of the target cell is greater than or equal to the first preset ratio, its cell type is marked as C1; if the coverage of the target cell is greater than or equal to the second preset ratio and less than the first preset ratio, its cell type is marked as C2; if the coverage of the target cell is less than the second preset ratio, its cell type is marked as C3.

[0053] In step S120, target user trajectory data intersecting with the airport area range may be extracted from the user trajectory data according to the cell type of the target cell.

[0054] In an exemplary embodiment, the user identification method may further include: generating user trajectory data according to the measurement report and S1MME signaling. And, if the user trajectory data is obtained according to the assisted global positioning system AGPS positioning information in the measurement report, marking the user trajectory data as the first positioning type; if the user trajectory data is obtained according to the AGPS positioning information in the measurement report and predicted based on the machine learning algorithm, marking the user trajectory data as the second positioning type; if the user trajectory data is obtained according to the latitude and longitude information of the primary service cell in the measurement report, marking the user trajectory data as the third positioning type; if the user trajectory data is obtained using the base station latitude and longitude information in the S1MME signaling, marking the user trajectory data as the fourth positioning type.

[0055] Specifically, MR precise positioning can be mainly used, supplemented by S1MME signaling, and user trajectory data can be fitted and generated through algorithms such as clustering and outlier removal, and the generated user trajectory data can be marked as the first positioning type, the second positioning type, the third positioning type, or the fourth positioning type. It has been explained above that MR can include TA, RSRP, PCI, EARFCN, and about 2% of MRs carry AGPS positioning information. S1MME is a control plane interface that can connect the base station and the mobility management entity (MME), and mainly completes the radio access bearer control of the S1 interface (i.e., the communication interface between the base station LTE eNodeB and the packet core network EPC), interface-specific operation and maintenance, and other functions.

[0056] In the disclosed embodiment, the method for acquiring user trajectory data of different positioning types is different. For MRs with AGPS positioning information, user trajectory data can be directly generated based on the AGPS positioning information, and the generated user trajectory data can be marked as the first positioning type. In addition, MRs with AGPS positioning information can be used to build a fingerprint library in combination with TA, RSRP, PCI, and EARFCN information in the MR. Then, based on the machine learning algorithm, the AGPS positioning information in the MR without AGPS positioning information can be predicted. Then, user trajectory data is generated based on the predicted AGPS positioning information, and the generated user trajectory data is marked as the second positioning type. In addition, if the TA, RSRP, PCI, and EARFCN information in the MR is missing and the AGPS positioning information in the MR cannot be predicted, the user trajectory data can be generated based on the longitude and latitude information of the main service cell in the MR, and marked as the third positioning type. In addition, the base station longitude and latitude information in the S1MME signaling can also be used to generate user trajectory data and marked as the fourth positioning type. In the process of generating user trajectory data using the base station longitude and latitude information in the S1MME signaling, the base station longitude and latitude information can be used to correct the base station longitude and latitude information in combination with the actual coverage range of the cell.

[0057] The user trajectory data of the first positioning type is obtained directly through the AGPS positioning information, the user trajectory data of the second positioning type is obtained by predicting the AGPS positioning information, the user trajectory data of the third positioning type is obtained through the longitude and latitude information of the main service cell, and the user trajectory data of the fourth positioning type is obtained through the longitude and latitude information of the base station. Therefore, the accuracy of the user trajectory data is from high to low: user trajectory data of the first positioning type, user trajectory data of the second positioning type, user trajectory data of the third positioning type, and user trajectory data of the fourth positioning type. Of course, the first positioning type, the second positioning type, the third positioning type, and the fourth positioning type are used to distinguish the acquisition method of the user trajectory data, and can also be expressed in other forms. For example, if the user trajectory data is obtained according to the AGPS positioning information in the MR, it is marked as the AGPS positioning type; if the user trajectory data is obtained based on the AGPS positioning information in the MR and predicted based on the machine learning algorithm, it is marked as the ML positioning type; if the user trajectory data is obtained according to the longitude and latitude information of the main service cell in the MR, it is marked as the SC positioning type; if the user trajectory data is obtained using the longitude and latitude information of the base station in the S1MME signaling, it is marked as the S1 positioning type.

[0058] It should be noted that in the process of generating user trajectory data, user privacy inclusion measures must be strictly followed, and user privacy data involved in MR and S1MME signaling must be encrypted or desensitized.

[0059] For target cells of different cell types, different trajectory extraction strategies are used to extract user trajectory data and mark the trajectory type. In an exemplary embodiment, according to the cell type of the target cell, the target user trajectory data that intersects with the airport area range is extracted from the user trajectory data, which may include:

[0060] (1) If the cell type of the target cell is the first type or the second type, extract user trajectory data of the first trajectory type from the user trajectory data, wherein the user trajectory data of the first trajectory type includes user trajectory data of the first positioning type, the second positioning type, the third positioning type, and the fourth positioning type in the target cell.

[0061] Considering that the first type and the second type of target cells have a lot of overlap with the airport area, that is to say, the probability that the first type and the second type of target cells are located in the central part of the airport area is relatively high, or if a user's trajectory is within the first type and the second type of target cells, then the user's trajectory can better represent his user type. Therefore, if the cell type of the target cell is the first type or the second type, then the user trajectory data of all positioning types in the cell are extracted. And the extracted user trajectory data is marked as the first trajectory type.

[0062] (2) If the cell type of the target cell is the third type or the fourth type, extract user trajectory data of the second trajectory type from the user trajectory data, wherein the user trajectory data of the second trajectory type includes user trajectory data of the first positioning type and the second positioning type in the target cell, and the user trajectory data of the second trajectory type is within the airport area.

[0063] Considering that the overlap between the third and fourth type target cells and the airport area is not as much as the overlap between the first and second type target cells and the airport area, if the cell type of the target cell is the third or fourth type, then some user trajectory data in the target cell is extracted. Specifically, the user trajectory data of the first positioning type and the second positioning type in the third or fourth type target cell can be extracted, and the extracted user trajectory data needs to be within the airport area. In addition, the extracted user trajectory data is marked as the second trajectory type.

[0064] (3) If the cell type of the target cell is the third type, extract user trajectory data of the third trajectory type from the user trajectory data, wherein the user trajectory data of the third trajectory type includes user trajectory data of the third positioning type in the target cell, and the user trajectory data of the third trajectory type is within the airport area.

[0065] In addition, for the third type of target cell, the user trajectory data of the third positioning type in the target cell can be extracted, and the extracted user trajectory data needs to be within the airport area and can also be marked as the third trajectory type.

[0066] In an exemplary embodiment, the user identification method may further include: acquiring a target user according to user trajectory data of a first trajectory type, user trajectory data of a second trajectory type, and user trajectory data of a third trajectory type. And, according to the cell type of the target cell, extracting target user trajectory data that intersects with the airport area from the user trajectory data, and may further include: extracting user trajectory data of a fourth trajectory type from the user trajectory data, wherein the user trajectory data of the fourth trajectory type is trajectory data of the target user outside the airport area.

[0067] In order to accurately identify the user type, in addition to extracting user trajectory data within the airport area, user trajectory data outside the airport area can also be extracted. Specifically, after obtaining the user trajectory data of the first trajectory type, the second trajectory type, and the third trajectory type, the extracted user trajectory data can be deduplicated to obtain the target user, and then the user trajectory data of the target user outside the airport area can be extracted, and the extracted user trajectory data can be marked as the fourth trajectory type. Among them, the specific range corresponding to the outside of the airport area can be determined according to actual conditions. For example, the airport is located in City A, and the outside of the airport area can be the area of ​​City A excluding the airport area.

[0068] In the embodiment of the present disclosure, after determining the target cell overlapping with the airport area, the cell type of the target cell can be determined according to the degree of overlap, and the positioning type of the user trajectory data can be determined according to the generation method of the user trajectory data. Then, for different target cells, different extraction strategies are used to extract the user trajectory data of the target cell. Table 1 is a trajectory extraction rule table.

[0069] Table 1 Trajectory extraction rules table

[0070]

[0071] Of course, the first trajectory type, the second trajectory type, the third trajectory type, and the fourth trajectory type are used to distinguish the extraction type of user trajectory data, and can also be expressed in other forms. For example, 0, 1, 2, and 3 can be used to represent the extraction type of the trajectory. It should be noted that when aggregating data by user dimension, if the same user has multiple trajectory types, then the smallest trajectory type value is extracted, that is, the user trajectory of the first trajectory type is extracted first, then the user trajectory of the second trajectory type, then the user trajectory of the third trajectory type, and finally the user trajectory of the fourth trajectory type. The reason for this is that the smaller the trajectory type value, the higher its credibility, that is, the more accurate the user type is when it is analyzed and identified.

[0072] Step S130 , identifying the user type of the target user trajectory data according to the indicator information of the target user trajectory data and the parameter information of the airport area.

[0073] Among them, the indicator information of the target user trajectory data may include: first time, second time, first distance, second distance, third distance, dwell time, trajectory type, number of repetitions, distance of trajectory points, and number of cells passed.

[0074] The first time is the earliest time of the user's trajectory data. For each target user's trajectory data, the minimum value of the data timestamp is obtained when different trajectory types in the trajectory data are extracted.

[0075] The second time is the latest time of the user trajectory data. For each target user's trajectory data, the latest value of the data timestamp is extracted when different trajectory types in the trajectory data are extracted.

[0076] The first distance is the farthest distance before entering the airport area in the user's trajectory data. The first distance is the farthest distance from the trajectory point to the center of the area before entering the airport area in the user's trajectory data. If the earliest time is in the area, the first distance is empty.

[0077] The second distance is the farthest distance during the period of entering the airport area in the user's trajectory data. The second distance is the distance between the earliest and latest trajectory points in the user's trajectory data. If there is a trajectory point outside the area, the farthest distance among these trajectory points is selected, and the selected distance is the second distance. If the area is not left in the middle, the second distance is empty.

[0078] The third distance is the farthest distance after leaving the airport area in the user's trajectory data. The third distance is the farthest distance from the trajectory point after leaving the airport area in the user's trajectory data to the center of the area. If the latest time is within the area, the third distance is empty.

[0079] The dwell time may include the dwell time within the airport area and the dwell time outside the airport area.

[0080] The number of dwelling times can be the number of marking times within the airport area and outside the airport area. For example, the user trajectory data is marked according to a 15-minute scale, and the whole day is divided into 96 moments, and the number of markings at each moment is counted.

[0081] The trajectory type is the extraction type of the target user's trajectory data. It should be noted that if the same user has multiple trajectory types, the smallest trajectory type value is extracted.

[0082] The number of repeated appearances may be the number of times the user repeatedly appears in and outside the airport area. It should be noted that the duration of each appearance is greater than a preset duration threshold, and the distance from the center of the airport area is greater than a preset distance threshold.

[0083] The distance sum of the trajectory points can be obtained by sorting all the trajectory points in the user trajectory data in time series, calculating the distance between two adjacent points, and finally accumulating and summing them.

[0084] The number of cells passed through can be the number a of target cells in the cells included in the user trajectory data. Of course, the ratio of cells passed through by the user can also be obtained, that is, the ratio a / b*100% of the number a of target cells in the cells included in the user trajectory data to the total number b of target cells.

[0085] After determining the scope of the airport area, you can configure the parameter information of the airport area, which may include: the round-trip time threshold Duration_OUT_IN, the maximum distance between the airport runway and the area center Distance_Center, the permanent days threshold Stay_Days_Limit, and the work time threshold Work_Time_Limit.

[0086] The threshold Duration_OUT_IN for the round-trip time between the airport and the nearest airport can be the round-trip time between the airport and the nearest airport, and the reserved service and travel time. For example, the nearest airport to Guangzhou Baiyun Airport is Changsha Airport in Hunan, with the shortest round-trip time being about 120 minutes and the reserved service and travel time being 300 minutes. The total is 420 minutes, so the threshold for the round-trip time between Guangzhou Baiyun Airport and the nearest airport is 420 minutes.

[0087] The maximum distance between the airport runway and the regional center, Distance_Center, can be used to evaluate the threshold of the distance between the earliest trajectory point of the arriving flight user and the airport regional center, and can also be used to evaluate the threshold of the distance between the last trajectory point of the departing flight user and the airport regional center. In general, 500 to 1,000 meters can be added to the actual measured distance.

[0088] The resident days threshold value Stay_Days_Limit is the number of days that a person appears in the airport area within a month, such as 14 days, and is used to determine whether the person is an airport staff member.

[0089] The working time threshold Work_Time_Limit includes the day shift working time threshold and the night shift working time threshold, which respectively represent the daytime working time and the night working time. The day shift working time threshold can be used to determine whether it is an airport day shift staff, such as being set to 6 hours. Correspondingly, the night shift time threshold can be used to determine whether it is an airport night shift staff, such as being set to 6 hours.

[0090] The parameter information of the airport area may also include the longitude and latitude coordinates of the center point of the area and the range of the sleep period. The range of the sleep period may be configured according to different time zones, such as from 0:00 to 5:00 in the morning, and the trajectory during the range of the sleep period may be interrupted.

[0091] User types may include: arriving passengers, departing passengers, transfer passengers, out-of-town passengers, local passengers, staff, frequent airport passengers, pick-up and drop-off passengers, and passers-by. Staff may include day shift staff, night shift staff, and permanent (day shift + night shift) staff.

[0092] In an exemplary embodiment, identifying the user type of the target user trajectory data based on the indicator information of the target user trajectory data and the parameter information of the airport area may include: obtaining the indicator information of the target user trajectory data, the user trajectory data information of the previous cycle of the target user trajectory data, and the user trajectory data information of the next cycle of the target user trajectory data; analyzing the target user trajectory data based on the obtained indicator information, the parameter information of the airport area, the user trajectory data information of the previous cycle, and the user trajectory data information of the next cycle, to identify the user type of the target user trajectory data.

[0093] When analyzing user trajectory data, in addition to considering the indicator information of the user trajectory data and the parameter information of the airport area, in order to improve the accuracy of user type identification, the user's trajectory data of the previous cycle and the trajectory data of the next cycle may also be considered. In the embodiment of the present disclosure, the user trajectory data is analyzed in a preset time period, such as analyzing the user's daily trajectory data to identify the user type of the user. In order to accurately identify the user type, the user trajectory data of the previous cycle and the user trajectory data of the next cycle may be combined. For example, if the target user trajectory data is the trajectory data of user 1 on Tuesday, then the trajectory data of user 1 on Monday and Wednesday may be combined for analysis. Table 2 is a table of user type identification rules.

[0094] Table 2 User type identification rules

[0095]

[0096]

[0097]

[0098] It should be noted that the preset values ​​in Table 2 can be configured according to actual conditions, such as adjusting according to different airport schedules. In addition, when the target user trajectory data meets the conditions in the identification rule, the user type of the target user trajectory data is the user type corresponding to the identification rule. If a target user trajectory data meets the following conditions, the user type of the target user trajectory data is arrival: (1) The trajectory point first appears in or near the airport area, that is, within the range defined by Distance_Center; (2) The last trajectory point appears outside the airport area (such as the urban area) and the distance is greater than Distance_Center; (3) There is no trajectory in the previous period, or the gap between the latest time of the user trajectory data in the previous period and the earliest time of the target user trajectory data is greater than Duration_OUT_IN; (4) a / b>=3 / b, that is, the user passes through at least three target cells; (5) The trajectory type is the first trajectory type or the second trajectory type.

[0099] It can be seen that in the user identification method provided by the embodiment of the present disclosure, the trajectory data features of each user type can be utilized, such as the sudden disappearance of the trajectory (flight departure) and the sudden appearance (flight arrival), to set identification rules for different user types, and then according to the set identification rules, the user type can be identified according to the indicator information of the trajectory data of the target user appearing in the airport area and the parameter information of the airport area, that is, the user's category label is determined, and multi-dimensional statistical analysis of airport passenger flow can be achieved, such as analysis of the user's origin.

[0100] In addition, in the disclosed embodiment, user trajectory data can be segmented and counted according to the time series of trajectory points, such as once every 15 minutes, that is, 1 hour is divided into 4 scales for statistics, or once every 10 minutes. The purpose is to count the number of user stays by marking the scales, and the length of stay cannot be simply calculated by the start time. Statistics are performed on the trajectory data within each scale to obtain indicator information such as the farthest distance from the airport area center, trajectory type, number of positioning times, earliest / latest time, and target cell passed.

[0101] On the basis of the above statistics by trajectory points, the following information can also be summarized and counted by day: the farthest distance before / during / after the user enters the area, the maximum duration of the gap between trajectory points inside and outside the area, the residence time, the number of marking times inside and outside the area during work / rest periods, the user trajectory type (multiple minimum values ​​are taken), the earliest and latest time of various positioning types, the total number of positioning times of each trajectory type, the ratio of the number of target cells passed to the total number of target cells in the airport area, the maximum duration inside and outside the area, and the earliest / latest time difference inside and outside the area. Based on these statistical information, the user type is identified by day. In addition, statistics can also be summarized by month. Specifically, the total number of days that users appear in the airport area, the average residence time and residence time in / outside the airport area, the number of days of various tags (that is, the number of days identified as each user type), the number of days on holidays, etc.

[0102] For the target user's trajectory data, the target user's trajectory data of the previous cycle can also be counted. If D is a certain day, the target user's trajectory data on D-1 day is extracted and counted. The earliest appearance time of the trajectory point on D-1 day, the latest appearance time of the trajectory point, the farthest and closest distance of the trajectory point from the regional center, whether the trajectory point falls within the regional range, the number of times it falls within the regional range, the total residence time, the number of residence times and other information can be counted. The time and distance between the latest time of the D-1 day trajectory and the earliest trajectory of D day can also be counted. In addition, the target user's trajectory data of the next cycle can also be counted. If D is a certain day, the target user's trajectory data on D+1 day is extracted and counted. The earliest appearance time of the trajectory point on D+1 day, the latest appearance time of the trajectory point, the farthest and closest distance of the trajectory point from the regional center, whether the trajectory point falls within the regional range, the number of times it falls within the regional range, the total residence time, the number of residence times and other information can also be counted. The time and distance between the earliest time of the D+1 day trajectory and the latest time of the D day can also be counted.

[0103] The user identification method provided by the embodiment of the present disclosure can analyze the coverage of each cell to obtain the target cell that overlaps with the airport area, and then extract the user trajectory that intersects with the airport area according to the cell type of the target cell. Then, these trajectories are analyzed using the indicator information contained in the trajectory and the parameter information of the airport area to identify the corresponding user type, which can reflect the actual network coverage of the area and avoid the miscalculation, omission or over-calculation problems that occur in the related technology of identification through base station coarse positioning and mobile phone number attribution. In addition, the user trajectory is extracted according to the cell type of the target cell to meet the analysis of users in the target cell, which can reduce the calculation amount for analyzing users in a large area of ​​cells and improve the accuracy of user identification.

[0104] Figure 3 FIG. 1 is a flow chart of a user identification method according to another exemplary embodiment. Figure 3As shown, the user identification method may include the following parts.

[0105] (1) Airport area selection and configuration. Select the airport area and the various scenes included in the airport area on the map, such as the airport departure hall, arrival hall and waiting room. Configure the parameter information of the airport area, such as the round-trip time threshold, the maximum distance between the airport runway and the area center, the number of days of residence threshold, the working time threshold, the longitude and latitude coordinates of the area center, the range of sleeping time, etc.

[0106] (2) Prediction of AGPS positioning information in MRs and calculation of cell coverage. A fingerprint library is constructed using MRs with AGPS positioning information and information such as TA and RSRP in the MRs. Then, based on a machine learning algorithm, the AGPS positioning information in MRs without AGPS positioning information is predicted. In addition, the actual coverage of each cell can be calculated using MRs with AGPS positioning information.

[0107] (3) User trajectory data generation. The user trajectory data is generated mainly by MR and supplemented by S1MME signaling through clustering and outlier elimination algorithms. In addition, according to different ways of generating user trajectory data, the positioning type of the user trajectory data is marked, such as marking it as the first positioning type, the second positioning type, the third positioning type, the fourth positioning type, and further marking it as the AGPS positioning type, the ML positioning type, the SC positioning type, and the S1 positioning type.

[0108] (4) Obtain the target cell corresponding to the airport area. The target cell is obtained by determining whether the airport area range overlaps with the area range of each cell. Then, according to the degree of overlap between the area range of each target cell and the airport area range, the cell type of each target cell is marked, such as marking it as the first type, the second type, the third type and the fourth type, and also marking it as C0, C1, C2 and C3.

[0109] (5) Extraction of target user trajectory data. Specifically, according to the trajectory extraction rules shown in Table 1, user trajectory data of different positioning types under the target cell can be extracted according to the cell type of each target cell. After extracting the user trajectory data in the target cell, the target user can be deduplicated, and then the trajectory data of these target users outside the area can be extracted. In addition, after extracting the target user trajectory data, the trajectory type of the trajectory data can be marked.

[0110] (6) Identify the user type of the target user's trajectory data. Specifically, the target user's trajectory data can be analyzed according to the identification rules shown in Table 2 to obtain the corresponding user type. In other words, statistics are obtained from the user dimension on the distance from the trajectory point to the center of the area in each period (front / middle / back), the residence time, the maximum duration of the gap between trajectory points, the sum of the distances between trajectory points, the number of residence marking times, the number of marking times during the working period / rest period, the proportion of the target cells passed through to the total target cells, the earliest and latest time, the trajectory type, and other information. Then, the user type is identified based on the statistical information, such as arrival, departure, transfer, round trip from other places, local round trip, passerby, and staff.

[0111] (7) Trajectory tracking analysis, regional user monthly statistics, re-identification of user types, and passenger flow statistics analysis. Monthly statistical analysis of regional user stay, and re-identification of regional users based on trajectory and monthly statistical data, and output of statistical analysis results.

[0112] Figure 4 FIG. 1 is a block diagram of a user identification device according to an exemplary embodiment. Figure 4 The device may include: an acquisition module 410, an extraction module 420, an identification module 430 and a marking module 440.

[0113] The acquisition module 410 may be used to acquire a target cell that overlaps with the airport area according to the coverage of each cell.

[0114] The extraction module 420 may be used to extract target user trajectory data that intersects with the airport area from the user trajectory data according to the cell type of the target cell.

[0115] The identification module 430 may be used to identify the user type of the target user trajectory data according to the indicator information of the target user trajectory data and the parameter information of the airport area.

[0116] In an exemplary embodiment, the acquisition module 410 may also be used to generate user trajectory data according to the measurement report and S1MME signaling. The marking module 440 may be used to mark the user trajectory data as the first positioning type if the user trajectory data is obtained according to the AGPS positioning information in the measurement report; mark the user trajectory data as the second positioning type if the user trajectory data is predicted based on the AGPS positioning information in the measurement report and based on the machine learning algorithm; mark the user trajectory data as the third positioning type if the user trajectory data is obtained according to the latitude and longitude information of the primary service cell in the measurement report; and mark the user trajectory data as the fourth positioning type if the user trajectory data is obtained using the base station latitude and longitude information in the S1MME signaling.

[0117] In an exemplary embodiment, the marking module 440 can also be used for: if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is 100%, the target cell is marked as the first type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is greater than or equal to a first preset ratio, the target cell is marked as the second type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is greater than or equal to a second preset ratio and less than the first preset ratio, the target cell is marked as the third type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is less than the second preset ratio, the target cell is marked as the fourth type.

[0118] In an exemplary embodiment, the extraction module 420 may also be used to: if the cell type of the target cell is the first type or the second type, extract user trajectory data of the first trajectory type from the user trajectory data, wherein the user trajectory data of the first trajectory type includes user trajectory data of the first positioning type, the second positioning type, the third positioning type and the fourth positioning type in the target cell; if the cell type of the target cell is the third type or the fourth type, extract user trajectory data of the second trajectory type from the user trajectory data, wherein the user trajectory data of the second trajectory type includes user trajectory data of the first positioning type and the second positioning type in the target cell, and the user trajectory data of the second trajectory type is within the airport area; if the cell type of the target cell is the third type, extract user trajectory data of the third trajectory type from the user trajectory data, wherein the user trajectory data of the third trajectory type includes user trajectory data of the third positioning type in the target cell, and the user trajectory data of the third trajectory type is within the airport area.

[0119] In an exemplary embodiment, the extraction module 420 may also be used to obtain a target user according to user trajectory data of the first trajectory type, user trajectory data of the second trajectory type, and user trajectory data of the third trajectory type.

[0120] In an exemplary embodiment, the extraction module 420 may also be used to extract user trajectory data of a fourth trajectory type from the user trajectory data, wherein the user trajectory data of the fourth trajectory type is trajectory data of the target user outside the airport area.

[0121] In an exemplary embodiment, the identification module 430 can also be used to: obtain indicator information of the target user trajectory data, user trajectory data information of the previous cycle of the target user trajectory data, and user trajectory data information of the next cycle of the target user trajectory data; analyze the target user trajectory data based on the obtained indicator information, parameter information of the airport area, user trajectory data information of the previous cycle, and user trajectory data information of the next cycle, and identify the user type of the target user trajectory data.

[0122] In an exemplary embodiment, the indicator information of the target user trajectory data may include: the first time, the second time, the first distance, the second distance, the third distance, the dwell time, the trajectory type, the number of repeated occurrences, the distance and the number of cells passed; wherein the first time is the earliest time of the user trajectory data, the second time is the latest time of the user trajectory data, the first distance is the farthest distance before entering the airport area in the user trajectory data, the second distance is the farthest distance during the period of entering the airport area in the user trajectory data, and the third distance is the farthest distance after leaving the airport area in the user trajectory data.

[0123] In an exemplary embodiment, parameter information of an airport area may include: a round-trip time threshold for different locations, a maximum distance between an airport runway and a regional center, a permanent residence days threshold, and a working hours threshold.

[0124] In an exemplary embodiment, user types may include arriving passengers, departing passengers, transfer passengers, out-of-town passengers, local passengers, staff, people who frequently travel to and from the airport, pick-up and drop-off personnel, and passers-by.

[0125] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0126] Figure 5 It is a structural block diagram of an electronic device for user identification according to an exemplary embodiment. It should be noted that the electronic device shown in the figure is only an example and should not bring any limitation to the function and scope of use of the embodiment of the present invention.

[0127] Refer to the following Figure 5 The electronic device 500 according to this embodiment of the present invention is described. Figure 5 The electronic device 500 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0128] like Figure 5As shown, the electronic device 500 is in the form of a general computing device. The components of the electronic device 500 may include but are not limited to: at least one processing unit 510, at least one storage unit 520, and a bus 530 connecting different system components (including the storage unit 520 and the processing unit 510).

[0129] The storage unit stores program codes, which can be executed by the processing unit 510, so that the processing unit 510 performs the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 510 can perform the following steps: Figure 1 In step S110 shown in the figure, a target cell overlapping with the airport area is obtained according to the coverage of each cell; in step S120, target user trajectory data intersecting with the airport area is extracted from the user trajectory data according to the cell type of the target cell; in step S130, the user type of the target user trajectory data is identified according to the indicator information of the target user trajectory data and the parameter information of the airport area.

[0130] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 5201 and / or a cache storage unit 5202 , and may further include a read-only storage unit (ROM) 5203 .

[0131] The storage unit 520 may also include a program / utility 5204 having a set (at least one) of program modules 5205, such program modules 5205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0132] Bus 530 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0133] The electronic device 500 may also communicate with one or more external devices 600 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 500, and / or communicate with any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 550. Furthermore, the electronic device 500 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 540. As shown, the network adapter 540 communicates with other modules of the electronic device 500 via a bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0134] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0135] The program product for implementing the above method according to the embodiment of the present invention may adopt a portable compact disk read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device.

[0136] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0137] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0138] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0139] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0140] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0141] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0142] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0143] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

[0144] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A user identification method, characterized in that: include: According to the coverage of each cell, obtain the target cell that overlaps with the airport area; According to the cell type of the target cell, target user trajectory data intersecting with the airport area range is extracted from the user trajectory data; Identifying a user type of the target user trajectory data according to the indicator information of the target user trajectory data and the parameter information of the airport area; The method further includes: if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is 100%, marking the target cell as the first type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is greater than or equal to a first preset ratio, marking the target cell as the second type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is greater than or equal to a second preset ratio and less than the first preset ratio, marking the target cell as the third type; if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is less than the second preset ratio, marking the target cell as the fourth type; And, extracting target user trajectory data having an intersection with the airport area from the user trajectory data according to the cell type of the target cell, including: if the cell type of the target cell is the first type or the second type, extracting user trajectory data of the first trajectory type from the user trajectory data, wherein the user trajectory data of the first trajectory type includes user trajectory data of the first positioning type, the second positioning type, the third positioning type and the fourth positioning type in the target cell; if the cell type of the target cell is the third type or the fourth type, extracting user trajectory data of the second trajectory type from the user trajectory data, wherein the user trajectory data of the second trajectory type includes user trajectory data of the first positioning type and the second positioning type in the target cell, and the user trajectory data of the second trajectory type is within the airport area; if the cell type of the target cell is the third type, extracting user trajectory data of the third trajectory type from the user trajectory data, wherein the user trajectory data of the third trajectory type includes user trajectory data of the third positioning type in the target cell, and the user trajectory data of the third trajectory type is within the airport area; Among them, the user trajectory data of the first positioning type is obtained directly through AGPS positioning information, the user trajectory data of the second positioning type is obtained through predicted AGPS positioning information, the user trajectory data of the third positioning type is obtained through the longitude and latitude information of the main service cell, and the user trajectory data of the fourth positioning type is obtained through the longitude and latitude information of the base station.

2. The method according to claim 1, characterized in that The method further comprises: generating the user trajectory data according to the measurement report and the S1MME signaling; and, If the user trajectory data is directly obtained according to the Assisted Global Positioning System AGPS positioning information in the measurement report, marking the user trajectory data as a first positioning type; If the user trajectory data is predicted based on the AGPS positioning information in the measurement report and based on a machine learning algorithm, marking the user trajectory data as a second positioning type; If the user trajectory data is obtained according to the latitude and longitude information of the primary serving cell in the measurement report, marking the user trajectory data as a third positioning type; If the user trajectory data is obtained by using the base station longitude and latitude information in the S1MME signaling, the user trajectory data is marked as the fourth positioning type.

3. The method according to claim 1, characterized in that The method further comprises: A target user is acquired according to the user trajectory data of the first trajectory type, the user trajectory data of the second trajectory type, and the user trajectory data of the third trajectory type.

4. The method according to claim 3, characterized in that According to the cell type of the target cell, target user trajectory data intersecting with the airport area is extracted from the user trajectory data, including: User trajectory data of a fourth trajectory type is extracted from the user trajectory data, wherein the user trajectory data of the fourth trajectory type is trajectory data of the target user outside the airport area.

5. The method according to claim 1, characterized in that Identifying the user type of the target user trajectory data according to the indicator information of the target user trajectory data and the parameter information of the airport area includes: Obtaining indicator information of the target user trajectory data, user trajectory data information of a previous period of the target user trajectory data, and user trajectory data information of a next period of the target user trajectory data; According to the acquired indicator information, the parameter information of the airport area, the user trajectory data information of the previous cycle and the user trajectory data information of the next cycle, the target user trajectory data is analyzed to identify the user type of the target user trajectory data.

6. The method according to any one of claims 1 to 5, characterized in that: The indicator information of the target user trajectory data includes: first time, second time, first distance, second distance, third distance, dwell time, trajectory type, number of repetitions, distance of trajectory points, and number of cells passed; wherein, The first time is the earliest time of the user trajectory data, the second time is the latest time of the user trajectory data, the first distance is the farthest distance in the user trajectory data before entering the airport area, the second distance is the farthest distance in the user trajectory data during entering the airport area, and the third distance is the farthest distance in the user trajectory data after leaving the airport area.

7. The method according to any one of claims 1 to 5, characterized in that: The parameter information of the airport area includes: a threshold for round-trip time between different locations, a maximum distance between the airport runway and the area center, a threshold for the number of days of residence, and a threshold for working hours.

8. The method according to any one of claims 1 to 5, characterized in that: The user types include: arriving passengers, departing passengers, transit passengers, out-of-town passengers, local passengers, staff, people who frequently travel to and from the airport, pick-up and drop-off personnel, and passers-by.

9. A user identification device, characterized in that: include: An acquisition module, used for acquiring target cells overlapping with the airport area according to the coverage of each cell; An extraction module, used for extracting target user trajectory data having an intersection with the airport area from the user trajectory data according to the cell type of the target cell; an identification module, configured to identify a user type of the target user trajectory data according to the indicator information of the target user trajectory data and the parameter information of the airport area; A marking module, for marking the target cell as a first type if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is 100%; marking the target cell as a second type if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is greater than or equal to a first preset ratio; marking the target cell as a third type if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is greater than or equal to a second preset ratio and less than the first preset ratio; marking the target cell as a fourth type if the ratio of the overlapping part of the coverage of the target cell and the airport area to the coverage of the target cell is less than the second preset ratio; Furthermore, the extraction module is further configured to extract user trajectory data of a first trajectory type from the user trajectory data if the cell type of the target cell is the first type or the second type, wherein the user trajectory data of the first trajectory type includes user trajectory data of a first positioning type, a second positioning type, a third positioning type, and a fourth positioning type in the target cell; if the cell type of the target cell is the third type or the fourth type, extract user trajectory data of a second trajectory type from the user trajectory data, wherein the user trajectory data of the second trajectory type includes user trajectory data of a first positioning type and a second positioning type in the target cell, and the user trajectory data of the second trajectory type is within the airport area; if the cell type of the target cell is the third type, extract user trajectory data of a third trajectory type from the user trajectory data, wherein the user trajectory data of the third trajectory type includes user trajectory data of a third positioning type in the target cell, and the user trajectory data of the third trajectory type is within the airport area; Among them, the user trajectory data of the first positioning type is obtained directly through AGPS positioning information, the user trajectory data of the second positioning type is obtained through predicted AGPS positioning information, the user trajectory data of the third positioning type is obtained through the longitude and latitude information of the main service cell, and the user trajectory data of the fourth positioning type is obtained through the longitude and latitude information of the base station.

10. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 8. 11 . A computer-readable storage medium, when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 8.

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