An airport passenger screening method and device based on signaling trajectory data

Abstract

The application provides an airport passenger screening method and device based on signaling trajectory data, the method comprising: obtaining mobile phone signaling trajectory data collected by a target airport within a preset collection period, and spatial coverage data corresponding to a base station set by the target airport; based on the spatial coverage data, using a plurality of spatiotemporal constraint rules to screen the mobile phone signaling trajectory data to determine at least one target user corresponding to the mobile phone signaling trajectory data; determining passenger data of each target user in the mobile phone signaling trajectory data, and based on the passenger data, constructing a target passenger data set of the target airport. Through the above method, the accuracy of passenger screening is improved while breaking through the limitations of single rule screening, fine classification of arriving, departing and double-attribute passengers is realized, and structured and high-quality passenger data is output and a standardized data set is constructed.

Description

Technical Field

[0001] This application relates to the fields of mobile communication data processing and passenger flow identification technology, and in particular to an airport passenger screening method and apparatus based on signaling trajectory data. Background Technology

[0002] Mobile phone signaling trajectory data can record the user's location changes, dwell time, movement routes and other spatiotemporal characteristics. In theory, it can achieve full coverage tracking of people within the airport area, providing the possibility for accurate differentiation between passengers and non-passengers. However, existing airport passenger screening methods based on users' mobile phone signaling trajectory data still have the following problems.

[0003] The spatiotemporal rules are simplistic and lack scenario adaptability. Existing methods mostly use a single rule of "staying time within the airport geofence exceeding a threshold" for filtering, without considering the special spatiotemporal characteristics of passengers at civil transport airports. This results in a large number of non-passenger users who pass through the airport or make short stops (such as commuters around the airport, people picking up or dropping off passengers, etc.) being misclassified as passengers. Furthermore, the data quality is out of touch with operational needs. Existing methods only focus on the binary classification of "passenger or non-passenger" and do not conduct customized filtering for departing and arriving passengers at civil transport airports, thus failing to directly support the construction of high-quality passenger behavior datasets. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide an airport passenger screening method and device based on signaling trajectory data. By pre-constructing a progressive multi-temporal and spatial constraint rule system adapted to civil transport airports, and based on mobile phone signaling trajectory data collected by the target airport within a preset collection period and spatial coverage data corresponding to the base stations set up at the target airport, the method accurately distinguishes passengers from various non-passenger groups, effectively avoids interference factors such as signal drift, and improves the accuracy of passenger screening while overcoming the limitations of single-rule screening. It achieves refined classification of arriving, departing, and dual-attribute passengers, outputs structured, high-quality passenger data, and constructs a standardized dataset. This overcomes the shortcomings of existing methods that can only perform binary classification, and provides technical support for civil transport airports to build high-quality passenger datasets and achieve refined operational decisions, thereby helping airports to achieve refined operational decisions.

[0005] This application provides an embodiment of an airport passenger screening method based on signaling trajectory data, the method comprising: The system acquires mobile phone signaling trajectory data collected at the target airport within a preset collection period, as well as spatial coverage data corresponding to the base stations set up at the target airport. Based on the spatial coverage data, the mobile phone signaling trajectory data is filtered using preset multi-temporal and spatial constraint rules to determine at least one target user corresponding to the mobile phone signaling trajectory data; The passenger data corresponding to each target user is determined from the mobile phone signaling trajectory data, and a target passenger dataset corresponding to the target airport is constructed based on the passenger data.

[0006] Furthermore, the acquisition of mobile phone signaling trajectory data collected at the target airport within a preset collection period, and spatial coverage data corresponding to the base stations set up at the target airport, includes: Within a preset collection period, raw mobile phone signaling trajectory data of at least one user corresponding to the target airport are collected. The original mobile phone signaling trajectory data is preprocessed to obtain mobile phone signaling trajectory data corresponding to each user; wherein, the mobile phone signaling trajectory data includes at least user identification information, timestamp information, and base station location information; For the base stations set up at the target airport, spatial coverage data corresponding to the base stations is obtained based on the location coding information corresponding to the base stations.

[0007] Furthermore, the multi-temporal constraint rules include preliminary data filtering rules, irrelevant passenger data exclusion rules, and inbound / outbound passenger discrimination rules; the step of filtering the mobile phone signaling trajectory data based on the spatial coverage data using preset multi-temporal constraint rules to determine at least one target user corresponding to the mobile phone signaling trajectory data includes: Based on the airport geofence information in the spatial coverage data, the mobile phone signaling trajectory data is filtered using the preliminary data filtering rules to determine multiple first candidate users in the mobile phone signaling trajectory data, and to determine the first user data corresponding to each first candidate user. Based on the airport location range information in the spatial coverage data, the first user data is filtered using the irrelevant passenger data exclusion rules to determine multiple second candidate users in the first user data, and the second user data corresponding to each second candidate user is determined. Based on the airport geofencing information, the second user data is filtered using the inbound and outbound passenger discrimination rules to determine at least one target user corresponding to the second user data.

[0008] Furthermore, based on the airport geofence information in the spatial coverage data, the preliminary data filtering rules are used to filter the mobile phone signaling trajectory data to determine multiple first candidate users corresponding to the mobile phone signaling trajectory data, and to determine the first user data corresponding to each first candidate user, including: The base station location information corresponding to each user in the mobile phone signaling trajectory data is compared with the airport geofence information in the spatial coverage data to determine whether there is a coverage record corresponding to the airport geofence information in the base station location information, and the determination result is obtained. Based on the judgment result, multiple first candidate users are selected from the mobile phone signaling trajectory data to find the coverage records corresponding to the airport geofence information in the base station location information; The first user data corresponding to each first candidate user is determined in the mobile phone signaling trajectory data.

[0009] Furthermore, the irrelevant passenger data exclusion rules include rules for excluding users transiting through airports, rules for excluding users with short-term connections, and rules for excluding users spending the night at airports. The step of filtering the first user data based on the airport location range information in the spatial coverage data using the irrelevant passenger data exclusion rules to determine multiple second candidate users corresponding to the first user data, and determining the second user data corresponding to each second candidate user, includes: The first user data is scanned to identify the continuous trajectory point sequence corresponding to each first candidate user, and it is determined whether each continuous trajectory point sequence exhibits a first continuous spatiotemporal pattern, so as to determine whether the first candidate user meets the exclusion rule for users passing through the airport. The first candidate users who meet the airport user exclusion rules are excluded, so as to select the first retained users from the first candidate users, and determine whether the continuous trajectory point sequence corresponding to each first retained user exhibits the second continuous spatiotemporal pattern, so as to determine whether the first retained user meets the short-term connection user exclusion rules. The first retained users who meet the short-term connection user exclusion rule are excluded, so as to filter out the second retained users from the first retained users, and determine whether the continuous trajectory point sequence corresponding to each second retained user shows airport overnight characteristics, so as to determine whether the second retained user meets the airport overnight user exclusion rule. The second retained users who meet the airport overnight user exclusion rules are excluded, so as to filter out multiple second candidate users from the second retained users and determine the second user data corresponding to each second candidate user.

[0010] Furthermore, the passenger identification rules include passenger identification rules for arrivals and passenger identification rules for departures; the step of filtering the second user data based on the airport geofencing information using the passenger identification rules to determine at least one target user corresponding to the second user data includes: In the second user data, determine the trajectory start-point information and trajectory end-point information corresponding to each second candidate user; Determine whether there is a coverage record corresponding to the airport geofence information in the trajectory starting point information to determine whether the second candidate user meets the arrival passenger identification rule, and determine whether there is a coverage record corresponding to the airport geofence information in the trajectory ending point information to determine whether the second candidate user meets the departure passenger identification rule; When the second candidate user meets the arrival passenger identification rule, the second candidate user is identified as an arrival passenger; when the second candidate user meets the departure passenger identification rule, the second candidate user is identified as a departure passenger. The arriving passengers and the departing passengers are identified as at least one target user corresponding to the second user data.

[0011] Furthermore, the step of determining the passenger data corresponding to each target user in the mobile phone signaling trajectory data, and constructing the target passenger dataset corresponding to the target airport based on the passenger data, includes: The passenger data corresponding to each target user is determined from the mobile phone signaling trajectory data; Based on the passenger data, structured passenger information corresponding to the target airport is generated; wherein, the structured passenger information includes multiple passenger information records, and each passenger information record includes at least user identification information, record date, passenger type tag information and mobile phone signaling trajectory point information; Based on the structured passenger information, a target passenger dataset corresponding to the target airport is constructed.

[0012] This application embodiment also provides an airport passenger screening device based on signaling trajectory data, the airport passenger screening device comprising: The data acquisition module is used to acquire mobile phone signaling trajectory data collected by the target airport within a preset collection period, as well as spatial coverage data corresponding to the base stations set up at the target airport. The rule filtering module is used to filter the mobile phone signaling trajectory data based on the spatial coverage data using preset multi-temporal and spatial constraint rules, so as to determine at least one target user corresponding to the mobile phone signaling trajectory data; The data construction module is used to determine the passenger data corresponding to each target user in the mobile phone signaling trajectory data, and construct the target passenger dataset corresponding to the target airport based on the passenger data.

[0013] Furthermore, when the data acquisition module is used to acquire mobile phone signaling trajectory data collected by the target airport within a preset acquisition period, and spatial coverage data corresponding to the base stations set up at the target airport, the data acquisition module is used to: Within a preset collection period, raw mobile phone signaling trajectory data of at least one user corresponding to the target airport are collected. The original mobile phone signaling trajectory data is preprocessed to obtain mobile phone signaling trajectory data corresponding to each user; wherein, the mobile phone signaling trajectory data includes at least user identification information, timestamp information, and base station location information; For the base stations set up at the target airport, spatial coverage data corresponding to the base stations is obtained based on the location coding information corresponding to the base stations.

[0014] Furthermore, the multi-temporal constraint rules include preliminary data filtering rules, irrelevant passenger data exclusion rules, and inbound / outbound passenger discrimination rules; when the rule filtering module is used to filter the mobile phone signaling trajectory data based on the spatial coverage data using preset multi-temporal constraint rules to determine at least one target user corresponding to the mobile phone signaling trajectory data, the rule filtering module is used to: Based on the airport geofence information in the spatial coverage data, the mobile phone signaling trajectory data is filtered using the preliminary data filtering rules to determine multiple first candidate users in the mobile phone signaling trajectory data, and to determine the first user data corresponding to each first candidate user. Based on the airport location range information in the spatial coverage data, the first user data is filtered using the irrelevant passenger data exclusion rules to determine multiple second candidate users in the first user data, and the second user data corresponding to each second candidate user is determined. Based on the airport geofencing information, the second user data is filtered using the inbound and outbound passenger discrimination rules to determine at least one target user corresponding to the second user data.

[0015] Furthermore, when the rule filtering module is used to filter the mobile phone signaling trajectory data based on the airport geofence information in the spatial coverage data and using the preliminary data filtering rules to determine multiple first candidate users corresponding to the mobile phone signaling trajectory data, and to determine the first user data corresponding to each first candidate user, the rule filtering module is used to: The base station location information corresponding to each user in the mobile phone signaling trajectory data is compared with the airport geofence information in the spatial coverage data to determine whether there is a coverage record corresponding to the airport geofence information in the base station location information, and the determination result is obtained. Based on the judgment result, multiple first candidate users are selected from the mobile phone signaling trajectory data to find the coverage records corresponding to the airport geofence information in the base station location information; The first user data corresponding to each first candidate user is determined in the mobile phone signaling trajectory data.

[0016] Furthermore, the irrelevant passenger data exclusion rules include rules for excluding users transiting through airports, rules for excluding users with short-term connections, and rules for excluding users staying overnight at airports. When the rule filtering module filters the first user data based on airport location range information in the spatial coverage data using the irrelevant passenger data exclusion rules to determine multiple second candidate users corresponding to the first user data, and to determine the second user data corresponding to each second candidate user, the rule filtering module is used to: The first user data is scanned to identify the continuous trajectory point sequence corresponding to each first candidate user, and it is determined whether each continuous trajectory point sequence exhibits a first continuous spatiotemporal pattern, so as to determine whether the first candidate user meets the exclusion rule for users passing through the airport. The first candidate users who meet the airport user exclusion rules are excluded, so as to select the first retained users from the first candidate users, and determine whether the continuous trajectory point sequence corresponding to each first retained user exhibits the second continuous spatiotemporal pattern, so as to determine whether the first retained user meets the short-term connection user exclusion rules. The first retained users who meet the short-term connection user exclusion rule are excluded, so as to filter out the second retained users from the first retained users, and determine whether the continuous trajectory point sequence corresponding to each second retained user shows airport overnight characteristics, so as to determine whether the second retained user meets the airport overnight user exclusion rule. The second retained users who meet the airport overnight user exclusion rules are excluded, so as to filter out multiple second candidate users from the second retained users and determine the second user data corresponding to each second candidate user.

[0017] Furthermore, the arrival and departure passenger discrimination rules include arrival passenger identification rules and departure passenger identification rules; when the rule filtering module is used to filter the second user data based on the airport geofencing information and using the arrival and departure passenger discrimination rules to determine at least one target user corresponding to the second user data, the rule filtering module is used to: In the second user data, determine the trajectory start-point information and trajectory end-point information corresponding to each second candidate user; Determine whether there is a coverage record corresponding to the airport geofence information in the trajectory starting point information to determine whether the second candidate user meets the arrival passenger identification rule, and determine whether there is a coverage record corresponding to the airport geofence information in the trajectory ending point information to determine whether the second candidate user meets the departure passenger identification rule; When the second candidate user meets the arrival passenger identification rule, the second candidate user is identified as an arrival passenger; when the second candidate user meets the departure passenger identification rule, the second candidate user is identified as a departure passenger. The arriving passengers and the departing passengers are identified as at least one target user corresponding to the second user data.

[0018] Furthermore, when the data construction module determines the passenger data corresponding to each target user in the mobile phone signaling trajectory data, and constructs the target passenger dataset corresponding to the target airport based on the passenger data, the data construction module is used to: The passenger data corresponding to each target user is determined from the mobile phone signaling trajectory data; Based on the passenger data, structured passenger information corresponding to the target airport is generated; wherein, the structured passenger information includes multiple passenger information records, and each passenger information record includes at least user identification information, record date, passenger type tag information and mobile phone signaling trajectory point information; Based on the structured passenger information, a target passenger dataset corresponding to the target airport is constructed.

[0019] This application embodiment also provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the airport passenger screening method based on signaling trajectory data described above are performed.

[0020] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the airport passenger screening method based on signaling trajectory data as described above.

[0021] The airport passenger screening method and apparatus based on signaling trajectory data provided in this application include: acquiring mobile phone signaling trajectory data collected from a target airport within a preset collection period, and spatial coverage data corresponding to base stations set up at the target airport; filtering the mobile phone signaling trajectory data based on the spatial coverage data using preset multi-temporal constraint rules to determine at least one target user corresponding to the mobile phone signaling trajectory data; determining passenger data corresponding to each target user in the mobile phone signaling trajectory data, and constructing a target passenger dataset corresponding to the target airport based on the passenger data.

[0022] Compared to existing technologies that rely solely on a single rule of "staying time within the airport geofence exceeding a threshold" for screening and focus on binary classification of "passengers or non-passengers," this new method pre-constructs a progressive multi-temporal and spatial constraint rule system adapted to civil transport airports. Based on mobile phone signaling trajectory data collected from the target airport within a preset collection period and spatial coverage data corresponding to the base stations set up at the target airport, it accurately distinguishes passengers from various non-passenger groups, effectively avoiding interference factors such as signal drift. While overcoming the limitations of single-rule screening, it improves the accuracy of passenger screening, achieving refined classification of arriving, departing, and dual-attribute passengers. It outputs structured, high-quality passenger data and constructs a standardized dataset, overcoming the shortcomings of existing methods that can only perform binary classification. This provides technical support for civil transport airports to build high-quality passenger datasets and achieve refined operational decisions, thereby assisting airports in realizing refined operational decisions.

[0023] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating an airport passenger screening method based on signaling trajectory data, provided as an embodiment of this application; Figure 2 A schematic diagram of the structure of an airport passenger screening device based on signaling trajectory data provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0027] Research has found that mobile phone signaling trajectory data can record a user's location changes, dwell time, movement patterns, and other spatiotemporal characteristics. Theoretically, this could enable full-coverage tracking of people within the airport area, providing the possibility for accurate differentiation between passengers and non-passengers. However, existing airport passenger screening methods based on users' mobile phone signaling trajectory data still have the following problems.

[0028] Currently, spatiotemporal rules are designed in a simplistic way and lack scenario adaptability. Existing methods mostly use a single rule of "staying time within the airport geofence exceeding a threshold" for screening, without considering the special spatiotemporal characteristics of passengers at civil transport airports. This results in a large number of non-passenger users who pass through the airport or make short stops (such as commuters around the airport, people picking up or dropping off passengers, etc.) being misclassified as passengers. Furthermore, data quality is out of touch with operational needs. Existing methods only focus on the binary classification of "passenger or non-passenger" and do not conduct customized screening for departing and arriving passengers at civil transport airports, thus failing to directly support the construction of high-quality passenger behavior datasets.

[0029] Based on this, this application provides an airport passenger screening method based on signaling trajectory data. By pre-constructing a progressive multi-temporal constraint rule system adapted to civil transport airports, and based on mobile phone signaling trajectory data collected by the target airport within a preset collection period and spatial coverage data corresponding to the base stations set up at the target airport, it accurately distinguishes passengers from various non-passenger groups, effectively avoids interference factors such as signal drift, and improves the accuracy of passenger screening while overcoming the limitations of single-rule screening. It achieves refined classification of arriving, departing, and dual-attribute passengers, outputs structured, high-quality passenger data, and constructs a standardized dataset, making up for the shortcomings of existing methods that can only perform binary classification. This provides technical support for civil transport airports to build high-quality passenger datasets and achieve refined operational decisions, thereby helping airports to achieve refined operational decisions.

[0030] Please see Figure 1 , Figure 1A flowchart illustrating an airport passenger screening method based on signaling trajectory data, provided as an embodiment of this application. Figure 1 As shown in the embodiments of this application, the airport passenger screening method is generally applicable to identifying passengers at civil transport airports through mobile phone signaling trajectory data and constructing a high-quality dataset of passenger behavior to support operational decisions at civil transport airports. The method includes: S101. Acquire the mobile phone signaling trajectory data collected by the target airport within a preset collection period, and the spatial coverage data corresponding to the base stations set up at the target airport.

[0031] In this embodiment of the application, mobile phone signaling trajectory data is a byproduct generated by the signaling interaction between the mobile phone and the network base station during the process of ensuring normal communication services for users.

[0032] The mobile phone signaling trajectory data includes, but is not limited to, user identification information, timestamp information, and base station location information.

[0033] For example, when a user's mobile device is powered on, it will continuously and passively exchange information with surrounding communication base stations in order to maintain network connectivity. This information includes the user's device identifier, timestamp, and location code of the base station, thus forming a spatiotemporal trajectory data of the user.

[0034] In this embodiment of the application, spatial coverage data refers to the three-dimensional spatial range, signal quality distribution information, and geographical location information of the communication base stations (such as 4G / 5G, private networks, Wi-Fi, etc.) deployed in or around the airport area that can effectively provide wireless signal services.

[0035] The spatial coverage data includes, but is not limited to, airport geofence information and airport location range information, as well as signal quality distribution information.

[0036] In this embodiment of the application, the preset collection period can be specifically determined according to the specific situation of the target airport and the passenger screening needs. For example, the preset collection period can be set to 24 hours.

[0037] In this way, by acquiring mobile phone signaling trajectory data collected from the target airport within the preset collection period and spatial coverage data corresponding to the base stations set up at the target airport, the basic data collection and preprocessing preparation is completed, providing high-quality data support for subsequent screening and analysis.

[0038] In one possible implementation of this application, step S101 may include: S1011. Within the preset collection period, collect the original mobile phone signaling trajectory data corresponding to at least one user at the target airport.

[0039] For example, within a preset collection period (preferably from 00:00 to 24:00 on a single day, which can be flexibly adjusted to continuous collection over multiple days or collection during specific time periods according to actual operational needs), raw mobile phone signaling trajectory data corresponding to at least one user within a 3-kilometer radius of the target airport can be obtained by establishing a data cooperation mechanism with mobile communication operators.

[0040] The original data includes at least basic information such as user equipment identifier (e.g., IMSI, IMEI, etc., anonymous identifiers that cannot be directly associated with personal identity), signaling generation timestamp, serving base station location code, and signaling interaction type, to ensure data compliance and integrity.

[0041] S1012. Preprocess the original mobile phone signaling trajectory data to obtain mobile phone signaling trajectory data corresponding to each user.

[0042] The preprocessing methods include, but are not limited to, using a unified timestamp format, a unified time range, and matching base station coordinates through base station location encoding.

[0043] For example, the raw mobile phone signaling trajectory data is standardized and preprocessed. The preprocessing operations include unifying the timestamp format to "YYYY-MM-DD HH:MM:SS", calibrating the time range to remove invalid data that exceeds the collection period, supplementing the geographical coordinates of the base station by accurately matching the base station location code with the GIS map, removing redundant and duplicate signaling records and abnormal data caused by signal drift, and finally obtaining the standardized mobile phone signaling trajectory data corresponding to each user.

[0044] S1013. For the base station set up at the target airport, obtain the spatial coverage data corresponding to the base station based on the location coding information corresponding to the base station.

[0045] In this embodiment of the application, for all communication base stations set up in the target airport area, based on key parameters such as the location code, signal transmission power, antenna gain, and terrain obstruction coefficient of each base station, and combined with professional GIS spatial analysis technology such as ArcGIS, the actual signal coverage range and signal strength distribution of each base station are delineated, and the spatial coverage data corresponding to the airport base station is integrated to form the spatial coverage data.

[0046] Here, the spatial coverage data may include geofencing information of the airport's core area and information on the extended location range around the airport, providing accurate spatial reference for subsequent multi-rule filtering.

[0047] As an example of acquiring the mobile phone signaling trajectory data and the spatial coverage data, for instance, a medium-sized civil transport airport is selected as the target airport. This airport has an annual passenger throughput of approximately 8 million and a total of 28 communication base stations, covering core areas such as the terminal building, apron, passenger transfer area, and parking lot. The preset collection period is from 00:00 to 24:00. By establishing compliant data cooperation with the two major local mobile communication operators, raw mobile phone signaling trajectory data of the target airport and its surrounding 3-kilometer radius are collected during this period. The data includes device identifiers (IMSI / IMEI anonymized), timestamps, base station location codes, signaling types, signal strengths, and other information for over 100,000 users. At the same time, parameters such as the location codes, transmission power, antenna gain, and terrain obstruction coefficients of the 28 communication base stations within the target airport are collected. Combined with the spatial analysis technology of ArcGIS version 10.8, the actual signal coverage range of each base station is delineated, generating airport geofences (covering core areas such as the terminal building, apron, and passenger transfer area, with boundary errors controlled within 5 meters) and airport location range data, forming a standardized spatial coverage dataset.

[0048] Furthermore, the collected raw mobile signaling trajectory data underwent standardized preprocessing. First, the timestamps were uniformly converted to the "YYYY-MM-DD HH:MM:SS" format, and signaling records exceeding the collection period from 00:00 to 24:00 on December 20, 2025 were removed, resulting in the removal of approximately 32,000 invalid data entries. Subsequently, the latitude and longitude coordinates of each signaling point were supplemented through precise matching between the base station location code and the ArcGIS map. Linear interpolation was used to correct coordinate deviations caused by base station signal drift (deviations were controlled within 10 meters). Finally, the data was grouped by user identifier, and redundant and duplicate signaling records and abnormal data with signal strength below the threshold were removed from each group to generate complete daily signaling trajectory data for each user. This ensured that each data entry contained three core fields: user ID, accurate timestamp, and base station latitude and longitude, ultimately resulting in 87,000 standardized signaling trajectory data entries.

[0049] S102. Based on the spatial coverage data, the mobile phone signaling trajectory data is filtered using preset multi-temporal constraint rules to determine at least one target user corresponding to the mobile phone signaling trajectory data.

[0050] In this embodiment of the application, the multi-temporal constraint rules include preliminary data filtering rules, irrelevant passenger data exclusion rules, and inbound and outbound passenger discrimination rules.

[0051] The preliminary data filtering rule is a preliminary mobile signaling trajectory data filtering rule based on airport geofences and trajectory endpoints. Specifically, this rule is used to screen users who may become passengers. Based on the spatial coverage of civil transport airport base stations, airport geofences are defined, and records in mobile signaling trajectory data within the geofence over a period of time are regarded as potential passengers traveling at the airport.

[0052] The irrelevant passenger data exclusion rules include rules for excluding users who pass through airports, rules for excluding users with short-term connections, and rules for excluding users who stay overnight at airports. These rules are used to exclude non-passenger users from mobile signaling data who, although involved in airports, do not exhibit behavioral patterns that match those of arriving or departing passengers.

[0053] The inbound and outbound passenger discrimination rules include inbound passenger identification rules and outbound passenger identification rules, which are used to achieve refined identification of inbound and outbound passenger types.

[0054] In this step, the multi-temporal constraint rules adopt a hierarchical and progressive design, which takes into account both screening efficiency and accuracy. The rules work together and are refined layer by layer to achieve accurate screening. The multi-temporal constraint rules are used to screen target users based on spatial coverage data.

[0055] In one possible implementation of this application, step S102 may include: S1021. Based on the airport geofence information in the spatial coverage data, the mobile phone signaling trajectory data is filtered using the preliminary data filtering rules to determine multiple first candidate users corresponding to the mobile phone signaling trajectory data, and the first user data corresponding to each first candidate user is determined.

[0056] In this step, the first candidate user is screened using preliminary data filtering rules based on airport geofencing information to ensure the comprehensiveness, accuracy and efficiency of the screening.

[0057] In one possible implementation of this application, step S1021 may include: S10211. The base station location information corresponding to each user in the mobile phone signaling trajectory data is compared with the airport geofence information in the spatial coverage data to determine whether there is a coverage record corresponding to the airport geofence information in the base station location information, and a judgment result is obtained.

[0058] In this step, a programming tool is used to traverse each signaling record in the standardized mobile signaling trajectory data. The latitude and longitude information of all base station locations corresponding to each user is compared one by one with the airport geofence boundary coordinates in the spatial coverage data. At the same time, a signal strength threshold is introduced as an auxiliary judgment criterion to eliminate non-true coverage records caused by signal drift. It is then determined whether there is at least one valid coverage record within the airport geofence in the user's base station location information, thus obtaining the comparison judgment result for each user.

[0059] S10212. Based on the judgment result, select multiple first candidate users from the mobile phone signaling trajectory data who have coverage records corresponding to the airport geofence information in the base station location information.

[0060] In this step, based on the judgment results, all users with valid coverage records within the airport geofence in the base station location information are selected as first candidate users to ensure that all users who may be associated with the airport are included in the subsequent analysis scope, effectively avoiding the omission of target passengers.

[0061] S10213, Determine the first user data corresponding to each first candidate user in the mobile phone signaling trajectory data.

[0062] In this step, complete mobile signaling trajectory data of each first candidate user within a preset collection period is extracted as the corresponding first user data. At the same time, the time series characteristics and spatial distribution characteristics of user signaling are fully preserved, laying a solid foundation for subsequent trajectory pattern analysis, rule matching and interference elimination.

[0063] S1022. Based on the airport location range information in the spatial coverage data, the first user data is filtered using the irrelevant passenger data exclusion rule to determine multiple second candidate users corresponding to the first user data, and the second user data corresponding to each second candidate user is determined.

[0064] In this step, the irrelevant passenger data exclusion rule is designed based on the behavioral characteristics of typical non-passenger groups in the airport scenario, accurately covering various non-passenger scenarios, and using this type of rule to screen second candidate users.

[0065] In one possible implementation of this application, step S1022 may include: S10221. Perform pattern scanning on the first user data to identify the continuous trajectory point sequence corresponding to each first candidate user, and determine whether each continuous trajectory point sequence exhibits a first continuous spatiotemporal pattern, so as to determine whether the first candidate user meets the route airport user exclusion rule.

[0066] Here, the first continuous spatiotemporal pattern is represented as "outside the airport, to inside the airport, to outside the airport".

[0067] In this embodiment of the application, trajectory pattern scanning and sequence analysis are performed on the first user data based on trajectory analysis tools to accurately identify the continuous trajectory point sequence corresponding to each first candidate user and the time interval and spatial distance of each trajectory point. It is determined whether the continuous trajectory point sequence exhibits a first continuous spatiotemporal pattern of "outside the airport, to inside the airport, to outside the airport", and the stay time inside the airport does not exceed 1 hour and the movement distance is relatively short. If this pattern is met, the first candidate user is determined to be a user passing through the airport, triggering the exclusion rule for users passing through the airport.

[0068] S10222. Exclude the first candidate users who meet the route airport user exclusion rules to select the first retained users from the first candidate users, and determine whether the continuous trajectory point sequence corresponding to each first retained user exhibits the second continuous spatiotemporal pattern, so as to determine whether the first retained user meets the short-term connection user exclusion rules.

[0069] Here, the second continuous spatiotemporal pattern is represented as "inside the airport, to outside the airport, to inside the airport".

[0070] In this embodiment, the first candidate users who meet the exclusion rules for users passing through the airport are excluded, and the remaining users are the first reserved users. The continuous trajectory point sequence of the first reserved users is further subjected to pattern recognition to determine whether it exhibits a second continuous spatiotemporal pattern of "inside the airport, to outside the airport, to inside the airport", and the stay time outside the airport does not exceed 30 minutes and the trajectory range is limited to the area around the airport. If the pattern is met, the first reserved user is determined to be a short-term connection user (a resident near the airport whose mobile phone occasionally connects to the airport base station, whose signal briefly covers the airport during daily commutes, or a resident around the airport whose connection is briefly due to the base station signal coverage), thus triggering the short-term connection user exclusion rules.

[0071] S10223. Exclude the first retained users who meet the short-term connection user exclusion rule, so as to select the second retained users from the first retained users, and determine whether the continuous trajectory point sequence corresponding to each second retained user shows airport overnight characteristics, so as to determine whether the second retained user meets the airport overnight user exclusion rule.

[0072] In this embodiment, the first reserved users who meet the short-term connection user exclusion rule are excluded, and the remaining users are designated as the second reserved users. Combining time constraints and trajectory stability characteristics, it is determined whether the continuous trajectory point sequence of each second reserved user has airport overnight characteristics. Specifically, the determination criteria are: whether the last valid signaling point of the user after 18:00 on the same day is located within the airport geofence, and whether the first valid signaling point before 06:00 on the next day is still located within the airport geofence, and whether the trajectory shows no significant movement. If both conditions are met, the second reserved user is determined to be an airport overnight user (mostly airport staff, security personnel, or stranded personnel), triggering the airport overnight user exclusion rule.

[0073] S10224. Exclude the second retained users who meet the airport overnight user exclusion rules, so as to filter out multiple second candidate users from the second retained users, and determine the second user data corresponding to each second candidate user.

[0074] In this step, the second-retained users who meet the airport overnight user exclusion rules are excluded, and the remaining users are the second-candidate users. At this time, the second-candidate users are basically the potential passenger group, and the complete signaling trajectory data of each second-candidate user is extracted as the second-user data.

[0075] S1023. Based on the airport geofencing information, the second user data is filtered using the inbound and outbound passenger discrimination rules to determine at least one target user corresponding to the second user data.

[0076] In this step, the rules for identifying arriving and departing passengers are designed based on the characteristics of the starting and ending points of passengers' travel trajectories and the spatiotemporal correlation patterns. These rules are used to determine target users.

[0077] In one possible implementation of this application, step S1023 may include: S10231. Determine the trajectory start-point information and trajectory end-point information for each of the second candidate users in the second user data.

[0078] In this step, the trajectory start-point information (location coordinates, time, and signal strength of the first valid signaling point of the day) and trajectory end-point information (location coordinates, time, and signal strength of the last valid signaling point of the day) of each second candidate user within the preset collection period are extracted from the second user data in a time series sorted order. At the same time, abnormal trajectory points caused by signal interruption or equipment shutdown are removed to ensure the validity of the trajectory data.

[0079] S10232. Determine whether there is a coverage record corresponding to the airport geofence information in the trajectory starting point information to determine whether the second candidate user meets the arrival passenger identification rule, and determine whether there is a coverage record corresponding to the airport geofence information in the trajectory ending point information to determine whether the second candidate user meets the departure passenger identification rule.

[0080] In this step, it is determined whether the trajectory starting point information is located within the airport geofence and the signal strength meets the standard. If it does, the second candidate user is determined to meet the arrival passenger identification rule (i.e., the passenger departs from the airport to other areas on the same day). At the same time, it is determined whether the trajectory ending point information is located within the airport geofence and the signal strength meets the standard. If it does, the second candidate user is determined to meet the departure passenger identification rule (i.e., the passenger arrives at the airport on the same day).

[0081] S10233. When the second candidate user meets the arrival passenger identification rule, the second candidate user is identified as an arrival passenger, and when the second candidate user meets the departure passenger identification rule, the second candidate user is identified as a departure passenger.

[0082] In this step, the second candidate user who meets the inbound passenger identification rules is identified as an inbound passenger, and the second candidate user who meets the outbound passenger identification rules is identified as an outbound passenger. If there are special cases where the trajectory start and end points are both within the fence and the trajectory has obvious round-trip characteristics (such as passengers traveling to and from the airport on the same day), they are marked as inbound and outbound dual-attribute passengers respectively to ensure comprehensive classification.

[0083] S10234. The arriving passengers and departing passengers are identified as at least one target user corresponding to the second user data.

[0084] In this step, arriving passengers, departing passengers, and passengers with dual attributes are collectively identified as target users, completing the accurate identification and classification of passenger identities and providing core data for subsequent dataset construction.

[0085] S103. Determine the passenger data corresponding to each target user from the mobile phone signaling trajectory data, and construct the target passenger dataset corresponding to the target airport based on the passenger data.

[0086] In this embodiment of the application, the target passenger dataset includes structured passenger information corresponding to the target airport.

[0087] The structured passenger information includes multiple passenger information records, each of which includes, but is not limited to, user identification information, record date, passenger type label information, and mobile phone signaling trajectory point information.

[0088] In this step, determining passenger data and constructing a target passenger dataset is to ensure the standardization, usability, and reliability of the dataset.

[0089] In one possible implementation of this application, step S103 may include: S1031. Determine the passenger data corresponding to each target user from the mobile phone signaling trajectory data.

[0090] In this step, the complete signaling trajectory sequence, passenger type label information (arrival / departure / dual attributes), key time node data, and signal strength characteristics corresponding to each target user are extracted from the mobile phone signaling trajectory data. These are used as the core passenger data corresponding to the target user, and a unique user identifier is established to map the passenger data to ensure that the data information is complete, unique, and traceable.

[0091] S1032. Based on the passenger data, generate structured passenger information corresponding to the target airport.

[0092] In this step, based on the passenger data, structured passenger information corresponding to the target airport is generated in accordance with preset data specifications and industry standards.

[0093] The structured passenger information includes multiple independent passenger information records. Each record includes at least user identification information, record date, passenger type label information (arrival / departure / dual attributes), detailed information on the origin and destination of the trajectory, key stay periods, characteristics of the stay area, and mobile phone signaling trajectory point sequence (including timestamp, latitude and longitude, and signal strength of each point).

[0094] S1033. Based on the structured passenger information, construct the target passenger dataset corresponding to the target airport.

[0095] In this step, the structured passenger information is comprehensively organized and its validity is verified in multiple dimensions. Invalid data with more than a preset number of missing trajectory points, incomplete key information, substandard signal strength, and logical contradictions are removed. At the same time, duplicate records are deduplicated, and finally, a standardized target passenger dataset corresponding to the target airport is constructed.

[0096] The target passenger dataset can be directly used for various operational decision-making scenarios such as airport passenger flow analysis, peak period prediction, service resource optimization, and route operation adjustment.

[0097] The airport passenger screening method based on signaling trajectory data provided in this application pre-constructs a progressive multi-temporal and spatial constraint rule system adapted to civil transport airports. Based on mobile phone signaling trajectory data collected by the target airport within a preset collection period and spatial coverage data corresponding to the base stations set up at the target airport, it accurately distinguishes passengers from various non-passenger groups, effectively avoids interference factors such as signal drift, and improves the accuracy of passenger screening while overcoming the limitations of single-rule screening. It achieves refined classification of arriving, departing, and dual-attribute passengers, outputs structured, high-quality passenger data, and constructs a standardized dataset. This method makes up for the shortcomings of existing methods that can only perform binary classification, and provides technical support for civil transport airports to build high-quality passenger datasets and achieve refined operational decisions, thereby helping airports to achieve refined operational decisions.

[0098] Please see Figure 2 , Figure 2 This is a schematic diagram of an airport passenger screening device based on signaling trajectory data, provided as an embodiment of this application. Figure 2 As shown, the airport passenger screening device 200 includes: The data acquisition module 210 is used to acquire mobile phone signaling trajectory data collected by the target airport within a preset collection period, and spatial coverage data corresponding to the base stations set up at the target airport. The rule filtering module 220 is used to filter the mobile phone signaling trajectory data based on the spatial coverage data using preset multi-temporal and spatial constraint rules, so as to determine at least one target user corresponding to the mobile phone signaling trajectory data; The data construction module 230 is used to determine the passenger data corresponding to each target user in the mobile phone signaling trajectory data, and construct the target passenger dataset corresponding to the target airport based on the passenger data.

[0099] Furthermore, when the data acquisition module 210 is used to acquire mobile phone signaling trajectory data collected by the target airport within a preset acquisition period, and spatial coverage data corresponding to the base stations set up at the target airport, the data acquisition module 210 is used to: Within a preset collection period, raw mobile phone signaling trajectory data of at least one user corresponding to the target airport are collected. The original mobile phone signaling trajectory data is preprocessed to obtain mobile phone signaling trajectory data corresponding to each user; wherein, the mobile phone signaling trajectory data includes at least user identification information, timestamp information, and base station location information; For the base stations set up at the target airport, spatial coverage data corresponding to the base stations is obtained based on the location coding information corresponding to the base stations.

[0100] Furthermore, the multi-temporal constraint rules include preliminary data filtering rules, irrelevant passenger data exclusion rules, and inbound / outbound passenger discrimination rules; when the rule filtering module 220 is used to filter the mobile phone signaling trajectory data based on the spatial coverage data using preset multi-temporal constraint rules to determine at least one target user corresponding to the mobile phone signaling trajectory data, the rule filtering module 220 is used to: Based on the airport geofence information in the spatial coverage data, the mobile phone signaling trajectory data is filtered using the preliminary data filtering rules to determine multiple first candidate users in the mobile phone signaling trajectory data, and to determine the first user data corresponding to each first candidate user. Based on the airport location range information in the spatial coverage data, the first user data is filtered using the irrelevant passenger data exclusion rules to determine multiple second candidate users in the first user data, and the second user data corresponding to each second candidate user is determined. Based on the airport geofencing information, the second user data is filtered using the inbound and outbound passenger discrimination rules to determine at least one target user corresponding to the second user data.

[0101] Furthermore, when the rule filtering module 220 filters the mobile phone signaling trajectory data based on the airport geofence information in the spatial coverage data and uses the preliminary data filtering rules to determine multiple first candidate users corresponding to the mobile phone signaling trajectory data, and determines the first user data corresponding to each first candidate user, the rule filtering module 220 is used to: The base station location information corresponding to each user in the mobile phone signaling trajectory data is compared with the airport geofence information in the spatial coverage data to determine whether there is a coverage record corresponding to the airport geofence information in the base station location information, and the determination result is obtained. Based on the judgment result, multiple first candidate users are selected from the mobile phone signaling trajectory data to find the coverage records corresponding to the airport geofence information in the base station location information; The first user data corresponding to each first candidate user is determined in the mobile phone signaling trajectory data.

[0102] Furthermore, the irrelevant passenger data exclusion rules include rules for excluding users transiting through airports, rules for excluding users with short-term connections, and rules for excluding users staying overnight at airports. When the rule filtering module 220 filters the first user data based on the airport location range information in the spatial coverage data using the irrelevant passenger data exclusion rules to determine multiple second candidate users corresponding to the first user data, and determines the second user data corresponding to each second candidate user, the rule filtering module 220 is used to: The first user data is scanned to identify the continuous trajectory point sequence corresponding to each first candidate user, and it is determined whether each continuous trajectory point sequence exhibits a first continuous spatiotemporal pattern, so as to determine whether the first candidate user meets the exclusion rule for users passing through the airport. The first candidate users who meet the airport user exclusion rules are excluded, so as to select the first retained users from the first candidate users, and determine whether the continuous trajectory point sequence corresponding to each first retained user exhibits the second continuous spatiotemporal pattern, so as to determine whether the first retained user meets the short-term connection user exclusion rules. The first retained users who meet the short-term connection user exclusion rule are excluded, so as to filter out the second retained users from the first retained users, and determine whether the continuous trajectory point sequence corresponding to each second retained user shows airport overnight characteristics, so as to determine whether the second retained user meets the airport overnight user exclusion rule. The second retained users who meet the airport overnight user exclusion rules are excluded, so as to filter out multiple second candidate users from the second retained users and determine the second user data corresponding to each second candidate user.

[0103] Furthermore, the arrival and departure passenger discrimination rules include arrival passenger identification rules and departure passenger identification rules; when the rule filtering module 220 is used to filter the second user data based on the airport geofence information and using the arrival and departure passenger discrimination rules to determine at least one target user corresponding to the second user data, the rule filtering module 220 is used to: In the second user data, determine the trajectory start-point information and trajectory end-point information corresponding to each second candidate user; Determine whether there is a coverage record corresponding to the airport geofence information in the trajectory starting point information to determine whether the second candidate user meets the arrival passenger identification rule, and determine whether there is a coverage record corresponding to the airport geofence information in the trajectory ending point information to determine whether the second candidate user meets the departure passenger identification rule; When the second candidate user meets the arrival passenger identification rule, the second candidate user is identified as an arrival passenger; when the second candidate user meets the departure passenger identification rule, the second candidate user is identified as a departure passenger. The arriving passengers and the departing passengers are identified as at least one target user corresponding to the second user data.

[0104] Furthermore, when the data construction module 230 determines the passenger data corresponding to each target user in the mobile phone signaling trajectory data, and constructs the target passenger dataset corresponding to the target airport based on the passenger data, the data construction module 230 is used to: The passenger data corresponding to each target user is determined from the mobile phone signaling trajectory data; Based on the passenger data, structured passenger information corresponding to the target airport is generated; wherein, the structured passenger information includes multiple passenger information records, and each passenger information record includes at least user identification information, record date, passenger type tag information and mobile phone signaling trajectory point information; Based on the structured passenger information, a target passenger dataset corresponding to the target airport is constructed.

[0105] The airport passenger screening device based on signaling trajectory data provided in this application embodiment, by pre-constructing a progressive multi-temporal and spatial constraint rule system adapted to civil transport airports, accurately distinguishes passengers from various non-passenger groups based on mobile phone signaling trajectory data collected by the target airport within a preset collection period and spatial coverage data corresponding to the base stations set up at the target airport. It effectively avoids interference factors such as signal drift, and improves the accuracy of passenger screening while breaking through the limitations of single rule screening. It achieves refined classification of arriving, departing, and dual-attribute passengers, outputs structured, high-quality passenger data, and constructs a standardized dataset. It makes up for the shortcomings of existing methods that can only perform binary classification, and provides technical support for civil transport airports to build high-quality passenger datasets and achieve refined operational decisions, thereby helping airports to achieve refined operational decisions.

[0106] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 300 includes a processor 310, a memory 320, and a bus 330.

[0107] The memory 320 stores machine-readable instructions executable by the processor 310. When the electronic device 300 is running, the processor 310 and the memory 320 communicate via the bus 330. When the machine-readable instructions are executed by the processor 310, they can perform the operations described above.Figure 1 The steps of the airport passenger screening method based on signaling trajectory data in the method embodiment shown are specifically implemented in the method embodiment and will not be repeated here.

[0108] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 The steps of the airport passenger screening method based on signaling trajectory data in the method embodiment shown are specifically implemented in the method embodiment and will not be repeated here.

[0109] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0111] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0112] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0113] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0114] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for screening airport passengers based on signaling trajectory data, characterized in that, The method includes: The system acquires mobile phone signaling trajectory data collected at the target airport within a preset collection period, as well as spatial coverage data corresponding to the base stations set up at the target airport. Based on the spatial coverage data, the mobile phone signaling trajectory data is filtered using preset multi-temporal and spatial constraint rules to determine at least one target user corresponding to the mobile phone signaling trajectory data; The passenger data corresponding to each target user is determined from the mobile phone signaling trajectory data, and a target passenger dataset corresponding to the target airport is constructed based on the passenger data.

2. The method according to claim 1, characterized in that, The acquisition of mobile phone signaling trajectory data collected at the target airport within a preset collection period, and spatial coverage data corresponding to the base stations set up at the target airport, includes: Within a preset collection period, raw mobile phone signaling trajectory data of at least one user corresponding to the target airport are collected. The original mobile phone signaling trajectory data is preprocessed to obtain mobile phone signaling trajectory data corresponding to each user; wherein, the mobile phone signaling trajectory data includes at least user identification information, timestamp information, and base station location information; For the base stations set up at the target airport, spatial coverage data corresponding to the base stations is obtained based on the location coding information corresponding to the base stations.

3. The method according to claim 1, characterized in that, The multi-temporal constraint rules include preliminary data filtering rules, irrelevant passenger data exclusion rules, and inbound / outbound passenger discrimination rules; the step of filtering the mobile phone signaling trajectory data based on the spatial coverage data using preset multi-temporal constraint rules to determine at least one target user corresponding to the mobile phone signaling trajectory data includes: Based on the airport geofence information in the spatial coverage data, the mobile phone signaling trajectory data is filtered using the preliminary data filtering rules to determine multiple first candidate users in the mobile phone signaling trajectory data, and to determine the first user data corresponding to each first candidate user. Based on the airport location range information in the spatial coverage data, the first user data is filtered using the irrelevant passenger data exclusion rules to determine multiple second candidate users in the first user data, and the second user data corresponding to each second candidate user is determined. Based on the airport geofencing information, the second user data is filtered using the inbound and outbound passenger discrimination rules to determine at least one target user corresponding to the second user data.

4. The method according to claim 3, characterized in that, Based on the airport geofence information in the spatial coverage data, the preliminary data filtering rules are used to filter the mobile phone signaling trajectory data to determine multiple first candidate users corresponding to the mobile phone signaling trajectory data, and to determine the first user data corresponding to each first candidate user, including: The base station location information corresponding to each user in the mobile phone signaling trajectory data is compared with the airport geofence information in the spatial coverage data to determine whether there is a coverage record corresponding to the airport geofence information in the base station location information, and the determination result is obtained. Based on the judgment result, multiple first candidate users are selected from the mobile phone signaling trajectory data to find the coverage records corresponding to the airport geofence information in the base station location information; The first user data corresponding to each first candidate user is determined in the mobile phone signaling trajectory data.

5. The method according to claim 3, characterized in that, The irrelevant passenger data exclusion rules include rules for excluding users transiting through airports, rules for excluding users with short-term connections, and rules for excluding users spending the night at airports. The process of filtering the first user data based on airport location range information in the spatial coverage data using the irrelevant passenger data exclusion rules to determine multiple second candidate users corresponding to the first user data, and determining the second user data corresponding to each second candidate user, includes: The first user data is scanned to identify the continuous trajectory point sequence corresponding to each first candidate user, and it is determined whether each continuous trajectory point sequence exhibits a first continuous spatiotemporal pattern, so as to determine whether the first candidate user meets the exclusion rule for users passing through the airport. The first candidate users who meet the airport user exclusion rules are excluded, so as to select the first retained users from the first candidate users, and determine whether the continuous trajectory point sequence corresponding to each first retained user exhibits the second continuous spatiotemporal pattern, so as to determine whether the first retained user meets the short-term connection user exclusion rules. The first retained users who meet the short-term connection user exclusion rule are excluded, so as to filter out the second retained users from the first retained users, and determine whether the continuous trajectory point sequence corresponding to each second retained user shows airport overnight characteristics, so as to determine whether the second retained user meets the airport overnight user exclusion rule. The second retained users who meet the airport overnight user exclusion rules are excluded, so as to filter out multiple second candidate users from the second retained users and determine the second user data corresponding to each second candidate user.

6. The method according to claim 3, characterized in that, The arrival and departure passenger identification rules include arrival passenger identification rules and departure passenger identification rules; the step of filtering the second user data based on the airport geofencing information using the arrival and departure passenger identification rules to determine at least one target user corresponding to the second user data includes: In the second user data, determine the trajectory start-point information and trajectory end-point information corresponding to each second candidate user; Determine whether there is a coverage record corresponding to the airport geofence information in the trajectory starting point information to determine whether the second candidate user meets the arrival passenger identification rule, and determine whether there is a coverage record corresponding to the airport geofence information in the trajectory ending point information to determine whether the second candidate user meets the departure passenger identification rule; When the second candidate user meets the arrival passenger identification rule, the second candidate user is identified as an arrival passenger; when the second candidate user meets the departure passenger identification rule, the second candidate user is identified as a departure passenger. The arriving passengers and the departing passengers are identified as at least one target user corresponding to the second user data.

7. The method according to claim 1, characterized in that, The step of determining passenger data corresponding to each target user from the mobile phone signaling trajectory data, and constructing a target passenger dataset corresponding to the target airport based on the passenger data, includes: The passenger data corresponding to each target user is determined from the mobile phone signaling trajectory data; Based on the passenger data, structured passenger information corresponding to the target airport is generated; wherein, the structured passenger information includes multiple passenger information records, and each passenger information record includes at least user identification information, record date, passenger type tag information and mobile phone signaling trajectory point information; Based on the structured passenger information, a target passenger dataset corresponding to the target airport is constructed.

8. An airport passenger screening device based on signaling trajectory data, characterized in that, The airport passenger screening device includes: The data acquisition module is used to acquire mobile phone signaling trajectory data collected by the target airport within a preset collection period, as well as spatial coverage data corresponding to the base stations set up at the target airport. The rule filtering module is used to filter the mobile phone signaling trajectory data based on the spatial coverage data using preset multi-temporal and spatial constraint rules, so as to determine at least one target user corresponding to the mobile phone signaling trajectory data; The data construction module is used to determine the passenger data corresponding to each target user in the mobile phone signaling trajectory data, and construct the target passenger dataset corresponding to the target airport based on the passenger data.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. The machine-readable instructions are executed by the processor to perform the steps of the airport passenger screening method based on signaling trajectory data as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the airport passenger screening method based on signaling trajectory data as described in any one of claims 1 to 7.

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