Smart airport operation system construction method based on 5G customized network

Abstract

The invention discloses a smart airport operation system construction method based on a 5G customized network, and relates to the technical field of airport scene operation management. The construction method of the smart airport operation system based on the 5G customized network comprises the following steps: S1, collecting smart airport operation environment data, preprocessing and storing the data, and constructing an airport situation event database; s2, constructing a multi-service communication channel through network slice configuration and an access mapping relation; s3, the edge side generates an edge intermediate result packet based on the unified data stream, and the cloud side constructs a unified event time axis according to the consistency judgment matrix; s4, performing time semantic connection analysis through edge intermediate result packet data; s5, cross-business domain fusion is carried out through the event timeline and the situation object data; and S6, performing disposal admission judgment based on the global operation situation data. The problems of non-uniform operation situation and lack of continuous basis for resource scheduling caused by inconsistent time semantics when the airport operation edge side events are converged at the cloud side are solved.

Description

Technical Field

[0001] This invention relates to the field of airport surface operation management technology, specifically a method for constructing a smart airport operation system based on a 5G customized network. Background Technology

[0002] As airport operations continue to expand, the construction of smart airports has become an important direction for promoting the digital and intelligent transformation of the civil aviation industry. With the development of IoT, 5G communication, and edge computing technologies, smart airports are gradually adopting networked and digital means to achieve network access and distributed data collection for various terminal devices.

[0003] For example, the invention patent announcement CN112053085B discloses an airport surface operation management system and method based on digital twins, belonging to the field of airport surface operation management technology. It constructs a digital environment for airport surface operations that is completely identical to the airport's operational status in real time, and overlays target status information, air traffic control information, and airport information onto the virtual scene to intuitively present the airport's operational status in real time. At the same time, it uses artificial intelligence methods to intelligently analyze video images, automatically completing functions such as detection, tracking, identification, positioning, attitude estimation, and behavior estimation of aircraft, vehicles, and personnel targets, thereby improving the intelligence level of video surveillance. Furthermore, it uses multi-source data fusion technology to match and fuse target status information on the airport surface with air traffic control and airport information, realizing intelligent matching and fusion between video images and heterogeneous business data.

[0004] For example, the invention patent announcement CN118428624B discloses a collaborative scheduling system and method for integrated operation of airport clusters. The system includes: an initial planning subsystem that acquires initial scheduling data and calculates the ETA (Earning Time Acquisition) and ETD (Earning Time To Difference) for each arriving flight and each departing flight based on the initial scheduling data; a single-airport integrated sequencing management subsystem that plans the corresponding runway scheduling plan and flight arrival / departure schedule based on the ETA and ETD of each airport's flights; and an airport cluster airspace integrated management subsystem that, with the goal of maximizing airport cluster throughput, modifies the runway scheduling plan and flight arrival / departure schedule for each airport and forms a conflict-free airspace scheduling plan. By transforming the integrated airport cluster scheduling problem into a macro-level airspace scheduling model and a micro-level single-airport scheduling model, and comprehensively considering the flight timing constraints between airports, integrated collaborative scheduling of the airport cluster is achieved.

[0005] Despite this, existing smart airport operation systems still face challenges such as inconsistent data standards, limited data flow across business domains, low efficiency in equipment and business process collaboration, unclear event tracking links, and slow response to changes in operational status. The lack of a unified format and alignment mechanism for data from different sources leads to significant information silos and redundant processing. Collaborative optimization of resource scheduling, fault handling, and anomaly response across multiple business domains is difficult, impacting the overall operational efficiency and safety assurance capabilities of the airport.

[0006] Therefore, to address the above issues, there is an urgent need for a method to construct a smart airport operation system based on a 5G customized network. Summary of the Invention

[0007] Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides a method for constructing a smart airport operation system based on a 5G customized network, which solves the problems of inconsistent temporal semantics leading to inconsistent operational status and lack of continuous basis for resource scheduling when edge-side event results are converged on the cloud side in existing airport operations.

[0009] Technical solution

[0010] To achieve the above objectives, the present invention provides the following technical solution: a method for constructing a smart airport operation system based on a 5G customized network, comprising: S1, collecting equipment operation status data, business processing behavior data, and communication operation data, preprocessing and storing them to construct an airport situation event database; S2, constructing multi-service communication channels through the service domain-level network slicing configuration and access mapping relationship of the 5G customized network; S3, generating edge intermediate result packets on the edge side based on a unified data stream, and writing the edge intermediate results into the event timeline mainline on the cloud side based on a consistency judgment matrix to construct a unified event timeline; S4, performing time semantic connection analysis through time, semantic, and version-related data in the edge intermediate result packets, and performing event mainline inclusion, alignment correction, and non-mainline diversion processing based on the time semantic analysis results; S5, performing cross-service domain fusion through the event timeline and situation object data, and performing situation playback and linkage display operations in the digital twin scenario based on the fusion analysis results; S6, performing handling access judgment based on the full-domain operation situation data, and executing instruction issuance, queue retention, and feedback based on the judgment results.

[0011] Further, the specific steps for collecting equipment operation status data, business processing behavior data, and communication operation data, preprocessing and storing them to construct an airport situational event database are as follows: Collecting smart airport operation environment data, including equipment operation status data, business processing behavior data, and communication operation data; Collecting equipment operation status data: Obtaining equipment operation status values, alarm link status, and resource occupancy status through security inspection equipment, baggage handling equipment, boarding gate facilities, apron vehicle terminals, operational security devices, and energy security devices; obtaining equipment identification codes through equipment control interfaces; obtaining location codes through the airport spatial coding table; and binding the aforementioned data with the collection time to form an equipment operation status time series; Collecting business processing behavior data: Obtaining corresponding business processing statuses through security inspection business records, baggage sorting control records, boarding gate management records, apron vehicle dispatch records, and operational security event records; and matching the timestamps with the equipment operation status time series to form a business processing behavior time series; The system collects communication operation data: It acquires bandwidth usage status, end-to-end latency measurements, transmission jitter measurements, and data packet loss statistics for each service domain network slice through the 5G customized network management interface, and aligns these with the device operation status time series to form a communication status time series. It performs outlier removal and missing segment completion processing on the device operation status data, service processing behavior data, and communication operation data. Missing segment completion uses linear interpolation between adjacent time points, and the interpolated segments are marked. Simultaneously, it performs scale alignment processing on continuous numerical fields, mapping them to a unified interval. After completing the unified format processing, a unified data stream is formed. The unified data stream field set includes device identifier code, location code, service domain code, event type code, event occurrence time, event end time, status judgment value, resource usage status, data dictionary version number, and quality flag. The unified data stream is stored, and an airport situation event database is constructed. Within the airport situation event database, a mapping relationship table, a device topology table, a situation scenario mapping table, and a handling access rule table are built.

[0012] Furthermore, the specific steps for constructing multi-service communication channels through the service domain-level network slicing configuration and access mapping relationship of the 5G customized network are as follows: The 5G customized network communication resources are divided into service domain-level slices. For each service domain's corresponding network slice, bandwidth quota parameters, end-to-end latency threshold parameters, transmission jitter threshold parameters, data packet loss threshold parameters, transmission priority identifier parameters, slice isolation control parameters, and transmission encryption control parameters are configured respectively. A deterministic mapping relationship table between terminal access identifiers and network slice identifiers is established, enabling the unified data stream generated by the field terminal and the analysis result stream output by the edge computing node to access the corresponding network slice channel according to the mapping relationship table.

[0013] Furthermore, the specific steps for generating edge intermediate result packets based on the unified data stream at the edge are as follows: Edge computing nodes are deployed in the terminal area, baggage sorting area, and apron area respectively. The edge computing nodes access the unified data stream through network slices configured for the corresponding business domain in the 5G customized network. The edge computing nodes perform local operation status parsing, video structured result aggregation, business queue statistics, local abnormal state identification, and time window aggregation processing of object trajectories based on the unified data stream, perform edge-side analysis, and output edge intermediate result packets.

[0014] Furthermore, the specific steps for the cloud side to write edge intermediate results into the main event timeline based on the consistency judgment matrix to construct a unified event timeline are as follows: First, obtain the unified data stream transmitted from the 5G customized network and edge intermediate result packets. Then, write the edge intermediate result packets into the event candidate queue according to their event numbers, and retrieve the main line record corresponding to the same event number from the event timeline as the comparison object. For each edge intermediate result packet in the candidate queue, construct a consistency judgment matrix, which includes event number matching items, event type code matching items, and time range matching items. Based on the consistency judgment matrix, perform item-by-item judgment to generate a consistency judgment flag and output the status judgment value: When all items in the consistency judgment matrix match, include the corresponding edge intermediate result packet in the main event timeline structure and update the main timeline time range. For the same event number, only one time-ordered main line record is retained. When there are unmatched items in the consistency judgment matrix, the edge intermediate result packet does not enter the main event timeline structure and is transferred to the subsequent time semantic processing flow.

[0015] Furthermore, the specific steps for time semantic alignment analysis using time, semantic, and version-related data in the edge intermediate result package are as follows: For each edge intermediate result package entering the time alignment processing range of the event timeline, the event occurrence time in the edge intermediate result package is read and compared with the reference time point of the main record of the same event number in the event timeline to obtain the time deviation value; the event type code, location code, and status judgment value in the edge intermediate result package are extracted and compared with the fields of the corresponding business domain main record in the event timeline to obtain the semantic consistency value; the data dictionary version number in the edge intermediate result package is read and compared with the main record of the event timeline to obtain the semantic consistency value. The relative positions of the data dictionary version numbers in the version evolution sequence are compared to obtain the version difference value. The time deviation value, semantic consistency value, and version difference value are respectively processed by tiered encoding to form time tiered encoding, semantic tiered encoding, and version tiered encoding. All edge intermediate result packets are combined into a metric encoding sequence. First, they are sorted in ascending order according to the time tiered encoding. If the time tiered encoding is the same, they are sorted in descending order according to the semantic tiered encoding. If the first two types of tiered encoding are the same, they are sorted in ascending order according to the version tiered encoding to obtain an ordered metric sequence. The sequential position number of each edge intermediate result packet in the ordered metric sequence is obtained to obtain the semantic deviation value, and it is written into the time alignment queue of the event timeline.

[0016] Further, the specific steps for incorporating the main event line, aligning and correcting it, and handling non-main event line diversion based on the temporal semantic analysis results are as follows: By comparing the semantic deviation value and the deviation threshold in real time, when the semantic deviation value is less than the deviation threshold, the corresponding edge intermediate result packet is extracted from the time alignment queue and incorporated into the main event line processing path. The main event time range, event status marker, and associated device set corresponding to the event number are updated, and the results are synchronously provided to the global operational status generation process to drive the status indicator update, resource occupancy status refresh, and scheduling decision calculation. When the semantic deviation value is greater than or equal to the deviation threshold, the corresponding edge intermediate result packet is retained in the time alignment queue and subjected to disordered rearrangement and time window correction based on the event occurrence time. After correction, the semantic deviation value is recalculated. When the corrected semantic deviation value is less than the deviation threshold, the corresponding edge intermediate result packet is moved from the time alignment queue into the main event line processing path. When the corrected semantic deviation value is greater than or equal to the deviation threshold, the event is removed from the time alignment queue and written into the non-main event semantic region of the event timeline. A semantic deviation identifier is attached, and the event is written into the airport status event database according to the event number, time range, and location code.

[0017] Furthermore, the specific steps for cross-business domain integration of the event timeline and the situation object data are as follows: In the process of generating the overall operation situation, obtain the main integration segment in the event timeline as the trigger source for status update, and maintain a status integration buffer corresponding to the generation of the overall operation situation; when a new main integration segment enters the specified time window, perform status update processing on the existing operation status in the status integration buffer according to the event type code, location code, and status determination value. The status update processing includes status addition, status replacement, and status continuation, continuously depicting the change process of the operation status of each business domain of the airport; at the end of the time window, the industrial cloud platform starts the processing of generating the overall operation situation, performs summary and integration across business domains, locations, and resource elements on the operation status in the status integration buffer, represents the overall operation situation object of the airport's comprehensive operation status within the corresponding time range, and outputs the situation number, event number, location code, and associated resource identifier of the overall operation situation object.

[0018] Furthermore, the specific steps for performing situation playback and linkage display operations in the digital twin scenario according to the fusion analysis results are as follows: Obtain the situation number, event number, location code, and associated resource identifier of the overall operation situation object; record the corresponding digital twin scenario node identifier, spatial coordinate code, and coordinate reference system identifier with the situation number as the main index field, and bind the location code and associated resource identifier to the spatial unit node and operation object node in the scenario one by one to construct a situation scenario mapping table between the overall operation situation object and the digital twin scenario; after the mapping is completed, implement a time axis synchronization mechanism: express and retain the time zone offset identifier with the UTC timestamp. The cloud side synchronously records the acquisition completion timestamp and the reception timestamp when receiving the edge intermediate result packet, and calculates the time drift estimation value within the time window of the same event number and writes it into the time alignment field. When the digital twin scenario loads the overall operation situation object, use the event occurrence time plus the time drift estimation value as the time axis drive benchmark to achieve continuous playback of the situation evolving over time; during the situation playback and interaction process, perform linkage positioning processing based on the situation number, event number, and location code. When any identifier is triggered, the digital twin scenario synchronously locates the corresponding spatial area and operation object, and presents the situation evolution status, resource occupancy change, alarm trigger and transmission link, and disposal execution progress in the corresponding stage in chronological order.

[0019] Furthermore, the specific steps for determining access based on the overall operational status data, and for issuing instructions, retaining them in the queue, and providing feedback based on the determination results are as follows: Extract the status number, associated event number, location code, resource occupancy status, and alarm link status from the overall operational status object. Match the location code with the device topology table in the resource ledger to identify the set of devices affected by a single action. Generate an access rule table based on historical action records in the event timeline and corresponding execution feedback results. Perform rule matching in the access rule table according to the resource occupancy status and alarm link status to generate an access determination result, and write the corresponding status event into the instruction candidate queue. The system compares the handling admission judgment result with the handling threshold. If the handling admission judgment result is less than the handling threshold, the situation event is packaged into an instruction package and sent to the edge computing node and field equipment for execution through the slice channel of the corresponding business domain. If the judgment result is greater than or equal to the handling threshold, the corresponding situation event is retained in the instruction candidate queue to wait for the update of the global operational situation object. After the instruction is executed, a receipt package is generated. Based on the associated event number in the receipt package, the execution result is linked back to the event timeline, driving the update of the event status and handling progress in the global operational situation object. The event timeline change record, slice communication quality record, and situation evolution record are written into the airport situation event database.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects: (1) This invention constructs a multi-service communication channel by configuring network slicing at the service domain level and mapping the terminal access identifier, thereby achieving differentiated protection and mutual isolation of different service domains in terms of bandwidth, latency, jitter, packet loss and priority. This achieves the effect of maintaining the communication stability of critical operating services under a unified bearer environment, effectively solving the problems of mixed transmission of multiple types of service traffic, competition for resources between critical and non-critical services, and difficulty in ensuring the communication continuity of the operation process in the prior art.

[0022] (2) This invention generates edge intermediate result packages based on a unified data stream on the edge side and introduces an event candidate queue and a consistency judgment matrix. On the cloud side, it constructs a time-ordered event timeline main line maintained by event number, so that the same event retains only one interpretation record on a unified time axis in different processing layers. This achieves a unified time view effect that connects edge processing and cloud-side operation monitoring, effectively solving the problems in the prior art where edge analysis results and cloud operation views lack time correspondence, the same operation state is repeatedly interpreted, and operation judgment depends on the experience of the processing level.

[0023] (3) This invention constructs time deviation value, semantic consistency value and version difference value by utilizing the time, semantic and version information in the edge intermediate result package, and performs grading encoding, sorting and rearrangement and time window correction processing in the time alignment queue, so that the records entering the main event timeline simultaneously meet the time sequence relationship, consistency requirements and version evolution constraints, thereby realizing the effect of repeatable judgment and correction of the time semantic relationship between edge results and historical records, effectively solving the problems in the prior art that asynchronous data transmission easily causes event sequence disorder, unclear state coverage relationship and difficulty in reliably tracing the running process.

[0024] (4) This invention constructs a global operational status object based on the main event timeline fusion segment in the global operational status generation process, and loads the global operational status object into the digital twin scene by combining the status scene mapping table and the time axis synchronization mechanism. This enables continuous playback and linkage display of the operational status across business domains, locations and resource elements in the three-dimensional scene, thereby achieving a unified and intuitive status display effect with time evolution information for the operation management link. This effectively solves the problems of fragmented views of different business platforms, disconnect between digital twin scene and underlying event data, and difficulty for operators to grasp the overall airport status in the existing technology.

[0025] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0026] Figure 1 This is a flowchart of a method for constructing a smart airport operation system based on a 5G customized network according to the present invention. Figure 2 This is a logic diagram of the event stream temporal semantic alignment, correction, and splitting processing of the present invention; Figure 3 This is a schematic diagram illustrating the time drift mapping association of the digital twin scenario of the airport's overall operational status in this invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Please see Figures 1-3This invention provides a technical solution: a method for constructing a smart airport operation system based on a 5G customized network, comprising: S1, collecting equipment operation status data, business processing behavior data, and communication operation data, preprocessing and storing them to construct an airport situation event database; S2, constructing multi-service communication channels through the service domain-level network slicing configuration and access mapping relationship of the 5G customized network; S3, generating edge intermediate result packets on the edge side based on a unified data stream, and writing the edge intermediate results into the main event timeline on the cloud side based on a consistency judgment matrix to construct a unified event timeline; S4, performing time semantic connection analysis through time, semantic, and version-related data in the edge intermediate result packets, and performing event mainline inclusion, alignment correction, and non-mainline diversion processing according to the time semantic analysis results; S5, performing cross-service domain fusion through the event timeline and situation object data, and performing situation playback and linkage display operations in the digital twin scenario according to the fusion analysis results; S6, performing handling access judgment based on the full-domain operation situation data, and executing instruction issuance, queue retention, and feedback according to the judgment results.

[0029] Specifically, the steps for collecting equipment operation status data, business processing behavior data, and communication operation data, preprocessing them, storing them, and then constructing an airport situational event database are as follows: Collecting smart airport operation environment data, including equipment operation status data, business processing behavior data, and communication operation data, to comprehensively reflect the operation status of each business domain of the airport at the same time; Collecting equipment operation status data: Obtaining equipment operation status values, alarm link status, and resource occupancy status through security screening equipment, baggage handling equipment, boarding gate facilities, apron vehicle terminals, operational security devices, and energy supply devices; simultaneously obtaining equipment identification codes through equipment control interfaces and location information through the airport spatial coding table. The system encodes and binds equipment operating status values, alarm link status, and resource occupancy status with corresponding collection times to form a time series of equipment operating status arranged continuously in time, used to depict the process of equipment operation changes. It also collects business processing behavior data: security check processing status is obtained from security check business records, baggage handling status from baggage sorting control records, passenger release status from boarding gate control records, vehicle task status from apron vehicle dispatch records, and security event status from operational security event records. These business processing statuses are then matched with the equipment operating status time series according to their timestamps to form a business processing behavior time series synchronized with the equipment status, used to describe the relationship between business activities and equipment operation. The correlation between data sources and communication operation data is analyzed. This involves acquiring bandwidth usage, end-to-end latency measurements, transmission jitter measurements, and data packet loss statistics for each service domain network slice via the 5G customized network management interface. The communication operation data is then timestamped according to the acquisition time and the device operation status time series to form a communication status time series, reflecting the impact of communication conditions on service operation. Outlier removal and missing segment completion are uniformly performed on device operation status data, service processing behavior data, and communication operation data. Missing segment completion uses linear interpolation between adjacent time points, and the interpolated segments are marked. Simultaneously, continuous numerical fields are scale-aligned and mapped to a unified interval to ensure that data from different sources are consistent in terms of magnitude. The data stream ensures comparability. After completing the unified format processing, the equipment operation status data, business processing behavior data, and communication operation data are integrated into a unified data stream. The unified data stream field set includes equipment identification code, location code, business domain code, event type code, event occurrence time, event end time, status judgment value, resource occupancy status, data dictionary version number, and quality tag, which are used for subsequent event timeline construction and time semantic analysis. The unified data stream is stored and an airport situation event database is built. In the airport situation event database, a mapping relationship table, equipment topology table, situation scene mapping table, and handling access rule table are built to provide a data foundation for subsequent multi-business domain operation status generation, digital twin linkage display, and handling decision-making.

[0030] In this implementation plan, by uniformly collecting, aligning and scaling equipment operation status data, business processing behavior data and communication operation data, the operation information scattered in different business domains, different devices and different communication slices is transformed into a unified data stream with consistent structure and continuous time. This data is then centrally stored in the airport situation event database, thereby providing a data foundation with comparability, traceability and consistent time benchmark for subsequent edge side analysis, event timeline construction, time semantic connection determination and full-domain operation situation generation.

[0031] Specifically, the steps for constructing multi-service communication channels through the service domain-level network slicing configuration and access mapping relationship of the 5G customized network are as follows: The 5G customized network communication resources are divided into service domain-level slices. For each service domain's corresponding network slice, within a set performance measurement period, based on continuous sampling results, bandwidth quota parameters, end-to-end latency threshold parameters, transmission jitter threshold parameters, data packet loss threshold parameters, transmission priority identifier parameters, slice isolation control parameters, and transmission encryption control parameters are configured respectively. The transmission encryption control parameters are limited to using the IPsec protocol family to achieve a match between security and latency requirements for different service domains. A deterministic mapping relationship table between terminal access identifiers and network slice identifiers is established. The deterministic mapping relationship table uses the terminal access identifier as the main index field and matches the on-site terminal access identifiers and target network slice identifiers one by one according to the matching order of IMSI first, IMEI second, MAC address third, and certificate fingerprint supplement. Binding: When multiple candidate matches exist, the identity identifier with the highest priority is selected as the binding basis according to the matching order, and the remaining candidates are recorded as conflict information for easy auditing and traceability. When the terminal access identifier changes, the corresponding entry in the deterministic mapping relationship table is updated through hot update and a re-association process is triggered. The re-association process re-matches the service domain network slice identifier based on the latest access identifier. When a match cannot be completed, the terminal access is uniformly backed to the general service slice channel according to the service domain communication policy. Under the constraints of the deterministic mapping relationship table, the unified data stream generated by the field terminal and the analysis result stream output by the edge computing node are stably connected to the corresponding network slice channel within the set measurement period. At the end of each measurement period, the operating status of each communication channel is evaluated based on the statistical results of the end-to-end delay threshold parameter, transmission jitter threshold parameter, and data packet loss threshold parameter, and the bandwidth quota parameter and transmission priority identifier parameter are adjusted accordingly.

[0032] In this implementation plan, 5G customized network communication resources are divided into slices according to service domains and configured with bandwidth quota parameters, end-to-end latency threshold parameters, transmission jitter threshold parameters, data packet loss threshold parameters, transmission priority identifier parameters, slice isolation control parameters, and transmission encryption control parameters. Combined with a one-to-one binding deterministic mapping relationship table with terminal access identifier as the main index, the unified data stream generated by the field terminal and the analysis result stream output by the edge computing node are stably carried in the network slice channel that matches the service requirements. This realizes the transformation of the communication channel selection process from experience-dependent to rule-driven, and the transformation of communication quality from post-event perception to statistical and adjustable according to the measurement cycle. In this way, it realizes the communication carrying capacity with clear security levels and controllable performance indicators for different services in the airport multi-service concurrent scenario. It effectively solves the problems of key and non-key services competing for link resources, the difficulty in timely detection of communication quality fluctuations, and the lack of differentiated protection according to services in the existing technology.

[0033] Specifically, the steps for generating edge intermediate result packets based on the unified data stream at the edge are as follows: Edge computing nodes are deployed in the terminal area, baggage sorting area, and apron area. These edge computing nodes access the unified data stream through network slices configured for their respective service domains within the 5G customized network. Based on the unified data stream, the edge computing nodes sequentially perform local operational status parsing, video structured result aggregation, service queue statistics, local anomaly state identification, and object trajectory time window aggregation processing to achieve real-time data analysis and event status extraction for each service domain. After analysis, the edge computing nodes output edge intermediate result packets. The field set of the edge intermediate result packets includes event number, time range, location code, semantic summary, version number, and source quality score. Event numbers are generated according to unified event numbering rules to ensure unique identification of events across regions and devices; the time range reflects the start and end times of the event in the unified data stream, facilitating event tracking and subsequent alignment processing; the location code identifies the event's occurrence area and specific spatial location, enabling spatial distribution management; the semantic summary is automatically extracted and encoded by edge nodes from core elements such as event type codes and status judgment values, facilitating subsequent consistency judgment and semantic connection in the cloud; the version number indicates the version evolution of the edge node's local data dictionary and event data, ensuring the accuracy of mainline fusion and time-series processing; the source quality score is comprehensively assigned based on the acquisition link status, data integrity, and processing latency, used for priority ranking and quality control during subsequent edge and cloud collaborative processing. Through the above structured edge intermediate result package, efficient collection, unique identification, and reliable traceability of event status across multiple business domains and regions at the airport are achieved, fully supporting key processes such as subsequent event timeline construction, semantic consistency analysis, and generation of overall operational status.

[0034] In this implementation plan, edge computing nodes deployed in the terminal building, baggage sorting area, and apron area perform real-time local parsing, structured aggregation, and multi-dimensional status statistics on the unified data stream. This automatically identifies key business events and abnormal states, and ultimately generates edge intermediate result packages with fields such as event number, time range, location code, semantic summary, version number, and source quality score. This enables efficient collection, unique identification, and tracing of event information across multiple business domains and regions of the airport, providing a high-quality data foundation for subsequent event timeline construction and generation of overall operational status.

[0035] Specifically, the cloud-side steps for constructing a unified event timeline by writing edge intermediate results into the main event timeline based on the consistency judgment matrix are as follows: First, obtain the unified data stream transmitted from the 5G customized network and edge intermediate result packets. Then, use these edge intermediate result packets as event-level inputs, writing them into the event candidate queue according to their event numbers. This queue is used to centrally manage the analysis results generated by the same event at different times and processing layers. Next, retrieve the mainline record from the event timeline that has the same event number as the edge intermediate result packets in the event candidate queue as the comparison object, ensuring that the judgment process always revolves around the same event. Finally, for each edge intermediate result packet in the candidate queue, construct a consistency judgment matrix. This matrix consists of event number matching items, event type code matching items, and time range matching items, used to characterize the edge intermediate results from three dimensions: identifier consistency, semantic consistency, and temporal continuity. The system identifies the matching relationship between intermediate result packages and existing event timeline records. Each matching item is evaluated against the consistency determination matrix, generating a corresponding consistency determination flag and outputting a status determination value. This flag indicates whether the current edge intermediate result package meets the conditions for inclusion in the event timeline. When all matching items in the consistency determination matrix are true, the corresponding edge intermediate result package is included in the event timeline structure, and the time range of the timeline is expanded or corrected. Simultaneously, only one consecutive timeline record is retained for the same event number to maintain the uniqueness and orderliness of the event timeline. When there are unmatched items in the consistency determination matrix, the corresponding edge intermediate result package does not enter the event timeline structure but is instead transferred to the subsequent time semantic processing flow. This allows for further analysis and processing of event results with temporal or semantic differences while maintaining the stability of the event timeline.

[0036] In this implementation plan, by determining the consistency between the intermediate result packages at the edge and the main event timeline records, event results from different times and different processing layers are uniformly screened and merged. This ensures that event results that meet the conditions of consistent identification, semantic consistency, and temporal continuity are stably included in the main event timeline, thereby forming a unique, orderly, and continuously updated event timeline. At the same time, event results with discrepancies are orderly diverted to subsequent time semantic processing flows, providing a clear and reliable event basis for subsequent time semantic connection analysis and operational status generation.

[0037] Specifically, the steps for time semantic connection analysis using time, semantic, and version-related data in the edge intermediate result package are as follows: For each edge intermediate result package entering the time alignment processing range of the event timeline, the event occurrence time in the edge intermediate result package is read, and the event occurrence time is compared with the reference time point recorded in the main line of the same event number in the event timeline as a pair of time benchmarks. Combined with the time granularity configuration, the temporal sequence and interval length between the two are calculated and determined. After filtering out noise time segments that exceed the tolerance range, the time deviation value used to characterize the degree of edge uplink rhythm offset is obtained; by extracting the edge intermediate result package... The event type code, location code, and status judgment value in the fruit package are compared field by field with the corresponding fields in the main record of the business domain in the event timeline. The comparison results are recorded for scenarios of complete field consistency, inconsistency, and missing data completion. After one round of comparison, the results are summarized into a semantic consistency value that represents the degree of semantic description fit. By reading the data dictionary version number in the edge intermediate result package, the data dictionary version number is located in the version evolution sequence maintained by the main record of the event timeline. The reference positions under the same event number are compared sequentially. The sequence distance and cross-version steps characterize the edge record and the main record. The version differences between older and younger versions are used to obtain version difference values ​​reflecting the synchronization of version evolution. Time deviation values, semantic consistency values, and version difference values ​​are each subjected to hierarchical encoding. Time deviation values ​​are divided into multi-level time hierarchical codes based on chronological order and interval size; semantic consistency values ​​are divided into multi-level semantic hierarchical codes based on field consistency; and version difference values ​​are divided into multi-level version hierarchical codes based on version differences, forming time hierarchical codes, semantic hierarchical codes, and version hierarchical codes. All intermediate edge result packets are combined into a metric encoding sequence according to time hierarchical codes, semantic hierarchical codes, and version hierarchical codes. First, the sequence is encoded according to time hierarchical codes. The codes are sorted in ascending order. If the time-series codes are the same, they are sorted in descending order according to the semantic-series codes. If the first two types of codes are the same, they are sorted in ascending order according to the version-series codes. This hierarchical sorting strategy yields an ordered metric sequence that prioritizes time, semantic fit, and version freshness. The sequential position number of each edge intermediate result package in the ordered metric sequence is obtained. The sequential position number is used as the semantic deviation value of the corresponding edge intermediate result package to characterize the relative temporal semantic offset of the edge intermediate result package within the current time alignment processing window. The semantic deviation value is then written into the time alignment queue of the event timeline.

[0038] In this implementation scheme, time deviation, semantic consistency, and version difference values ​​are constructed for the edge intermediate result packets that enter the time alignment processing range of the event timeline, and then uniformly converted into semantic deviation values. This completes the sorting and classification of edge intermediate result packets within the same time alignment window, establishes a quantifiable time semantic reference benchmark for mainline attachment processing, and achieves consistent constraints on the time sequence, semantic fit, and version relationship between edge-up results and the main event timeline, effectively reducing state conflicts caused by time sequence disorder.

[0039] Specifically, the specific steps for incorporating the main event flow, aligning and correcting alignment, and handling non-main event flow based on the temporal semantic analysis results are as follows: By comparing semantic deviation values ​​and deviation thresholds in real time, such as... Figure 2 This is a logic diagram for event flow time semantic alignment, correction, and diversion processing in this embodiment. When the semantic deviation value is less than the deviation threshold, the corresponding edge intermediate result packet is extracted from the time alignment queue and included in the main processing path of the event timeline. The main time range, event status marker, and associated device set corresponding to the event number are updated in a timely manner. At the same time, the processing results are synchronously provided to the global operational status generation process to drive real-time updates of status indicators, refresh of resource occupancy status, and calculation of scheduling decisions. When the semantic deviation value is greater than or equal to the deviation threshold, the corresponding edge intermediate result packet is retained in the time alignment queue, and out-of-order rearrangement and time window correction processing based on the event occurrence time are performed to further improve the adaptive processing capability for out-of-order and latency anomalies. After correction, the semantic deviation value is recalculated. If the corrected semantic deviation value is less than the deviation threshold, the corresponding edge intermediate result packet is moved from the time alignment queue into the main processing path of the event timeline. If the corrected semantic deviation value is greater than or equal to the deviation threshold, the event is removed from the time alignment queue and written into the non-main semantic region of the event timeline, with a semantic deviation identifier attached, and written into the airport status event database according to the event number, time range, and location code. Through the above semantic correction process, in this embodiment, compared with the uncorrected baseline, the main line misalignment rate is reduced by ≥30%, and the average alignment delay is shortened by ≥25%, which significantly improves the accuracy of event main line identification and the timeliness of operational status data processing.

[0040] In this implementation scheme, by setting semantic deviation values ​​and deviation thresholds, dynamic classification and efficient correction of edge intermediate result packets are achieved, ensuring that data that meets the conditions enters the main processing path of the event timeline in a timely manner, driving timely updates of event status and generation of overall operational status. For data that does not meet the conditions, the accuracy of event merging and the robustness of time-series processing are further improved through disordered rearrangement and time window correction, significantly reducing the false merging rate and shortening the alignment delay, thereby improving the accuracy of event mainline identification and the real-time performance of airport operational status data processing.

[0041] Specifically, the steps for cross-business domain fusion through event timelines and situational object data are as follows: In the overall operational situation generation process, the main line fusion segment in the event timeline is obtained as the state update trigger source, and a state fusion buffer corresponding to the overall operational situation generation is maintained. The state fusion buffer adopts a structure with location code and business domain code as a joint index. The record fields include event number, event type code, state judgment value, resource occupancy status, associated resource identifier, and time range, which are used to temporarily store the operational status from different event main lines within the same time window. When a new main line fusion segment enters the specified time window, the existing operational status in the state fusion buffer is updated according to the event type code, location code, and state judgment value. Specifically, when there are no records with the same location code and business domain code in the buffer, a new status is added. When there are records with the same location code and business domain code but different event type codes or changes in state judgment value, a status replacement is performed. When the item type code and status judgment value are consistent, the execution status continues and the corresponding time range is extended. When multiple status records appear in the same location code within the same time window, conflict resolution is carried out according to the priority principle of the time order of the main record in the event timeline. If the time order is the same, priority is determined according to the quality mark corresponding to the status judgment value, and only one valid status record is retained. At the end of the time window, the industrial cloud platform starts the full-domain operation status generation process, which performs cross-business domain, cross-location, and cross-resource element aggregation and integration on the operation status in the status fusion buffer. The aggregation process adopts the fusion operator of aggregating the number of operation statuses by business domain code, aggregating resource occupancy status by location code, and merging associated resource identifiers by event type code to form a full-domain operation status object that represents the comprehensive operation status of the airport within the corresponding time range. The status number, event number, location code, and associated resource identifier of the full-domain operation status object are output, providing clear data basis for subsequent digital twin linkage display and handling access judgment.

[0042] In this implementation plan, a state fusion buffer is introduced to centrally manage and orderly update the main fusion segments in the event timeline. This integrates and resolves the operational states scattered across different business domains, locations, and resource elements within a unified time window, forming a comprehensive operational state with controllable conflicts and a unique expression. Based on this, a global operational situation object that reflects the overall operation of the airport is generated, thus providing a stable, continuous, spatially and business-oriented situational foundation for subsequent situation display and decision-making.

[0043] Specifically, the specific steps for performing situation playback and linkage display operations in the digital twin scenario according to the fusion analysis results are as follows: Obtain the situation number, event number, location code, and associated resource identifier of the global operation situation object. Register a situation record in the data management layer for each global operation situation object, using the situation number as the main index field to record the corresponding digital twin scenario node identifier, spatial coordinate code, and coordinate reference system identifier. When constructing the situation scenario mapping table, bind the location code and associated resource identifier to the spatial unit nodes and operation object nodes in the scenario one by one, so that there is a unique mapping path between the two-dimensional identification space and the three-dimensional scenario space for the same situation number, forming a retrievable and traceable situation scenario mapping table between the global operation situation object and the digital twin scenario; After completing the mapping, implement a time axis synchronization mechanism, expressing and retaining the time zone offset identifier with the UTC timestamp. The cloud side synchronizes and records the acquisition completion timestamp and reception timestamp when receiving the edge intermediate result packet. By sliding the time window, compare the acquisition completion timestamp and reception timestamp for the same event number within the specified time range, calculate the time drift estimation value and write it into the time alignment field, providing a unified time correction basis for the subsequent loading of the global operation situation object. The digital twin scenario uses the event occurrence time plus the time drift estimation value as the time axis driving benchmark when loading the global operation situation object, and schedules the situation instances according to the order of the time axis driving fields, realizing continuous playback of the situation evolving over time; During the situation playback and interaction process, perform linkage positioning processing based on the situation number, event number, and location code. When any identifier is triggered in the interaction interface, the associated service generates a linkage message containing the situation number, event number, and location code and delivers it to the digital twin scenario rendering module. The rendering module synchronizes and locates the corresponding spatial area and operation object in the three-dimensional scenario according to the situation scenario mapping table, and presents the situation evolution status, resource occupancy change, alarm trigger and transmission link, and disposal execution progress in the corresponding stage in chronological order.

[0044] In this implementation case, situation S001 corresponds to event number E1001, event type is security check anomaly, location code is T1_A01_security check area, associated resource identifiers are DEV_SEC_001, CAM_T1_032, scene node ID is SCENE_SEC_A01, spatial coordinates are 120.3, 30.2, 15.5, event occurrence time is 2023-10-01-08:15:30, event status is in progress, time drift is 120, and mapping status is bound; situation S002 corresponds to event number E1002, event type is luggage congestion, location code is BHS. The _T03_ sorting area is associated with resource identifiers DEV_BHS_012 and ROBOT_005, scene node ID SCENE_BHS_T03, spatial coordinates 120.4, 30.3, 8.2, event occurrence time 2023-10-01-08:20:15, event status "processing", time drift 85, and mapping status "bound". Situational state S003 corresponds to event number E1003, event type "boarding delay", location code GATE_205_gate, associated resource identifiers DEV_GATE_205 and SCREEN_205, scene node ID... The event is designated SCENE_GATE_205, with spatial coordinates of 120.5, 30.4, 12.8, an event occurrence time of 2023-10-01-09:05:20, an event status of "Unresolved," a time drift of 150 seconds, and a mapping status of "Bound." Situational situation S004 corresponds to event number E1004, an event type of vehicle malfunction, a location code of APR_B2_tapering lane, associated resource identifiers of VEH_APR_023 and GPS_023, a scene node ID of SCENE_APR_B2, spatial coordinates of 120.6, 30.5, 3.5, and an event occurrence time of 2023- At 10:30:45 on October 1, 2023, the event status was "Emergency," the time drift was 65 seconds, and the mapping status was "Bound." Situation S005 corresponds to event number E1005, the event type is "Perimeter Intrusion," the location code is PERIM_N07_Fence, the associated resource identifiers are CAM_PERIM_055 and MOTION_007, the scene node ID is SCENE_PERIM_N07, the spatial coordinates are 120.7, 30.6, 2.0, the event occurrence time was 2023-10-01-14:45:10, the event status is "Alert," the time drift is 200 seconds, and the mapping status is "Bound."

[0045] Table 1. Time Drift Mapping Association Table for Airport Overall Operation Status Digital Twin Scenario

[0046] like Figure 3The diagram shown is a schematic representation of the time drift mapping association in the digital twin scenario of airport full-domain operational status provided in this application embodiment. (See Table 1 and...) Figure 3 It can be seen that there are significant differences in the combination of location encoding, associated resource identifiers, and time drift among different situation numbers. Situation S001 corresponds to the security check anomaly scenario, with the scenario node ID corresponding one-to-one with the spatial coordinates of the security check area, and a time drift value of 120, indicating that there is an accumulation of transmission delay between edge acquisition and cloud reception of the security check event; Situation S004 corresponds to the vehicle malfunction scenario, with a time drift value of 65. Combined with the event status being emergency, it can be found that vehicle alarm events are prioritized in the mapping record on the time axis, which is more conducive to ensuring the operational safety of the taxiway area; Situation S005 corresponds to the perimeter intrusion scenario, with a time drift value of 200. Under the condition that the situation status is alarm, the mapping status remains bound, indicating that even if the transmission link length increases, the digital twin scenario can still complete the location and situation loading based on the situation scene mapping table. Overall, various events such as security check anomalies, baggage congestion, boarding delays, vehicle malfunctions, and perimeter intrusions are uniformly registered through a combination of situation number, event number, location code, and scene node ID. The time drift field provides a quantitative basis for the time axis correction of the digital twin scene, and the mapping status field ensures the traceability and retrospectiveness of the operational situation in the three-dimensional scene. This enables the time-related display of operational events from multiple business domains under a unified spatial view, effectively solving the problems of scattered correspondence between scene events and digital twin scenes and reliance on experience-based judgment for time synchronization in existing technologies.

[0047] In this implementation plan, by constructing a situational scenario mapping table and implementing a timeline synchronization mechanism, a one-to-one correspondence is established between the global operational situational objects and the spatial unit nodes and operational object nodes in the digital twin scenario in terms of time and space. This enables continuous playback and linked positioning display of the operational situation in the three-dimensional scenario, allowing operation and management personnel to intuitively track situational evolution, resource occupancy changes, alarm links, and handling progress based on a unified timeline. This enhances the collaborative efficiency and visualization support capabilities of operation monitoring and command and dispatch.

[0048] Specifically, the steps for determining access based on the overall operational status data, and for issuing instructions, retaining them in the queue, and providing feedback based on the determination results are as follows: Extract the status number, associated event number, location code, resource occupancy status, and alarm link status from the overall operational status object. Match the location code against the device topology table in the resource ledger item by item to identify the set of target devices affected by a single action. Generate an access rule table based on historical action records in the event timeline and corresponding execution feedback results. This table includes rule items for resource occupancy status and alarm link status. Perform rule matching on the access rule table according to the current resource occupancy status and alarm link status, outputting the access determination results, and writing eligible status events into the instruction candidate queue. For each status event to be handled, compare the access determination result with a preset handling threshold. If the determination result is less than the threshold, encapsulate the status event into an instruction package and send it to the edge computing node for synchronous execution with the field devices through the slice channel of the corresponding business domain. The minimum set of fields in the instruction packet includes the target device set, operation type, operation time limit, rollback strategy, instruction priority, and idempotency ID, and is uniquely identified by instruction number, associated situation number, associated event number, location code, and idempotency ID. If the handling admission judgment result is greater than or equal to the handling threshold, the corresponding situation event is retained in the instruction candidate queue, awaiting a subsequent update of the global operational situation object to trigger a re-judgment. After the field equipment completes the instruction, it generates a receipt packet, which contains the instruction number, associated event number, idempotency ID, execution time, execution result, status snapshot, and failure reason. All receipt packets are indexed by the associated event number and idempotency ID and linked back to the event timeline, realizing full-link closed-loop traceability of the instruction process. The event timeline drives the real-time update of the event status and handling progress in the global operational situation object based on the receipt content, and synchronously archives the event timeline change record, slice communication quality record, and situation evolution record to the airport situation event database, comprehensively improving the accuracy, traceability, and system collaboration efficiency of instruction handling.

[0049] In this implementation plan, this step extracts key information from the overall operational status object, combines it with the equipment topology table and the disposal access rule table, automatically identifies affected equipment and determines disposal access conditions, dynamically filters and issues instruction packages, ensuring that the instruction content has core fields such as target equipment set, operation type, operation time limit, rollback strategy, priority, and idempotent ID, and achieves unique tracking throughout the entire process with instruction number and idempotent ID. After the field equipment completes the instruction, it sends back a receipt package. All receipt results are accurately linked back to the event timeline, driving real-time updates of status and disposal progress. At the same time, it realizes closed-loop archiving and traceable management of the entire instruction disposal chain, thereby improving the disposal response efficiency and control coordination capabilities in the operation of the smart airport.

[0050] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0051] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for constructing a smart airport operation system based on a 5G customized network, characterized in that, Includes the following steps: S1 collects equipment operating status data, business processing behavior data, and communication operation data, preprocesses and stores them to build an airport situational event database; S2 constructs multi-service communication channels through the service domain-level network slicing configuration and access mapping relationship of the 5G customized network. S3: The edge side generates edge intermediate result packages based on the unified data stream, and the cloud side writes the edge intermediate results into the event timeline main line to construct a unified event timeline based on the consistency judgment matrix. S4 performs time semantic connection analysis by using time, semantic and version-related data in the edge intermediate result package, and performs event mainline inclusion, alignment correction and non-mainline diversion processing based on the time semantic analysis results; S5 integrates event timelines and situational object data across business domains, and performs situational playback and linkage display operations in digital twin scenarios based on the integration analysis results. S6 performs access control determination based on the overall operational status data, and issues instructions and retains queues and provides feedback based on the determination results. 2.The method of claim 1, wherein the method is characterized by: The specific steps for constructing the airport situational event database after preprocessing and storing the collected equipment operation status data, business processing behavior data, and communication operation data are as follows: Collect smart airport operation environment data, which includes equipment operation status data, business processing behavior data, and communication operation data. Collect equipment operation status data: Obtain equipment operation status values, alarm link status and resource occupancy status through security inspection equipment, baggage handling equipment, boarding gate facilities, apron vehicle terminals, operation security devices and energy guarantee devices; obtain equipment identification codes through equipment control interfaces; obtain location codes through airport spatial coding tables; and bind the aforementioned data with the collection time to form a time series of equipment operation status. Collect business processing behavior data: Obtain the corresponding business processing status through security check business records, baggage sorting control records, boarding gate management records, apron vehicle dispatch records and operation security event records, and match the timestamps with the equipment operation status time series to form a business processing behavior time series; Collect communication operation data: Obtain the bandwidth occupancy status, end-to-end latency measurement value, transmission jitter measurement value and data packet loss statistics of each service domain network slice through the 5G customized network management interface, and align them with the device operation status time series to form a communication status time series; Outlier removal and missing segment completion are performed on equipment operation status data, business processing behavior data, and communication operation data. Missing segment completion uses linear interpolation between adjacent time points and marks the interpolated segments. At the same time, continuous numerical fields are scale-aligned and mapped to a unified interval. After completing the unified format processing, a unified data stream is formed. The unified data stream field set includes equipment identification code, location code, business domain code, event type code, event occurrence time, event end time, status judgment value, resource occupancy status, data dictionary version number, and quality tag. The unified data stream is stored and an airport situation event database is constructed. Within the airport situation event database, mapping relationship tables, equipment topology tables, situation scenario mapping tables, and handling access rule tables are built.

3. The method for constructing a smart airport operation system based on a 5G customized network according to claim 1, characterized in that: The specific steps for constructing multi-service communication channels through the service domain-level network slicing configuration and access mapping relationship of the 5G customized network are as follows: The 5G customized network communication resources are divided into service domain-level slices. For each service domain corresponding network slice, bandwidth quota parameters, end-to-end latency threshold parameters, transmission jitter threshold parameters, data packet loss threshold parameters, transmission priority identifier parameters, slice isolation control parameters, and transmission encryption control parameters are configured respectively. Establish a deterministic mapping table between terminal access identifiers and network slice identifiers, so that the unified data stream generated by the field terminal and the analysis result stream output by the edge computing node can be connected to the corresponding network slice channel according to the mapping table.

4. The method for constructing a smart airport operation system based on a 5G customized network according to claim 1, characterized in that: The specific steps for generating the edge intermediate result package based on the unified data stream at the edge are as follows: Edge computing nodes are deployed in the terminal area, baggage sorting area, and apron area. The edge computing nodes access the unified data stream through network slices configured for the corresponding business domain in the 5G customized network. The edge computing nodes perform local operation status parsing, video structured result aggregation, business queue statistics, local abnormal status identification, and time window aggregation processing of object trajectories based on the unified data stream, perform edge-side analysis, and output edge intermediate result packets.

5. The method for constructing a smart airport operation system based on a 5G customized network according to claim 1, characterized in that: The specific steps for constructing a unified event timeline by writing intermediate edge results into the main event timeline based on the consistency determination matrix on the cloud side are as follows: The unified data stream and edge intermediate result packets transmitted from the 5G customized network are obtained. First, the edge intermediate result packets are written into the event candidate queue according to the event number, and the main line record corresponding to the same event number is retrieved from the event timeline as the comparison object. For each edge intermediate result packet in the candidate queue, a consistency judgment matrix is ​​constructed. The consistency judgment matrix includes event number matching items, event type code matching items, and time range matching items. Based on the consistency determination matrix, each item is judged to generate a consistency determination flag and output the status determination value: when all items in the consistency determination matrix match, the corresponding edge intermediate result package is included in the main structure of the event timeline and the main time range is updated. For the same event number, only one time-ordered main line record is retained; when there are unmatched items in the consistency determination matrix, the edge intermediate result package is not included in the main structure of the event timeline and is transferred to the subsequent time semantic processing flow.

6. The method for constructing a smart airport operation system based on a 5G customized network according to claim 1, characterized in that: The specific steps for performing time-semantic coherence analysis using time, semantic, and version-related data in the edge intermediate result package are as follows: For each edge intermediate result packet that enters the time alignment processing range of the event timeline, the time deviation is obtained by reading the event occurrence time in the edge intermediate result packet and comparing it with the reference time point of the main record of the same event number in the event timeline; the semantic consistency is obtained by extracting the event type code, location code, and status judgment value in the edge intermediate result packet and performing a field-by-field consistency comparison with the fields of the corresponding business domain main record in the event timeline; and the version difference is obtained by reading the data dictionary version number in the edge intermediate result packet and comparing it with the relative position of the data dictionary version number in the main record of the event timeline in the version evolution sequence. The time deviation value, semantic consistency value, and version difference value are respectively processed by performing segmented encoding to form time segmented encoding, semantic segmented encoding, and version segmented encoding; All edge intermediate result packets are assembled into a metric coding sequence. First, they are sorted in ascending order according to time-based coding. If the time-based coding is the same, they are sorted in descending order according to semantic coding. If the first two types of coding are the same, they are sorted in ascending order according to version coding to obtain an ordered metric sequence. The semantic deviation value is obtained by obtaining the sequential position number of each edge intermediate result packet in the ordered metric sequence and writing it into the time alignment queue of the event timeline.

7. The method for constructing a smart airport operation system based on a 5G customized network according to claim 1, characterized in that: The specific steps for performing event mainline inclusion, alignment correction, and non-mainline traffic splitting based on the temporal semantic analysis results are as follows: By comparing semantic deviation values ​​and deviation thresholds in real time, when the semantic deviation value is less than the deviation threshold, the corresponding edge intermediate result package is extracted from the time alignment queue and included in the main processing path of the event timeline. The main time range, event status marker and associated device set corresponding to the event number are updated, and the results are synchronously provided to the global operation status generation process to drive status indicator updates, resource occupancy status refresh and scheduling decision calculation. When the semantic deviation value is greater than or equal to the deviation threshold, the corresponding edge intermediate result packet is retained in the time alignment queue and scrambled and time window corrected based on the event occurrence time is performed. After the correction is completed, the semantic deviation value is recalculated. When the corrected semantic deviation value is less than the deviation threshold, the corresponding edge intermediate result packet is moved from the time alignment queue into the main processing path of the event timeline. When the corrected semantic deviation value is greater than or equal to the deviation threshold, the event is removed from the time alignment queue and written into the non-main semantic region of the event timeline. A semantic deviation identifier is attached and the event is written into the airport situation event database according to the event number, time range, and location code.

8. The method for constructing a smart airport operation system based on a 5G customized network according to claim 1, characterized in that: The specific steps for cross-business domain fusion using event timelines and situational object data are as follows: In the overall operational status generation process, the main fusion segment in the event timeline is used as the trigger source for status updates, and a status fusion buffer corresponding to the overall operational status generation is maintained. When a new main fusion segment enters the specified time window, the existing operational status in the status fusion buffer is updated based on the event type code, location code, and status judgment value. The status update process includes status addition, status replacement, and status continuation, continuously depicting the change process of the operational status of each business domain of the airport. At the end of the time window, the industrial cloud platform starts the overall operational status generation process, which summarizes and integrates the operational status in the status fusion buffer across business domains, locations, and resource elements, representing the overall operational status of the airport within the corresponding time range, and outputs the status number, event number, location code, and associated resource identifier of the overall operational status object.

9. The method for constructing a smart airport operation system based on a 5G customized network according to claim 1, characterized in that: The specific steps for performing situational replay and interactive display operations in the digital twin scenario based on the fusion analysis results are as follows: Obtain the status number, event number, location code, and associated resource identifier of the global operational status object; use the status number as the main index field to record the corresponding digital twin scene node identifier, spatial coordinate code, and coordinate reference system identifier, and bind the location code and associated resource identifier to the spatial unit nodes and operational object nodes in the scene one by one, constructing a status scene mapping table between the global operational status object and the digital twin scene; after completing the mapping, implement a time axis synchronization mechanism: express using UTC timestamps and retain time zone offset identifiers, and the cloud side synchronously records the acquisition completion timestamp and reception timestamp when receiving intermediate result packets from the edge. The event is staggered, and the estimated time drift value is calculated and written into the time alignment field within the time window of the same event number. When the digital twin scene loads the global operational status object, the event occurrence time superimposed with the estimated time drift value is used as the time axis driving benchmark to realize the continuous playback of the status evolution over time. During the status playback and interaction process, the location processing is linked based on the status number, event number and location code. When any identifier is triggered, the digital twin scene synchronously locates the corresponding spatial area and operational object, and presents the status evolution status, resource consumption changes, alarm triggering and transmission links and handling execution progress in the corresponding stage in chronological order.

10. The method for constructing a smart airport operation system based on a 5G customized network according to claim 1, characterized in that: The specific steps for determining access based on the overall operational status data, and for issuing instructions, retaining data in the queue, and providing feedback based on the determination results are as follows: Extract the status number, associated event number, location code, resource occupancy status, and alarm link status from the overall operational status object. Match the location code with the device topology table in the resource ledger to identify the set of devices affected by a single action. Generate an action admission rule table based on historical action records in the event timeline and the corresponding execution receipt results. Perform rule matching in the action admission rule table according to the resource occupancy status and alarm link status to generate an action admission judgment result, and write the corresponding status event into the instruction candidate queue. The decision-making results for handling access are compared with the handling threshold. When the decision-making results are less than the handling threshold, the situation event is packaged into an instruction package and sent to the edge computing node and field equipment for execution through the slice channel of the corresponding business domain. When the decision-making results are greater than or equal to the handling threshold, the corresponding situation event is kept in the instruction candidate queue to wait for the update of the global operational situation object. After the instruction is executed, a receipt package is generated. Based on the associated event number in the receipt package, the execution result is linked back to the event timeline, driving the update of the event status and handling progress in the global operational situation object. The event timeline change record, slice communication quality record, and situation evolution record are written into the airport situation event database.

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