An event-driven spatiotemporal network dynamic construction method

Abstract

The application discloses a kind of event-driven spatiotemporal network dynamic construction method, the method comprises the following steps: step S1, input the basic data of airport clean water vehicle scheduling, and the specific composition of each type of data and data type are clear;Step S2, based on basic data, extract and define three types of mutually exclusive and complete core driven event set E with priority attribute={E1, E2, E3};Wherein, E1 is task event;E2 is vehicle event;E3 is geographic position state linkage event;Step S3, based on basic data and core driven event set, through event linkage trigger logic, the constraint condition of each type of arc segment is quantitatively deduced, generates job arc, driving arc, water supplement arc and adaptive priority arc, completes the dynamic construction of spatiotemporal network.The application has the advantages of greatly reducing network calculation dimension and improving real-time scheduling efficiency.

Description

Technical Field

[0001] This invention relates to the field of airport ground support scheduling optimization technology, and in particular to an event-driven method for dynamic construction of spatiotemporal networks. Background Technology

[0002] Against the backdrop of a continuously improving globalized air transport network, the demand for air travel is steadily increasing, leading to the continuous expansion of the operational scale of modern large-scale hub airports. Flight takeoffs and landings, passenger throughput, and cargo volume are all showing a year-on-year upward trend. As a crucial node in the air transport chain, airport ground handling services directly connect core aspects such as flight takeoffs and landings, passenger transfers, and cargo transshipment. Their service efficiency and response speed not only affect flight punctuality but also impact the airport's operational reputation and core competitiveness. Therefore, the industry is placing increasingly stringent demands on ground handling services to be available around the clock, with high precision and rapid response.

[0003] Water truck scheduling is a core support component of the airport ground support system, ensuring normal flight operations. It primarily provides drinking water and cleaning water to arriving flights. The rationality of its scheduling directly impacts flight punctuality and departure efficiency, and also relates to airport water resource utilization efficiency and operational cost control. To achieve optimal matching between water trucks, water refueling tasks, and airport site resources, the scheduling optimization process heavily relies on precise spatiotemporal network modeling technology. This technology abstracts spatiotemporal behaviors such as vehicle movement, task execution, and site occupancy into network nodes and arcs, providing a quantitative analysis basis for scheduling decisions. Currently, mainstream spatiotemporal network modeling solutions in the industry generally adopt the core logic of "fixed-step discretization of the time axis." Specifically, this involves dividing the time axis within the scheduling cycle into equal time slices (e.g., 1 minute / 5 minutes / 10 minutes), constructing network nodes at corresponding positions within each time slice, and pre-generating various arcs between nodes. While this solution can meet the basic modeling needs of small-scale scheduling scenarios, it suffers from significant inherent performance bottlenecks in large-scale scheduling scenarios involving multiple tasks and vehicles at large airports. To ensure scheduling accuracy to match actual operational needs, the time step must be set to a minimum. This operation directly leads to a geometric increase in the number of nodes and arcs in the spatiotemporal network, causing a severe "variable explosion" problem. This problem manifests as a sharp increase in the computational dimension of the scheduling model, excessive memory consumption, and excessively long algorithm execution time, ultimately resulting in a delay in scheduling decision output. This fails to meet the timeliness requirements of real-time scheduling of airport water trucks. For example, in scenarios such as dense flight arrivals during the morning rush hour or sudden emergency flight water refill requests, the decision delay of the traditional solution may lead to operational chaos such as flight delays, idle vehicle resources, and task backlogs.

[0004] Therefore, addressing the core requirements of large-scale and real-time dispatching of water trucks at major airports, it is crucial to overcome the technical bottlenecks of traditional fixed-step discretization modeling. This necessitates the development of a spatiotemporal network dynamic construction technology that accurately identifies key events during the dispatching process (such as task generation, vehicle status changes, and site availability changes), and uses event triggering as its core. This technology enables on-demand generation of arc segments rather than batch pre-generation across the entire time axis, becoming a key path to improve dispatching optimization efficiency. The research and application of this technology can significantly reduce the computational cost of the dispatching model and improve solution efficiency. It also enhances the dynamic matching capability of water truck resources, improves resource utilization and task completion rates, thereby ensuring the responsiveness of airport ground support dispatching and reducing flight delays caused by dispatching delays. This has significant practical implications and application value for promoting the intelligent and efficient upgrading of airport ground support services. Summary of the Invention

[0005] The purpose of this invention is to provide an event-driven method for dynamically constructing spatiotemporal networks, which has the advantages of significantly reducing the computational dimension of the network and improving real-time scheduling efficiency.

[0006] To achieve the above objectives, this invention provides an event-driven method for dynamically constructing a spatiotemporal network. The method includes: Step S1, inputting basic data for airport water truck scheduling and defining the specific composition and data type of each type of data; Step S2, based on the basic data, extracting and defining a complete set of three mutually exclusive core driving events E={E1,E2,E3} with priority attributes; where E1 is a task event; E2 is a vehicle event; and E3 is a geographic location status linkage event; Step S3, based on the basic data and the core driving event set, quantifying and deriving the constraints of each type of arc segment through event linkage triggering logic, and generating the operation arc. , driving arc Water replenishment arc and adaptive priority dwell arc This completes the dynamic construction of the spatiotemporal network.

[0007] Preferably, in step S1, the basic data includes task information, vehicle information, and site information; the task information is the data related to each water truck water filling task to be executed, and all of it is quantifiable and traceable structured data; the vehicle information is the data related to each water truck participating in the dispatch; and the site information is the key geographical location and real-time status data related to water truck dispatch within the airport.

[0008] Preferably, the task information includes: task identifier, execution location, required net water volume, time window constraint, and urgency level; the vehicle information includes: vehicle identifier, maximum water capacity, initial location, initial remaining water volume, and current load rate; the site information includes: location type, planar coordinates, available service capacity, occupancy status, and accessibility status.

[0009] Preferably, task event E1 is generated based on the task information from step S1, corresponding to each water-adding task. , The task number represents the trigger scenario where a water truck is needed to perform water replenishment. The core parameters directly reuse all the data from the task information, and the priority is completely consistent with the task urgency level.

[0010] Preferably, vehicle event E2: generated based on the vehicle information in step S1, corresponding to each water truck. , The vehicle serial number represents the scenario where "the change in the status of the water truck can trigger subsequent operations". The core parameters reuse data such as vehicle identifier, maximum water capacity, and initial remaining water volume. The trigger constraint is "the vehicle is idle and available and has not been assigned any subsequent tasks".

[0011] Preferably, the geographic location status linkage event E3: Based on the site information generated in step S1, key geographic locations are bound to real-time status, representing the scenario where "changes in location availability can trigger vehicle operation". Core parameters reuse data such as location type, coordinates, and available service capacity, and the triggering constraint is determined by the occupancy status. Reachability State A joint decision.

[0012] Preferably, the working arc constraint is: by Linked triggering, with time constraints based on task information. Water volume constraints combine the required net water volume for the task with the initial remaining water volume in the vehicle, while location constraints match the task execution location and site. , state.

[0013] Preferably, the travel arc constraint is: by The linkage is triggered, the time constraint is calculated from the site coordinates, the load constraint limits the current vehicle load rate to ≤80%, and the path constraint requires the site accessibility status. .

[0014] Preferably, the water replenishment arc constraint is: by The system is triggered in conjunction with the vehicle's water level status. The trigger constraint is that the vehicle's initial remaining water level is less than the total water requirement of the subsequently assigned tasks. The time constraint is derived from the water replenishment duration, calculated using the following formula: ,in Let j be the maximum water capacity of vehicle j. The water replenishment rate is preset to a standardized rate; site constraints are taken from the occupancy status of the water supply station. Accessibility state and available service capacity.

[0015] Preferably, the adaptive priority dwell arc constraint is: by Priority attribute driven, combined Location status and dwell time are set according to the urgency of the task, and interruption constraints are associated with the high priority attributes of new tasks and the vehicle's matching status.

[0016] In summary, compared with the prior art, the event-driven spatiotemporal network dynamic construction method provided by this invention has the following beneficial effects:

[0017] First, the present invention proposes an event-driven method for dynamic construction of spatiotemporal networks, which breaks through the technical bottleneck of traditional fixed-step discretization modeling. It adopts an event-driven mechanism to realize the on-demand generation of arc segments, rather than the batch pre-generation of the entire time axis, fundamentally solving the "variable explosion" problem and improving the solution efficiency and real-time response capability of the scheduling model.

[0018] Second, the present invention proposes an event-driven spatiotemporal network dynamic construction method, which constructs a standardized basic data system, and allows data to be reused and linked in a closed loop between steps without the need for additional data, thus ensuring the feasibility and stability of the technical solution.

[0019] Third, the present invention proposes an event-driven spatiotemporal network dynamic construction method that precisely matches the three core elements of tasks, vehicles, and sites to meet the large-scale and real-time requirements of airport water truck scheduling. Through the coordinated linkage of four types of arcs, it achieves full coverage of the scheduling process, which can effectively improve the utilization rate of water truck resources and the task completion rate, and reduce the risk of flight delays.

[0020] Fourth, the event-driven spatiotemporal network dynamic construction method proposed in this invention defines three types of core driving events that satisfy mutual exclusion and completeness. The arc segment constraints are all derived from measured data, the parameters are clearly quantified, and the method has repeatability and generalizability. Attached Figure Description

[0021] Figure 1 This is a flowchart of an event-driven spatiotemporal network dynamic construction method proposed in this invention. Detailed Implementation

[0022] The following will be combined with the appendix in the embodiments of the present invention. Figure 1 The technical solutions, structural features, objectives and effects achieved in the embodiments of the present invention will be described in detail.

[0023] It should be noted that the accompanying drawings are in a very simplified form and use non-precise proportions. They are only used to facilitate and clarify the purpose of illustrating the embodiments of the present invention, and are not intended to limit the implementation conditions of the present invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportional relationship, or adjustments to the size should still fall within the scope of the technical content disclosed in the present invention, provided that they do not affect the effects and objectives that the present invention can produce.

[0024] It should be noted that, in this invention, relational terms such as "first" and "second" are used merely 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 the expressly listed elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0025] like Figure 1 As shown, this invention proposes an event-driven method for dynamically constructing spatiotemporal networks, the method comprising:

[0026] Step S1: Input the basic data for airport water truck dispatch (task information, vehicle information, site information), and clarify the specific composition and data type of each type of data;

[0027] The purpose of step S1 is to provide standardized support for subsequent event definition and constraint derivation.

[0028] Step S2: Based on the basic data, extract and define three mutually exclusive and complete core driving event sets E={E1,E2,E3} with priority attributes; where E1 is the task event; E2 is the vehicle event; and E3 is the geographic location status linkage event (see below for details).

[0029] The significance of step S2 lies in providing a precise trigger source and quantitative parameter support for subsequent arc segment generation, which is the core technical link to realize on-demand arc segment generation.

[0030] Step S3: Based on the basic data and the core driving event set, the constraints of various arc segments are quantified and derived through event linkage triggering logic to generate the operation arc. , driving arc Water replenishment arc and adaptive priority dwell arc This enables the dynamic construction of spatiotemporal networks.

[0031] This step is the core technology implementation link to achieve "reducing computational dimensions and improving real-time performance".

[0032] Specifically, as mentioned above, in step S1, the basic data includes task information, vehicle information, and site information.

[0033] Task Information: The core consists of data related to each pending water truck refill task, all of which is quantifiable and traceable structured data. Specifically, it includes: Task Identifier (a unique string / numeric code used for task uniqueness identification and traceability), Execution Location (airport location coordinates or location number, defining the operational space location), and Required Clean Water Volume (unit: ...). (Water quantity constraint core parameter) and time window constraint (earliest start time) Latest completion time All timestamps are minute-level timestamps starting from 00:00 on the current day, and urgency levels are ( (1 represents the highest priority, 2 represents the normal priority, and 3 represents the lowest priority; these are used for priority association constraints).

[0034] Vehicle Information: The core data consists of information about each water truck participating in the dispatch process. This includes: vehicle identifier (a unique string / numeric code used for vehicle dispatch tracking), maximum water capacity (unit: ...). Rated water tank capacity), initial position (parking position at the start of the scheduling cycle, coordinates or station number), initial remaining water volume (unit: The remaining water volume in the tank at the start of scheduling, and the current load rate (real-time calculated parameter, the percentage of the current remaining water volume to the maximum water capacity, used for load limit constraints).

[0035] Site Information: The core data consists of key geographical locations and real-time status data related to water truck dispatch within the airport. Specifically, this includes: location type (aircraft stand, water station, and vehicle parking area, clearly defining functional attributes), planar coordinates (e.g., Gaussian plane coordinates, used to calculate travel time), available service capacity (the number of water trucks that can be served simultaneously), occupancy status (binary variable, 0 for unoccupied, 1 for occupied), and reachability status (binary variable, 1 for reachable, 0 for inaccessible, a core parameter for path constraints).

[0036] Specifically, in step S2, based on the basic data, extract and define three mutually exclusive and complete sets of core driving events E={E1,E2,E3} with priority attributes;

[0037] The three types of events strictly satisfy "mutual exclusion" and "completeness". Mutual exclusion means that the three types of events have no overlap, and only the associated logic of one type of event is triggered at the same time to avoid conflicts. Completeness means that it fully covers the key scenarios generated by all triggering arcs in the scheduling process without omission. The priority attribute is derived from the task urgency level and vehicle status priority in step S1. The vehicle status priority is defined as follows: idle and available vehicles have higher priority than vehicles performing tasks, and vehicles with sufficient water have higher priority than vehicles that need water replenishment.

[0038] Among them, task event E1 is generated based on the task information in step S1, corresponding to each water-adding task. ( (This is the task sequence number), representing the trigger scenario of "requiring a water truck to perform water replenishment operations". The core parameters directly reuse all the data of the task information (task identifier, execution location, required amount of purified water, etc.), and the priority is completely consistent with the task urgency level.

[0039] Vehicle event E2: Generated based on vehicle information from step S1, corresponding to each water truck. ( (Vehicle serial number), representing the scenario where "the change in the status of the water truck can trigger subsequent operations". The core parameters reuse data such as vehicle identifier, maximum water capacity, and initial remaining water volume. The trigger constraint is "the vehicle is idle and available and has not been assigned any subsequent tasks".

[0040] Geographic location status linkage event E3: Based on the site information generated in step S1, it binds key geographic locations with real-time status, representing a scenario where "changes in location availability can trigger vehicle operation." Core parameters reuse data such as location type, coordinates, and available service capacity, and the triggering constraint is determined by the occupancy status. Reachability State A joint decision.

[0041] Specifically, in step S3, based on the basic data and the core driving event set, the constraints of various arc segments are quantified and derived through event linkage triggering logic to generate the operation arc. , driving arc Water replenishment arc and adaptive priority dwell arc This completes the dynamic construction of the spatiotemporal network.

[0042] In a specific embodiment, the operation arc is generated. , driving arc Water replenishment arc and adaptive priority dwell arc The constraints will be introduced one by one.

[0043] Working arc constraint: by Linked triggering, with time constraints based on task information. Water volume constraints combine the required net water volume for the task with the initial remaining water volume in the vehicle, while location constraints match the task execution location and site. , state.

[0044] Driving arc constraint: by The linkage is triggered by time constraints calculated from site coordinates (distance / airport vehicle speed limit), load constraints limit the current vehicle load rate to ≤80%, and path constraints require site accessibility status. .

[0045] Water replenishment arc constraint: by The linkage is triggered and associated with the vehicle's water status, with the trigger constraint that the vehicle's initial remaining water level is less than the total water requirement of the subsequently assigned tasks (i.e., ...). ,in Let J be the initial residual water volume of vehicle j. (Total water requirement for all subsequent tasks assigned to vehicle j); the time constraint is taken from the water replenishment duration, calculated using the following formula: ,in Let j be the maximum water capacity of vehicle j. The preset standardized water replenishment rate is set (referencing the standard settings in the airport ground support industry). Site constraints are derived from the occupancy status of the water supply station. Accessibility state And available service capacity (the number of currently idle water stations is ≥1).

[0046] Adaptive priority dwell arc constraint: by Priority attribute driven, combined Location status and dwell time are set according to the urgency of the task (high priority ≤ 5min, non-high priority ≤ 30min). Interruption constraints are associated with the high priority attribute of the new task and the vehicle matching status.

[0047] The driving arc is the core connecting arc segment, responsible for connecting the operation arc, water replenishment arc, and dwelling arc (the vehicle arrives at the work position, water station, or dwelling position via the driving arc); the operation arc is the core target arc segment, and the water replenishment arc provides it with water supply guarantee (the water replenishment arc is triggered when the remaining water is insufficient, and after water replenishment is completed, the vehicle travels to the work position via the driving arc); the dwelling arc is an auxiliary arc segment, used in scenarios where the vehicle has no immediate operation and is waiting for subsequent tasks, and can be interrupted by high-priority task events, quickly switching to the driving arc to travel to the work position. The four types of arc segments work together to cover the entire process of water truck dispatching, forming a complete spatiotemporal network.

[0048] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A method for dynamically constructing spatiotemporal networks based on event-driven principles, characterized in that: The method includes: Step S1: Input the basic data for airport water truck dispatching and clarify the specific composition and data type of each type of data; Step S2: Based on the basic data, extract and define three mutually exclusive and complete core driving event sets E={E1,E2,E3} with priority attributes; where E1 is the task event; E2 is the vehicle event; and E3 is the geographic location status linkage event. Step S3: Based on the basic data and the core driving event set, the constraints of various arc segments are quantified and derived through event linkage triggering logic to generate the operation arc. , driving arc Water replenishment arc and adaptive priority dwell arc This completes the dynamic construction of the spatiotemporal network.

2. The event-driven spatiotemporal network dynamic construction method according to claim 1, characterized in that, In step S1, the basic data includes task information, vehicle information, and site information; The task information consists of data related to each water truck water filling task to be performed, all of which are quantifiable and traceable structured data; The vehicle information includes data related to each water truck participating in the dispatch. The site information includes key geographical locations and real-time status data related to the dispatch of water trucks within the airport.

3. The event-driven spatiotemporal network dynamic construction method according to claim 2, characterized in that, The task information includes: task identifier, execution location, required net water volume, time window constraint, and urgency level; the vehicle information includes: vehicle identifier, maximum water capacity, initial location, initial remaining water volume, and current load rate; the site information includes: location type, planar coordinates, available service capacity, occupancy status, and accessibility status.

4. The event-driven spatiotemporal network dynamic construction method according to claim 3, characterized in that, Task event E1: Generated based on the task information from step S1, corresponding to each water-adding task. , The task number represents the trigger scenario where a water truck is needed to perform water replenishment. The core parameters directly reuse all the data from the task information, and the priority is completely consistent with the task urgency level.

5. The event-driven spatiotemporal network dynamic construction method according to claim 4, characterized in that, Vehicle Event E2: Generated based on the vehicle information from step S1, corresponding to each water truck. , The vehicle serial number represents the scenario where "a change in the status of the water truck can trigger subsequent operations". The core parameters reuse data such as vehicle identifier, maximum water capacity, and initial remaining water volume. The trigger constraint is "the vehicle is idle and available and has not been assigned any subsequent tasks".

6. The event-driven spatiotemporal network dynamic construction method according to claim 5, characterized in that, Geographic location status linkage event E3: Based on the site information generated in step S1, it binds key geographic locations with real-time status, representing the scenario where "changes in location availability can trigger vehicle operation." Core parameters reuse data such as location type, coordinates, and available service capacity, and the trigger constraint is determined by the occupancy status. Reachability State A joint decision.

7. The event-driven spatiotemporal network dynamic construction method according to claim 6, characterized in that, Working arc constraint: by Linked triggering, with time constraints based on task information. Water volume constraints combine the required net water volume for the task with the initial remaining water volume in the vehicle, while location constraints match the task execution location and site. , state.

8. The event-driven spatiotemporal network dynamic construction method according to claim 7, characterized in that, Driving arc constraint: by The linkage is triggered, the time constraint is calculated from the site coordinates, the load constraint limits the current vehicle load rate to ≤80%, and the path constraint requires the site accessibility status. .

9. The event-driven spatiotemporal network dynamic construction method according to claim 8, characterized in that, Water replenishment arc constraint: by The system is triggered in conjunction with the vehicle's water level status. The trigger constraint is that the vehicle's initial remaining water level is less than the total water requirement of the subsequently assigned tasks. The time constraint is derived from the water replenishment duration, calculated using the following formula: ,in Let j be the maximum water capacity of vehicle j. The water replenishment rate is preset to a standardized rate; site constraints are taken from the occupancy status of the water supply station. Accessibility state and available service capacity.

10. The event-driven spatiotemporal network dynamic construction method according to claim 9, characterized in that, Adaptive priority dwell arc constraint: by Priority attribute driven, combined Location status and dwell time are set according to the urgency of the task, and interruption constraints are associated with the high priority attributes of new tasks and the vehicle's matching status.

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