An electromagnetic catapult scheduling method and system for multiple aircrafts on a runway

Abstract

The application provides an electromagnetic catapult scheduling method and system for take-off and landing field multi-aircraft cooperation, effectively solving the problem that the prior art cannot meet the operation rule that emergency critical materials must be absolutely prioritized when facing a sudden high-priority task. The method comprises the following steps: acquiring multi-source scheduling input data of at least one unmanned aerial vehicle, and preprocessing the multi-source scheduling input data to obtain a scheduling decision parameter set; processing the scheduling decision parameter set to generate a comprehensive scheduling sequence list of all unmanned aerial vehicles; combining the comprehensive scheduling sequence list with real-time state data of each catapult runway, assigning a target catapult runway to each unmanned aerial vehicle through a resource allocation algorithm, and planning the catapult timing on each runway to generate a final scheduling scheme; issuing the catapult runway corresponding to the final scheduling scheme and controlling the catapult runway to enter a ready state, so that the unmanned aerial vehicles are catapulted and taken off in turn, and the feedback results of the take-off and landing field are monitored in real time during the execution.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) scheduling technology, and more specifically, to an electromagnetic catapult scheduling method and system for multi-UAV collaborative launch at take-off and landing sites. Background Technology

[0002] With the rapid development of the low-altitude economy and smart logistics, utilizing fixed-wing drones for medium- and long-distance regional freight has become a key technological direction for improving logistics efficiency. In hub-and-spoke scenarios, how to achieve continuous, efficient, and reliable takeoff of multiple cargo drones within limited takeoff and landing space is the core bottleneck restricting the large-scale operation of this model. Electromagnetic catapult technology, due to its advantages such as smooth acceleration, high controllability, and no need for a long runway, is regarded as an ideal solution for fixed-wing drones to take off in confined spaces, giving rise to the technical requirements for multi-drone coordinated launch and continuous launch.

[0003] To address the need for multi-aircraft catapult launches, existing technologies have primarily explored two aspects: hardware coordination of the catapult system and task scheduling algorithms.

[0004] At the hardware coordination level, existing patents focus on achieving multi-drone launches through parallel or sequential control of physical structures. For example, Chinese Patent Publication No. CN111924124A discloses a multi-flight electromagnetic launch device and method for vehicle-mounted small UAVs, and Patent Publication No. CN112173153B discloses a continuous-fire UAV electromagnetic catapult system and UAV hangar. At the task scheduling algorithm level, existing research largely focuses on trajectory planning for general-purpose UAVs or task allocation under single constraints. For instance, some methods aim to optimize the search path for multiple UAVs through swarm intelligence algorithms, or plan take-off and landing point selection in joint delivery modes. Other patents are beginning to incorporate specific logistics elements into decision-making, such as the take-off and landing device, take-off and landing system, and unmanned delivery system in Chinese Patent Publication No. CN110382354B. A review of existing technologies reveals a significant technological gap at the intersection of "multi-launch resource coordination" and "customized dynamic scheduling for freight scenarios." Hardware coordination solutions address the issue of "simultaneous launch of multiple aircraft," but fail to resolve the intelligent decision-making challenges regarding "launching order and runway." Furthermore, general scheduling algorithms struggle to deeply adapt to the complex, dynamic, and potentially conflicting multi-objective optimization needs of freight operations. This deficiency directly leads to severe "scheduling paradoxes" or "scoring equilibrium traps" in actual operations, particularly when facing sudden high-priority tasks.

[0005] Specifically, if a simple fixed-weight multi-factor linear scoring model is used for scheduling and ranking, the following situation may occur: A drone A carrying extremely high-priority cargo but with a large weight may have its overall score surpassed by another drone B carrying ordinary cargo but with a lighter weight and immediate launch capability, due to its slightly longer launch preparation time and negative contribution to runway occupancy. From a mathematical model perspective, this ranking result is "reasonable"; however, from the core logic of freight operations, this completely violates the ironclad operational rule that "urgent and critical materials must have absolute priority." This logical paradox, caused by the algorithm model's failure to place business rules at the highest decision-making level, is unavoidable by existing static or shallow scheduling strategies. The direct consequences are delays in the transportation of critical materials, and global business losses resulting from localized optimization of site resources. In severe cases, it will cast doubt on the credibility and practicality of the entire intelligent scheduling system, failing to meet the high reliability and adaptability requirements of modern freight hubs. Summary of the Invention

[0006] In view of this, the purpose of this application is to provide a method and system for electromagnetic catapult scheduling with multi-aircraft coordination at take-off and landing sites, which effectively solves the problem that existing technologies cannot meet the ironclad rule of absolute priority for emergency critical materials when facing sudden high-priority tasks, and cannot meet the high reliability and high adaptability requirements of modern freight hubs.

[0007] In a first aspect, embodiments of this application provide a method for multi-aircraft coordinated electromagnetic catapult scheduling at takeoff and landing sites, the method comprising: Acquire multi-source scheduling input data of at least one UAV, and preprocess the multi-source scheduling input data according to the data type to obtain a scheduling decision parameter set; The scheduling decision parameter set is processed by a rule filtering layer and a dynamic sorting algorithm to determine the scheduling priority of the UAVs and generate a comprehensive scheduling order list of all UAVs. Combining the comprehensive scheduling order list with the real-time status data of each catapult runway, a target catapult runway is allocated to each UAV through a resource allocation algorithm, and the catapult sequence on each runway is planned to generate the final scheduling scheme. The system issues the catapult runway corresponding to the final scheduling plan and controls the catapult runway to enter the ready state so that the UAVs can be launched in sequence. During the execution, the system monitors the feedback results of the take-off and landing field in real time.

[0008] In conjunction with the first aspect, this application provides a first possible implementation of the first aspect, wherein the step of processing the scheduling decision parameter set through a rule filtering layer and a dynamic sorting algorithm to determine the scheduling priority of the UAV includes: The scheduling decision parameter set is processed sequentially by the rule filtering layer and the dynamic sorting algorithm to obtain the forced sorting result and the score sorting result. The comprehensive scheduling order list is generated by combining the forced sorting result and the scoring sorting result.

[0009] In conjunction with the first aspect, this application provides a second possible implementation of the first aspect, wherein the step of processing the scheduling decision parameter set sequentially through the rule filtering layer and the dynamic sorting algorithm to obtain a forced sorting result and a scoring sorting result includes: The rule filtering layer detects whether there are freight tasks in the scheduling decision parameter set whose freight priority has reached an extreme threshold. If so, the aforementioned freight tasks are forcibly prioritized, the forced sorting result is generated, and the priority determination flag is activated to dynamically adjust the dynamic sorting algorithm.

[0010] In conjunction with the first aspect, this application provides a third possible implementation of the first aspect, wherein activating the priority determination flag to dynamically adjust the dynamic sorting algorithm includes: A priority decision flag is generated based on the task number of the freight task, and the priority decision flag is activated based on the forced sorting result; Temporary weights are obtained by combining the weights corresponding to the freight priority in the dynamic sorting algorithm with the amplification factor, so as to dynamically adjust the dynamic sorting algorithm.

[0011] In conjunction with the first aspect, this application provides a fourth possible implementation of the first aspect, wherein the step of processing the scheduling decision parameter set sequentially through the rule filtering layer and the dynamic sorting algorithm to obtain a forced sorting result and a scoring sorting result includes: The comprehensive scheduling priority score of each unordered drone is calculated based on the dynamically adjusted and sorted algorithm. The comprehensive scheduling priority scores are sorted to generate the score ranking result based on the sorted comprehensive scheduling priority scores.

[0012] In conjunction with the first aspect, this application provides a fifth possible implementation of the first aspect, wherein a target catapult runway is allocated to each UAV using a resource allocation algorithm, and the catapult timing sequence on each runway is planned to generate a final scheduling scheme, including: A catapult runway status mapping table is established using the comprehensive scheduling sequence list and the real-time status data of each catapult runway. Based on the catapult runway status mapping table, the target runway corresponding to each UAV in the comprehensive scheduling order list is selected. Based on the UAV, the planned target launch time and target runway of the UAV are written into the final scheduling scheme until all UAVs are processed.

[0013] In conjunction with the first aspect, this application provides a sixth possible implementation of the first aspect, wherein selecting the runway corresponding to each UAV in the integrated scheduling order list based on the catapult runway state mapping table includes: Based on the catapult runway status mapping table, a set of first runways that meet the first runway conditions is selected. Select a second set of runways for the UAV from the first set of runways, and then select the target runway from the second set of runways.

[0014] In conjunction with the first aspect, this application provides a seventh possible implementation of the first aspect, wherein preprocessing the multi-source scheduling input data according to the data type to obtain a scheduling decision parameter set includes: Set corresponding preprocessing methods for the freight priority, current load, site status data, and queue length; The preprocessing methods are executed respectively to preprocess the freight priority, current load, site status data and queue number to obtain the scheduling decision parameter set.

[0015] In conjunction with the first aspect, this application provides an eighth possible implementation of the first aspect, wherein, after real-time monitoring of the feedback results from the takeoff and landing field during execution, the following is included: Determine whether the task order in the historical integrated scheduling order list contradicts the order that should be followed in the business core principle rule base based on the corresponding task parameters; If the behavior is violated, the scheduling instance is identified as a paradoxical case, and optimization analysis is performed based on the paradoxical case.

[0016] Secondly, embodiments of this application provide a multi-aircraft coordinated electromagnetic catapult scheduling system for takeoff and landing fields, the system comprising: The acquisition module is used to acquire multi-source scheduling input data of at least one UAV, and preprocess the multi-source scheduling input data according to the data type to obtain a set of scheduling decision parameters; The processing module is used to process the scheduling decision parameter set through a rule filtering layer and a dynamic sorting algorithm, determine the scheduling priority of the UAV, and generate a comprehensive scheduling order list of all UAVs. The allocation module is used to combine the comprehensive scheduling order list with the real-time status data of each catapult runway, allocate a target catapult runway to each UAV through a resource allocation algorithm, and plan the catapult sequence on each runway to generate a final scheduling scheme. The distribution module is used to distribute the catapult runway corresponding to the final scheduling scheme and control the catapult runway to enter the ready state so that the UAVs can be launched in sequence, and to monitor the feedback results of the take-off and landing field in real time during the execution process.

[0017] This application provides a multi-aircraft collaborative electromagnetic catapult scheduling method for takeoff and landing fields. The method first acquires multi-source scheduling input data for at least one UAV, and preprocesses the multi-source scheduling input data according to data type to obtain a scheduling decision parameter set. Next, it processes the scheduling decision parameter set through a rule filtering layer and a dynamic sorting algorithm to determine the scheduling priority of the UAVs and generate a comprehensive scheduling order list for all UAVs. Then, combining the comprehensive scheduling order list with the real-time status data of each catapult runway, it allocates a target catapult runway to each UAV using a resource allocation algorithm and plans the catapult timing sequence on each runway to generate a final scheduling scheme. Finally, it issues the catapult runways corresponding to the final scheduling scheme and controls the catapult runways to enter a ready state for the UAVs to take off sequentially, while monitoring the feedback results from the takeoff and landing field in real time during execution. Based on the above methods, an independent rule filtering layer with the highest decision-making authority is placed before the traditional multi-factor weighted scoring and ranking model. This layer does not participate in the comprehensive scoring calculation, but directly decides and forces the ranking of input tasks based on preset, inviolable hard business rules. This not only achieves the effect of meeting the ironclad operational rule that emergency critical materials must be given absolute priority when facing sudden high-priority tasks, but also achieves significant results in the intersection of "multi-launch resource collaboration" and "customized dynamic scheduling of freight scenarios". It solves the problem that the ironclad operational rule that emergency critical materials must be given absolute priority cannot be met when facing sudden high-priority tasks, and avoids the problem that delays in the transportation of critical materials and the global business loss caused by local optimization of site resources, which in severe cases will question the credibility and practicality of the entire intelligent scheduling system. It also meets the high reliability and high adaptability operation requirements of modern freight hubs. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This paper presents a flowchart of an electromagnetic catapult scheduling method for multi-aircraft coordinated launch at a takeoff and landing field, according to an embodiment of this application. Figure 2This paper presents another flowchart of an electromagnetic catapult scheduling method for multi-aircraft coordinated launch at a takeoff and landing field, as provided in an embodiment of this application. Figure 3 This paper shows the overall architecture diagram of the multi-track collaborative scheduling terminal provided in an embodiment of this application; Figure 4 A flowchart illustrating the process of obtaining a comprehensive scheduling order list provided in an embodiment of this application is shown; Figure 5 A flowchart illustrating the final scheduling scheme provided in an embodiment of this application is shown; Figure 6 The flowchart of scheduling aftereffect evaluation and model iteration provided in the embodiments of this application is shown; Figure 7 The diagram shows a structural block diagram of an electromagnetic catapult scheduling system for multi-aircraft coordination at a takeoff and landing field, provided in an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0021] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0023] The target technology has significant technological gaps at the intersection of "multi-launch resource coordination" and "customized dynamic scheduling for freight scenarios." Hardware coordination solutions solve the problem of "launching multiple aircraft simultaneously," but fail to address the intelligent decision-making issues of "what order to launch and which runway to use." Furthermore, general scheduling algorithms struggle to deeply adapt to the complex, dynamic, and potentially conflicting multi-objective optimization needs of freight operations. This deficiency directly leads to serious "scheduling paradoxes" or "scoring equilibrium traps" in actual operations, especially when facing sudden high-priority tasks.

[0024] Based on this, the embodiments of this application provide a method and system for electromagnetic catapult scheduling with multi-aircraft coordination in takeoff and landing fields, which will be described below through embodiments.

[0025] Example 1 To facilitate understanding of this embodiment, a detailed description of the electromagnetic catapult scheduling method for multi-aircraft coordinated launch at takeoff and landing sites disclosed in this application embodiment will be provided first. For example... Figure 1 The diagram shown is a flowchart of a multi-aircraft coordinated electromagnetic catapult scheduling method for takeoff and landing fields. Figure 2 The diagram shows another flowchart of a multi-aircraft coordinated electromagnetic catapult scheduling method for takeoff and landing fields. This application provides a multi-aircraft coordinated electromagnetic catapult scheduling method for takeoff and landing fields, the method comprising: S101. Obtain multi-source scheduling input data of at least one UAV, and preprocess the multi-source scheduling input data according to the data type to obtain a scheduling decision parameter set; S102. The scheduling decision parameter set is processed through a rule filtering layer and a dynamic sorting algorithm to determine the scheduling priority of the UAV and generate a comprehensive scheduling order list of all UAVs. S103. Combining the comprehensive scheduling sequence list with the real-time status data of each catapult runway, a target catapult runway is allocated to each UAV through a resource allocation algorithm, and the catapult sequence on each runway is planned to generate the final scheduling scheme. S104. Issue the catapult runway corresponding to the final scheduling scheme and control the catapult runway to enter the ready state so that the UAVs can be launched in sequence, and monitor the feedback results of the take-off and landing field in real time during the execution process.

[0026] The method described in this application can be implemented on a multi-runway collaborative scheduling terminal, such as... Figure 3As shown, the multi-runway collaborative scheduling terminal includes a multi-source data acquisition module, a communication interaction module, and an catapult resource allocation module. The catapult resource allocation module includes modular catapult runway group 1, modular catapult runway group 2, ..., modular catapult runway group N. The multi-source data acquisition module includes a UAV status acquisition unit, a cargo information acquisition unit, a site and runway status acquisition unit, and a route planning data interface. The UAV status acquisition unit is connected to the UAV swarm, the cargo information acquisition unit is connected to the cargo order management system, the site and runway status acquisition unit is connected to the site sensor network, and the route planning data interface is connected to the air traffic planning system. The communication interaction module connects the catapult execution equipment group and the UAV swarm.

[0027] The drone described in this application is a fixed-wing drone for cargo transport that is to be launched.

[0028] In step S101, this application, based on the multi-runway collaborative scheduling terminal, first initializes the terminal parameters upon startup, and then collects multi-source scheduling input data from at least one UAV based on the multi-source data acquisition module. The multi-source scheduling input data is real-time. The multi-source data acquisition module includes a UAV status acquisition unit, which establishes communication with the flight control computer of each UAV via a wireless data link, receives and parses real-time status data packets from the flight control computer, and extracts the UAV's current payload value, remaining battery percentage, catapult hook mechanism ready status Boolean value, and pre-stored maximum payload parameters for the model. Simultaneously, the cargo information acquisition unit connects to the cargo hub's order management system via a standard application programming interface, and queries and extracts the corresponding cargo priority code, cargo type code, and destination coordinates from the order management system based on the task number bound to the UAV. The site and runway status are also considered. The status acquisition unit uses a sensor network deployed in the catapult runway area, including photoelectric sensors and pressure sensors, to sense the physical occupancy of each runway in real time. It then fuses and calculates the sensor signals to output the occupancy, vacancy, or fault status of each runway. Simultaneously, it integrates the status of all runways to calculate the overall real-time area occupancy rate of the airfield. In addition, the flight path planning data interface interacts with an independent air traffic planning system through an internal communication bus to obtain approved UAV flight path plan data covering a predetermined future time period. The multi-source data acquisition module timestamps and unifies the format of all collected raw data, packages it into the multi-source scheduling input data, and preprocesses the multi-source scheduling input data according to its data type. Different data types correspond to different preprocessing methods, thereby obtaining the scheduling decision parameter set corresponding to the multi-source scheduling input data.

[0029] In a specific implementation of step S101, one embodiment is as follows: Preprocessing the multi-source scheduling input data according to the data type to obtain a scheduling decision parameter set, including: S1011. Set corresponding preprocessing methods for the freight priority, current load, site status data and queue number respectively; S1012. Perform the preprocessing method respectively to preprocess the freight priority, current load, site status data and queue number to obtain the scheduling decision parameter set.

[0030] In steps S1011-S1012, the multi-source scheduling input data is first cleaned to remove outliers that clearly exceed the physical reasonable range, and missing secondary parameters are filled with data from the previous valid period. Then, preprocessing is performed based on the preprocessing method corresponding to the data type: for cargo priority in cargo information, the text or code-based priority description is converted into a numerical quantification value according to a predefined mapping table, where "urgent" is mapped to 10, "high" to 8, "medium" to 6, and "low" to 4. For the current payload in the UAV status data, the preprocessing module reads... The maximum rated payload parameter of this aircraft model is used to calculate the payload ratio. Then, the calibration parameter table of the electromagnetic catapult system is consulted. This calibration parameter table defines a continuous or piecewise linear mapping relationship from the payload ratio to the relative catapult energy demand coefficient. The standardized payload adaptation coefficient is obtained by looking up the table or interpolation. The site status data includes site occupancy data. For site occupancy data, it is directly output as a floating-point number between 0 and 1. For the number of drones queuing in the site, it is converted into a normalized coefficient proportional to the queue length. Finally, all the converted numerical parameters are encapsulated according to a predetermined data structure to generate a unified set of scheduling decision parameters. This process transforms the complex physical world and business rules into consistent quantitative indicators that the algorithm can process, laying a precise data foundation for subsequent rule adjudication and model optimization.

[0031] In step S102, as Figure 4As shown, after obtaining the scheduling decision parameter set corresponding to the multi-source scheduling input data, this application processes the scheduling decision parameter set through a rule filtering layer and a dynamic sorting algorithm to determine the scheduling priority of the UAV. The rule filtering layer applies at least one hard scheduling rule, configured to: when a task with a cargo priority quantification value reaching a preset extreme priority threshold is detected, forcibly place the task at the absolute forefront of the current scheduling sequence and generate a priority determination flag; the dynamic sorting algorithm non-linearly adjusts the weight coefficients according to the priority determination flag. If the priority determination flag is activated, the weight coefficients associated with the cargo priority quantification value are temporarily and significantly increased to suppress the influence of other factors in the scoring. The adjusted dynamic sorting algorithm is then invoked to calculate the comprehensive scheduling priority score for each UAV not subject to the forced sorting by the hard scheduling rule; the dynamic sorting algorithm is based on a comprehensive evaluation model including cargo priority quantification value, load adaptation coefficient, and site occupancy optimization coefficient, wherein the comprehensive scheduling priority score is calculated using a weighted sum formula; combining the forced sorting result output by the rule filtering layer and the score sorting result calculated by the dynamic sorting algorithm, the comprehensive scheduling order list is generated.

[0032] In a specific implementation of step S102, one embodiment is as follows: The process of processing the scheduling decision parameter set through a rule filtering layer and a dynamic sorting algorithm to determine the scheduling priority of the UAV includes: S1021. The scheduling decision parameter set is processed sequentially through the rule filtering layer and the dynamic sorting algorithm to obtain the forced sorting result and the score sorting result; S1022. Combine the forced sorting result with the scoring sorting result to generate the comprehensive scheduling order list.

[0033] In step S1021- In S1022, the scheduling decision parameter set is processed sequentially by the rule filtering layer and the dynamic sorting algorithm to obtain a forced sorting result and a score sorting result. Specifically, the rule layer first processes the forced sorting result of the scheduling decision parameter set and generates a priority determination flag. The dynamic sorting algorithm adjusts the weight coefficients nonlinearly according to the priority determination flag to obtain the score sorting result. Combining the forced sorting result and the score sorting result, the comprehensive scheduling order list is generated. That is, the temporary sorting list corresponding to the extreme priority tasks in the forced sorting result output by the rule filtering layer is completely extracted and used as the first area of ​​the comprehensive scheduling order list, namely the absolute priority area. The score list in the score sorting result calculated by the dynamic sorting algorithm is sorted from high to low to form the second area, namely the ordinary priority area. The comprehensive scheduling order list fixes the extreme priority tasks that must jump the queue at the top, and then follows the ordinary tasks according to their scores. Finally, a complete takeoff order list is formed that conforms to the core business principle of prioritizing urgent tasks while also taking into account scheduling efficiency such as adapting to factors such as load and site conditions. This accurately solves the pain points of low efficiency of pure forced sorting and chaotic priority of pure score sorting.

[0034] In a specific implementation of step S1021, one embodiment is as follows: the scheduling decision parameter set is processed sequentially through the rule filtering layer and the dynamic sorting algorithm to obtain a forced sorting result and a scoring sorting result, including: A1. The rule filtering layer detects whether there are freight tasks in the scheduling decision parameter set whose freight priority has reached an extreme threshold. A2. If so, then the aforementioned freight task is forcibly prioritized, the forced sorting result is generated, and the priority determination flag is activated to dynamically adjust the dynamic sorting algorithm.

[0035] In steps A1-A2, the cargo priority quantification value of each UAV is extracted from the scheduling decision parameter set to achieve the effect of detecting whether there are cargo tasks with cargo priority reaching an extreme threshold in the scheduling decision parameter set through the rule filtering layer. The hard scheduling rule built into the rule filtering layer is defined as follows: traverse all extracted cargo priority quantification values, compare each quantification value with a preset extreme priority threshold, the extreme priority threshold corresponding to the highest urgency level in the business logic; once any cargo priority quantification value is found to be equal to the extreme priority threshold, the rule filtering layer immediately identifies the cargo task corresponding to the quantification value as an extreme priority task, wherein the cargo task is executed by the UAV. Once any cargo priority quantification value is found to be equal to the extreme priority threshold, the rule filtering layer immediately identifies the cargo task corresponding to the quantification value as an extreme priority task, the rule filtering layer then activates the priority determination flag of the cargo task, the flag contains the task number of the identified extreme priority task, and places the extreme priority task at the top of a temporary sorting list; after the rule filtering layer completes the traversal, it outputs the forced sorting result, the forced sorting result including the priority determination flag and the temporary sorting list.

[0036] This application introduces an independent rule filtering layer with the highest decision-making authority before the traditional multi-factor weighted scoring and ranking model. This layer does not participate in the comprehensive score calculation but directly adjudicates and forces the ranking of input tasks based on preset, inviolable hard business rules. Subsequently, the optimization model only dynamically ranks tasks not processed by this layer. This architecture ensures the absolute authority of the core business logic, preventing the system from sacrificing key principles while pursuing optimal overall efficiency, and resolving the fundamental contradiction that may arise between algorithm optimization and ironclad business rules in existing technologies.

[0037] In a specific implementation of step A2, one embodiment is as follows: the activation of the priority determination flag to dynamically adjust the dynamic sorting algorithm includes: A21. Generate a priority decision flag based on the task number of the freight task, and activate the priority decision flag based on the forced sorting result; A22. Temporary weights are obtained by combining the weights and amplification factors corresponding to the freight priority in the dynamic sorting algorithm, so as to dynamically adjust the dynamic sorting algorithm.

[0038] In steps A21-A22, this application generates a priority determination flag based on the task number of the freight task through the rule filtering layer. The dynamic sorting algorithm pre-stores a set of basic weight coefficients, including a first basic weight coefficient corresponding to the quantified value of the freight priority. The second basic weighting coefficient corresponding to the load-bearing adaptation coefficient And the third basic weight coefficient corresponding to the site occupancy optimization coefficient. When the input priority determination flag is inactive, the basic weight coefficient set is directly used for subsequent calculations; when the input priority determination flag is active, a nonlinear adjustment function is triggered, which generates an amplification factor much greater than 1. ; Calculate the first weighting coefficient that is temporarily effective At the same time, maintain the second weighting coefficient With the third weighting coefficient The priority determination flag remains unchanged, as it is activated based on the priority quantification value of the freight task. When the priority quantification value is greater than the corresponding extreme priority threshold, the priority determination flag is activated, and the dynamic sorting algorithm will use the priority flag determined by the priority determination flag. , , A temporary set of weight coefficients is constructed to replace the basic set of weight coefficients and is used to calculate the overall scheduling priority score, so that the extreme priority task can gain an overwhelming advantage in mathematical score.

[0039] When the rule filtering layer detects a task whose freight priority has reached a preset extreme priority threshold, it not only outputs a forced sorting instruction but also triggers a priority determination flag. Upon receiving this flag, the dynamic sorting algorithm does not simply skip the task but instead initiates a non-linear adjustment function for the weighting coefficients. This function significantly and temporarily increases the values ​​of priority-related weighting parameters, giving them a dominant influence in subsequent scoring calculations for other tasks. This ensures that, when an extreme priority task exists, the entire sorting list generation logic is mathematically and automatically tilted towards the optimal scheduling environment for that task, achieving a precise and dynamic mapping from business rules to mathematical model parameters.

[0040] In the specific implementation of step S1021, another embodiment exists as follows: the scheduling decision parameter set is processed sequentially through the rule filtering layer and the dynamic sorting algorithm to obtain the forced sorting result and the scoring sorting result, including: B1. Calculate the comprehensive scheduling priority score for each unordered drone based on the dynamically adjusted dynamic sorting algorithm. B2. Sort the comprehensive scheduling priority scores to generate the score sorting result based on the sorted comprehensive scheduling priority scores.

[0041] In steps B1-B2, the dynamic scheduling priority score for each un-forced-sorted UAV, calculated using the dynamically adjusted and dynamically sorted algorithm described in this application, is calculated based on a temporary set of weight coefficients. For each un-forced-sorted UAV, its cargo priority quantification value is extracted from the scheduling decision parameter set. Load adaptability coefficient and site occupancy optimization coefficient The freight priority quantification value The numerical mapping is directly derived from the preprocessed cargo information; a higher value indicates a higher business priority. The load capacity adaptation coefficient... Through formula Calculated, where This is the current payload of the drone. This is the maximum rated load capacity of this model. This is an energy demand mapping function calibrated based on the characteristics of the electromagnetic catapult system. This function converts the load ratio into a relative catapult energy demand coefficient, and the site occupancy optimization coefficient is also mentioned. Through formula Calculated, where This represents the current site occupancy rate. This represents the current length of the queue of drones awaiting launch. and The preset positive coefficient; the comprehensive scheduling priority score Through weighted sum formula The calculation shows that, among which , , The first, second, and third weight coefficients from the temporary weight coefficient set are used for calculation. After completion, the output includes each drone and its corresponding score. The rating list is the rating sorting result.

[0042] In step S103, as Figure 5 As shown, after obtaining the comprehensive scheduling order list, this application combines the comprehensive scheduling order list with the real-time status data of each catapult runway, wherein the real-time status data of each catapult runway is updated in real time. A resource allocation algorithm is used to allocate target catapult runways to each UAV and plan the catapult sequence on each runway to generate a final scheduling scheme. The resource allocation algorithm aims to enable continuous takeoff of multiple aircraft, avoid runway occupancy conflicts, and avoid post-takeoff flight path conflicts; that is, the resource allocation algorithm is a multi-objective function. This application first sets a scheduling scheme for one UAV and repeats the steps of setting the scheduling scheme for this UAV until all UAVs in the comprehensive scheduling order list are processed, outputting the complete final scheduling scheme for all UAVs.

[0043] In the specific implementation of step S103, one embodiment is as follows: A target catapult runway is allocated to each UAV using a resource allocation algorithm, and the catapult timing sequence on each runway is planned to generate a final scheduling scheme, including: S1031. Establish a catapult runway status mapping table using the comprehensive scheduling order list and the real-time status data of each catapult runway. S1032. Based on the catapult runway status mapping table, select the target runway corresponding to each UAV in the comprehensive scheduling order list. S1033. Based on the UAV, the planned target launch start time and target runway of the UAV are written into the final scheduling scheme until all UAVs are processed.

[0044] In steps S1031-S1033, the resource allocation algorithm described in this application first initializes an empty final scheduling scheme and establishes a catapult runway status mapping table based on the real-time status data of each catapult runway. This table records the number, current status, estimated release time, and associated catapult energy output capability of each runway. The resource allocation algorithm processes each UAV sequentially, starting from the top of the integrated scheduling order list. For the currently processed UAV, the resource allocation algorithm calculates the required catapult energy range based on its model parameters and payload, and then queries the catapult runway status mapping table based on the catapult energy range to select a runway that enables the UAV to launch. The runway with the shortest waiting time and conforming to the runway load balancing strategy is selected as the target catapult runway. Then, a specific catapult launch time is assigned to the UAV, which must be later than the previous mission release time of the target runway and allow sufficient preparation time. At the same time, the UAV-runway matching relationship and catapult time are written into the final scheduling scheme. Finally, the target runway status in the catapult runway status mapping table is updated to pre-occupancy, and its expected release time is updated, thus generating the scheduling scheme for the UAV. The above process is repeated until all UAVs in the integrated scheduling order list are processed, and the complete final scheduling scheme containing all UAVs is output.

[0045] In a specific implementation of step S1032, one embodiment is as follows: Based on the catapult runway status mapping table, the runways corresponding to each UAV in the integrated scheduling order list are selected, including: S10321. Based on the catapult runway state mapping table, select the first runway set that meets the first runway conditions; S10322. Select a second set of runways for the UAV from the first set of runways, and select the target runway from the second set of runways.

[0046] In steps S10321-S10322, when this application queries the catapult runway status mapping table based on the catapult energy range to obtain the target runway, it first selects runways that are currently idle and whose catapult energy output capability matches the energy demand range as a first candidate set. Then, the resource allocation algorithm excludes runways from the first candidate set that have spatial or temporal conflicts with the UAV's planned flight path during the expected takeoff time, obtaining a second candidate set. From the second candidate set, and from the second runway set, a runway that minimizes the UAV's waiting time and conforms to the runway load balancing strategy is selected as its target catapult runway. This ensures both current scheduling efficiency and long-term scheduling stability, thereby achieving the goal of first selecting a first candidate set of basic available runways based on both physical availability and energy matching, then eliminating risky runways with flight path conflicts, and finally selecting the optimal solution with the shortest waiting time and load balancing from the remaining runways, synchronizing the landing catapult time with the runway status. The entire process ensures the safe takeoff of the UAV while taking into account scheduling efficiency and reasonable resource allocation, and is a key link connecting the sorting list and the final execution plan.

[0047] In step S104, this application receives the final scheduling scheme based on the multi-runway collaborative scheduling terminal, and converts the part of the final scheduling scheme involving catapult resource allocation into a specific equipment control command sequence. The equipment control command includes the target catapult runway number, the identifier code of the designated UAV, and the predetermined absolute time for catapult launch. The multi-runway collaborative scheduling terminal sends the command sequence to the catapult resource allocation module through the communication interaction module. The catapult resource allocation module parses the command and sends a preparation command to the corresponding modular catapult runway controller. The preparation command triggers the catapult runway to perform energy pre-charging, guide rail self-check, and physical hook-up docking with the designated UAV. Simultaneously, the multi-runway collaborative scheduling terminal sends the command sequence to the corresponding modular catapult runway controller through the communication interaction module. The assigned UAV sends a mission command, including its assigned runway number and estimated launch time. The UAV then moves to the designated runway and completes a pre-launch self-check. The launch resource allocation module continuously monitors the readiness status feedback of each launch runway and the UAV's readiness status feedback. When both conditions are met and the scheduled launch time arrives, the resource launch module sends a launch start command to the launch runway controller. Throughout the execution process, the multi-source data acquisition module continuously operates, sending the actual launch time of each runway, the actual takeoff time of the UAV, and any fault alarm information as real-time monitoring feedback data back to the multi-runway collaborative scheduling terminal. The terminal compares the actual execution data with the final scheduling scheme to form an execution monitoring log.

[0048] In a specific implementation of step S104, one embodiment includes: after real-time monitoring of the feedback results from the takeoff and landing field during execution, the following steps are taken: S1041. Determine whether the task order in the historical integrated scheduling order list contradicts the order that should be followed in the business core principle rule base based on the corresponding task parameters; S1042. If violated, the scheduling instance is identified as a paradox case, and optimization analysis is performed based on the paradox case.

[0049] In steps S1041-S1042, the method provided in this application also includes scheduling post-effect evaluation and model iteration steps, the specific execution process of which is as follows: Figure 6 As shown, this process begins after a predetermined number of scheduling cycles are completed or upon receiving a manual trigger command. First, a historical scheduling record dataset is collected from the log database on the multi-runway collaborative scheduling terminal. This dataset includes a historical scheduling decision parameter set, a historical comprehensive scheduling order list, historical final scheduling schemes, and corresponding actual launch execution result data. The actual launch execution result data includes the actual launch time, actual energy consumption, and mission completion status of each UAV. Subsequently, an offline analysis module loads a preset business core principle rule base. This rule base contains at least one rule: if a task has the highest cargo priority, it must obtain the highest scheduling priority within the current scheduling window. The offline analysis module compares the historical comprehensive scheduling order list with the historical scheduling decision parameter set to check whether the sorting results within each scheduling cycle violate the business principles. The module identifies any rule in the core principle rule base; for cases violating the rule, it marks them as paradoxical cases and extracts all input parameters and intermediate calculation results for that case; the module then launches a simulation analysis engine, adjusting the extreme priority threshold in the rule filtering layer or the nonlinear adjustment function of the weight coefficients in the dynamic ranking algorithm while keeping other parameters unchanged, and re-simulates and calculates the scheduling ranking; the simulation analysis engine evaluates whether paradoxical cases are eliminated under different adjustment strategies and their impact on the overall scheduling efficiency index; finally, the offline analysis module generates a model parameter adjustment suggestion report, which recommends the optimal parameter adjustment scheme that eliminates identified paradoxical cases while minimizing the negative impact on overall scheduling performance; after a security confirmation process, this report is used to automatically or manually update the relevant parameters of the rule filtering layer and the dynamic ranking algorithm.

[0050] The specific process of simulation calculation based on the simulation analysis engine in the offline analysis module is as follows: When the offline analysis module identifies a paradox case, the engine extracts the complete historical scheduling decision parameter set of the case. The engine first creates a sandbox copy of the rule filtering layer and the dynamic sorting algorithm, with its initial parameters consistent with the online version when the paradox case was generated. In the first simulation, the engine adjusts the extreme priority threshold of the rule filtering layer, changing the threshold from its initial value... Modify to a more stringent value This causes a high-priority task that did not originally trigger the hard rule to be captured under the new threshold, and then the sandbox copy step S102 is run to obtain the first simulated sorting result. In the second simulation, the engine restored the threshold to... However, the nonlinear adjustment function of the weight coefficients in the dynamic sorting algorithm is modified, specifically the amplification factor in the active state is adjusted. From initial value Increase to Then, step S102 is run again to obtain the second simulated sorting result. The engine calculates separately. and Relative to the original paradoxical ordering results The degree of improvement is measured by the advance of the paradoxical task in the sorting list and the change in the overall scheduling efficiency index; the engine will use the first simulation sorting results Second simulation sorting results The corresponding improvement data and the adjustment parameters used. and As a key output, the model parameter adjustment suggestion report is written into it for subsequent decision-making.

[0051] In practical use, the UAV status acquisition unit communicates with the flight control systems of each UAV via a wireless data link to obtain its current status. Specifically, this includes: UAV-A's current payload is 1.2 tons, battery level is 85%, and the ejection hook mechanism is in "ready" status; its maximum rated payload is 1.5 tons. The statuses of other UAVs are obtained in a similar manner: UAV-B payload is 1.0 ton, UAV-C payload is 0.8 tons, UAV-D payload is 0.9 tons, UAV-E payload is 1.1 tons, and UAV-F payload is 0.7 tons. The cargo information acquisition unit reads mission attributes from the freight order management system via an interface. Specifically, the cargo carried by UAV-A and UAV-B is marked as "emergency medical supplies," and the system assigns it the freight priority code "urgent"; UAV-E and UAV-F carry "industrial parts," with a priority of "high"; UAV-C and UAV-D carry "general daily necessities," with a priority of "medium"; the site and runway status acquisition unit senses the physical environment through a deployed sensor network. Real-time status display: Runways R1 and R2 are in an "idle" state; Runway R3 is in a "fault" locked state due to equipment self-check failure; Runway R4 is currently occupied by a UAV undergoing pre-takeoff checks. Overall calculation shows that the current site occupancy rate is 60%, and the queue length of UAVs awaiting catapult launch is 6. Simultaneously, the flight path planning data interface retrieves approved flight plans from the air traffic management system. The data indicates that UAV-A and UAV-C have the same destination airport, and their initial departure routes overlap in airspace, requiring a minimum 3-minute interval between takeoffs to avoid potential conflicts.

[0052] The scheduling terminal cleans, standardizes, and quantizes the collected raw data to generate a unified set of scheduling decision parameters. All data is verified to be error-free, and no outliers need to be removed.

[0053] For cargo information, text priority is converted into numerical freight priority quantification values ​​according to predefined mapping rules. In this embodiment, "Express" is mapped to 10, "High" to 8, and "Medium" to 6. Therefore, the quantification values ​​of UAV-A and UAV-B are... UAV-E and UAV-F UAV-C and UAV-D For UAV payload data, it needs to be converted into a payload adaptation coefficient related to the energy requirements of the catapult system. First, calculate the payload ratio of each UAV; for example, the payload ratio of UAV-A is... Subsequently, the pre-calibrated energy demand mapping function of the electromagnetic catapult system was queried. The function is defined in this example as The load-bearing adaptability coefficient of UAV-A was calculated. The remaining drones The value is calculated using the same method; site occupancy optimization coefficient. It is a comprehensive negative indicator reflecting the current level of congestion and queuing pressure at the venue. In this embodiment, according to the formula... and take , occupancy rate and normalized queue length Substitute and calculate to obtain the current unified The following table shows a partial example of the final generated scheduling decision parameter set: Table 1. Some parameters and data in the scheduling decision parameter set.

[0054] This step is crucial for resolving the "scoring paradox." The scheduling terminal first inputs the parameter set into a preset rule filtering layer. This layer defines a hard scheduling rule: if the freight priority quantification value reaches an extreme threshold... If so, then priority is forced. After traversing the parameter set, it was found that UAV-A and UAV-B... The conditions are met. The rule filtering layer immediately activates the "priority decision flag" and forces UAV-A and UAV-B to be placed at the absolute front of the current batch scheduling queue (assuming that A precedes B in the order of task reception), forming a preliminary decision list.

[0055] Subsequently, the dynamic ranking algorithm non-linearly adjusts the scoring weights based on the activation flag. The base weights are set as follows: , , When the flag is activated, the adjustment function is triggered, introducing an amplification factor. The first weighting coefficient, which is temporarily effective, is calculated. The second and third weighting coefficients and The value remains unchanged at 0.3. This adjustment aims to give high-priority tasks an overwhelming advantage in scoring.

[0056] Next, a temporary weight is used to calculate the comprehensive scheduling priority score for the UAVs (UAV-C to UAV-F) that were not forcibly sorted. The scoring formula is as follows: Taking UAV-E as an example ( Its rating is In contrast, without weight adjustment, its score is only 3.2384, which clearly demonstrates the amplifying effect of non-linear adjustment on priority.

[0057] After calculating the scores of all UAVs, and combining the forced sorting results from the rule-based filtering layer, a final comprehensive scheduling order list is generated: [UAV-A, UAV-B, UAV-E, UAV-F, UAV-D, UAV-C]. It is worth noting that UAV-C, carrying general cargo and whose flight path conflicts with UAV-A, is reasonably placed last.

[0058] The resource allocation algorithm receives the integrated scheduling order list and the real-time status information of each runway (R1 and R2 are idle, R3 is faulty, and R4 is pre-occupied but is expected to be released in 2 minutes).

[0059] The algorithm starts processing from the first item in the list. When allocating catapult resources for UAV-A, its high-energy catapult requirements must be met, and flight path conflicts with UAV-C must be considered. Both R1 and R2 meet the energy requirements, so the algorithm selects R1 and schedules its catapult launch time at T+0 minutes (immediate preparation). Next, UAV-B is processed. To avoid conflicts and achieve continuous operation, the algorithm allocates runway R2 to it and schedules its catapult launch time at T+2 minutes, maintaining a safe interval with UAV-A's takeoff. When processing UAV-E, runway R4 has been released and is idle. The algorithm selects R4 from R1, R2, and R4, which allows for the earliest catapult launch and meets the load balancing principle, scheduling its catapult launch time at T+5 minutes. Following this logic, the algorithm allocates target runways and precise catapult launch times to all UAVs, ultimately generating a detailed scheduling scheme Gantt chart that clearly defines the operational arrangements for each runway at each time, enabling continuous, conflict-free takeoffs of multiple aircraft.

[0060] The dispatch terminal sends the final plan to the catapult resource allocation module and all relevant units. The catapult resource allocation module controls runway R1 for the UAV-A catapult launch. The data acquisition module confirms that UAV-A successfully deorbited at T+0.5 minutes. The system then proceeds with the planned catapult launch of UAV-B from runway R2.

[0061] Throughout the entire execution process, the system monitors the status of each stage in real time. For example, at T+3 minutes, the ejection energy feedback value of UAV-B was detected to be slightly lower than the expected setting, but still within the system tolerance range. The dispatch terminal recorded this information without interrupting the established process. All UAVs were ejected from the field in an orderly manner according to the dispatch plan.

[0062] After the day's scheduling tasks were completed, the offline analysis module automatically started to perform a post-event evaluation of the scheduling logs. The analysis revealed that in another scheduling batch in the afternoon, there was a flight with a "high" priority (…). But the load capacity is very light. The drone's overall score surpassed that of a drone with a priority of "Extreme Urgent" ( It should have been 10, but due to data entry error it was marked as 9.5) and the load was relatively large. The drone. Due to the "urgent" mission The value did not reach the extreme threshold of 10, failing to trigger the hard rule and resulting in a slight "sorting paradox" case. For this case, the simulation analysis engine initiated a parameter optimization process. The engine first extracted the complete data for this case. The first simulation attempted to modify the rule threshold, setting... The threshold was lowered from 10 to 9.8. After a resimulation, the "urgent" task was correctly captured and forced ahead of schedule, resolving the paradox, but the overall average scheduling wait time increased by 5%. The second simulation restored the threshold. However, the amplification factor will be adjusted nonlinearly. The weighting was increased from 15 to 25. Simulation results show that, with the significantly enhanced weighting, the "urgent" task score significantly outperformed the others, the ranking was corrected, the paradox was resolved, and the impact on overall scheduling efficiency was less than 1%.

[0063] The offline analysis module generates an evaluation report, recommending a second simulation strategy, which involves keeping the threshold constant and adjusting the amplification factor. The value was updated to 25. This suggestion, after security review by the system administrator, took effect during system maintenance the following day, completing one adaptive iterative optimization of the model parameters.

[0064] Example 2 This application also provides an electromagnetic catapult scheduling system for multi-aircraft coordination at takeoff and landing sites, such as... Figure 7 The diagram shows a block diagram of a multi-aircraft coordinated electromagnetic catapult scheduling system for takeoff and landing fields. The functions implemented by this system correspond to the steps described above in executing a multi-aircraft coordinated electromagnetic catapult scheduling method for takeoff and landing fields on a terminal device. This device can be understood as a server component including a processor. The multi-aircraft coordinated electromagnetic catapult scheduling system for takeoff and landing fields described in this application includes: The acquisition module 701 is used to acquire multi-source scheduling input data of at least one UAV, and preprocess the multi-source scheduling input data according to the data type to obtain a scheduling decision parameter set; The processing module 702 is used to process the scheduling decision parameter set through a rule filtering layer and a dynamic sorting algorithm, determine the scheduling priority of the UAV, and generate a comprehensive scheduling order list of all UAVs. The allocation module 703 is used to combine the comprehensive scheduling order list with the real-time status data of each catapult runway, allocate a target catapult runway to each UAV through a resource allocation algorithm, and plan the catapult sequence on each runway to generate a final scheduling scheme. The distribution module 704 is used to distribute the catapult runway corresponding to the final scheduling scheme and control the catapult runway to enter the ready state so that the UAVs can be launched in sequence, and to monitor the feedback results of the take-off and landing field in real time during the execution process.

[0065] In one feasible implementation, the processing module includes: The scheduling decision parameter set is processed sequentially by the rule filtering layer and the dynamic sorting algorithm to obtain the forced sorting result and the score sorting result. The comprehensive scheduling order list is generated by combining the forced sorting result and the scoring sorting result.

[0066] In one feasible implementation, the processing module further includes: The rule filtering layer detects whether there are freight tasks in the scheduling decision parameter set whose freight priority has reached an extreme threshold. If so, the aforementioned freight tasks are forcibly prioritized, the forced sorting result is generated, and the priority determination flag is activated to dynamically adjust the dynamic sorting algorithm.

[0067] In one feasible implementation, the processing module also includes: A priority decision flag is generated based on the task number of the freight task, and the priority decision flag is activated based on the forced sorting result; Temporary weights are obtained by combining the weights corresponding to the freight priority in the dynamic sorting algorithm with the amplification factor, so as to dynamically adjust the dynamic sorting algorithm.

[0068] In one feasible implementation, the processing module further includes: The comprehensive scheduling priority score of each unordered drone is calculated based on the dynamically adjusted and sorted algorithm. The comprehensive scheduling priority scores are sorted to generate the score ranking result based on the sorted comprehensive scheduling priority scores.

[0069] In one feasible implementation, the allocation module includes: A catapult runway status mapping table is established using the comprehensive scheduling sequence list and the real-time status data of each catapult runway. Based on the catapult runway status mapping table, the target runway corresponding to each UAV in the comprehensive scheduling order list is selected. Based on the UAV, the planned target launch time and target runway of the UAV are written into the final scheduling scheme until all UAVs are processed.

[0070] In one feasible implementation, the allocation module further includes: Based on the catapult runway status mapping table, a set of first runways that meet the first runway conditions is selected. Select a second set of runways for the UAV from the first set of runways, and then select the target runway from the second set of runways.

[0071] In one feasible implementation, the acquisition module includes: Set corresponding preprocessing methods for the freight priority, current load, site status data, and queue length; The preprocessing methods are executed respectively to preprocess the freight priority, current load, site status data and queue number to obtain the scheduling decision parameter set.

[0072] In one feasible implementation, the distribution module includes: Determine whether the task order in the historical integrated scheduling order list contradicts the order that should be followed in the business core principle rule base based on the corresponding task parameters; If the behavior is violated, the scheduling instance is identified as a paradoxical case, and optimization analysis is performed based on the paradoxical case.

[0073] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0074] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0075] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0076] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a platform server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0077] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for coordinated electromagnetic catapult launch scheduling at takeoff and landing sites, characterized in that, The method includes: Acquire multi-source scheduling input data of at least one UAV, and preprocess the multi-source scheduling input data according to the data type to obtain a scheduling decision parameter set; The scheduling decision parameter set is processed by a rule filtering layer and a dynamic sorting algorithm to determine the scheduling priority of the UAVs and generate a comprehensive scheduling order list of all UAVs. Combining the comprehensive scheduling order list with the real-time status data of each catapult runway, a target catapult runway is allocated to each UAV through a resource allocation algorithm, and the catapult sequence on each runway is planned to generate the final scheduling scheme. The system issues the catapult runway corresponding to the final scheduling plan and controls the catapult runway to enter the ready state so that the UAVs can be launched in sequence. During the execution, the system monitors the feedback results of the take-off and landing field in real time.

2. The method according to claim 1, characterized in that, The process of processing the scheduling decision parameter set through a rule filtering layer and a dynamic sorting algorithm to determine the scheduling priority of the UAV includes: The scheduling decision parameter set is processed sequentially by the rule filtering layer and the dynamic sorting algorithm to obtain the forced sorting result and the score sorting result. The comprehensive scheduling order list is generated by combining the forced sorting result and the scoring sorting result.

3. The method according to claim 2, characterized in that, The process of sequentially processing the scheduling decision parameter set through the rule filtering layer and the dynamic sorting algorithm to obtain forced sorting results and score sorting results includes: The rule filtering layer detects whether there are freight tasks in the scheduling decision parameter set whose freight priority has reached an extreme threshold. If so, the aforementioned freight tasks are forcibly prioritized, the forced sorting result is generated, and the priority determination flag is activated to dynamically adjust the dynamic sorting algorithm.

4. The method according to claim 3, characterized in that, The activation priority determination flag dynamically adjusts the dynamic sorting algorithm, including: A priority decision flag is generated based on the task number of the freight task, and the priority decision flag is activated based on the forced sorting result; Temporary weights are obtained by combining the weights corresponding to the freight priority in the dynamic sorting algorithm with the amplification factor, so as to dynamically adjust the dynamic sorting algorithm.

5. The method according to claim 2, characterized in that, The process of sequentially processing the scheduling decision parameter set through the rule filtering layer and the dynamic sorting algorithm to obtain forced sorting results and score sorting results includes: The comprehensive scheduling priority score of each unordered drone is calculated based on the dynamically adjusted and sorted algorithm. The comprehensive scheduling priority scores are sorted to generate the score ranking result based on the sorted comprehensive scheduling priority scores.

6. The method according to claim 1, characterized in that, A resource allocation algorithm is used to assign target catapult runways to each drone and to plan the catapult sequence on each runway to generate a final scheduling scheme, including: A catapult runway status mapping table is established using the comprehensive scheduling sequence list and the real-time status data of each catapult runway. Based on the catapult runway status mapping table, the target runway corresponding to each UAV in the comprehensive scheduling order list is selected. Based on the UAV, the planned target launch time and target runway of the UAV are written into the final scheduling scheme until all UAVs are processed.

7. The method according to claim 6, characterized in that, Based on the catapult runway status mapping table, the runways corresponding to each UAV in the comprehensive scheduling order list are selected, including: Based on the catapult runway status mapping table, a set of first runways that meet the first runway conditions is selected. Select a second set of runways for the UAV from the first set of runways, and then select the target runway from the second set of runways.

8. The method according to claim 1, characterized in that, The multi-source scheduling input data is preprocessed according to its data type to obtain a set of scheduling decision parameters, including: Set corresponding preprocessing methods for the freight priority, current load, site status data, and queue length; The preprocessing methods are executed respectively to preprocess the freight priority, current load, site status data and queue number to obtain the scheduling decision parameter set.

9. The method according to claim 1, characterized in that, The process of monitoring the feedback results from the takeoff and landing field in real time during execution includes: Determine whether the task order in the historical integrated scheduling order list contradicts the order that should be followed in the business core principle rule base based on the corresponding task parameters; If the behavior is violated, the scheduling instance is identified as a paradoxical case, and optimization analysis is performed based on the paradoxical case.

10. A multi-aircraft coordinated electromagnetic catapult scheduling system for takeoff and landing fields, characterized in that, The system includes: The acquisition module is used to acquire multi-source scheduling input data of at least one UAV, and preprocess the multi-source scheduling input data according to the data type to obtain a set of scheduling decision parameters; The processing module is used to process the scheduling decision parameter set through a rule filtering layer and a dynamic sorting algorithm, determine the scheduling priority of the UAV, and generate a comprehensive scheduling order list of all UAVs. The allocation module is used to combine the comprehensive scheduling order list with the real-time status data of each catapult runway, allocate a target catapult runway to each UAV through a resource allocation algorithm, and plan the catapult sequence on each runway to generate a final scheduling scheme. The distribution module is used to distribute the catapult runway corresponding to the final scheduling scheme and control the catapult runway to enter the ready state so that the UAVs can be launched in sequence, and to monitor the feedback results of the take-off and landing field in real time during the execution process.

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