An unmanned aerial vehicle ground-based electromagnetic take-off and landing transportation method, device and electronic equipment

Abstract

The application provides a UAV land-based electromagnetic take-off and landing transportation method and device and electronic equipment, effectively solving the problem that the existing UAV land-based electromagnetic take-off and landing transportation system cannot provide a high-frequency continuous take-off and landing transportation environment for UAVs in a logistics park. The method comprises the following steps: generating a UAV transportation scheme and generating a flight path sequence according to received order information, various state information of the UAV, site information of the logistics park and real-time airspace information; performing power demand prediction of a direct-current bus based on the flight path sequence to obtain predicted power and predicted power constraints, and outputting a power distribution instruction corresponding to the predicted power; obtaining operation control instructions according to the optimized power distribution instructions and the UAV transportation scheme, and executing the operation control instructions to perform logistics transportation; and collecting operation data generated by the UAV during logistics transportation in real time, identifying abnormal information in the operation data to adjust the logistics transportation of the UAV in real time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle transportation, in particular to an unmanned aerial vehicle land-based electromagnetic take-off and landing transportation method and device and electronic equipment. BACKGROUND

[0002] With the rapid development of modern logistics industry, unmanned aerial vehicle logistics transportation system has become an important technical direction to improve regional distribution efficiency and reduce labor costs. In particular, fixed-wing unmanned aerial vehicles have great application potential in regional logistics distribution due to their advantages of large load, long range and high speed. To support the large-scale application of fixed-wing unmanned aerial vehicles in logistics parks, the construction of land-based take-off and landing transportation systems is particularly critical. In order to facilitate the deployment of fixed-wing unmanned aerial vehicles in logistics parks, the take-off distance needs to be controlled, so a catapult and arresting system is used, such as the invention patent with patent number CN113697123B, which discloses a carrier-based aircraft catapult device that provides initial acceleration for unmanned aerial vehicles through linear motors or rotary motors combined with gear and rack mechanisms; such as the invention patent with patent number CN105438496B, which discloses a catapult arresting device that realizes braking and energy absorption of unmanned aerial vehicles through a pulley set, a cylinder and an energy recovery device.

[0003] However, the above-mentioned system usually adopts a centralized energy management architecture, and its control strategy is designed based on single-machine take-off and landing or low-frequency operation mode. There are obvious limitations in dealing with high-frequency, multi-machine continuous take-off and landing scenarios in logistics parks. In particular, when deployed in space-limited scenarios such as rooftops, the system's energy buffer capacity is insufficient, and the energy scheduling flexibility is poor. Specifically, when multiple unmanned aerial vehicles perform intensive take-off and landing operations, the DC bus will face a sharp instantaneous power mutation: the catapult system needs to draw a large amount of power from the power grid at the start of the moment, while the arresting system feeds back a large amount of energy to the power grid at the braking moment. This sharp change in power in a short period of time will cause the DC bus voltage to drop or surge, the system frequency to fluctuate, and thus cause problems such as control accuracy degradation, device protection circuit misoperation, and energy recovery efficiency reduction. In severe cases, voltage fluctuations may cause the entire take-off and landing process to be interrupted, and even sensitive power electronic devices may be damaged.

[0004] Therefore, there is an urgent need for an unmanned aerial vehicle land-based electromagnetic take-off and landing transportation system that can maintain stable operation of the DC bus in high-frequency continuous take-off and landing scenarios. This problem has become a key technical bottleneck restricting the large-scale commercial application of logistics unmanned aerial vehicles. SUMMARY

[0005] Therefore, the present application aims to provide a UAV land-based electromagnetic take-off and landing transportation method, device and electronic equipment, which effectively solves the problem that the existing UAV land-based electromagnetic take-off and landing transportation system cannot provide a high-frequency continuous take-off and landing transportation environment for UAVs in a logistics park.

[0006] In a first aspect, the embodiments of the present application provide a UAV land-based electromagnetic take-off and landing transportation method, which is suitable for a take-off and landing transportation system including a task scheduling module, a path planning module, an energy coordination module, an execution control module and a state monitoring module. The method comprises:

[0007] The task scheduling module generates a UAV transportation scheme corresponding to the order information according to the received order information, various state information of the UAV, site information of the logistics park and real-time airspace information; the UAV transportation scheme includes a UAV take-off and landing strategy and a task allocation table;

[0008] The path planning module generates a flight path sequence based on the order information, the UAV transportation scheme and performance parameter data of the UAV, and inputs the flight path sequence to the energy coordination module;

[0009] The energy coordination module performs power demand prediction of a direct current bus based on the flight path sequence to obtain predicted power and predicted power constraints, and outputs a power distribution instruction corresponding to the predicted power;

[0010] The execution control module optimizes the power distribution instruction based on the predicted power constraints and the flight path sequence, and obtains a running control instruction according to the optimized power distribution instruction and the UAV transportation scheme, controls the UAV and the corresponding auxiliary flight equipment to execute the running control instruction to perform logistics transportation;

[0011] The state monitoring module collects running data generated by the UAV during logistics transportation in real time, identifies abnormal information in the running data through an abnormality detection algorithm to adjust the logistics transportation of the UAV in real time.

[0012] In combination with the first aspect, the embodiments of the present application provide a first possible implementation manner of the first aspect, wherein the energy coordination module performs power demand prediction of a direct current bus based on the flight path sequence to obtain predicted power, and outputs a power distribution instruction corresponding to the predicted power, which comprises:

[0013] Collecting first influence data of influence factors of the power demand of the direct current bus, and obtaining second influence data related to the first influence data in the flight path sequence;

[0014] The predicted power is obtained by fusing the first influence data and the second influence data, and the power distribution instruction is obtained based on the predicted power.

[0015] With reference to the first aspect, in a second possible implementation of the first aspect, the power distribution instruction is obtained based on the predicted power, and the method further includes:

[0016] Based on the power distribution instruction, a hybrid energy storage control module in the take-off and landing transportation system is called to calculate a power distribution ratio and a device state control instruction in a hierarchical manner.

[0017] The actual power of the target device is controlled to be consistent with the target power in the power distribution instruction by using the power distribution ratio and the device state control instruction.

[0018] With reference to the first aspect, in a third possible implementation of the first aspect, the power distribution instruction is optimized by the execution control module based on the predicted power constraint and the flight path sequence, and the method further includes:

[0019] A time sequence optimization model for the power distribution instruction is established based on the predicted power constraint and the flight path sequence.

[0020] An optimization target of the power distribution instruction is calculated by using the time sequence optimization model, so as to optimize the power distribution instruction in each cycle.

[0021] With reference to the first aspect, in a fourth possible implementation of the first aspect, the optimization target of the power distribution instruction is calculated by using the time sequence optimization model, and the method further includes:

[0022] A plurality of optimization dimensions for the optimization target are set, and an optimization sub-target corresponding to each optimization dimension is configured; the optimization dimensions include a power dimension and a take-off and landing dimension.

[0023] Control data is generated by fusing the optimization sub-targets corresponding to the optimization dimensions, so as to perform optimization based on the control data.

[0024] With reference to the first aspect, in a fifth possible implementation of the first aspect, the real-time adjustment of the logistics transportation of the unmanned aerial vehicle includes:

[0025] It is determined whether the abnormal information meets an abnormal condition set in advance for the abnormal information; the abnormal condition corresponds to a type of abnormal information.

[0026] If yes, a reconfiguration instruction is generated based on the abnormal information, so that the energy coordination module adjusts the unmanned aerial vehicle transportation scheme based on the reconfiguration instruction.

[0027] With reference to the first aspect, in a seventh possible implementation form of the first aspect, the task scheduling module generates the unmanned aerial vehicle transportation scheme corresponding to the order information according to the received order information, the plurality of state information of the unmanned aerial vehicle, the site information of the logistics park, and real-time airspace information, and then includes the following steps:

[0028] Obtaining a plurality of real-time current parameters of the plurality of auxiliary flight devices in the blocking stage, to adjust the real-time monitored energy conversion efficiency to a target state based on the plurality of real-time current parameters;

[0029] Outputting the real-time current parameters in the target state to the control module corresponding to the blocking stage, to adjust the plurality of auxiliary flight devices.

[0030] With reference to the first aspect, in a seventh possible implementation form of the first aspect, the task scheduling module generates the unmanned aerial vehicle transportation scheme corresponding to the order information according to the received order information, the plurality of state information of the unmanned aerial vehicle, the site information of the logistics park, and real-time airspace information, and then includes the following steps:

[0031] Based on the site type in the site information, determining a cargo loading position of the unmanned aerial vehicle transportation scheme; the cargo loading position includes a roof;

[0032] Collecting real-time roof data of the roof, generating a load adjustment instruction based on the real-time roof data, and moving the cargo to the roof based on the load adjustment instruction.

[0033] In a second aspect, embodiments of the present application provide an unmanned aerial vehicle land-based electromagnetic take-off and landing transportation device, which is suitable for a take-off and landing transportation system, the take-off and landing transportation system including a task scheduling module, a path planning module, an energy coordination module, an execution control module, and a state monitoring module, and the device includes:

[0034] The task scheduling module generates an unmanned aerial vehicle transportation scheme corresponding to the order information according to received order information, a plurality of state information of the unmanned aerial vehicle, site information of a logistics park, and real-time airspace information; the unmanned aerial vehicle transportation scheme includes an unmanned aerial vehicle take-off and landing strategy and a task allocation table;

[0035] The path planning module generates a flight path sequence based on the order information, the unmanned aerial vehicle transportation scheme, and performance parameter data of the unmanned aerial vehicle, and inputs the flight path sequence to the energy coordination module;

[0036] The energy coordination module predicts the power demand of the direct current bus based on the flight path sequence to obtain predicted power and predicted power constraints, and outputs a power allocation instruction corresponding to the predicted power;

[0037] The transportation module is configured to optimize the power distribution instruction based on the predicted power constraint and the flight path sequence by the execution control module, and obtain operation control instructions according to the optimized power distribution instruction and the unmanned aerial vehicle transportation scheme, so as to control the unmanned aerial vehicle and the corresponding auxiliary flight equipment to execute the operation control instructions to perform the logistics transportation.

[0038] The adjustment module is configured to collect operation data generated by the unmanned aerial vehicle during the logistics transportation in real time by the state monitoring module, identify abnormal information in the operation data by an abnormality detection algorithm, and adjust the logistics transportation of the unmanned aerial vehicle in real time.

[0039] In a third aspect, an electronic device is provided, which includes a processor, a memory and a bus, the memory stores machine readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, and the machine readable instructions are executed by the processor to perform the steps of any one of the unmanned aerial vehicle land-based electromagnetic take-off and landing transportation methods.

[0040] The unmanned aerial vehicle land-based electromagnetic take-off and landing transportation method provided by the embodiment of the application is suitable for a take-off and landing transportation system, the take-off and landing transportation system comprises a task scheduling module, a path planning module, an energy coordination module, an execution control module and a state monitoring module, the method first generates an unmanned aerial vehicle transportation scheme corresponding to order information according to the received order information, a plurality of state information of unmanned aerial vehicles, site information of a logistics park and real-time airspace information through the task scheduling module; the unmanned aerial vehicle transportation scheme comprises an unmanned aerial vehicle take-off and landing strategy and a task allocation table; secondly, the path planning module generates a flight path sequence based on the order information, the unmanned aerial vehicle transportation scheme and performance parameter data of the unmanned aerial vehicle, and inputs the flight path sequence to the energy coordination module; then the energy coordination module performs power demand prediction of a direct current bus based on the flight path sequence to obtain predicted power and a predicted power constraint, and outputs a power distribution instruction corresponding to the predicted power; next, the execution control module optimizes the power distribution instruction based on the predicted power constraint and the flight path sequence, and obtains a running control instruction according to the optimized power distribution instruction and the unmanned aerial vehicle transportation scheme, controls the unmanned aerial vehicle and corresponding auxiliary flight equipment to execute the running control instruction to perform logistics transportation, and finally the state monitoring module collects running data generated by the unmanned aerial vehicle during logistics transportation in real time, identifies abnormal information in the running data through an abnormality detection algorithm to adjust logistics transportation of the unmanned aerial vehicle in real time. Based on the above method, the unmanned aerial vehicle in the logistics park can realize the effect of high-frequency continuous take-off and landing, effectively solves the problem that the existing unmanned aerial vehicle land-based electromagnetic take-off and landing transportation system cannot provide a high-frequency continuous take-off and landing transportation environment for the unmanned aerial vehicle in the logistics park, protects the safety of the equipment in the unmanned aerial vehicle land-based electromagnetic take-off and landing transportation system, avoids the phenomenon of sudden drop or surge of the direct current bus voltage, and further ensures the control accuracy, improves the accuracy of the action of the equipment protection circuit and the energy recovery efficiency and the like. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0042] Figure 1 A flowchart of an unmanned aerial vehicle land-based electromagnetic take-off and landing transportation method provided by an embodiment of the application is shown;

[0043] Figure 2 Another flowchart of an unmanned aerial vehicle land-based electromagnetic take-off and landing transportation method provided by an embodiment of the application is shown;

[0044] Figure 3 A flowchart of a process for optimizing the power distribution instruction is shown.

[0045] Figure 4 A structural block diagram of a UAV land-based electromagnetic take-off and landing transport device is shown.

[0046] Figure 5 A structural block diagram of an electronic device is shown. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of description and illustration, and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowchart shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can not be implemented in sequence, and the steps without logical context relationship can be reversed in sequence or implemented simultaneously. In addition, one or more other operations can be added to the flowchart or removed from the flowchart under the guidance of the content of the present application by those skilled in the art.

[0048] In addition, the described embodiments are only some of the embodiments of the present application, not all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0049] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0050] The current UAV land-based electromagnetic take-off and landing transport system usually adopts a centralized energy management architecture, and its control strategy is designed based on single machine take-off or low frequency operation mode. There are obvious limitations in coping with high frequency, multi-machine continuous take-off and landing scenes in logistics parks. Especially when deployed in space-limited scenarios such as rooftops, the system has insufficient energy buffer capacity and poor energy scheduling flexibility. In severe cases, voltage fluctuations may cause the entire take-off and landing process to be interrupted, and even sensitive power electronic devices may be damaged.

[0051] Based on this, the present application provides a method, apparatus, and electronic device for unmanned aerial vehicle (UAV) land-based electromagnetic take-off and landing transportation, which will be described below through embodiments.

[0052] Example 1

[0053] To facilitate understanding of this embodiment, a detailed description of a UAV land-based electromagnetic take-off and landing transportation method disclosed in this application embodiment will be provided first. For example... Figure 1 The diagram shown is a flowchart of a land-based electromagnetic take-off and landing transportation method for unmanned aerial vehicles (UAVs). Figure 2 The diagram shows another flowchart of a UAV land-based electromagnetic take-off and landing transportation method. This application provides a UAV land-based electromagnetic take-off and landing transportation method applicable to take-off and landing transportation systems. The take-off and landing transportation system includes a task scheduling module, a path planning module, an energy coordination module, an execution control module, and a status monitoring module. The method includes:

[0054] S101. The task scheduling module generates a drone transportation plan corresponding to the received order information, various status information of the drone, site information of the logistics park, and real-time airspace information; the drone transportation plan includes drone take-off and landing strategies and a task allocation table.

[0055] S102. The path planning module generates a flight path sequence based on the order information, the drone transportation plan, and the drone's performance parameter data, and inputs the flight path sequence into the energy coordination module.

[0056] S103. The energy coordination module predicts the power demand of the DC bus based on the flight path sequence to obtain the predicted power and the predicted power constraint, and outputs the power allocation command corresponding to the predicted power.

[0057] S104. The execution control module optimizes the power allocation command based on the predicted power constraint and flight path sequence, and obtains the operation control command according to the optimized power allocation command and the UAV transportation scheme, and controls the UAV and the corresponding auxiliary flight equipment to execute the operation control command to carry out logistics transportation.

[0058] S105. The status monitoring module collects the operational data generated by the UAV during logistics transportation in real time, and identifies abnormal information in the operational data through an anomaly detection algorithm to adjust the logistics transportation of the UAV in real time.

[0059] The take-off and landing transportation system in the application not only includes a task scheduling module, a path planning module, an energy coordination module, an execution control module and a state monitoring module, but also includes an energy recovery optimization module, a system optimization module, a roof structure monitoring module, a fault-tolerant processing module, a hybrid energy storage control module, a central control module and a multi-machine cooperative control module, so as to improve the richness of the functions of the take-off and landing transportation system and ensure the accuracy of energy processing and the system in the take-off and landing transportation system.

[0060] In step S101, the task scheduling module connects a logistics management platform in a logistics park with unmanned aerial vehicles, captures order information about logistics transportation from the logistics management platform, and the order information That is, the order information includes delivery time, arrival time, weight and path information, which is used to evaluate the energy consumption demand and time constraint of the unmanned aerial vehicle executing the logistics transportation task, and to obtain various state information, site information and real-time airspace information. The various state information includes various states of the unmanned aerial vehicle during logistics transportation, such as flying, waiting to fly and ending flying. The site information includes the ground, the roof and the corresponding flyable position. The real-time airspace information is specific information about whether the unmanned aerial vehicle can fly in the real-time sky, including the number of unmanned aerial vehicles that can fly. According to the received order information, the various state information of the unmanned aerial vehicle, the site information of the logistics park and the real-time airspace information, a pre-set scheduling algorithm is called to generate an unmanned aerial vehicle transportation scheme corresponding to the order information. The unmanned aerial vehicle transportation scheme includes an unmanned aerial vehicle take-off and landing strategy and a task allocation table. The unmanned aerial vehicle take-off and landing strategy includes a roof strategy and a ground strategy. That is, the task scheduling module determines whether the ground take-off and landing can be performed according to the site information of the logistics park. If not, the unmanned aerial vehicle take-off and landing strategy only has the roof strategy.

[0061] The task scheduling module also optimizes the target selects the optimal task allocation scheme , wherein represents the energy consumption cost, represents the delay cost, and balances the energy efficiency and the logistics timeliness, that is During the calculation, the task scheduling module considers the power demand of the unmanned aerial vehicle take-off and landing and the power constraint of the energy coordination scheduling output, so as to ensure that the logistics transportation task allocation will not cause instantaneous power peak value overrun or high energy storage system load. Finally, the task scheduling module generates a task allocation table which clearly indicates the task start and end time of each unmanned aerial vehicle. The task allocation table is the scheduling strategy of the unmanned aerial vehicle, and the task allocation table is output to the path planning module to generate a flight path sequence that meets the time window, power constraint and path optimization requirements, so as to ensure the efficient, stable and safe operation of the unmanned aerial vehicle when multiple unmanned aerial vehicles cooperatively execute the logistics task.

[0062] In the implementation process of step S101, there is an embodiment that: after the task scheduling module generates the unmanned aerial vehicle transportation scheme corresponding to the order information according to the received order information, various state information of the unmanned aerial vehicle, site information of the logistics park, and real-time airspace information, the task scheduling module comprises:

[0063] S1011, determining a goods loading position of the unmanned aerial vehicle transportation scheme based on a site type in the site information; the goods loading position comprises a roof;

[0064] S1012, collecting real-time roof data of the roof, generating a load adjustment instruction based on the real-time roof data, and moving goods to the roof based on the load adjustment instruction.

[0065] In steps S1011-S1012, the task scheduling module obtains the site type in the site information, and determines the goods loading position based on the site type in the site information. If the ground type in the site type can also be used for take-off and landing of the unmanned aerial vehicle, the goods loading position is the ground. If the ground type in the site type cannot be used for take-off and landing of the unmanned aerial vehicle, the goods loading position is the roof. The task scheduling module collects real-time roof data of the roof based on the roof structure monitoring module, and generates a load adjustment instruction based on the real-time roof data , wherein represents the load power to be reduced, represents the equipment position adjustment amount, and the real-time roof data comprises stress distribution data and strain data of a roof bearing structure, which are used to accurately reflect the instantaneous mechanical load borne by the roof during take-off and landing of multiple unmanned aerial vehicles. The roof structure monitoring module inputs the real-time roof data comprising the stress distribution data and strain data of the roof bearing structure into a pre-set finite element analysis model, calculates the von Mises stress of each sampling point , and obtains a structure safety factor by ratio analysis in formula (1):

[0066] (1);

[0067] The roof structure monitoring module takes the minimum safety factor as a critical state of the overall bearing capacity of the roof, and realizes sensitive detection of the local stress concentration area. When the calculated is less than a set minimum safety threshold , the roof structure monitoring module immediately generates a load adjustment instruction , wherein is the instantaneous power of the unmanned aerial vehicle to be reduced to reduce the stress of the roof structure. To fine-tune the landing device or equipment position to optimize load distribution, and output the load adjustment instructions to the multi-machine cooperative control module in real time for dynamic adjustment of the unmanned aerial vehicle launch and arrest timing and power distribution strategy, so as to effectively alleviate the roof bearing pressure, while maintaining the timing synchronization of the multi-machine cooperative landing of the unmanned aerial vehicle and the system power constraint, ensuring the safety of the roof structure and the stable operation of the entire electromagnetic landing and transportation system, and the structure safety factor output by the roof structure monitoring module to the execution control module.

[0068] In step S102, the path planning module generates a flight path sequence based on the order information, the unmanned aerial vehicle transportation scheme and the performance parameter data of the unmanned aerial vehicle, wherein the path planning module generates flight path information for the unmanned aerial vehicle based on the performance parameter data of the unmanned aerial vehicle, the flight path information including global path and real-time obstacle avoidance path, thereby ensuring the accuracy of the obtained unmanned aerial vehicle transportation scheme, and the flight path sequence generated includes the planned launch time and the predicted arrest time of each unmanned aerial vehicle, wherein i and j represent the indexes of launch and arrest events respectively, and the flight path sequence is input to the energy cooperative module for processing by the energy cooperative module based on the flight path sequence.

[0069] In step S103, the energy cooperative module receives the flight path sequence output by the path planning module, and performs power demand prediction of the DC bus based on the flight path sequence to obtain predicted power and predicted power constraint, wherein the energy cooperative module has established a DC bus total power demand prediction model and a timing optimization model for predicted power constraint in advance, wherein the timing optimization model is established based on auxiliary equipment in the landing and transportation system excluding the unmanned aerial vehicle, and the auxiliary equipment includes but is not limited to generators, converters, super capacitors and lithium batteries, and the DC bus total power demand prediction model is represented as , wherein and represent the power curves of typical single launch events and arrest events respectively, to describe the instantaneous power change of each event in the occurrence process A. This model generates the instantaneous power demand curve of the microgrid in the entire landing period by shifting and superimposing the power curves of each unmanned aerial vehicle event according to their planned time, ensuring that the landing and transportation system can predict power peaks and fluctuation trends in advance, and the energy cooperative module outputs the power distribution instructions corresponding to the predicted power, which is represented as , wherein and represent the instantaneous power output values of the super capacitor and the lithium battery respectively, and the DC bus voltage fluctuation , under the condition of meeting the power balance constraint The power smoothing distribution is realized. The optimization process uses the super capacitor for the rapid response of the instantaneous high power demand, the lithium battery provides energy compensation and continuous energy supply, and the grid power is used as a stable reference to ensure that the DC bus voltage fluctuates minimally while meeting the instantaneous power demand of all unmanned aircraft launch and arrest events, ensuring the reliability of energy supply and system dynamic stability during multi-aircraft coordinated take-off and landing.

[0070] In the implementation process of step S103, one embodiment is that the energy coordination module performs power demand prediction of the DC bus based on the flight path sequence to obtain predicted power, and outputs a power distribution instruction corresponding to the predicted power, including:

[0071] S1031, collect first influence data of factors affecting the power demand of the DC bus, and obtain second influence data related to the flight path sequence;

[0072] S1032, fuse the first influence data and the second influence data to obtain the predicted power, and obtain the power distribution instruction based on the predicted power.

[0073] In steps S1031-S1032, the energy coordination module pre-collects factors affecting the power demand of the DC bus, including typical single launch events and arrest events of unmanned aerial vehicles, and obtains the corresponding power curves and , respectively, collects first influence data of the factors, and obtains second influence data related to the flight path sequence, the second influence data is the planned launch time and the predicted arrest time in the flight path sequence, fuses the first influence data and the second influence data to obtain the predicted power, that is, the calculated predicted power , and obtains the instantaneous power output values of the super capacitor and the lithium battery based on the predicted power, and performs calculation based on the instantaneous power output values of the super capacitor and the lithium battery to obtain the power distribution instruction .

[0074] In the implementation process of step S1032, one embodiment is that after obtaining the power distribution instruction based on the predicted power, it includes:

[0075] S10321、based on the power allocation instruction, calling the hybrid energy storage control module in the vertical take-off and landing transportation system to calculate the power allocation ratio and the device state control instruction in layers;

[0076] S10322, through the power allocation ratio and the device state control instruction, controlling the actual power of the target device to be consistent with the target power in the power allocation instruction.

[0077] In steps S10321-S10322, the energy coordination module outputs the hybrid energy storage control module in the vertical take-off and landing transportation system, and the hybrid energy storage control module in the vertical take-off and landing transportation system adopts a double-layer control structure to calculate in layers. The upper layer adjusts the power allocation ratio in real time by solving , wherein represents the voltage deviation, and is the proportional integral coefficient, that is, the output of the upper layer directly determines the target power ratio and , realizes the closed-loop alignment with the energy coordination module; the lower layer controls the switching state of the bidirectional DC-DC converter through pulse width modulation, realizes the coordinated control of the super capacitor terminal voltage and the lithium battery terminal voltage , so as to obtain the calculated power allocation ratio and the device state control instruction; the hybrid energy storage control module outputs the converter duty ratio control signal through the power allocation ratio and the device state control instruction, which is used to adjust the power flow in real time, ensures that the actual power allocation and tracks the instruction value, controls the actual power of the converter included in the target device to be consistent with the target power in the power allocation instruction, realizes the coordinated cooperation of the super capacitor responding to the instantaneous power fluctuation and the lithium battery providing the sustained energy supply, so as to maintain the stability of the DC bus voltage and the dynamic balance of the power in the process of multi-aircraft cooperative vertical take-off and landing, and improve the energy response speed and operation reliability of the entire electromagnetic vertical take-off and landing transportation system.

[0078] In step S104, after receiving the power allocation instruction output by the energy coordination module, the execution control module coordinates the action timing of the launching device and the arresting device, realizes the timing arrangement of multi-aircraft take-off and landing through the control algorithm, outputs the device control signal to each auxiliary flight device for configuration, and calls the multi-aircraft cooperative control module. The multi-aircraft cooperative control module optimizes the power allocation instruction based on the predicted power constraint and the flight path sequence, so as to realize the accurate matching of the power provided by each device in the vertical take-off and landing transportation system and the order information, that is, according to and and the predicted power constraint optimization, and obtain operation control instructions according to the optimized power distribution instructions and the unmanned vehicle transportation scheme, and control the unmanned vehicle to execute the operation control instructions according to the power distribution instructions according to the unmanned vehicle transportation scheme and the corresponding auxiliary flight equipment to perform logistics transportation.

[0079] In the implementation process of step S104, there is an embodiment that: Figure 3 As shown in the figure, the execution control module optimizes the power distribution instructions based on the predicted power constraint and the flight path sequence, including:

[0080] S1041, establish a timing optimization model for the power distribution instructions based on the predicted power constraint and the flight path sequence;

[0081] S1042, calculate the optimization target of the power distribution instructions through the timing optimization model, to optimize the power distribution instructions of each period.

[0082] In steps S1041-S1042, the execution control module calls the multi-machine cooperative control module, establishes a timing optimization model for the power distribution instructions based on the predicted power constraint and the flight path sequence, obtains the planned take-off time and landing time and the corresponding instantaneous power demand of each unmanned vehicle, and uses them to establish complete take-off and landing timing constraints. The timing optimization model is represented by formula (2):

[0083] (2);

[0084] Where N and M represent the number of planned take-off and landing events, respectively. By minimizing the sum of squares of deviations between actual take-off and landing times and ideal times, time accurate synchronization of multi-machine take-off and landing is achieved. The module uses a model predictive control algorithm to solve the constrained optimization problem in each control period.

[0085] In the implementation process of step S1042, there is an embodiment that:

[0086] A1, set multiple optimization dimensions for the optimization target, and configure optimization sub-targets corresponding to the optimization dimensions; the optimization dimensions include power dimensions and take-off and landing dimensions;

[0087] A2, fuse the optimization sub-targets corresponding to each optimization dimension to generate control data, to optimize based on the control data.

[0088] In steps A1-A2, the multi-machine cooperative control module sets multiple optimization dimensions for the optimization target, the optimization dimensions including a power dimension and a take-off and landing dimension, and configures optimization sub-targets corresponding to the optimization dimensions, the optimization sub-target corresponding to the power dimension being , ensuring that the instantaneous power does not exceed the maximum bearing capacity of the hybrid energy storage system and the power grid, and the optimization sub-target corresponding to the take-off and landing dimension being expressed in take-off and landing intervals, specifically , ensuring the safe separation and cooperative sequence among the unmanned aerial vehicles. In the optimization solving process, the module combines the power distribution instructions output by the energy cooperative scheduling with the power curve required by the path of each unmanned aerial vehicle, dynamically adjusts the specific time of ejection and blocking, makes the total power curve change smoothly within the instantaneous peak value, and meets the unmanned aerial vehicle flight safety and path planning requirements. The module finally generates device start and stop time instructions , accurately schedules the actions of each ejection device and blocking device to the execution unit, realizes power constraint optimization and timing synchronization control of the multi-machine cooperative take-off and landing process, and thus guarantees the safe, efficient and stable operation of the unmanned aerial vehicle take-off and transport system.

[0089] In step S105, the state monitoring module acquires running data generated by the unmanned aerial vehicle during logistics transportation in real time based on multiple sensors, wherein the acquisition frequency is a high sampling frequency of 10 kHz, and the running data includes a direct current bus voltage signal and a current signal , which are used to capture the instantaneous power fluctuation and bus dynamic characteristics during the take-off and landing process of the unmanned aerial vehicle, and identify abnormal information in the running data through an abnormality detection algorithm, the abnormal information including voltage fluctuation and frequency abnormality; wherein the voltage fluctuation is decomposed under multiple scale parameters by inputting the collected voltage signal into a wavelet transform operator , and calculating a voltage fluctuation index , the state monitoring module generates an abnormal alarm signal when the voltage fluctuation index , wherein represents the voltage deviation amplitude, represents the frequency deviation, represents a pre-set threshold value of the voltage fluctuation index; the voltage fluctuation intensity of different frequency components is reflected by energy accumulation, realizing multi-scale analysis of the dynamic fluctuation of the direct current bus, and the abnormal alarm signal is input to the fault handling module for processing.

[0090] In the specific implementation process of step S105, there is an embodiment that the real-time adjustment of the logistics transportation of the unmanned aerial vehicle includes:

[0091] S10511, determining whether the abnormal information meets an abnormal condition set in advance for the abnormal information; the abnormal condition corresponds to the type of abnormal information;

[0092] S10512, if yes, generating a reconfiguration instruction based on the abnormal information, so that the energy coordination module adjusts the unmanned aerial vehicle transportation scheme based on the reconfiguration instruction.

[0093] In steps S10511-S10512, after receiving the abnormal information output by the state monitoring module, the fault-tolerant processing module quickly responds based on the abnormal information and the power distribution instruction provided by the energy coordination and scheduling module. The fault-tolerant processing module determines whether the abnormal information meets an abnormal condition set in advance for the abnormal information; the abnormal condition corresponds to the type of abnormal information; the abnormal condition includes an abnormal condition corresponding to voltage fluctuation and an abnormal condition corresponding to frequency. The abnormal condition corresponding to voltage fluctuation is If the voltage deviation exceeds a set threshold That is , a power reduction instruction is generated to reduce the instantaneous power output and alleviate the bus overvoltage or fluctuation amplitude; the abnormal condition corresponding to frequency is If the frequency deviation exceeds a set threshold That is , a frequency adjustment instruction is generated to adjust the equivalent frequency of the bus by adjusting the energy storage unit or grid input power to ensure system dynamic stability; the fault-tolerant processing module then generates a reconfiguration instruction based on the abnormal information , wherein represents the standby loop switching state, to the energy coordination module and hybrid energy storage control module based on the reconfiguration instruction, the energy coordination module adjusts the unmanned aerial vehicle transportation scheme based on the current state information, realizes the instantaneous adjustment of the energy storage unit power distribution, the switching of the standby loop, and the overall operation state optimization of the electromagnetic take-off and landing system, thereby quickly suppressing voltage and frequency abnormalities during the multi-unmanned aerial vehicle cooperative take-off and landing process, ensuring the stability of the DC bus and the safety of the equipment, triggering the corresponding module fast response strategy for adjusting the output power of the super capacitor and lithium battery or modifying the equipment start-stop timing, thereby suppressing the bus voltage fluctuation and maintaining the stable operation of the system, ensuring the energy reliability and dynamic safety during the multi-unmanned aerial vehicle cooperative take-off and landing process. The application also calls the central control module to generate a system state report for the take-off and landing transportation system according to the abnormal information and the real-time state information of the take-off and landing transportation system, thereby displaying, and the central control module can also realize autonomous adjustment of the scheduling strategy for unmanned aerial vehicle scheduling.

[0094] In the implementation process of step S105, there is another embodiment that the control of the unmanned aerial vehicle and the corresponding auxiliary equipment to execute the operation control instruction for logistics transportation includes:

[0095] S10521, obtain a plurality of real-time current parameters of the plurality of auxiliary flight equipment in the blocking stage, and adjust the real-time monitored energy conversion efficiency to a target state based on the plurality of real-time current parameters;

[0096] S10522, output the real-time current parameters in the target state to the control module corresponding to the blocking stage, to adjust the plurality of auxiliary flight equipment.

[0097] In steps S10521-S10522, the state monitoring module calls the energy recovery module, which collects the mechanical power of a plurality of auxiliary flight equipment such as generators and converters in the blocking stage and the electric energy output power , calculates the instantaneous energy conversion efficiency , which is used to quantify the real-time performance of the energy conversion from kinetic energy to electric energy by the blocking device, the energy recovery module inputs the energy conversion efficiency to the optimization algorithm, adjusts the generator excitation current and the converter switching frequency included in the real-time current parameter, dynamically changes the generator magnetic field strength and the switching characteristics of the power electronic device, realizes fine control of the energy conversion process, and the optimization algorithm takes the target function minimization as the criterion, and through iterative solution, obtains the parameter combination that makes the energy conversion efficiency as close to 1 as possible within the blocking period, the energy recovery module outputs the optimized parameter set composed of real-time current parameters , which is directly transmitted to the control module of the plurality of auxiliary flight equipment corresponding to the blocking stage, for adjusting the excitation current and the converter switching action, so that the actual recovered power accurately tracks the optimal state, thereby improving the energy recovery efficiency in the blocking stage, reducing the load of the power grid and the energy storage system, and ensuring the maximum energy utilization and system dynamic stability in the process of multi-machine cooperative take-off and landing of the unmanned aerial vehicle.

[0098] The system optimization module provided in the application also collects the operation data of each subsystem in real time, including the instantaneous power output of the energy storage unit and the electromagnetic take-off and landing device, and the actual energy consumption of the unmanned aerial vehicle executing the task, to calculate the energy utilization rate , which quantifies the efficiency of the system in converting input power into effective working energy. At the same time, the module obtains the reliability index of each auxiliary flight equipment , and obtains the overall system reliability through product operation , reflecting the robustness of multi-aircraft coordinated take-off and landing and energy storage system operation. The system optimization module inputs these operation data into a multi-objective optimization algorithm, comprehensively considers the maximization of energy efficiency, the improvement of system reliability, and power constraint conditions, and generates system improvement suggestions , including adjustment of power rating of energy storage and take-off and landing equipment , maintenance cycle optimization , and system topology improvement scheme , for improving energy utilization efficiency, prolonging equipment life, and optimizing multi-aircraft coordinated scheduling capability. Finally, the system optimization module outputs the improvement suggestions to the system maintenance and management platform, realizing continuous performance optimization and long-term stable operation management of the take-off and landing transportation system.

[0099] The unmanned aerial vehicle land-based electromagnetic take-off and landing transportation system provided by the application has the following beneficial effects:

[0100] (1) The unmanned aerial vehicle land-based electromagnetic take-off and landing transportation system receives a flight path sequence containing planned take-off time and predicted stopping time of the unmanned aerial vehicle, predicts the instantaneous power demand in the whole period by superimposing the power curves of each take-off and landing event, adopts a hybrid energy storage system composed of supercapacitors and lithium batteries, allocates the output power of the two types of energy storage units according to the demand, and ensures the direct current bus power balance through optimization, solves the problem of sudden drop or surge of direct current bus voltage caused by power mutation during high-frequency multi-aircraft take-off and landing, improves the energy scheduling flexibility, and guarantees the energy supply reliability of multi-aircraft coordinated take-off and landing;

[0101] (2) The unmanned aerial vehicle land-based electromagnetic take-off and landing transportation system adjusts the power allocation ratio of supercapacitors and lithium batteries in real time according to the direct current bus voltage deviation through the upper layer proportional integral (PI) control algorithm, and determines the target output power of the two types of energy storage units; the lower layer controls the switching state of the bidirectional DC-DC converter through pulse width modulation (PWM) technology, accurately adjusts the terminal voltage of the supercapacitors and lithium batteries, ensures that the actual output power is consistent with the target power, improves the response speed of the hybrid energy storage control module to instantaneous power fluctuation, accurately tracks the power allocation instruction, effectively maintains the stability of the direct current bus voltage, and avoids false operation of the equipment due to voltage fluctuation;

[0102] (3) The unmanned aerial vehicle land-based electromagnetic take-off and landing transportation system improves the existing single machine or low-frequency take-off and landing control mode, and proposes a timing optimization and constraint control method + technical means for multi-machine cooperative take-off and landing: an optimization model is established with the goal of minimizing the deviation between the actual take-off and landing time and the ideal time, a model predictive control algorithm is used, and in each control period, the instantaneous total power upper limit, take-off and landing interval range and other constraint conditions are considered to calculate and output the start and stop time instructions of each launching device and arresting device, realize the timing arrangement of multi-machine take-off and landing actions, avoid power over-limit and equipment action conflict during multi-machine take-off and landing, realize multi-machine timing accurate synchronization, and ensure the safety and operation efficiency of high-frequency multi-machine cooperative take-off and landing;

[0103] (4) The unmanned aerial vehicle land-based electromagnetic take-off and landing transportation system integrates logistics task scheduling, path planning, energy cooperative scheduling, hybrid energy storage control, multi-machine cooperative control, state monitoring, fault handling, roof structure safety monitoring, energy recovery optimization and other modules, forms a closed-loop operation system of "task-path-energy-control-safety-recovery", breaks through the technical bottleneck that the existing system cannot cope with multi-machine high-frequency take-off and landing, realizes the large-scale and stable operation of logistics unmanned aerial vehicles, effectively improves the regional logistics distribution efficiency, and reduces the labor and energy costs.

[0104] Embodiment 2

[0105] The application also provides an unmanned aerial vehicle land-based electromagnetic take-off and landing transportation device, as shown in Figure 4 As shown in a block diagram of an unmanned aerial vehicle land-based electromagnetic take-off and landing transportation device, the functions implemented by the automatic test auxiliary device for vehicle software correspond to the steps of the above-mentioned method for automatically testing vehicle software on a terminal device. The device can be understood as a component of a server including a processor, and the unmanned aerial vehicle land-based electromagnetic take-off and landing transportation device described in the application is suitable for a take-off and landing transportation system, which includes a task scheduling module, a path planning module, an energy cooperative module, an execution control module and a state monitoring module, and the device includes:

[0106] The receiving module 401, the task scheduling module generates an unmanned aerial vehicle transportation scheme corresponding to the order information according to the received order information, a plurality of state information of unmanned aerial vehicles, site information of a logistics park and real-time airspace information; the unmanned aerial vehicle transportation scheme includes an unmanned aerial vehicle take-off and landing strategy and a task allocation table;

[0107] The generating module 402 is configured to generate a flight path sequence based on the order information, the unmanned aerial vehicle transportation scheme and the performance parameter data of the unmanned aerial vehicle, and input the flight path sequence to the energy cooperative module;

[0108] The output module 403 is configured to perform power demand prediction on the DC bus based on the flight path sequence by the energy coordination module to obtain predicted power and predicted power constraints, and output power distribution instructions corresponding to the predicted power.

[0109] The transportation module 404 is configured to optimize the power distribution instructions based on the predicted power constraints and the flight path sequence by the execution control module, and obtain operation control instructions by the unmanned aerial vehicle transportation scheme according to the optimized power distribution instructions, to control the unmanned aerial vehicle and the corresponding auxiliary flight equipment to perform the operation control instructions to perform logistics transportation.

[0110] The adjustment module 405 is configured to collect operation data generated by the unmanned aerial vehicle during logistics transportation in real time by the state monitoring module, and identify abnormal information in the operation data by an abnormality detection algorithm to adjust the logistics transportation of the unmanned aerial vehicle in real time.

[0111] In an available implementation, the output module comprises:

[0112] The collection module is configured to collect first influence data of influence factors of the power demand of the DC bus, and obtain second influence data related to the flight path sequence.

[0113] The fusion module is configured to fuse the first influence data and the second influence data to obtain the predicted power, and obtain the power distribution instructions based on the predicted power.

[0114] In an available implementation, the output module further comprises:

[0115] The calling module is configured to call a hybrid energy storage control module in the take-off and landing transportation system based on the power distribution instructions to calculate power distribution ratios and device state control instructions in a hierarchical manner.

[0116] The control module is configured to control the actual power of the target device to be consistent with the target power in the power distribution instructions by the power distribution ratios and the device state control instructions.

[0117] In an available implementation, the transportation module comprises:

[0118] The establishment module is configured to establish a time sequence optimization model for the power distribution instructions based on the predicted power constraints and the flight path sequence.

[0119] The calculation module is configured to calculate an optimization target of the power distribution instructions by the time sequence optimization model to optimize the power distribution instructions in each period.

[0120] In an available implementation, the transportation module further comprises:

[0121] The setting module is configured to set multiple optimization dimensions for the optimization target and configure optimization sub-targets corresponding to the optimization dimensions; the optimization dimensions include a power dimension and a take-off and landing dimension;

[0122] The optimization module is configured to fuse the optimization sub-targets corresponding to each optimization dimension to generate control data, and perform optimization based on the control data.

[0123] In an available implementation, the adjusting module comprises:

[0124] The judging module is configured to judge whether the abnormal information satisfies an abnormal condition set in advance for the abnormal information; the abnormal condition corresponds to the type of the abnormal information.

[0125] The configuration module is configured to, if yes, generate a reconfiguration instruction based on the abnormal information, so that the energy coordination module performs unmanned aerial vehicle transportation scheme adjustment based on the reconfiguration instruction.

[0126] In an available implementation, the adjusting module further comprises:

[0127] The obtaining module is configured to obtain multiple real-time current parameters of the multiple auxiliary flight devices in the blocking stage, so as to adjust the real-time monitored energy conversion efficiency to a target state based on the multiple real-time current parameters.

[0128] The blocking module is configured to output the real-time current parameters in the target state to a control module corresponding to the blocking stage, so as to adjust the multiple auxiliary flight devices.

[0129] In an available implementation, the receiving module comprises:

[0130] The determining module is configured to determine a cargo loading position of the unmanned aerial vehicle transportation scheme based on a site type in the site information; the cargo loading position includes a roof.

[0131] The moving module is configured to collect real-time roof data of the roof, generate a load adjustment instruction based on the real-time roof data, and move the cargo to the roof based on the load adjustment instruction.

[0132] Embodiment 3

[0133] The application also provides an electronic device, such as Figure 5As shown, it comprises: a processor 501, a memory 502 and a bus 503, the memory 502 stores machine readable instructions executable by the processor 501, when the electronic device is running, the processor 501 and the memory 502 communicate through the bus 503, the machine readable instructions are executed by the processor 501 to execute any one of the steps of the unmanned aerial vehicle land-based electromagnetic take-off and landing transportation method.

[0134] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the above-described system and device can refer to the corresponding process in the method embodiment, and will not be repeated in this application. In the several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented by other means. The above-described device embodiments are only schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some communication interface, device or module, which can be electrical, mechanical or other forms.

[0135] The modules described as separate components can or can not be physically separated, and the components shown as modules can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0136] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0137] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a nonvolatile computer readable storage medium executable by a processor. Based on this understanding, the technical solutions of the present application essentially or say the part of the prior art or the part of the technical solutions of the present application can be embodied in the form of software products, and the computer software product is stored in a storage medium, including a plurality of instructions for making a computer device (which can be a personal computer, a platform server, or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: U disk, mobile hard disk, ROM, RAM, magnetic disk or optical disk and various program code storage media.

[0138] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A land-based electromagnetic take-off and landing transportation method for unmanned aerial vehicles (UAVs), characterized in that, Applicable to takeoff and landing transportation systems, the takeoff and landing transportation system includes a task scheduling module, a path planning module, an energy coordination module, an execution control module, and a status monitoring module, the method includes: The task scheduling module generates a drone transportation plan corresponding to the received order information, various drone status information, logistics park site information, and real-time airspace information; the drone transportation plan includes drone take-off and landing strategies and a task allocation table. The path planning module generates a flight path sequence based on the order information, the drone transportation plan, and the drone's performance parameter data, and inputs the flight path sequence into the energy coordination module; The energy coordination module predicts the power demand of the DC bus based on the flight path sequence to obtain the predicted power and the predicted power constraint, and outputs the power allocation command corresponding to the predicted power. The execution control module optimizes the power allocation command based on the predicted power constraint and flight path sequence, and obtains the operation control command according to the optimized power allocation command and the UAV transportation plan, and controls the UAV and the corresponding auxiliary flight equipment to execute the operation control command for logistics transportation; The status monitoring module collects the operational data generated by the drone during logistics transportation in real time, and identifies abnormal information in the operational data through an anomaly detection algorithm to adjust the logistics transportation of the drone in real time. The energy coordination module predicts the power demand of the DC bus based on the flight path sequence to obtain the predicted power, and outputs the power allocation command corresponding to the predicted power, including: Collect first impact data of factors affecting the power demand of the DC bus, and obtain second impact data related to the first impact data in the flight path sequence; the first impact data includes power curves corresponding to typical single ejection events and arresting events of the UAV; the second impact data includes planned ejection time and expected arresting time in the flight path sequence; The predicted power is obtained by fusing the first impact data and the second impact data, and the power allocation instruction is obtained based on the predicted power.

2. The method according to claim 1, characterized in that, After obtaining the power allocation instruction based on the predicted power, the process includes: Based on the power allocation command, the hybrid energy storage control module in the take-off and landing transportation system is invoked to calculate the power allocation ratio and equipment status control command in a hierarchical manner. By using the power allocation ratio and the device status control command, the actual power of the target device is controlled to be consistent with the target power in the power allocation command.

3. The method according to claim 1, characterized in that, The execution control module optimizes the power allocation command based on the predicted power constraints and flight path sequence, including: A time-series optimization model for the power allocation command is established based on the predicted power constraints and the flight path sequence. The optimization objective of the power allocation command is calculated using the timing optimization model to optimize the power allocation command for each cycle.

4. The method according to claim 3, characterized in that, The step of calculating the optimization objective of the power allocation command using the timing optimization model includes: Multiple optimization dimensions are set for the optimization objective, and optimization sub-objectives corresponding to the optimization dimensions are configured; the optimization dimensions include power dimension and takeoff and landing dimension; Control data is generated by integrating the optimization sub-objectives corresponding to each optimization dimension, and optimization is performed based on the control data.

5. The method according to claim 1, characterized in that, The real-time adjustment of the drone's logistics transportation includes: Determine whether the abnormal information meets the pre-set abnormal conditions for the abnormal information; the abnormal conditions correspond to the type of abnormal information. If so, a reconfiguration instruction is generated based on the abnormal information, so that the energy coordination module can adjust the UAV transportation plan based on the reconfiguration instruction.

6. The method according to claim 1, characterized in that, The control of the drone and its corresponding auxiliary equipment to execute the operation control commands for logistics transportation includes: Multiple real-time current parameters of multiple auxiliary flight devices during the arrest phase are acquired, and the real-time monitored energy conversion efficiency is adjusted to the target state based on the multiple real-time current parameters. The real-time current parameters under the target state are output to the control module corresponding to the arrest phase in order to adjust the multiple auxiliary flight devices.

7. The method according to claim 1, characterized in that, The task scheduling module generates a drone transportation plan corresponding to the received order information, various drone status information, logistics park site information, and real-time airspace information. This includes: Based on the site type in the site information, the cargo loading location of the drone transportation plan is determined; the cargo loading location includes rooftops; Collect real-time roof data, generate load adjustment instructions based on the real-time roof data, and move goods to the roof based on the load adjustment instructions.

8. A land-based electromagnetic take-off and landing transport device for unmanned aerial vehicles (UAVs), characterized in that, Applicable to take-off and landing transportation systems, the take-off and landing transportation system includes a task scheduling module, a path planning module, an energy coordination module, an execution control module, and a status monitoring module, the device includes: The receiving module and the task scheduling module generate a drone transportation plan corresponding to the received order information, various status information of the drone, site information of the logistics park, and real-time airspace information; the drone transportation plan includes drone take-off and landing strategies and a task allocation table. A generation module is used by the path planning module to generate a flight path sequence based on the order information, the drone transportation plan, and the drone's performance parameter data, and input the flight path sequence into the energy coordination module; The output module is used by the energy coordination module to predict the power demand of the DC bus based on the flight path sequence to obtain the predicted power and the predicted power constraint, and output the power allocation command corresponding to the predicted power. The transportation module is used by the execution control module to optimize the power allocation command based on the predicted power constraint and flight path sequence, and to obtain the operation control command according to the optimized power allocation command and the UAV transportation scheme, and to control the UAV and the corresponding auxiliary flight equipment to execute the operation control command for logistics transportation. The adjustment module is used by the status monitoring module to collect the operation data generated by the UAV during logistics transportation in real time, and to identify abnormal information in the operation data through an anomaly detection algorithm in order to adjust the logistics transportation of the UAV in real time. Output module, including: The collection module is used to collect first impact data of the factors affecting the power demand of the DC bus, and to obtain second impact data related to the first impact data in the flight path sequence; the first impact data includes the power curves corresponding to typical single ejection events and arresting events of the UAV; the second impact data includes the planned ejection time and the expected arresting time in the flight path sequence. A fusion module is used to fuse the first impact data and the second impact data to obtain the predicted power, so as to obtain the power allocation instruction based on the predicted power.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of a UAV land-based electromagnetic take-off and landing transportation method as described in any one of claims 1 to 7.

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