124 results about "Air traffic management" patented technology

The invention discloses a low-altitude unmanned aerial vehicle flight statistical system, and relates to the technical field of air traffic management. A flight statistical system of a low-altitude unmanned aerial vehicle comprises a multi-source sensing fusion module, a twin airspace modeling module, a behavior portrait modeling module, a non-cooperative target recognition module, a flight statistical analysis module and a self-adaptive scheduling module. According to the invention, a three-dimensional twin airspace model of a monitoring area is constructed based on high-precision map data, a building three-dimensional model and flight information, and space mapping and collision risk analysis of a flight path are carried out, so that real restoration of the flight path and environment collision prediction are realized; the behavior feature vector is generated and the random forest algorithm is adopted to carry out classification modeling, and the behavior portrait of the unmanned aerial vehicle is constructed, so that accurate identification and classification of the behavior pattern of the unmanned aerial vehicle are realized.

The invention discloses an unmanned aerial vehicle non-visual flight management method based on an unmanned area three-dimensional model, and belongs to the technical field of unmanned aerial vehicle air traffic management and autonomous flight control, and the method comprises the steps: fusing multi-source heterogeneous data to generate a probability map of a data confidence field; calculating a space-time risk potential field based on the map and the kinematics state of the unmanned aerial vehicle; performing adaptive perception and route optimization planning according to the risk potential field; and a closed-loop feedback signal is generated according to the flight execution deviation so as to regulate and control the data fusion and risk calculation process, and data assimilation is carried out. According to the technical scheme, environment modeling fusing multi-source data and confidence evaluation is adopted, dynamic risk calculation of kinematics and global closed-loop feedback regulation are combined, and the safety, autonomy and environment adaptability of non-visual flight of an unmanned aerial vehicle in a complex unmanned area can be remarkably improved.

The invention relates to the technical field of air traffic management, and particularly provides an airport scene aircraft taxiing conflict real-time prediction and dynamic scheduling method, which comprises the following steps: collecting the motion state of an aircraft and airport road network data, and generating the original trajectory of the aircraft by adopting a hidden Markov model; predicting a future taxiing trajectory of the aircraft to obtain a predicted trajectory; screening potential conflict aircrafts by adopting a dynamic R-Tree spatial index technology, judging whether tracks between the potential conflict aircrafts are intersected or not, and generating a dynamic risk value; executing a dynamic scheduling decision based on the dynamic risk value, and generating an aircraft bypass path by adopting a path generation algorithm; rolling time domain control is adopted, and a local path is re-planned based on data obtained in real time. According to the method, prediction of the taxiing trajectory of the aircraft is fully considered, the risk value of the taxiing conflict of the aircraft is pre-judged in advance, and dynamic path adjustment is performed, so that the labor cost is reduced, and the efficiency and safety of airport ground operation are improved.

The invention belongs to the technical field of air traffic management, and relates to an air flight flow prediction method based on a lightweight adaptive network, which comprises the following steps of: firstly, acquiring data and fusing the data to generate a flow time sequence matrix indexed by airport identification and timestamp; secondly, constructing a self-adaptive dynamic graph; performing mixed graph convolution on the output dynamic graph and node features to form a spatial feature tensor, and performing frequency domain enhancement on the spatial feature tensor to obtain an event enhancement sequence; then processing the event strengthening sequence through an hour-level large convolution kernel and a minute-level expansion small convolution kernel, amplifying a congestion peak based on trend-fluctuation gating after time alignment, and outputting a fusion feature sequence; and finally, carrying out recursive decoding and prediction to obtain the predicted air flight flow. According to the method provided by the invention, under the conditions of dynamically changing airspace topology and strong noise and multi-scale coupled time sequence data, a set of lightweight spatial-temporal model is constructed and updated online in a self-adaptive manner, so that high-precision multi-step prediction of the multi-element flight flow can be realized.

The embodiment of the invention discloses a flight path data analysis method based on a depth auto-encoder and a generative adversarial network, relates to the technical field of air traffic management, and adopts a cubic spline interpolation method to enable each flight path to have the same length; a deep auto-encoder (DAE) and a generative adversarial network (GAN) are used as a basic framework to establish a flight trajectory data analysis framework, and two tasks of trajectory anomaly detection and flow pattern recognition are synchronously executed; in the pre-training stage, on the basis of DAE, a discriminator is integrated to establish an abnormal trajectory detection task, and an abnormal score function is formulated in combination with reconstruction loss and discriminator loss; in the fine tuning stage, a Gaussian mixture model (GMM) is adopted for low-dimensional representation of the trajectory in a potential space output by an encoder to establish a flow pattern recognition task, and in order to enhance clustering, a loss function is constructed in combination with clustering distribution enhancement. The method is suitable for civil aviation air traffic management.

The invention discloses a mercuric chloride computing power driven dynamic link joint optimization method and system, a terminal and a storage medium. The method comprises the steps that a link prediction network predicts performance change of each link according to communication link performance data and historical data; the environment sensing network predicts the influence of the environment on the link performance according to the real-time state data of the unmanned aerial vehicle and the environment data; the flight control task resolving module obtains a flight control instruction priority according to the task type and the task requirement; and the reinforcement learning decision module dynamically selects an optimal communication mode according to the performance change of each link, the influence of the environment on the performance of the link and the priority of the flight control instruction. According to the method, the link state is predicted based on the mercuric chloride computing power and by fusing the priority instruction based on the flight control task demand, the communication mode is dynamically switched in real time according to the prediction result, high-reliability, low-delay and high-robustness communication guarantee is realized in a low-altitude economic scene, and core support is provided for users such as unmanned aerial vehicles and air traffic management.

The invention belongs to the technical field of air traffic management, and relates to a flight time adjustment management method and system in a special scene. According to the method, the flight release priority sequence is constructed, the airport taxiway network topology and the dynamic limitation information are combined, the taxiing path is planned, the space-time conflict is detected, and the dynamic adjustment of flight ground operation and the coordination verification of the runway access time window are realized. The problems of frequent flight ground taxiing path conflicts, low scheduling efficiency and difficulty in accurate take-off time control in special scenes are solved, the safety and efficiency of airport ground operation are effectively improved, the flight taxiing time and the runway waiting time are reduced, the flight release sorting and the runway use plan are optimized, and the flight taxiing efficiency is improved. The flight scheduling cooperation capability in a complex operation environment is enhanced, and the orderliness and stability of flight operation are guaranteed.

The invention belongs to the technical field of air traffic management, and discloses a control tower and flight guarantee command room decision instruction transmission method based on edge nodes, priorities are distributed for instructions through an intelligent grading model, the high-priority instructions can reserve resources in advance and are transmitted preferentially, the resources can be preempted in emergency, and the control tower and flight guarantee command room decision instruction transmission efficiency is improved. A multi-path transmission strategy is automatically switched when a certain path fails, data integrity is ensured in combination with fragment verification and block chain recording, instruction loss caused by a single point of failure is avoided through edge-cloud redundant backup, and the problems that instruction transmission is prone to interference and insufficient in safety in a traditional method are solved; through intelligent priority grading, resources are inclined to high-priority instructions, resource waste is avoided, a collaborative scheduling mechanism of an edge node cluster can dynamically adjust resource allocation according to a real-time load, and a transmission quality feedback and adaptive optimization strategy is continuously optimized, so that the system can still efficiently process a large number of instructions in a flight take-off and landing intensive period.

The invention discloses an aircraft deployment method based on a dynamic airspace network and reinforcement learning, and relates to the field of air traffic management, and the method comprises the steps: obtaining flight information and task priority weight information of each flight in a historical time period and a current time period; dynamically dividing the airspace into an independent sub-airspace and a shared airspace by using flight information in a historical time period; calculating the safe area of each flight through a space-time buffer zone model, and determining a flight path according to the safe area; and according to the flight information, the airspace division result and the flight path, a release action instruction set including height adjustment, course correction and speed change is generated, and real-time deployment is performed on the flight. The technical effects of the invention lie in that airspace changes can be responded in real time, flight paths of flights are optimized, potential conflicts are reduced, and the airspace utilization rate is improved, so that the air traffic control efficiency and the flight safety are improved.

The invention provides a flight path prediction method fusing space perception and time-frequency conversion, and relates to the technical field of computers and air traffic management. The method comprises the following steps: firstly, constructing a flight path data set, preprocessing a complete flight path in the flight path data set to obtain a flight path sequence, and generating a training sample; a flight path prediction model is constructed for flight path prediction; the method comprises the following steps: firstly, carrying out feature dimension expansion on a flight path sequence by a flight path prediction model to obtain flight path high-dimensional feature representation; and learning a spatial structure dependency relationship and a time dependency relationship in the flight path sequence high-dimensional feature representation to generate a final flight path prediction result. And finally, constructing a loss function of the flight path prediction model based on the idea of supervised learning, and training the constructed flight path prediction model to obtain a trained flight path prediction model. According to the method, the prediction accuracy is improved while the model complexity is reduced.

Techniques for outputting landing area conditions to aircraft using standardized message protocols are provided. In an example method, a computing device accesses a configuration for generating messages using a predetermined message format for a first air traffic management device located at a first landing area. The computing device updates the configuration with information about a condition at the first landing area using the predetermined message format. The computing device outputs, using the first air traffic management device, a message based on the configuration, including transmitting the message to a second air traffic management device associated with an aircraft within a predetermined distance of the first landing area.

The invention provides an intelligent scheduling management system and method for urban air traffic, and relates to the field of urban air traffic management, and the system comprises an aircraft information collection and processing module, an airspace division and management module, a flight plan management module, a central scheduling decision module, a communication and instruction transmission module, and a monitoring and early warning module. The method has the beneficial effects that through real-time flight conflict detection and intelligent scheduling decision making, the safety is effectively improved, the collision risk can be accurately identified and avoided, and the abnormal state of the aircraft can be timely handled; by applying an intelligent optimization algorithm, the operation efficiency is greatly improved, a take-off and landing sequence and a flight route are reasonably planned, and airspace resources are efficiently utilized; complex factors are comprehensively considered, and the system is highly suitable for complex environments such as building shielding and severe weather in urban low-altitude flight.

The invention discloses a report generation method and device, equipment and a storage medium, and relates to the technical field of air traffic management, and the method comprises the steps: obtaining the current navigation data and meteorological data of a target aircraft, and constructing corresponding target data based on the navigation data and meteorological data; the navigation data comprises radar data and flight plan data of a target aircraft; determining the flight conflict probability of the target aircraft according to the target data through a preset conflict probability prediction model; the preset conflict probability prediction model is constructed based on a long and short term memory network; determining a conflict risk level of the target aircraft based on the flight conflict probability of the target aircraft and preset historical conflict data; and generating a conflict report of the target aircraft based on the flight conflict probability and the conflict risk level. Through deep fusion of the large language model and air traffic control field knowledge, a more comprehensive conflict analysis basis can be provided, automatic report generation is realized, and the accuracy of conflict detection is significantly improved.

The invention discloses a low-altitude airspace capacity dynamic evaluation method and system based on a time sequence diagram neural network, and belongs to the technical field of low-altitude traffic management, and the method comprises a data collection and fusion module which is used for accessing a multi-source heterogeneous data source, and carrying out the cleaning, standardization and fusion; the dynamic space-time diagram construction module is used for constructing a dynamic space-time diagram evolved along with time according to the processed multi-source heterogeneous data; a time sequence prediction engine module which is embedded with a time sequence diagram neural network model and is used for receiving the time sequence of the dynamic space-time diagram and outputting a predicted operation risk score; and the capacity evaluation and quantification module is used for calculating the dynamic operation capacity based on the predicted operation risk score, and providing services for an upper management platform through an application program interface. According to the method, real-time calculation and accurate prediction of a core decision variable, namely the airspace operation capacity, are realized, so that the most fundamental scientific basis is provided for refined and intelligent management of the airspace, and the safety and efficiency of urban low-altitude traffic are remarkably improved.

The invention relates to the field of air traffic management, in particular to a flight conflict resolution method considering uncertainty of man-machine interaction and air-ground communication, which comprises the following steps: acquiring conflict resolution condition information when detecting that an aircraft track has a potential conflict, establishing a controller workload constraint based on controller task occupation time length information, and determining whether the conflict resolution condition information is a conflict resolution condition; establishing a safety interval constraint based on the aircraft flight state, and establishing an aircraft performance constraint based on the aircraft performance parameters; establishing a release track search grid, determining a decision variable, determining a target function based on a robust index, and searching in a limited search area in the release track search grid based on the constraint condition and the target function to obtain an optimal decision variable; generating a conflict resolution track and a controller workload timeline according to the optimal decision variable and conflict resolution condition information; according to the method, the robustness and the safety of the planned release trajectory can be improved.

The invention discloses a flight flow intelligent prediction and air traffic collaborative optimization system based on big data, and relates to the technical field of air traffic management. Comprising the steps that a data acquisition module acquires multi-source data from a civil aviation database and aligns the multi-source data to generate a fusion data matrix; the traffic prediction module extracts traffic feature vectors, generates a feature similarity matrix by calculating distribution differences, and generates a basic traffic prediction result based on migration prediction model parameters; the disturbance correction module detects a sudden disturbance event based on real-time weather and airspace state data and corrects a basic prediction result; the capacity adjusting module generates a capacity adjusting scheme when the capacity deviation value exceeds a threshold value according to the correction result and the airport operation capability data; and the collaborative optimization module finally generates a multi-airport collaborative scheduling scheme through a game equilibrium algorithm based on the correction result, the capacity scheme and the regional coordination data. According to the invention, the accuracy of flight flow prediction and the efficiency of multi-airport collaborative management are effectively improved.

The invention relates to the technical field of civil aviation air traffic management, and discloses a pre-post air traffic controller fatigue objective prediction method and system, and the method comprises the steps: presenting a mental movement alertness task to a user; the method comprises the following steps: in the process of executing a mental movement alertness task by a user, acquiring eye image data containing the user through image acquisition equipment, and recording the response time and error rate of the user to the mental movement alertness task; calculating to obtain an eyelid closure parameter based on the eye image data; calculating a reaction time parameter and an error rate parameter based on the reaction time and the error rate; and determining a comprehensive fatigue value of the user based on a weighted combination of the eyelid closure parameter, the response time parameter and the error rate parameter. The invention aims to provide an objective fatigue prediction method and system for a pre-post air traffic controller so as to overcome the defects that a traditional subjective scale is insufficient in reliability, a standard objective test consumes too long time and is not suitable for pre-class rapid screening.

The invention relates to the technical field of unmanned aerial vehicle detection and identification and air traffic management, and discloses an unmanned aerial vehicle detection and identification system based on radio frequency signal feature extraction, which comprises a radio frequency signal acquisition module, a feature database module and a signal processing and analysis module, according to the system, bandwidth filtering is firstly performed on collected signals, then a multi-dimensional time sequence feature sequence capable of representing communication behaviors is constructed, and finally, the multi-dimensional time sequence feature sequence and a template sequence are aligned and compared section by section through a sliding window mechanism to complete recognition. According to the method and the system, the dependence on a single signal energy threshold value is eliminated, and the system can still carry out identification according to the internal time logic continuity even if the signal is partially interfered in a complex electromagnetic environment through the mechanism, so that the robustness and the confidence coefficient of detection are improved, and quick response under low computing power is realized.

The invention discloses a low-altitude route planning method based on mixed granularity grids, and belongs to the technical field of air traffic management for unmanned aerial vehicles. The problems that in the prior art, according to traditional low-altitude air route planning based on airspace grids of a unified scale, the fine-granularity air route planning efficiency is low, and a coarse-granularity route cannot be achieved are solved. An elliptical boundary is constructed according to starting and ending point coordinates of the unmanned aerial vehicle to obtain an elliptical region; the building is trimmed, and a building subset is constructed; performing spatial domain grid coding on the elliptical region to obtain an elliptical region grid; performing fine-grained grid division on the overlapped grids, and further judging whether the grids in the elliptical region intersect with the building subsets or not to obtain a separated grid set; and constructing an undirected graph, searching in the undirected graph through an A * path search algorithm to obtain a shortest path between a starting point and an ending point, and taking the shortest path as a low-altitude air route planning path. The airspace network modeling efficiency is effectively improved, and the method can be applied to low-altitude route planning.

The invention relates to the technical field of air traffic management, and discloses an in-post controller fatigue prediction method based on a work type and a circadian rhythm, and the method comprises the steps: obtaining a face video frame in a controller post, and carrying out the calculation to obtain a PERCLOS time sequence representing an eyelid closing proportion; obtaining an exogenous factor related to the control task; the PERCLOS time sequence and the ectogenic factors are coded and input to a trained time sequence neural network model, and the time sequence neural network model is configured to apply different weights to the work types, the circadian rhythms and the work requirements so as to carry out differential modeling; and generating a fatigue level curve covering a preset prediction time window based on the output of the time sequence neural network model. The invention aims to solve the technical problems that a fatigue monitoring method in the prior art is lagged in response and insufficient in adaptability to dynamic working scenes, and provides a method capable of actively and accurately predicting the future fatigue development trend of the controller.

The invention relates to a flight plan conflict judgment method based on flight interval regulation. The method comprises the following steps: discretizing flight plan track data to generate flight plan track grid data; generating a flight plan track gridding buffer area based on a flight interval rule, and judging a potential flight conflict detection block in combination with a limited flight area; judging whether time overlapping exists among the flight plans or not at each potential flight conflict detection block, and when time overlapping exists, performing conflict judgment on flight plan tracks, limited flight areas of the potential flight conflict detection blocks and potential flight plan conflict tracks; and judging a potential flight plan conflict track pair based on a vertical interval rule of the flight interval, calculating a relative track relation of the potential flight plan conflict track pair, and judging an actual conflict between the flight plans based on the relative track relation and the flight interval rule. The flight plan conflict in the complex airspace can be automatically judged, and the airspace safety and the intelligent level of air traffic management are effectively improved.

The invention discloses a multi-source data fusion unmanned aerial vehicle low-altitude air traffic flow distribution method and system, and relates to the technical field of unmanned aerial vehicle air traffic management. The method comprises the following steps: identifying the model of a target non-cooperative unmanned aerial vehicle based on multi-source data and acquiring performance parameters of the target non-cooperative unmanned aerial vehicle; integrating the predicted communication delay of the target area and the unmanned aerial vehicle density to evaluate an avoidance risk coefficient; defining a potential risk range in combination with the performance state of the cooperative unmanned aerial vehicle, and dynamically correcting the potential risk range into a real-time risk range according to the real-time motion state of the non-cooperative unmanned aerial vehicle and the regional risk coefficient; and based on a Q-learning reinforcement learning algorithm, fusing the three-dimensional building map and the real-time risk range of each unmanned aerial vehicle, and carrying out cooperative dynamic allocation on the cooperative unmanned aerial vehicle traffic flow. According to the invention, accurate perception and global active avoidance of the threat of the non-cooperative unmanned aerial vehicle are realized, and the safety and operation efficiency of low-altitude airspace traffic management are effectively improved.

The invention relates to an adaptive heuristic conflict-free flight path planning method and system with robustness, belongs to the technical field of air traffic management, and is particularly used for flight conflict detection and release. The method is used for rapidly generating the robust conflict-free track meeting the safety interval constraint, can flexibly switch the local search strategy and the global search strategy according to the search stage, achieves effective balance between optimization performance and efficiency, and is especially suitable for an air traffic control automation system needing to dynamically update conflict-free track suggestions at the second-level frequency. The adaptive heuristic conflict-free flight path planning system comprises a conflict detection module, a conflict resolution module, a visualization module and an interaction module, and the conflict resolution module uses the conflict resolution module to generate a conflict-free flight path for a flight.

The invention relates to the technical field of air traffic management monitoring, in particular to an airport flight delay prediction system and method.The system comprises a data preprocessing module, a spatial feature module, a time feature module and a feature fusion and output module.The method comprises the steps that firstly, data preprocessing is conducted based on spatial-temporal feature decoupling, and standardized input is provided for subsequent modules; processing the problem of insufficient spatial heterogeneity by adopting a spatial feature module of a double attention mechanism, and considering short-time sudden fluctuation and a long-period rule through a time feature module parallel expansion convolution and dynamic pooling strategy; and finally, hierarchical attention-guided space-time fusion is used for splicing the multi-scale features of the bidirectional LSTM through expansion convolution, the recognition precision of the model is improved, lightweight output driven by affine transformation replaces a full-connection layer with LayerNorm, the feature expression ability is ensured, the reasoning time delay is optimized, and delay prediction is completed.

The application relates to the technical field of air traffic management, in particular to a UAV integrated conflict management method and system applied to low-altitude airspace, which comprises the following steps: a UAV air traffic management service provider receives service request information sent by a ground control station, and sends flight demand information to a public information service provider; the public information service provider processes the flight demand information according to a priority order; the UAV air traffic management service provider carries out non-hotspot and non-conflict path planning according to the use permission, airspace information and service request information sent by the public information service provider, obtains planning path information and sends the planning path information to the ground control station; the ground control station determines task allocation information based on the planning path information, sends the task allocation information to a UAV controlled by the ground control station, and enables the UAV to execute the planned path; and the application can realize non-hotspot and non-conflict path planning simultaneously.

The application discloses a kind of unmanned plane flight plan pre-dispatching tool and method, wherein the tool includes: information input module, target constraint generation module, dispatching model establishment module, dispatching scheme generation module and dispatching scheme release module;The unmanned plane flight plan pre-dispatching method is realized using the above tool, including the following steps: step 1, receiving various information;Step 2, generating the target constraint of unmanned plane flight plan pre-dispatching;Step 3, establishing the target planning model of unmanned plane flight plan pre-dispatching;Step 4, solving the generation of unmanned plane flight plan pre-dispatching scheme;Step 5, release scheme, complete the unmanned plane flight plan pre-dispatching.The application provides implementation tool and technical basis for low-altitude flight service, urban air traffic management, unmanned plane command control.

The invention discloses an unmanned aerial vehicle flight conflict resolution method, which belongs to the technical field of air traffic management intelligent decision making, and comprises the steps of constructing an airspace state space and an unmanned aerial vehicle state space based on state information of an airspace and state information of an unmanned aerial vehicle, the uncertainty is quantified to obtain an airspace state space containing the uncertainty and an unmanned aerial vehicle state space, the unmanned aerial vehicle flight conflict resolution process is modeled as an interaction process between agents in a multi-agent reinforcement learning decision model, and a flight conflict resolution strategy space and a flight conflict resolution reward function of the agents are constructed; and building a flight conflict resolution decision training framework based on multi-agent reinforcement learning, and training the multi-agent reinforcement learning decision model to obtain a flight conflict resolution strategy. According to the method, uncertainty of a low-altitude airspace environment and an unmanned aerial vehicle main body is considered, intelligent agent reinforcement learning is combined, flight conflict resolution strategy selection is carried out, and the autonomous decision-making capability of unmanned aerial vehicle flight conflict resolution can be improved.

The invention particularly relates to an unmanned aerial vehicle cluster downloading data load balancing algorithm based on micro-service, which comprises the following steps of: replacing periodic synchronization by combining multi-round sampling and instant calculation, and ensuring that average response time can reflect the real-time state of the micro-service; and the optimal micro-service node is dynamically screened by monitoring the micro-service response state in real time, so that the efficient distribution of the data downloaded by the unmanned aerial vehicle in the micro-service cluster is realized. On the basis, a real-time response time decision and hot registration dynamic management are deeply combined, so that not only is the optimality of data distribution ensured through response time, but also the capacity expansion cost is reduced through hot registration. According to the invention, the technical problems of inaccurate response time perception, dependence on manual intervention on capacity expansion and shrinkage, long-term resource occupation of fault nodes and encumberment of overall data flow in a mass unmanned aerial vehicle data downloading scene in the existing load balancing scheme are solved; the method is suitable for scenes requiring massive unmanned aerial vehicle collaborative operation, such as regional emergency monitoring, large-scale training and urban air traffic management.

The invention discloses an air traffic stability control method, which belongs to the technical field of intelligent management of air traffic management, and comprises the following steps: constructing an aircraft flight space-time sequence based on high-altitude cruise aircraft information; calculating a relative flight position interval and a relative flight speed between the aircrafts based on the flight space-time sequence of the aircrafts; based on the relative flight position interval and the relative flight speed between the aircrafts, constructing an aircraft car-following model containing time-delay band bidirectional attention; based on the aircraft car-following model, constructing air traffic stability judgment conditions; and based on the air traffic stability judgment condition, the aircrafts in the unstable state are screened for feedback control, and the air traffic stability is guaranteed. According to the method, the air traffic stability condition can be judged, feedback control is performed by screening aircrafts in an unstable state, and the air traffic stability is guaranteed.

The application discloses a kind of low airspace auditing systems of multi-agent collaborative driving, comprising: operation person management platform, for storing and maintaining operation person basic information and carrying out airspace application;Local low altitude management platform, for receiving airspace application information, and pre-trial is carried out to airspace application;Air traffic management agency low altitude platform, for receiving airspace pre-trial information sent by local low altitude management platform, and final approval is carried out to airspace application;Multi-agent collaborative platform receives the airspace application information submitted by operation person management platform, constructs approval procedure, and carries out space calculation;In combination with approval procedure and calculation result, airspace application information is sent to local low altitude management platform and pre-trial is carried out.The application realizes low airspace unified application, step-by-step approval, and shields complex approval relationship for low altitude user, and low altitude user only needs to submit airspace application to local low altitude management platform through operation person management platform, without considering the specific jurisdiction involved in applying airspace.