1554 results about "Runway" patented technology

A display system of an aircraft includes a display device defining a display area, a vision system for generating an image of an operating environment of the aircraft on the display area, data processors operatively coupled to the display device and the vision system, and non-transitory machine-readable memory operatively coupled to the one or more data processors and storing instructions executable by the one or more processors and configured to cause the one or more processors to, when a condition of the aircraft is met, cause an alteration of a display of flight information on the display area to declutter a cutout region in the image generated by the vision system, the cutout region devoid of imaging generated by the vision system and displaying an Approach Lighting System (ALS) of a runway of intended landing for the aircraft.

The invention provides an automatic pavement settlement monitoring method and system based on a shield undercrossing airport runway. The dynamic database is constructed by collecting operation parameters such as shield tunneling machine propelling pressure, cutterhead torque and synchronous grouting pressure in real time and combining three-dimensional deformation data obtained through laser scanning. Strain sensing units are arranged in the soil layers to form a layered monitoring network, strain changes of the soil layers of different depths are collected, and spatial correlation is established between the strain changes and the tunneling position. And analyzing a spatial attenuation rule through a dynamic association processing unit, and generating a multi-dimensional feature data set. And establishing a settlement prediction model, fusing the shield dynamic load sequence and the layered strain conduction characteristics, and outputting settlement distribution information. And the system automatically compares the settlement difference value of the adjacent areas with a preset safety threshold value, and generates a graded early warning signal after verification of the three-dimensional deformation data space. According to the technical scheme provided by the invention, real-time three-dimensional prediction and spatial verification type graded early warning of settlement of the airport runway are realized.

The invention relates to the technical field of constructional engineering, in particular to a cement concrete durability optimization method and system for an airport runway, and the method comprises the steps: building a data collection frame, and collecting the data of the airport runway; preprocessing the collected data, and performing preliminary analysis based on the processed data to obtain main factors influencing the durability of the cement concrete; based on the preliminary analysis result, using machine learning and deep learning technologies to construct a durability prediction model; a self-learning mechanism is introduced, a feedback loop is established, actual operation data is compared with a prediction result, and model parameters are dynamically adjusted; and based on a prediction result of the durability prediction model, combining a plurality of durability indexes, and adopting a multi-objective optimization algorithm to generate an optimal cement concrete formula and a construction process scheme so as to obtain a final durability optimization scheme. By means of the method, the defects of a traditional method in the aspects of data accuracy and model adaptability are effectively overcome, and the durability optimization level of the airport runway cement concrete is improved.

The invention relates to the technical field of airport safety monitoring, and discloses an image-based airport runway foreign matter intelligent detection and clearance auxiliary processing system and method. The acquisition module is used for acquiring original image data of a runway and a clearance area in real time through a high-definition camera; adopting an improved YOLOv11 target detection algorithm to identify birds, unmanned aerial vehicles or foreign matter targets in the clearance area; an improved YOLOv11 backbone is used as a main body feature extractor, and original image data is converted into a multi-scale feature map; the neck assembly combines feature maps of different scales and transmits the feature maps to the head assembly for prediction; the head assembly performs target prediction based on the reconstructed feature map; wherein a C2f block in the YOLOv11 neck assembly is replaced by a C3k2 block, and an intersection stage part and a space attention block are introduced; and a bird repelling or cleaning module is dispatched according to the detection result. According to the invention, the monitoring efficiency can be effectively improved, the detection precision is improved, clearance auxiliary processing is optimized, and the safety of the airport runway is improved.

The invention discloses a data-mechanism fused airport runway surface water and ice condition prediction method. The method comprises the following steps: establishing a theoretical model of a runway surface temperature field and a water and ice condition; thermal parameter inversion is carried out on the theoretical model based on actually measured data, and a dynamic theoretical model adapted to an actual application scene is obtained through multi-time iteration optimization of model parameters; and utilizing the fused time sequence data and the dynamic theoretical model to predict the water and ice condition on the surface of the airport runway, and providing dynamic evolution information of the icing process. According to the method, the problems of insufficient real-time performance and accuracy of runway icing prediction in the prior art are solved, the icing time, the ice layer thickness and the ice layer range of the runway surface can be accurately predicted, real-time deicing decision support is provided, and the runway operation safety and the airport operation efficiency are effectively improved.

A craft is provided comprising: a propulsion system; a fuselage coupled with the propulsion system, wherein the fuselage is configured to carry a payload and fuel for the propulsion system; and a pre-assembled wing coupled with the fuselage; wherein the craft is designed to take-off without using a runway and does not have landing gear to land.

The invention discloses a fixed dual-runway FOD detection method based on multi-source circumferential scanning, and belongs to the technical field of data fusion detection, and the method specifically comprises the steps: synchronously obtaining dual-runway multi-polarization echo data through a fixed circumferential scanning rada; performing time-varying clutter suppression by combining an adaptive clutter cancellation method matched with a dynamic background template and a runway material feature library, and generating a time-frequency domain three-dimensional feature matrix through time-frequency analysis; synchronously collecting and preprocessing image data, and marking a target area through a constant false alarm rate algorithm and image detection; a multi-source data geographic coordinate mapping model is established, data registration and cross validation are completed under a runway physical coordinate system, and real-time tracking of a real FOD target is realized based on radar and image fusion data through multi-dimensional confirmation of radar polarization / micro-motion features and image visual features; according to the method, the FOD detection accuracy and the position identification precision are improved through multi-source data fusion, the false alarm rate is effectively reduced, and a guarantee is provided for safe operation of an airport.

The invention provides a multi-scale cross-space learning general aviation aircraft landing runway detection method, and relates to the technical field of computer vision. The method comprises the following steps: firstly, collecting a runway image data set when a general aviation aircraft lands, preprocessing the data set, and dividing the data set into a training set and a test set; then, an AES-YOLOv8 network model for detecting a multi-scale runway target is constructed; the model is based on a YOLOv8s network of multi-scale cross-space learning, and a Backbone network comprises a plurality of improved lightweight channel attention convolution modules LA-AKConv; an EMA attention mechanism is introduced after a spatial pyramid pooling module SPPF of the Backbone network; the Neck network comprises a plurality of C2fSCConv modules fused by C2f and a space and channel reconstruction convolution module SCConv; and the AES-YOLOv8 network model is trained based on the preprocessed data set to obtain a multi-scale cross-space learning airport runway detection model, and the runway state of the general aviation aircraft during landing is detected in real time through the multi-scale cross-space learning airport runway detection model.

The invention relates to an ADS-B (Automatic Dependent Surveillance-Broadcast)-based civil aviation flight pre-arrival data calculation method, which integrates ADS-B data, meteorological satellite data, airport runway state monitoring data and air traffic control real-time instruction data through multi-source data fusion, and dynamically adjusts the weight of each data source according to the flight stage of a flight. A dynamic adjustment time synchronization interval mechanism is adopted, a short synchronization interval is set in the take-off stage and the landing stage so as to capture rapidly-changed data, and the synchronization interval is prolonged in the cruise stage so as to optimize computing resources. Meanwhile, the deep learning model based on the Transform architecture utilizes a multi-head self-attention mechanism and position coding to enhance the feature capture capability, and reduces the calculation burden through a sparse activation strategy. The system is provided with a real-time monitoring layer, log records and visual analysis tools are integrated, and interpretability and traceability of the model are ensured. An efficient aviation management decision is supported, and the efficiency and safety of overall aviation traffic management are improved.

The invention relates to a micro-settlement control method for an extra-large-span flat-top straight-wall tunnel under the dynamic load action of an aircraft, and relates to the technical field of tunnel construction. The control method comprises the following steps: S10, uniformly spreading the weight of the airplane to each airplane wheel, and calculating to obtain a static load P1 of the airplane; s20, vertical kinetic energy of the aircraft is converted into impact force, dynamic additional force F is obtained and evenly spread to each aircraft wheel, and the dynamic load P2 of the aircraft is obtained through calculation; s30, adding the static load P1 and the dynamic load P2 to obtain an aircraft landing impact load P3; s40, the distance r from the load acting point to the calculation point and the burial depth h of the tunnel are obtained, and settlement S of the tunnel vault is calculated through the aircraft landing impact load; s50, through Moire-Coulomb model simulation analysis, the influence of the aircraft dynamic load on tunnel construction settlement is obtained; and S60, making a corresponding construction scheme according to the influence of the airplane dynamic load on tunnel construction settlement. The construction problem of undercrossing the airport runway by the tunnel can be solved, and settlement of the airport runway is controlled.

The invention provides a shield excavation face deformation monitoring method and system for undercrossing an airport runway, and the method comprises the steps: generating a pressure load response characteristic based on the pressure fluctuation time sequence data of a cutter head hydraulic cylinder in a shield tunneling machine in combination with dynamic load distribution data and static load distribution data; the deformation track of the shield excavation face and the displacement change of a bottom soil body undercrossing the airport runway are obtained, point cloud registration processing is carried out, registered data are obtained, and two-dimensional deformation monitoring data of the shield excavation face and the bottom soil body in the horizontal direction and the vertical direction are extracted; based on the pressure load response characteristics, the two-dimensional deformation monitoring data and the surface settlement monitoring data of the undercrossing airport runway, obtaining a deformation trend prediction result of the shield excavation face; according to the method, high-precision real-time monitoring and trend prediction of collaborative deformation of the shield excavation face and the runway structure in the underpass airport runway scene are achieved, and runway operation safety and shield construction accurate control are guaranteed.

The invention provides an aircraft landing stage short-time abnormal risk early warning and visual simulation adaptive method based on deep learning, and the method comprises the following steps: synchronizing a multi-source sensor signal, separating the multi-source sensor signal into an impact channel, a wind shear channel and a residual channel, and mapping the multi-source sensor signal into aircraft-runway-wind field graph node features; features are extracted through a space-time diagram convolutional network, and a future trajectory is predicted in combination with a dual-channel attention decoder under physical constraints such as an energy attenuation template; high-risk actions are rapidly inhibited on the basis of real-time risks, the risk probability is calculated through weighted submerged space sampling, graded alarms are output, meanwhile, the alarms drive visual simulation to be adaptively updated, and model online safety iteration is achieved through hard sample mining after landing. According to the method, physical driving modeling, space-time fusion perception and probabilistic risk assessment are adopted, the dependence of a traditional method on an empirical threshold value is overcome, and the early warning precision, the confidence coefficient and the landing safety are remarkably improved.

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 airport available runway monitoring and early warning system and method based on multi-source data fusion are high in real-time performance, good in forecasting effect and large in monitoring range. The system comprises a laser wind finding radar (1), a CFD numerical simulation module (2), a mesoscale weather forecast module (3), a machine learning model (4) and a system server (5). The method comprises the following steps that: a system server performs data assimilation by combining real-time wind shear detection data measured by a laser wind measurement radar and forecast data of a current time point output by a mesoscale weather forecast module, processes the assimilated data by applying a CFD numerical simulation module, and calculates the real-time wind shear detection data by combining terrain and building parameters near an airport; according to the method, real-time sub-meter gridding three-dimensional wind field data is generated, and a sub-meter three-dimensional wind field structure is reconstructed, so that future wind shear of an airport is forecast and early warned, and then available runway prediction is carried out. The method is applied to the technical field of wind field prediction and judgment.

The invention relates to the technical field of airport safety guarantee, and particularly discloses an airport bird situation monitoring and early warning bird repelling equipment linkage control method comprising the following steps: obtaining bird situation data vectors collected by sensors, calculating fused bird situation data vectors, and obtaining bird flock information; constructing a bird situation risk assessment model based on machine learning, combining the fused bird situation data with an aircraft take-off and landing plan, a runway use state and airport surrounding environment information to form an input feature vector, calculating a risk value through a risk assessment function, and dividing risk levels according to the risk value; according to a risk grade result of the risk assessment module, corresponding bird repelling equipment is controlled to work according to a hierarchical linkage bird repelling strategy; and in the bird repelling process, the risk level is re-evaluated according to the new bird situation data, the parameters of the bird repelling equipment are adjusted, and the bird repelling strategy is optimized by using a reinforcement learning algorithm. According to the invention, the defects of monitoring range and precision of a single sensor are effectively overcome, and the reliability of bird monitoring is greatly improved.

The invention provides a method and a system for generating deviation guide information in an approaching landing section of an aircraft and a readable storage medium, and the method comprises the steps: S1, real image collection and processing: capturing an outboard environment image through an airborne visual sensor, and extracting key feature points of a target landing airport runway; s2, virtual image generation: based on the airport three-dimensional high-precision map, generating an approach glide image under a visual angle of a virtual camera; s3, image feature registration: carrying out spatial alignment on the key feature points of the runway in the real image and the key feature points of the runway in the virtual image; the pixel deviation of the runway center point in the real image and the virtual image is calculated, glide guidance is provided for a pilot or is directly input into an automatic flight system.The method can be used for the aircraft in the approaching landing section, the enhanced outboard visual display is provided for the pilot in the cockpit based on the equivalent ILS deviation guidance method, and the visual guidance effect is improved. And providing approaching glide deviation guide information.

The invention discloses a departure peak period-oriented flight driving and taking-off collaborative optimization method, which comprises the following steps of: firstly, accurately identifying an optimal release control rate based on historical flight data, secondly, improving the sliding time prediction accuracy by utilizing integrated learning, and then, establishing a departure flight collaborative scheduling model according to an actual operation rule so as to realize the departure flight collaborative scheduling. And establishing a conflict mechanism to set flight priorities. A solving algorithm adopts a hybrid algorithm combining a genetic algorithm and tabu search, effective combination of global search and local search is ensured, and falling into a local optimal trap is avoided. The scheduling scheme covers key indexes such as runway throughput and average taxiing time, the airport scene congestion condition is relieved, the operation efficiency and safety of an airport in the departure peak period are improved, flight delay is reduced, and the utilization rate of runway time slot resources is increased.

The invention discloses a novel concrete pavement hardness detection device, and belongs to the technical field of detection. The invention discloses a novel concrete pavement hardness detection device, and belongs to the technical field of road engineering detection. The device comprises a springback tester, an ultrasonic tester and an analysis system, and equipment credibility parameters are generated through a springback confidence model (quantitative impact energy / angular deviation) and an ultrasonic confidence model (quantitative frequency / probe centering error); combining a pavement characteristic model (analyzing water content / temperature / steel bar density) to output an environment adaptation coefficient; further dynamically calculating a springback risk coefficient and an ultrasonic risk coefficient based on a risk model (fused vibration / carbonization depth); and finally, outputting a calibration intensity value through the intensity correction model. The method solves the defects of weak anti-interference capability and insufficient precision of a traditional method, effectively improves the detection efficiency, and is suitable for high-precision scenes such as expressways and airport runways.

The invention belongs to the technical field of infrastructure structures and materials, and discloses an intrinsic self-prestress ECC material, a surface layer structure prepared from the intrinsic self-prestress ECC material and application of the intrinsic self-prestress ECC material. A low-dosage volume expansion component and a building and curing combined strategy are utilized, the bottleneck that self-stress and a construction window period are not coordinated is broken through, the highest free expansion deformation of the intrinsic self-prestress ECC material in the temperature and humidity curing process is controlled to range from 1500 mu epsilon to 10000 mu epsilon, the later-period service free volume deformation is kept to be 55% higher than the early-period highest free expansion deformation, and the service life of the intrinsic self-prestress ECC material is prolonged. And the dual requirements of intrinsic self-prestress and construction windows are innovatively met. The maintenance-free seamless ultrathin target of the ECC surface layer structure is achieved through high-performance intrinsic self-prestress and self-healing. The method is widely applied to the fields of ocean ultra-thin large-buoyancy floating bodies, road surfaces, bridge floors, airport runways, ultra-thin box culverts, concrete panels, pumped storage power station panels, anti-seepage panels, floor panels, canal linings, tunnel linings, old structure updating and repairing and the like.

The invention relates to a tilting rotor unmanned aerial vehicle, in particular to an unmanned aerial vehicle combining a flying wing type structure and a multi-shaft tilting rotor. A completely fused flying wing type fuselage is formed by a low-speed wing shape, and no other fuselage characteristics exist except that bilaterally symmetrical wings are directly connected and fused in the middle; a left tilting rotor frame and a right tilting rotor frame are mounted at the left end and the right end of the flying wing type fuselage respectively; at least one to at most six brackets extend from the front part and the rear part respectively, and symmetrical tilting rotor frames are mounted on the brackets; motors and paddles are mounted on all the tilting rotor frames; and an undercarriage is mounted below the flying wing type fuselage. During tilting, a single motor and a single blade are not tilted, but a plurality of power units formed by one or more motors and blades are tilted in groups through a tilting rotor frame. Therefore, the structure is simplified, and the safety and the maneuverability are improved. The landing gear has two types, one type is a foot type landing gear suitable for vertical take-off and landing in a soft site, and the other type is a wheel type landing gear suitable for short-track take-off and landing in a hardened site.

The invention relates to the technical field of flight management, in particular to a flight departure guarantee resource allocation scheduling optimization management method, which comprises the following steps: according to a runway, a running sequence and a time interval corresponding to a flight shift list, constructing a flight state diagram for the flight shift list in a preorder and postorder manner; determining the transfer cost of each state in the flight state transfer process by taking the maximum capacity and the minimum adjustment time interval of each node in the flight state diagram in a unit time period as constraint conditions, and sequencing the flights into a time-adjusted flight list according to the transfer cost; performing iterative solution by using the timing flight list, determining an optimal adjustment time interval in unit time, and determining an adjustment quantity in a unit time period according to the adjustment time interval in combination with time slot allocation; when the adjustment quantity accords with a preset constraint rule, performing data combination on the corresponding flights, and regarding the combined data as an output adjustment scheme; the efficiency and the performability of flight scheduling processing are realized.

The invention discloses a lightweight segmentation method, device and equipment for a low-visibility runway of a plateau airport and a medium, and relates to the field of airport runway image segmentation. The method comprises the following steps: constructing a plateau region airport runway image data set under extreme climate; a YOLOv8n-seg model is used as a reference model, a StarNet network is used as a trunk module in the YOLOv8n-seg model, a gating channel local attention mechanism is fused before down-sampling operation of the YOLOv8n-seg model, a high-frequency feature fusion segmentation head is used as a head network of the YOLOv8n-seg model, and a lightweight Tibetan area airport runway segmentation model is obtained; and training the model by using a plateau region airport runway image data set, and segmenting the airport runway by using the lightweight Tibetan region airport runway segmentation model. According to the method, the adaptability to extreme scenes can be improved, and efficient and high-precision segmentation of the plateau airport runway under the extreme climate is realized.

The invention discloses an airport runway FOD inspection and operation safety guarantee system in the field of airport runway safety detection. A ground control platform receives three-mode image data of an unmanned aerial vehicle platform and sends flight control data; the unmanned aerial vehicle platform receives the flight control data, executes flight actions, collects three-mode image data and sends the three-mode image data to the ground control platform; the ground control platform comprises an FOD detection module, a map module and a task scheduling module; the FOD detection module receives the three-mode image data and performs video image recognition to generate image recognition data; a gridding electronic map is arranged in the map module, and the FOD detection module superposes the image recognition data on the gridding electronic map to generate map obstacle data; and the task scheduling module sends the flight control data to the unmanned aerial vehicle platform. According to the invention, three-mode image recognition is carried out on the airport scene through the unmanned aerial vehicle platform to enhance the FOD inspection detection accuracy, and the unmanned aerial vehicle platform is utilized to increase the monitoring range, so that emergencies can be found in time.

The invention relates to the technical field of airport safety monitoring, in particular to an airport runway intrusion identification method based on an ESNB algorithm, and the method comprises the steps: constructing an airport runway intrusion data set; the method comprises the following steps: constructing an improved Inception U-Net network, and obtaining a teacher network based on the improved Inception U-Net network; based on the teacher network, obtaining an optimal student network through a multi-objective evolutionary algorithm; based on the teacher network and the airport runway intrusion data set, performing knowledge distillation on the optimal student network to obtain an ESNB network; according to the scheme, the ESNB network is deployed in an airport and is used for airport runway intrusion recognition, and the problems that existing airport runway intrusion recognition is low in efficiency and high in computing power requirement can be effectively solved.

The invention belongs to the technical field of airport light layout, particularly relates to an airport light generation method based on an AIP document and image data, and aims to solve the problems that layout and attribute configuration are wrong and airport light requirements are difficult to meet due to fixed setting and manual planning of a traditional light system. According to the method, the geometric attributes and the spatial relationship of the runway / taxiway are extracted by analyzing the AIP text, and the satellite image and the GIS data are aligned to obtain accurate image data; carrying out standardization, feature matching and fusion superposition on the two types of data to generate a runway and taxiway layout map; according to the layout map and the rule base, grid sampling is combined with self-adaptive processing to obtain airport lamp positions; a cognitive rule and a multi-target model are constructed, target attributes are solved by using an MOPSO algorithm, and actual attributes are controlled in cooperation with an enhanced Hi-IDRL algorithm. According to the invention, layout and attribute dynamic optimization is realized, and diversified light simulation requirements of the airport are met.

The invention, which relates to the technical field of airport construction scheduling, discloses a dynamic-monitoring-based non-stop construction regulation and control method and system for an airport runway project, comprising the following steps: collecting real-time dynamic data and fusing multi-source information; performing airport runway construction scheduling based on intelligent prediction and adaptive optimization; construction conflict prediction and optimization scheduling are simulated through simulation; and comparing the construction progress with flight scheduling data in real time, and dynamically scheduling an adjustment mechanism. According to the dynamic scheduling optimization algorithm based on artificial intelligence, by inputting and analyzing real-time data, it is ensured that construction is arranged in the time period with the low flight flow, and therefore interference on flight take-off and landing is reduced. The construction conflict prediction and analogue simulation mechanism ensures smooth proceeding of construction and flights through accurate analogue simulation, and construction schedule monitoring and adjustment based on multi-dimensional feedback dynamically adjusts construction arrangement by monitoring comparison between the construction schedule and flight scheduling in real time to ensure that the construction schedule is not lagged.

Embodiments of the present disclosure are directed in part toward a runway assessment and landing safety (RALS) service comprising a runway condition model configured to provide enhanced runway condition assessment and vehicle landing safety for one or more vehicles. Embodiments are configured to process runway condition data aggregated from a plurality of runway condition data sources that describe a surface condition associated with a plurality of sections of a runway in order to generate adjusted runway condition codes for the plurality of sections of the runway. The adjusted runway condition codes are generated by the runway condition model based on a confidence associated with initial runway condition codes associated with the plurality of sections of the runway. Embodiments are configured to generate required landing parameters for the plurality of sections of the runway in order to determine a required runway length for a respective vehicle.

An urban air mobility building-top skyport for landing, loading and unloading, servicing, and parking short field takeoff and landing (STOL) aircraft, which includes a rotatable flight deck having at least one runway on which aircraft takeoff and land; a rotatable lower deck immediately below the flight deck and including an aircraft taxiway surrounding an enclosed central terminal with an elevator bank; and first and second aircraft ramps connecting the flight deck and the lower deck on which to taxi aircraft between the flight deck and the lower deck. A battery-swap station robotically replaces depleted batteries with fully charged batteries without the need for aircraft to cease moving at slow taxi speeds at any point during the process.

The invention relates to an airport operation guarantee capability evaluation and analysis method, which belongs to the field of airport operation and maintenance, and comprises the following steps: intelligently predicting the number of entering airplanes and the number of entering and exiting passenger flows of a target airport in a future time interval by using an airport operation and maintenance analysis model; sending a runway increase request when the number of entering aircrafts exceeds the aircraft capacity threshold value of the target airport in the future time interval, and sending a passenger flow overrun signal when the number of entering and exiting passenger flows is larger than the upper limit of the number of entering and exiting passenger flows of the target airport in the set interval duration. The invention also relates to an airport operation guarantee capability evaluation and analysis system. According to the invention, in order to solve the technical problem that it is difficult to consider time-sharing passenger transport demand meeting and airport operation and maintenance resource saving in the prior art, the airport operation and maintenance analysis model is introduced to predict the number of entering airplanes and the number of entering and exiting passenger flows of the target airport in the future time interval; and executing airport operation guarantee capability evaluation analysis and airport operation and maintenance resource dynamic adjustment, thereby solving the technical problems.

The invention discloses a takeoff point rear side operation safety interval calculation method and system under the influence of engine jet flow, and the method combines laser wind finding radar measurement experiment, data assimilation, computational fluid dynamics (CFD) numerical simulation and other methods, and measures the engine jet flow data of a takeoff aircraft at an airport runway end through a laser wind finding radar; constructing a double-engine nozzle model by using a computational fluid mechanics method, correcting a numerical simulation model by using a data assimilation method in combination with high-credibility laser radar wind measurement experimental data, and calculating an influence distance of typical aircraft engine jet flow; a model of the airplane under the influence of engine jet flow is established by using a CFD method, the model and the model are combined to calculate the minimum safety interval of rear side crossing, and reliable data reference is provided for a multi-runway airport to use a take-off point rear side runway crossing mode. The accurate safety interval between the front and rear airports is defined, and the overall operation efficiency of airports in China is remarkably improved.