A tethered airship-based real-time wind speed prediction method

Abstract

The application is a kind of real-time wind speed prediction method based on tethered airship, relating to the technical field of atmospheric meteorological parameter detection, comprising: mapping the continuous prediction sequence of high-altitude wind speed as the equivalent working wind speed of the height of the port hoisting equipment, calculating the instantaneous overturning moment and anti-overturning stable moment of the hoisting equipment, and generating the dynamic safety margin factor sequence in the future time window. In the application, the huge windward skin of the tethered airship is used as a spatial perception carrier, combined with a flexible pressure matrix, to upgrade the single-point meteorological observation to a three-dimensional spatial observation network; the Bernoulli equation is introduced to inverse the initial state of the wind field, and the Navier-Stokes partial differential equation and Reynolds number law are used to calculate the turbulence evolution, so that the whole wind speed prediction process is completely constructed on the basis of the strict law of momentum conservation.

Description

Technical Field

[0001] This invention relates to the field of atmospheric meteorological parameter detection technology, and in particular to a method for real-time wind speed prediction based on a tethered airship. Background Technology

[0002] In today's increasingly integrated global trade environment, large hub ports, as core nodes in the international logistics chain, directly impact the economic efficiency of the entire supply chain through the continuity and safety of their loading and unloading operations. Modern container ports are equipped with a large number of ultra-large, tall lifting machines, such as quay cranes, rubber-tired gantry cranes, and rail-mounted gantry cranes. These lifting devices have physical characteristics such as large windward area, high center of gravity, and large operating height, making them extremely sensitive to transient wind speeds during operation. When encountering sudden strong gusts or localized severe convective weather, failure to take timely windproof and anchoring measures can easily lead to catastrophic safety accidents such as crane slippage, structural deformation, or even complete overturning.

[0003] Currently, the port industry both domestically and internationally faces the following problems in storm warning and typhoon prevention scheduling management: Existing conventional wind speed monitoring methods at ports rely heavily on point anemometers installed on top of cranes or on ground-based meteorological towers. These methods cannot accurately reflect the true wind field evolution within the free atmospheric boundary layer hundreds of meters above the ground, resulting in wind prevention and control scheduling always being in a state of "lagging response".

[0004] Traditional forecasting methods only output a scalar value of future wind speed, failing to convert wind speed into a quantitative indicator of the dynamic instability risk of crane equipment, and thus cannot meet the needs of administrative management and scheduling.

[0005] Therefore, a real-time wind speed prediction method based on tethered airships is proposed to address the aforementioned problems. Summary of the Invention

[0006] The purpose of this invention is to propose a real-time wind speed prediction method based on a tethered airship in order to solve the above-mentioned problems.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A real-time wind speed prediction method based on a tethered airship includes: By using a tethered airship to hover in the target airspace, real-time data on the distribution of the space pressure field is collected, and environmental static pressure, environmental thermodynamic absolute temperature, and airship attitude data are acquired simultaneously. The air density is calculated based on the obtained environmental static pressure and environmental thermodynamic absolute temperature. The collected pressure field distribution data is converted into the instantaneous relative wind speed at each node on the airship surface. Motion compensation is performed in combination with the posture data to restore the absolute wind speed vector field at high altitude. Using the upper-level absolute wind speed vector field as the initial field, the instantaneous Reynolds number is calculated. When the instantaneous Reynolds number exceeds the turbulence critical abrupt change threshold, the gust disturbance evolution function is superimposed, and the continuous prediction sequence of upper-level wind speed within the future time window is solved and output. The continuous forecast sequence of high-altitude wind speed is mapped to the equivalent operating wind speed at the height of port crane equipment. The instantaneous overturning moment and anti-overturning stabilizing moment of the crane equipment are calculated, and a dynamic safety margin factor sequence is generated within the future time window. Risk assessment of lifting equipment is performed based on dynamic safety margin factor sequence. The assessment results are then used as constraints in a preset model for solution, generating the optimal equipment start-up and shutdown schedule and issuing scheduling instructions.

[0008] Preferably, the acquisition of spatial pressure field distribution data specifically includes: A flexible thin-film pressure sensor matrix arranged in a latitude and longitude grid topology is deployed on the windward side of the airship; The pressure sensor matrix is ​​used to sense the local total pressure on the corresponding micro-area of ​​the airship skin surface, and a synchronous calibration mechanism based on a unified timestamp is introduced to strictly bind all the collected local total pressure, environmental static pressure and pose state data at the same time to generate a time-series pressure dataset matrix.

[0009] Preferably, the step of converting the collected pressure field distribution data into instantaneous relative wind speeds at various nodes on the airship surface includes: Extract the instantaneous pitch and yaw angles from the pose state data as the primary key of the composite index; Spatial interpolation addressing is performed in a pre-stored multidimensional digital lookup table to obtain the local pressure coefficient corresponding to the current pose; By combining the local air pressure coefficient, the dynamically calculated air density, and the pressure difference between the local total pressure and the ambient static pressure, the instantaneous relative wind speed at each grid node is calculated in reverse according to Bernoulli's equation.

[0010] Preferably, the process of combining pose state data for motion compensation to reconstruct the upper-level absolute wind speed vector field specifically includes: The instantaneous relative wind speeds at each node are integrated by area weighting to synthesize the equivalent relative wind speed vector acting on the airship as a whole. The first derivative of the obtained three-dimensional spatial coordinates of the airship with respect to time is calculated to extract the instantaneous linear velocity vector of the airship in the inertial coordinate system. Using the vector synthesis rule, the equivalent relative wind speed vector is superimposed with the instantaneous linear velocity vector, eliminating the interference of the airship's own motion, and outputting the high-altitude absolute wind speed vector field.

[0011] Preferably, the process of calculating the instantaneous Reynolds number and superimposing the gust disturbance evolution function includes: The instantaneous modulus and aerodynamic viscosity coefficient of the upper-altitude absolute wind speed vector field are obtained, and the instantaneous Reynolds number is calculated in real time by combining the turbulent characteristic length of the port's upper-altitude characteristic vortex. When the instantaneous Reynolds number is determined to be greater than the set critical turbulence mutation threshold, the gust disturbance evolution function is activated. The gust disturbance evolution function uses instantaneous turbulent kinetic energy, turbulent kinetic energy viscous dissipation rate and wind speed pulsation dominant frequency as evolution parameters for superposition compensation.

[0012] Preferably, the process of solving the Navier-Stokes partial differential equation includes: Construct a three-dimensional transient partial differential equation that includes fluid convection acceleration terms, atmospheric pressure gradient force terms, and viscous dissipation terms; By employing the numerical difference method and setting a discretized time step, the upper-level absolute wind speed vector field at the current moment is used as the initial condition for iterative recursive solution to generate a continuous upper-level wind speed prediction sequence within the future time window.

[0013] Preferably, the process of mapping the equivalent operating wind speed to the height of port crane equipment includes: Connect to the port container terminal operating system to obtain the average number of stacked containers in the current yard, and dynamically update the underlying surface roughness length parameter accordingly; Based on the logarithmic wind speed profile of the atmospheric boundary layer, the predicted high-altitude wind speed is converted downwards into the equivalent operating wind speed by utilizing the surface roughness length parameter and the proportional relationship between the airship's stationary altitude and the wind centroid height of the lifting equipment.

[0014] Preferably, the process of generating the dynamic safety margin factor sequence within the future time window includes: The static thrust torque caused by the instantaneous wind load is calculated by combining the equivalent operating wind speed with the aerodynamic shape coefficient and effective windbreak area of ​​the lifting equipment. The instantaneous overturning moment is obtained by superimposing the static thrust torque with the internal additional inertial torque generated when the lifting equipment is under load. The inherent anti-overturning stabilizing moment of the lifting equipment is divided by the instantaneous overturning moment to construct the dynamic safety margin factor sequence that reflects the safety boundary.

[0015] Preferably, the risk assessment process includes: Preset a tiered risk assessment threshold that includes safety thresholds, warning thresholds, and danger thresholds; Within the prediction time window, when the dynamic safety margin factor is lower than the danger threshold, the operational availability status of the corresponding lifting equipment is forcibly set to shutdown, and a windproof anchoring program instruction is generated.

[0016] Preferably, the multi-objective operations research scheduling optimization model includes: The objective function is constructed with the dual optimization objectives of minimizing the total time of berthed ships in port and minimizing the cost of restricted lifting equipment being idled due to wind. By introducing a dynamically allocated economic weight coefficient, and under the constraints of collision prevention in the physical space of adjacent lifting equipment and the constraints of the three-dimensional space stack operation sequence of the container yard, the objective function is optimized and solved to generate the optimal equipment start-up and shutdown schedule and loading and unloading task redistribution matrix.

[0017] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention utilizes the large windward skin of a tethered airship as a space sensing carrier, combined with a flexible pressure matrix, to upgrade single-point meteorological observation into a three-dimensional space observation network; by introducing the Bernoulli equation to invert the initial state of the wind field, and using the Navier-Stokes partial differential equation and the Reynolds number law to calculate the turbulence evolution, the entire wind speed prediction process is completely based on the strict law of conservation of momentum.

[0018] 2. This invention establishes a cross-domain dimensionality reduction transformation mechanism to map high-altitude predicted wind speed to the actual operating height of lifting machinery. Combining the aerodynamic structural characteristics of the equipment, it dynamically calculates the external overturning moment and the inherent stabilizing moment, and extracts a dynamic safety margin index reflecting the equipment's wind resistance capability. This safety margin is used as a core mandatory constraint and substituted into an operations research scheduling model with the dual objectives of optimal operating efficiency and minimum downtime and idle costs for joint solution. This mechanism realizes a seamless transformation from abstract meteorological data to specific automated work orders, enabling meteorological early warning to directly empower port business management. Attached Figure Description

[0019] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0020] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0021] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0022] Example 1

[0023] Its specific implementation method is combined with the appendix Figure 1 Please provide a detailed explanation.

[0024] Appendix Figure 1 The flowchart of a real-time wind speed prediction method based on a tethered airship provided in this embodiment of the invention illustrates the complete steps from hovering the tethered airship in the target airspace to generating the optimal equipment start-up and shutdown schedule and issuing scheduling instructions.

[0025] In this embodiment, it includes: Step 1: Real-time pressure sensing at multiple points on the airship. The tethered airship hovers in the target airspace and collects the spatial pressure field distribution data in real time through the pressure sensor matrix deployed on its surface. At the same time, it acquires the environmental static pressure, environmental thermodynamic absolute temperature and the airship's position and attitude data. The core engineering objective of this step is to utilize the long-term hovering capability of a flexible airship (tethered airship) in a designated airspace to construct a high-resolution, large-scale space wind load sensitive surface, thereby obtaining wide-area multidimensional meteorological and attitude dynamic raw data that far exceeds that of traditional point anemometers.

[0026] First, a physical detection platform is constructed. Fixed or vehicle-mounted mobile anchoring vehicles are installed in the unobstructed safety area at the junction of the front shoreline and the rear storage yard of the port container terminal. Each anchoring vehicle is equipped with a constant tension variable frequency winch system, connected to a high-altitude flexible airship via a high-strength photoelectric composite tethering cable. The internal core of this photoelectric composite tethering cable is composed of a composite twist of load-bearing aramid fiber, twisted-pair copper power supply core, and single-mode optical fiber, and is externally wrapped with a wear-resistant polyurethane sheath. The airship's main gasbag is filled with inert helium gas with buoyancy capabilities. By controlling the winch's extension and retraction length, the airship is stably deployed in a target airspace above the port at a vertical altitude between hundreds of meters. This airspace completely covers the highest lifting point of the port's highest-specification quay crane equipment, forming a spatial detection profile overlooking the entire port operation area.

[0027] Secondly, a sensor matrix network was deployed on the airship's skin surface. To transform the airship into a "giant space wind speed detector," a layer of non-permeable, flexible thin-film pressure sensor matrix was tightly bonded and fixed to the nose cone and windward side areas outside the airship's gasbag, where they primarily bear wind loads. This sensor matrix is ​​not randomly distributed but strictly follows a three-dimensional curved surface with a latitude and longitude grid topology. The pressure sensor matrix is ​​defined as consisting of multiple rows and columns of independent sensing units, specifically... Action and To precisely locate each sensor in the spatial coordinate system, the first... line, number The physical location coordinates of the sensors in the column are in a spherical coordinate system. , where parameters Represents the azimuth angle of the node on the cross-section of the airship, parameter This represents the elevation angle of the node on the longitudinal section of the airship. Each individual flexible thin-film sensor unit is responsible for sensing and outputting in real time the normal stress (i.e., total pressure) borne by the corresponding micro-area on the airship skin surface, and its mathematical symbol is defined as... .

[0028] At the same time, multiple auxiliary physical quantity acquisition channels must be activated simultaneously to ensure that subsequent equation calculations have absolutely reliable boundary conditions and reference benchmarks: The first auxiliary channel is for static pressure and thermodynamic reference data acquisition. High-precision piezoresistive absolute pressure gauges and platinum resistance temperature sensors are installed at the center of the airship's internal gasbags and in the sheltered area of ​​the tail fin where it is not directly impacted by airflow. The pressure gauge in the sheltered area at the tail is used to collect the absolute background static pressure of the high-altitude environment in which the airship is located in real time, defined as a parameter. Temperature sensors are used to collect the absolute thermodynamic temperature of the environment.

[0029] The second auxiliary channel is for acquiring the rigid body dynamics and attitude state of the airship itself. A high-frequency microelectromechanical system (MEMS) inertial measurement unit (IMU) is rigidly installed inside the airship's onboard pod. This unit consists of mutually orthogonal three-axis gyroscopes, three-axis accelerometers, and three-axis magnetometers. Utilizing a Kalman filter data fusion algorithm, it outputs the airship's Euler angle attitude in three-dimensional space in real time at an extremely high sampling rate, specifically including the pitch angle parameters of the airship's rotation around its lateral axis. Yaw angle parameters of rotation about its vertical axis and the roll angle parameter about its longitudinal axis .

[0030] The third auxiliary channel is for absolute spatial coordinate tracking. An antenna module of the Real-Time Dynamic Differential Global Positioning System (RTK-GPS) is installed on the top of the airship. This module works in conjunction with a base station deployed on the port grounds to acquire the airship's instantaneous three-dimensional spatial coordinates with centimeter-level accuracy. ,in Longitude coordinates The coordinates are in the latitude direction. These are elevation coordinates.

[0031] After data acquisition is completed at the hardware sensing level, the system immediately enters the spatiotemporal alignment preprocessing stage. Due to the different response time constants of each sensor and the microsecond-level delay in data transmission, in order to eliminate the false disturbance data caused by the "wind swing effect" resulting from the elastic deformation of the cables in strong winds, this system introduces a unified timestamp synchronization calibration mechanism based on atomic clocks through an onboard field-programmable gate array (FPGA) hardware main control chip.

[0032] A globally unified data sampling frequency of no less than 20Hz (i.e., 20 complete samples per second) is set. All acquired analog voltage signals must be converted into digital sequences by a high-precision analog-to-digital converter within an extremely short time window and synchronized with the data at the same time. Strict binding is performed. Ultimately, step one will generate a temporal stress dataset matrix containing all spatial topological relationships, mathematically expressed as: ; This matrix forms the original driving force basis for all subsequent purely hydrodynamic calculations.

[0033] Step 2: Bernoulli wind resistance calculation. Based on the obtained environmental static pressure and environmental thermodynamic absolute temperature, the air density is calculated. Based on Bernoulli's equation, the collected pressure field distribution data is converted into the instantaneous relative wind speed of each node on the airship surface. Motion compensation is performed in combination with the attitude state data to restore the high-altitude absolute wind speed vector field. After providing a large dataset of multi-source physical quantities in step one, the core task of step two is to use the classical governing equations of fluid mechanics to accurately transform the measured non-uniform pressure distribution matrix on the airship surface into the overall aerodynamic drag acting on the airship surface. Based on this, the absolute wind speed vector field at high altitude after removing the airship's own motion interference is solved in reverse. This process completely abandons the empirical weight fitting commonly used in machine learning and fully follows the laws of conservation of mass and momentum.

[0034] First, instantaneous dynamic correction of the air density in the space environment is performed. In traditional near-surface wind measurement techniques, air density is often set as a constant under a standard atmosphere (e.g., ...). However, in the actual high-altitude environment of ports, the vertical gradients of temperature and air pressure vary extremely significantly. Treating these as constants would lead to huge systematic errors. Therefore, this method introduces the ideal gas law for real-time correction. Based on the high-altitude background static pressure collected in real-time in step one... In addition to the ambient thermodynamic absolute temperature, the instantaneous air density parameters of the airspace where the airship is currently located are calculated. : ; Explanation of all parameters in the formula: The absolute static pressure of the environment undisturbed by airflow, obtained in step one, is expressed in Pascals (Pa). The specific gas constant for dry air, based on the composition of the Earth's atmosphere, has its physical value precisely set as follows: ; The absolute thermodynamic temperature of the environment is strictly defined in Kelvin (K), and its value is equal to the value of Celsius plus... This formula ensures the density parameter (Unit is) The calculations are absolutely physically rigorous and will not drift due to temperature differences in region and season.

[0035] Subsequently, Bernoulli's equations for inviscid flow of incompressible fluids (this assumption is physically sound and extremely rigorous, as typical low-altitude wind speeds are far below Mach number 0.3) are introduced. According to the principle of energy conservation, along the same streamline, the sum of the fluid's static pressure energy and kinetic energy remains constant. For a tethered airship hovering in a wind field, the airship's skin surface acts as a giant obstruction. When the incoming airflow contacts the various tiny nodes of the sensor matrix, its kinetic energy is converted, causing a sharp increase in local static pressure, forming stagnation points or stagnant regions.

[0036] For any point on the surface of the airship that is positioned at the first... line, number The flexible pressure sensor nodes in the array sense the instantaneous total pressure. The following mathematical and physical mapping relationship exists between the incoming flow velocity and the flow velocity: ; In this equation, the parameters It is a known quantity, derived from real-time readings from the sensor matrix, and its unit is Pascal (Pa). and Similarly, these are known environmental constants that have already been calculated; parameters This represents the target physical quantity that needs to be solved, namely the instantaneous velocity modulus of the airflow relative to that node of the airship, expressed in meters per second (m / s); while the parameters... This represents the dimensionless local air pressure coefficient at that specific 3D grid node.

[0037] In order to accurately obtain The present invention does not rely on any fuzzy inference, but instead establishes a high-dimensional aerodynamic database in advance. Before the system is officially put into port operation, computational fluid dynamics (CFD) simulation software or large low-speed wind tunnel experimental equipment are required to conduct wind calibration tests on the airship shape used in this invention within the full envelope. By changing the pitch and yaw angles of the incoming flow, the airship surface is recorded under different incoming flow attitudes. The local air pressure coefficient response pattern at the coordinate points. This rigorously calibrated data is compiled into a high-density, multi-dimensional digital lookup table and stored in the controller memory of the ground data center. Within each discrete time slice of actual port operation, the system controller extracts the airship pitch angle from the real-time IMU output in step one. With yaw angle As the primary key of the composite index, multidimensional bilinear spatial interpolation addressing is performed in the lookup table to accurately obtain the address of any node in the current pose. The corresponding true air pressure coefficient .

[0038] Based on the rigorous parameter definitions and acquisition methods described above, the Bernoulli equation is transformed algebraically and solved inversely to calculate the instantaneous relative wind speed modulus sequence at each independent grid node on the airship surface, denoted as . : ; In this formula, the absolute value sign is used to prevent the complex square root anomaly caused by the pressure difference being negative due to small high-frequency noise from the sensor or local wake vortices, thus ensuring the robustness of the mathematical calculation.

[0039] Thus far, through analysis By transforming the single, spatially uninformed wind speed scalar into a relative wind speed distribution map covering the entire windward surface of the airship, this invention provides extremely valuable basic physical data for subsequent identification of non-uniform gust shear (such as a sudden increase in wind speed on the left side compared to the right side).

[0040] Step 3: Reynolds number evolution wind speed short-term prediction. The upper-level absolute wind speed vector field is used as the initial field to calculate the instantaneous Reynolds number. When the instantaneous Reynolds number exceeds the turbulence critical change threshold, the gust disturbance evolution function is superimposed. By solving the Navier-Stokes partial differential equation, the continuous prediction sequence of upper-level wind speed within the future time window is output. At the current moment after obtaining the output of step two. Absolute wind speed vector field at high altitude Subsequently, by introducing the Navier-Stokes partial differential equations from fluid mechanics and combining them with the Reynolds number evolution law, the underlying evolutionary mechanisms of pure physics and aerodynamics are used to predict a future period of time (i.e., the prediction time window). The trend of wind speed variation over time and space, especially the "sudden gust peaks" that have a devastating impact on port cranes.

[0041] First, establish the dynamic governing equations for the wind field in the target airspace. Then, use the absolute wind speed calculated in step two... As the initial field boundary conditions, under the microscopic meteorological scale of the port, air is treated as an incompressible viscous fluid. Based on the law of conservation of momentum, the following transient partial differential equations (simplified Navier-Stokes equations) in three-dimensional space are constructed: ; The first term on the left side of the equation This is the local derivative of the fluid velocity vector over time, which is the transient rate of change of future wind speed that needs to be integrated to solve. The second term on the left side of the equation The term represents convective acceleration, indicating the migration effect of the upper-level wind field on spatial coordinates. in, The gradient operator in three-dimensional space (i.e., the Hamiltonian operator) is represented as a vector differentiation operation in Cartesian coordinates. This is the core mathematical tool for characterizing wind speed shear; The first term on the right side of the equation The atmospheric pressure gradient force term is where The distribution of upper-air pressure fields is provided by background data from ground-based weather radar or regional weather stations; The second term on the right side of the equation For viscous dissipation, where The kinematic viscosity of air. For the Laplace operator, this term characterizes the loss of kinetic energy of the wind field due to internal air friction; The third term on the right side of the equation The term is the Coriolis force bias caused by the Earth's rotation. Considering that the time window for wind protection prediction of port cranes is usually short (generally set to 10 to 30 minutes), the rate of change of this term is extremely small in a short period of time, and it can be approximated as a preset constant vector for static compensation.

[0042] Secondly, we introduce a method based on the instantaneous Reynolds number. The evolution of turbulent disturbances is analyzed. Strong winds at high altitudes near ports are often not steady laminar flow, but rather accompanied by intense turbulent vortices. When large-scale vortices break down into smaller vortices, they often release enormous energy, forming gusts. To accurately predict these nonlinear abrupt changes, this method calculates the characteristic Reynolds number in real time: ; In this formula: The instantaneous air density obtained in step two; The modulus is the current absolute wind speed; The dynamic viscosity coefficient of air (unit: ) ); The characteristic length of turbulence.

[0043] In a specific embodiment of the present invention Instead of taking a fixed value, it takes the Taylor microscale of the characteristic vortex at high altitude of the port, which is obtained by autocorrelation function analysis of the high-frequency pressure pulsation sequence measured by the airship pressure sensor.

[0044] Based on the energy cascade theory of computational fluid dynamics, this invention sets a critical turbulence mutation threshold in the local controller. When the system detects At this point, the physical mechanism indicates that wind energy is being violently transferred to high-frequency, small-scale vortices, and a strong gust of wind is about to occur in the airspace ahead. The system immediately activates and superimposes the gust disturbance evolution function. : ; The parameters are specified as follows: This represents the absolute physical moment at which the prediction is currently initiated. The viscous dissipation rate of turbulent kinetic energy; It is the instantaneous turbulent kinetic energy, and its value is determined by half of the sum of the variances of the fluctuating wind speeds in the three orthogonal directions measured by the airship; To determine the dominant frequency of wind speed fluctuations, the system uses Fast Fourier Transform (FFT) to extract the power spectral density of historical 10-minute wind speed data and assigns the frequency corresponding to its peak value to... ; This is the unit vector representing the main pulsation direction of gusts in three-dimensional space, extracted from the covariance matrix of historical wind speed data.

[0045] Finally, the differential equations are solved using numerical difference methods. Since it is mathematically impossible to directly obtain analytical solutions to the Navier-Stokes equations, the high-performance computing cluster in this system's data center uses the Euler backward difference method to discretize and solve the aforementioned time derivatives.

[0046] Here we provide supplementary definitions for the core parameters: Strict Definition The time step for discretization (usually taken as a value to ensure computational convergence) is used for the solution. Instant Second).

[0047] The system from the current moment Beginning, with Perform an iterative loop for the increment. Let... For the future The predicted time (i.e.) ), then the future Predicted absolute wind speed vector at any time The recursive solution formula is: ; Through this iterative process, as the computation time progresses to... The iteration stops at this point. At this point, the method has generated the future... Within a time period, with a time resolution of Set of continuous high-altitude wind speed prediction sequences This series of data is entirely based on Newtonian mechanics and fluid dynamics, possessing absolute physical interpretability and causal logic.

[0048] Step 4: Port hoisting instability threshold assessment. The continuous high-altitude wind speed prediction sequence is mapped to the equivalent operating wind speed at the height of the port hoisting equipment. The instantaneous overturning moment and anti-overturning stabilizing moment of the hoisting equipment are calculated, and a dynamic safety margin factor sequence is generated within the future time window. For port dispatchers, wind speed (m / s) is merely a meteorological indicator. This indicator must be reduced in dimensionality and converted into a dynamic anti-overturning safety margin factor for specific port cranes. Step four completes this crucial cross-domain physical quantity mapping.

[0049] In the first phase, a logarithmic profile mapping of high-altitude wind speed to the surface working layer is performed.

[0050] As a high-altitude sensing platform, the airship typically hovers at an altitude of... (For example, set at a height of 200 meters). The quay cranes, which are most vulnerable to wind damage in ports, typically have a windward centroid height of [missing information]. (For example, at an altitude of 50 meters). Due to surface friction, wind speed decreases logarithmically with altitude. This invention, based on the logarithmic wind speed profile of the atmospheric boundary layer, predicts the high-altitude wind speed at the airship's location. Mapped down to the equivalent operating wind speed acting on the height of the port's cranes. : ; in, This invention introduces a dynamic roughness parameter to reflect the surface roughness characteristics of the port terrain, taking into account the dynamic changes in the stacking height of container yards. The system is connected to the port's Container Terminal Operating System (TOS) to obtain the average number of stacked containers in the yard in real time and update the parameter dynamically accordingly. The value of (usually fluctuating between 0.5 meters and 1.5 meters) is used to ensure the accuracy of the downward mapping of wind speed.

[0051] The second stage involves establishing structural wind load and overturning moment models for port cranes.

[0052] The lifting equipment affected by wind (taking a quay crane as an example) is treated as a set of rigid bodies subjected to force, and the future... The horizontal thrust (i.e., wind load) generated by instantaneous gust wind pressure on the overall structure of the equipment. : ; The parameters are defined as follows: The aerodynamic shape coefficient of the crane gantry and main beam truss structure (calculated from the steel structure factory drawings of this model of equipment); The effective windproof and shielding area of ​​the crane facing the wind direction in its current position; This is the compensation coefficient for wind pressure height variation.

[0053] The physical essence of catastrophic accidents such as overturning, slipping, or derailment of lifting machinery lies in the fact that the overturning moment caused by external wind forces exceeds the stabilizing moment generated by the equipment's own weight. (Calculating the future) Instantaneous overturning moment borne by the crane : ; Formula Explanation: The first term on the right side of the equation represents the overturning tendency caused by static wind pressure, where... The vertical lever arm length is from the equivalent center of the wind load to the windward running wheel (i.e., the overturning fulcrum) on the bottom rail of the crane; the second term on the right side of the equation represents the internal additional inertial torque generated by the crane during loaded operation (such as trolley emergency stop, hoisting mechanism acceleration and deceleration), where The total number of moving parts. For the first The mass of each moving part (including the container being lifted), For its instantaneous acceleration, This is the vertical height of the component from the overturning fulcrum.

[0054] The third stage involves the construction of dynamic safety margin factors and sequence output.

[0055] Conversely, calculate the inherent anti-overturning stabilizing moment of the crane. : ; in: The total mass of the crane (including the current load); This is the standard gravitational acceleration; It is the horizontal distance from the crane's overall center of gravity on the horizontal plane to the overturning fulcrum.

[0056] Based on the dynamic comparison of the above torques, this invention defines a sequence of "dynamic safety margin factors" to measure the safety boundary of equipment. : ; This coefficient It is a dimensionless parameter. From the perspective of engineering physics, when When the value continues to decrease and approaches 1.0, it means that the anti-overturning moment is about to be unable to suppress the wind overturning moment, and the equipment is getting closer and closer to the critical point of structural instability and overturning of the whole machine.

[0057] The final output of this step is: in the future Within the predicted time window, a time-based sequence is generated for each crane operating or on standby within the port. The horizontal axis is represented by the safety margin factor. The curve represents the continuous change on the vertical axis. This curve provides irrefutable data support for the next steps in port administration, resource allocation, and equipment windproof anchoring scheduling.

[0058] Step 5: Issue dynamic scheduling instructions for the port. Based on the dynamic safety margin factor sequence, assess the risk of the crane equipment. Use the assessment results as constraints to solve the multi-objective operations research scheduling optimization model, generate the optimal equipment start-up and shutdown schedule, and issue scheduling instructions. Steps one through four described above have already completed the meteorological perception, fluid dynamics calculations, and quantitative assessment of dynamic risks in the physical world. The core function of this step is to obtain the equipment dynamic safety margin factor sequence for the future time period. This translates into specific business operations, administrative management, and equipment scheduling instructions, enabling full-chain business operations.

[0059] First, a spatial matrix of the wind protection safety risks of all lifting equipment in the port is constructed. A set of numbered indexes is established for all large lifting machinery (including all quay cranes and yard cranes) currently in operation or on standby at the container terminal. ,in This represents the sum of the natural numbers of currently managed devices. Data center servers represent each specific device. Real-time tracking and loading of its future prediction time window Dynamic safety margin factor sequence .

[0060] The system has a strict three-tiered risk assessment threshold and response strategy rule preset internally: Level 1 security threshold (Based on the wind resistance rating of different equipment, the value is usually taken as follows) Left and right): When judged At that time, the system determined that the three-dimensional microenvironment in which the equipment was located was within a safe operating range. The system issued instructions allowing the equipment to maintain its rated lifting speed and trolley travel speed at full load without any administrative intervention.

[0061] Level 2 warning threshold (Usually a value of 1.2): When determining At that time, the system determines that the equipment is about to be subjected to the lateral thrust of strong gusts of wind and is in a restricted operating area. The system will automatically generate a flexible speed limit command, and through the programmable logic controller (PLC), forcibly reduce the lifting and lowering speed of the equipment's spreader and the translation speed of the trolley and carriage to 50% of the rated value, so as to reduce the additional internal inertial torque in step four of the system.

[0062] Level 3 Danger Threshold (Usually a value of 1.05): When determining When the system determines that the equipment has reached the critical point of being blown over by the wind, slipping, or derailing, it is in the shutdown warning zone. The system will immediately interrupt the work order of the equipment and forcibly issue windproof anchoring procedure instructions (such as lowering windproof iron shoes, rail clamps, or inserting ground anchors).

[0063] Secondly, based on the aforementioned predicted equipment availability time window, a multi-objective operations research dynamic scheduling optimization model is constructed and solved.

[0064] In the commercial operation of large ports, the shutdown of individual equipment due to wind can disrupt the entire ship's loading and unloading logistics chain. Therefore, it is essential to reallocate resources globally before gusts arrive. A mixed-integer programming operations research scheduling model is established with the dual optimization objectives of "minimizing the total time berthed in port (increasing port throughput)" and "minimizing the cost of idle equipment due to wind (reducing enterprise operating expenses)."

[0065] Define the objective function for overall port scheduling optimization. as follows: ; The parameters are specified as follows: This represents the total number of cargo ships currently berthed and operating at the berths. For the first The estimated total time (in hours) for a cargo ship from berthing to departure after loading and unloading is completed. For the first The economic cost function of a crane's idle time per unit time due to triggering the wind prevention shutdown warning (unit: yuan / hour). and This is a dimensionless economic weight coefficient dynamically assigned by the system. When the port is currently experiencing congestion (severe vessel queuing), the system automatically increases it. The weighting is adjusted automatically when port operations are relatively idle but energy costs are high. The weight.

[0066] When solving this objective function, the system must satisfy the following strict physical and logical constraints: including physical space collision avoidance safety distance constraints between adjacent lifting equipment, three-dimensional spatial stack operation sequence constraints within the container yard ("the top layer container must be moved before the bottom layer container can be grabbed"), and the core constraint dominated by wind speed prediction, namely: in any case determined as In a future time segment, the first The operational availability Boolean variable for the device is unconditionally forced to be 0.

[0067] The underlying core of the system scheduling center uses the branch and bound method combined with dynamic programming to solve the mixed integer programming equation at high speed, generating an optimal equipment start-up and shutdown schedule that avoids the risk of strong winds and a container loading and unloading task redistribution matrix.

[0068] Finally, the scheduling instructions are issued and execution feedback is provided: The system translates the obtained optimal scheduling matrix into standardized Equipment Control System (ECS) industrial messages and Electronic Work Order Dispatch Streams of the Container Terminal Production Operating System (TOS).

[0069] For the driver in the high-altitude control room, the system provides precise advance warnings via the industrial touchscreen terminal on the control panel (e.g., "The system predicts that a destructive gust of wind will occur in 12 minutes and 30 seconds, causing the safety margin to drop below the critical value. Please immediately complete the current single hoisting cycle and execute the rail clamping and windproof anchoring operation"). For the automated horizontal transport equipment at the bottom (such as Automated Guided Vehicles (AGVs) and unmanned trucks), the system automatically redirects the destination of their planned routes from under the dangerous quay crane that is about to be shut down to a sheltered storage area that is not severely affected by gusts, thereby fundamentally avoiding large-scale traffic congestion and loading / unloading chain paralysis caused by strong winds.

[0070] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0071] It should be noted that, in this document, the use of relational terms such as "first" and "second" is merely for distinguishing one entity or operation from another, and does not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0072] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0073] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0074] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A tethered airship-based real-time wind speed prediction method, characterized in that, include: By using a tethered airship to hover in the target airspace, real-time data on the distribution of the space pressure field is collected, and environmental static pressure, environmental thermodynamic absolute temperature, and airship attitude data are acquired simultaneously. The air density is calculated based on the obtained environmental static pressure and environmental thermodynamic absolute temperature. The collected pressure field distribution data is converted into the instantaneous relative wind speed at each node on the airship surface. Motion compensation is performed in combination with the posture data to restore the absolute wind speed vector field at high altitude. Using the upper-level absolute wind speed vector field as the initial field, the instantaneous Reynolds number is calculated. When the instantaneous Reynolds number exceeds the turbulence critical abrupt change threshold, the gust disturbance evolution function is superimposed, and the continuous prediction sequence of upper-level wind speed within the future time window is solved and output. The continuous forecast sequence of high-altitude wind speed is mapped to the equivalent operating wind speed at the height of port crane equipment. The instantaneous overturning moment and anti-overturning stabilizing moment of the crane equipment are calculated, and a dynamic safety margin factor sequence is generated within the future time window. Risk assessment of lifting equipment is performed based on dynamic safety margin factor sequence. The assessment results are then used as constraints in a preset model for solution, generating the optimal equipment start-up and shutdown schedule and issuing scheduling instructions.

2. The method for real-time wind speed prediction based on a tethered airship according to claim 1, characterized in that, The collection of spatial pressure field distribution data specifically includes: A flexible thin-film pressure sensor matrix arranged in a latitude and longitude grid topology is deployed on the windward side of the airship; The pressure sensor matrix is ​​used to sense the local total pressure on the corresponding micro-area of ​​the airship skin surface, and a synchronous calibration mechanism based on a unified timestamp is introduced to strictly bind all the collected local total pressure, environmental static pressure and pose state data at the same time to generate a time-series pressure dataset matrix.

3. The method for real-time wind speed prediction based on a tethered airship according to claim 1, characterized in that, The collected pressure field distribution data is converted into instantaneous relative wind speeds at various nodes on the airship surface, including: Extract the instantaneous pitch and yaw angles from the pose state data as the primary key of the composite index; Spatial interpolation addressing is performed in a pre-stored multidimensional digital lookup table to obtain the local pressure coefficient corresponding to the current pose; By combining the local air pressure coefficient, the dynamically calculated air density, and the pressure difference between the local total pressure and the ambient static pressure, the instantaneous relative wind speed at each grid node is calculated in reverse according to Bernoulli's equation.

4. The method for real-time wind speed prediction based on a tethered airship according to claim 3, characterized in that, The process of reconstructing the absolute wind speed vector field at high altitudes by combining pose state data with motion compensation specifically includes: The instantaneous relative wind speeds at each node are integrated by area weighting to synthesize the equivalent relative wind speed vector acting on the airship as a whole. The first derivative of the obtained three-dimensional spatial coordinates of the airship with respect to time is calculated to extract the instantaneous linear velocity vector of the airship in the inertial coordinate system. Using the vector synthesis rule, the equivalent relative wind speed vector is superimposed with the instantaneous linear velocity vector, eliminating the interference of the airship's own motion, and outputting the high-altitude absolute wind speed vector field.

5. The method for real-time wind speed prediction based on a tethered airship according to claim 1, characterized in that, The process of calculating the instantaneous Reynolds number and superimposing the gust disturbance evolution function includes: The instantaneous modulus and aerodynamic viscosity coefficient of the upper-altitude absolute wind speed vector field are obtained, and the instantaneous Reynolds number is calculated in real time by combining the turbulent characteristic length of the port's upper-altitude characteristic vortex. When the instantaneous Reynolds number is determined to be greater than the set critical turbulence mutation threshold, the gust disturbance evolution function is activated. The gust disturbance evolution function uses instantaneous turbulent kinetic energy, turbulent kinetic energy viscous dissipation rate and wind speed pulsation dominant frequency as evolution parameters for superposition compensation.

6. The method for real-time wind speed prediction based on a tethered airship according to claim 5, characterized in that, The process of solving the Navier-Stokes partial differential equations includes: Construct a three-dimensional transient partial differential equation that includes fluid convection acceleration terms, atmospheric pressure gradient force terms, and viscous dissipation terms; By employing the numerical difference method and setting a discretized time step, the upper-level absolute wind speed vector field at the current moment is used as the initial condition for iterative recursive solution to generate a continuous upper-level wind speed prediction sequence within the future time window.

7. The method for real-time wind speed prediction based on a tethered airship according to claim 1, characterized in that, The process of mapping the equivalent operating wind speed to the height of port cranes includes: Connect to the port container terminal operating system to obtain the average number of stacked containers in the current yard, and dynamically update the underlying surface roughness length parameter accordingly; Based on the logarithmic wind speed profile of the atmospheric boundary layer, the predicted high-altitude wind speed is converted downwards into the equivalent operating wind speed by utilizing the surface roughness length parameter and the proportional relationship between the airship's stationary altitude and the wind centroid height of the lifting equipment.

8. The method for real-time wind speed prediction based on a tethered airship according to claim 7, characterized in that, The process of generating the dynamic safety margin factor sequence within the future time window includes: The static thrust torque caused by the instantaneous wind load is calculated by combining the equivalent operating wind speed with the aerodynamic shape coefficient and effective windbreak area of ​​the lifting equipment. The instantaneous overturning moment is obtained by superimposing the static thrust torque with the internal additional inertial torque generated when the lifting equipment is under load. The inherent anti-overturning stabilizing moment of the lifting equipment is divided by the instantaneous overturning moment to construct the dynamic safety margin factor sequence that reflects the safety boundary.

9. The method for real-time wind speed prediction based on a tethered airship according to claim 1, characterized in that, The process of risk assessment includes: Preset a tiered risk assessment threshold that includes safety thresholds, warning thresholds, and danger thresholds; Within the prediction time window, when the dynamic safety margin factor is lower than the danger threshold, the operational availability status of the corresponding lifting equipment is forcibly set to shutdown, and a windproof anchoring program instruction is generated.

10. A method for real-time wind speed prediction based on a tethered airship according to claim 9, characterized in that, Multi-objective operations research scheduling optimization models include: The objective function is constructed with the dual optimization objectives of minimizing the total time of berthed ships in port and minimizing the cost of restricted lifting equipment being idled due to wind. By introducing a dynamically allocated economic weight coefficient, and under the constraints of collision prevention in the physical space of adjacent lifting equipment and the constraints of the three-dimensional space stack operation sequence of the container yard, the objective function is optimized and solved to generate the optimal equipment start-up and shutdown schedule and loading and unloading task redistribution matrix.

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