Method and device for controlling hoisting posture of large-span thin-wall arched corrugated steel structure

Abstract

The invention provides a large-span thin-wall arched corrugated steel structure hoisting attitude control method and device, and relates to the field of data processing. And through multiple types of observation nodes, acquiring characteristics of slow change of an overall attitude, local micro-vibration response and rapid wind direction swing, constructing an attitude energy memory cloud picture, and analyzing an association relationship thereof to identify an abnormal energy orbit. And when the abnormal energy orbit tends to be closed, the attitude crosses the windward angle band and the orbit is sheared through shearing attitude offset and coordination control. And after shearing, a random rewriting channel is utilized to enable the attitude to fall into an energy rarefied area and avoid generation of a new self-excitation orbit. And a double-layer structure of a main attitude trajectory and an abnormal trajectory layer is constructed, so that the self-excitation behavior is limited in the abnormal trajectory layer. And limiting the residence time according to an energy high-risk area, and triggering shearing and random rewriting when the residence time exceeds a threshold value, so as to prevent chronic bias. By implementing the technical scheme, the hoisting posture control precision of the large-span thin-wall arched corrugated steel structure in an environment with extremely low wind speed and rapid wind direction swing can be conveniently improved.

Description

A method and device for controlling the hoisting posture of a large-span thin-walled arched corrugated steel structure. Technical Field

[0001] This application relates to the technical field of data processing, specifically to a method and device for controlling the hoisting posture of a large-span thin-walled arched corrugated steel structure. Background Technology

[0002] In the construction of large-scale spatial structures, extra-long span bridges, and ultra-long roof projects, thin-walled arched corrugated steel components have gradually become key load-bearing units due to their light weight, large span, smooth overall curves, and high cross-sectional utilization efficiency. These components typically require multi-point coordinated aerial repositioning, attitude adjustment, and high-precision installation in highly open, wind-prone, and mechanically restricted construction environments. As project scale continues to increase, the span and length of individual components significantly improve, placing unprecedented demands on the stability, predictability, and anti-interference capabilities of attitude control during the hoisting process.

[0003] Meanwhile, during the hoisting of large-span thin-walled arched corrugated steel structures, the significant curvature changes and periodic corrugated groove structure of the components, coupled with the weak local stiffness of the thin walls and the high sensitivity of surface geometry, cause them to exhibit unique fluid-structure interaction characteristics different from traditional plate and shell components under complex aerodynamic environments. When the hoisting operation is conducted in an environment with extremely low wind speeds but rapidly oscillating wind direction, the corrugated groove geometry alters the local airflow adhesion pattern, leading to periodic separation and reattachment of the boundary layer near the groove edges, and the formation of small-scale vortex stagnation zones in local areas. Driven by the rapid oscillation of wind direction, these vortex stagnation zones generate self-excited micro-scale disturbances with stable frequencies, causing the thin-walled arched components to exhibit extremely low-amplitude but persistent aerodynamic "self-sustaining micro-oscillations." These self-excited micro-oscillations do not exhibit resonance excited by the structure's natural frequency, nor do they possess the characteristics of forced vibrations applied by external forces; rather, they originate from independent cycles of aerodynamic hysteresis effects. However, traditional attitude control methods are mostly based on rigid body assumptions or linear wind load models, which make it difficult to identify and suppress subtle self-excited disturbances caused by the combined effects of thin-walled geometric characteristics and local flow hysteresis, resulting in low accuracy of hoisting attitude control.

[0004] Therefore, there is an urgent need for a method and device for controlling the hoisting posture of large-span thin-walled arched corrugated steel structures. Summary of the Invention

[0005] This application provides a method and device for controlling the hoisting attitude of a large-span thin-walled arched corrugated steel structure, which facilitates improving the accuracy of hoisting attitude control of the large-span thin-walled arched corrugated steel structure in environments with extremely low wind speeds but rapidly swaying wind direction.

[0006] The first aspect of this application provides a method for hoisting attitude control of a large-span thin-walled arched corrugated steel structure. The method includes: acquiring overall attitude slow changes, local micro-vibration responses, and wind direction and speed observation trajectories at preset wind speeds by deploying main attitude observation nodes, aerodynamic sensitive observation nodes, and wind direction and speed observation nodes on the large-span thin-walled arched corrugated steel member; constructing an attitude energy memory cloud map containing wind direction sway characteristics, local micro-vibration energy distribution, and overall attitude slowdown; and analyzing the wind direction sway characteristics, local micro-vibration energy distribution, and overall attitude slowdown within a continuous time window based on the attitude energy memory cloud map. The correlation between body attitude easing and abnormal energy trajectory is determined, and an abnormal energy trajectory that forms a continuous energy accumulation in a specific attitude neighborhood and is phase-locked with the rapid oscillation of wind direction is identified. If the abnormal energy trajectory is determined to be close-range or near-closed in attitude space, the shear attitude offset is calculated based on the phase position of the abnormal energy trajectory and the rapid oscillation trend of wind direction. By applying coordinated tension micropulses to the slings, coordinating the tower crane trolley displacement adjustment, and the component height change, the steel component attitude is made to cross the windward angle zone where the abnormal energy trajectory is located within a preset time, and the abnormal energy is sheared in the attitude energy memory cloud map. The trajectory; based on the current energy density distribution and current wind direction oscillation trend of the sheared attitude energy memory cloud map, controlled random disturbances are applied to the cable tension distribution and tower crane trolley displacement through a random rewrite channel introduced within a preset amplitude and time constraint range, so that the attitude state point of the sheared steel component falls into the energy-sparse region and prevents the formation of a new periodic self-excited trajectory; a two-layer attitude representation structure consisting of a main attitude trajectory and an abnormal trajectory layer is constructed. The main attitude trajectory is used as the basis for hoisting control based on the long-term window data and geometric constraints of the main attitude observation node, and the abnormal trajectory layer is used as the basis for hoisting control based on the aerodynamic sensitive observation node. Short-time window data is used to determine whether to trigger track shearing, so as to limit the self-sustaining micro-oscillation to the internal loop of the abnormal trajectory layer without affecting the main attitude trajectory. Based on the attitude energy memory cloud map after shearing, high-risk energy areas and low-energy areas are delineated. The main attitude trajectory is controlled to pass through the low-energy areas and a limit range is set for the dwell time in the high-risk energy areas. If the dwell time exceeds the limit range and the attitude energy memory cloud map after shearing shows the appearance of an abnormal energy track, track shearing and random rewriting are triggered first to prevent the self-sustaining micro-oscillation from evolving into a chronic bias of the main attitude trajectory.

[0007] A second aspect of this application provides a hoisting attitude control device for a large-span thin-walled arched corrugated steel structure. The device includes an acquisition module and a processing module. The acquisition module is used to acquire, through main attitude observation nodes, aerodynamic sensitive observation nodes, and wind direction and speed observation nodes deployed on the large-span thin-walled arched corrugated steel structure, the overall attitude slow change, local micro-vibration response, and wind direction and speed rapid oscillation trajectory at a preset wind speed, respectively, and construct an attitude energy memory cloud map containing wind direction oscillation characteristics, local micro-vibration energy distribution, and overall attitude slowdown. The processing module is used to analyze the wind direction oscillation characteristics, local micro-vibration energy distribution, and overall attitude slowdown trajectory within a continuous time window based on the attitude energy memory cloud map. The correlation between local micro-vibration energy distribution and the overall attitude easing is established, and an abnormal energy trajectory that forms a continuous energy accumulation within a specific attitude neighborhood and is phase-locked with the rapid oscillation of the wind direction is identified. The processing module is further configured to, if it is determined that the abnormal energy trajectory tends to be closed or nearly closed in the attitude space, calculate the shear attitude offset based on the phase position of the abnormal energy trajectory and the rapid oscillation trend of the wind direction, and, by applying coordinated tension micro-pulses to the slings, coordinating with the tower crane trolley displacement adjustment and component height changes, ensure that the steel component's attitude crosses the windward angle zone where the abnormal energy trajectory is located within a preset time, and shear the offset in the attitude energy memory cloud map. The processing module is further configured to, based on the current energy density distribution and current wind direction oscillation trend of the sheared attitude energy memory cloud map, apply controlled random disturbances to the sling tension distribution and tower crane trolley displacement through a random rewrite channel introduced within a preset amplitude and time constraint range, so that the attitude state point of the sheared steel component falls into an energy-sparse region and prevents the formation of a new periodic self-excited trajectory; the processing module is further configured to construct a two-layer attitude representation structure consisting of a main attitude trajectory and an abnormal trajectory layer, using the main attitude trajectory based on the long-term window data and geometric constraints of the main attitude observation node as the basis for hoisting control, and using the abnormal trajectory layer based on the wind direction oscillation trend of the sheared attitude energy memory cloud map. The short-time window data of the dynamic sensitive observation node is used to determine whether to trigger track shearing, so as to limit the self-sustaining micro-oscillation to the internal loop of the abnormal trajectory layer without affecting the main attitude trajectory. The processing module is also used to delineate the high-energy risk area and the low-energy area based on the attitude energy memory cloud map after shearing, control the main attitude trajectory to pass through the low-energy area and set a limit range for the dwell time in the high-energy risk area. If the dwell time exceeds the limit range and the attitude energy memory cloud map after shearing shows the appearance of an abnormal energy track, track shearing and random rewriting are triggered first to prevent the self-sustaining micro-oscillation from evolving into a chronic bias of the main attitude trajectory.

[0008] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, and both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method described above.

[0009] A fourth aspect of this application provides a non-transitory computer-readable storage medium storing instructions that, when executed, perform the method described above.

[0010] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] By deploying master attitude observation nodes, aerodynamically sensitive observation nodes, and wind direction and speed observation nodes on the component, the slow changes in overall attitude, local micro-vibration response, and wind direction sway characteristics can be simultaneously acquired. The coupled behavior of these three factors is mapped into a traceable energy distribution model using attitude energy memory cloud maps, allowing the previously difficult-to-detect self-sustaining micro-oscillations to be identified as anomalous energy trajectories. When the anomalous energy trajectory tends to close, the self-excited cycle is actively cut off by shearing attitude offset, causing the component attitude to quickly cross the windward angle zone. Subsequently, controlled random perturbations are applied using a random rewrite channel, scattering attitude state points to energy-sparse regions, preventing the re-formation of new anomalous energy trajectories. This dynamically disrupts the phase-locked relationship caused by aerodynamic hysteresis, thus suppressing the self-excitement source. The constructed dual-layer attitude representation structure separates the master attitude trajectory from the anomalous trajectory layer, ensuring the master attitude trajectory remains stable in the long term, unaffected by local micro-vibrations, while the anomalous trajectory layer is dedicated to real-time identification of self-sustaining micro-oscillations and triggering shearing actions, achieving a control structure that is "macroscopically stable and microscopically sensitive." Furthermore, by delineating high-risk and low-energy areas in the attitude energy memory cloud map and setting an upper limit on the dwell time in the high-risk areas, the hoisting path can avoid the attitude being in the self-excitation zone for a long time at the planning and execution levels, thereby reducing the probability of self-sustaining micro-oscillations from the source. Therefore, it is convenient to improve the accuracy of hoisting attitude control for large-span thin-walled arched corrugated steel structures in environments with extremely low wind speeds but rapid wind direction changes. Attached Figure Description

[0012] Figure 1 is a flowchart illustrating a method for controlling the hoisting posture of a large-span thin-walled arched corrugated steel structure according to an embodiment of this application.

[0013] Figure 2 is a schematic diagram of a module of a hoisting attitude control device for a large-span thin-walled arched corrugated steel structure provided in an embodiment of this application;

[0014] Figure 3 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0015] Explanation of reference numerals in the attached figures: 21. Acquisition module; 22. Processing module; 31. Processor; 32. Communication bus; 33. User interface; 34. Network interface; 35. Memory. Detailed Implementation

[0016] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0017] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0018] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0019] To address the aforementioned technical problems, this application provides a method for controlling the hoisting attitude of a large-span thin-walled arched corrugated steel structure. Referring to Figure 1, which is a flowchart illustrating the method for controlling the hoisting attitude of a large-span thin-walled arched corrugated steel structure according to an embodiment of this application, the method is applied to a server and includes steps S110 to S160, as follows:

[0020] S110. By setting up main attitude observation nodes, aerodynamic sensitive observation nodes, and wind direction and speed observation nodes on the large-span thin-walled arched corrugated steel member, the slow change of overall attitude, local micro-vibration response, and rapid oscillation trajectory of wind direction under preset wind speed are obtained respectively, and an attitude energy memory cloud map containing wind direction oscillation characteristics, local micro-vibration energy distribution and overall attitude slowing is constructed.

[0021] Specifically, the server refers to the computing platform involved in hoisting attitude control and data processing. It can be an industrial server in an edge computer cabinet at the construction site, or a host computer system connected to the tower crane control system. The server continuously receives raw data from various observation nodes, performs data cleaning, time synchronization, feature extraction, and attitude analysis, and constructs an attitude energy memory cloud map based on this data. For example, an industrial server placed in a control room near the bridge site, connected to the tower crane and sensor network via fiber optic cable, can process all hoisting attitude-related data in real time and issue control commands. The main attitude observation nodes refer to the sensing nodes deployed on the large-span thin-walled arched corrugated steel structure, primarily used to characterize the slow changes in overall attitude. They are installed near the component's center of gravity, shear center, or locations with high structural stiffness. For example, a three-axis tilt sensor or a set of inertial attitude measurement units are installed in the middle of the arch crown and on both sides near the lifting points. These main attitude observation nodes can reflect the slow changes in the overall attitude of the entire arch component during hoisting, such as pitch, yaw, and torsion. Aerodynamically sensitive observation nodes refer to high-sensitivity sensing nodes deployed on the surface of large-span thin-walled arched corrugated steel components in areas with strong local aerodynamic effects to capture local micro-vibration responses. Examples include high-frequency micro-accelerometers attached to the root of the corrugated groove on the windward side, near the crest transition zone, or in weak mid-span areas. These aerodynamically sensitive observation nodes can still detect minute vibrations caused by local vortex retention and boundary layer separation hysteresis under extremely low wind speed conditions, providing a data foundation for subsequent analysis of local micro-vibration energy distribution. Wind direction and speed observation nodes refer to environmental observation nodes used to monitor the rapid oscillation trajectory of wind direction at preset wind speeds. They are generally installed at high altitudes unobstructed by the components, such as ultrasonic or mechanical anemometers deployed at the top of tower crane booms, temporary supports above components, or independent wind masts near bridge sites. These wind direction and speed observation nodes can continuously record how the wind direction oscillates back and forth within a preset wind speed range, the oscillation angle, and the oscillation speed, thus forming a rapid oscillation trajectory of the wind direction. This provides a basis for subsequent judgment of the coupling relationship between wind direction oscillation characteristics and local micro-vibration responses.

[0022] The overall slow attitude change refers to the gradual attitude change of the large-span thin-walled arched corrugated steel structure over a longer time scale, as observed through the main attitude observation node, rather than instantaneous high-frequency jitter. Local micro-vibration response refers to the small high-frequency vibrations occurring in a local area, measured through aerodynamically sensitive observation nodes. These vibrations have very low amplitude but relatively stable frequency. The rapid wind direction oscillation trajectory at a preset wind speed refers to selecting one or more extremely low wind speed ranges in the engineering design or control strategy, such as a range of approximately 0.1 to 1 meter per second, where the wind vane indicates a rapid oscillation of the wind direction within a range of 0 to 180 degrees with a period of approximately 0.5 to 1 second, manifesting as the wind direction jumping back and forth between east-northeast and west-southwest. Such a time series is considered a rapid wind direction oscillation trajectory at a preset wind speed in attitude analysis. Wind direction oscillation characteristics refer to the structured feature information extracted from the rapid wind direction oscillation trajectory at a preset wind speed, including the magnitude of the wind direction oscillation, the frequency of the wind direction oscillation, the rhythm of the wind direction oscillation direction switching, and the tendency of the wind direction to deviate to one side within a specific time period. Local micro-vibration energy distribution refers to the local micro-vibration response measured by various aerodynamically sensitive observation nodes within a time window, which is converted into spatial distribution information reflecting "where the vibration is more intense and more persistent". Overall attitude descent refers to the magnitude of the slow change in overall attitude within a time window, used to describe whether the main attitude remains essentially unchanged or only experiences very limited slow drift during that period. Attitude energy memory cloud map refers to a multi-dimensional state memory representation structure formed by the server statistically analyzing and visualizing wind direction oscillation characteristics, local micro-vibration energy distribution, and overall attitude descent collected within multiple time windows in attitude space. It can be understood as a spatiotemporal distribution map with attitude as the horizontal axis and the coupling strength between local energy and wind direction as color or height.

[0023] Furthermore, the server first establishes a structural coordinate system on the large-span thin-walled arched corrugated steel member, with the arch axis direction, span direction, and vertical direction as references. The installation positions of each main attitude observation node, each aerodynamic sensitive observation node, and each wind direction and speed observation node are calibrated using the same structural coordinate system. A unified time synchronization reference is configured for all acquisition devices, for example, using the same time server or the same timing signal to synchronize the clocks of each acquisition terminal. Under this time synchronization reference, the server continuously receives the overall attitude mitigation data output by the main attitude observation node, the local micro-vibration response data output by the aerodynamic sensitive observation node, and the wind direction sway characteristic data output by the wind direction and speed observation node. Multiple overlapping time windows are constructed according to a preset time window length and time window sliding step size. For each time window, within the corresponding time range, the server maps the overall attitude slow change result obtained by the main attitude observation node within that time range to the overall attitude mitigation attribute of an attitude state point. The server then maps the overall attitude slow change result obtained by the aerodynamic sensitive observation node within that time range, after frequency band selection and energy... The local micro-vibration energy distribution obtained by quantity integration or envelope analysis is mapped to the local micro-vibration energy distribution attribute of the attitude state point. The wind direction swing amplitude, wind direction swing frequency, and wind direction swing direction reversal feature obtained by statistical analysis through wind direction and wind speed observation nodes within the time range are mapped to the wind direction swing feature attribute of the attitude state point. A storage unit is allocated for the attitude state point in the attitude energy memory cloud map, so that the overall attitude buffer attribute, local micro-vibration energy distribution attribute, and wind direction swing feature attribute of the attitude state point within the time window are recorded in the storage unit, thereby completing the synchronous slicing and attitude state point writing for each time window.

[0024] The server treats multiple occurrences of each attitude state point across different time windows as multiple temporal observations of the same attitude state point. It updates the attributes in the corresponding storage units using a time-weighted approach, ensuring that newer observations contribute more to memory while older observations decay slowly. For a storage unit corresponding to an attitude state point in the t-th time window, the overall attitude slowdown attribute, local micro-vibration energy distribution attribute, and wind direction sway characteristic attribute can be represented as attribute vectors, and accumulated using the following time-weighted update method:

[0025]

[0026] in, This represents the memory attribute vector of the attitude state point after the update in the t-th time window, including the overall attitude slow memory, local micro-vibration energy distribution memory, and wind direction sway feature memory of the attitude state point at the current moment; This represents the memory attribute vector of the attitude state point after the update in the previous time window, which is the result of accumulating observations from multiple historical frames; This represents the current observation attribute vector generated synchronously by the main attitude observation node, aerodynamic sensitive observation node, and wind direction and speed observation node within the t-th time window for the attitude state point. It includes the overall attitude descent, local micro-vibration energy distribution, and wind direction sway characteristics corresponding to the time window. The time-weighted coefficient ranges from zero to one. A value closer to one indicates that more historical memory is retained, while a value closer to zero indicates that the current observation is given greater weight. Through the above time-weighted update method, the memory attribute vector in the storage unit is obtained by superimposing the memory attributes of the previous time window and the observation attributes of the current time window according to weights when each time window rolls. This allows the attitude energy memory cloud map to retain the historical coupling relationship between wind direction sway characteristics, local micro-vibration energy distribution and overall attitude descent in the attitude space, while also reflecting the latest aerodynamic environment and local response changes in a timely manner, thus forming an attitude energy memory cloud map that combines historical memory and real-time perception capabilities.

[0027] S120. Based on the attitude energy memory cloud map, analyze the correlation between wind direction sway characteristics, local micro-vibration energy distribution and overall attitude descent in a continuous time window, and determine the abnormal energy trajectory that forms a continuous energy accumulation in a specific attitude neighborhood and is phase-locked with the rapid sway of the wind direction.

[0028] Specifically, a specific attitude neighborhood refers to a set of attitudes in attitude space that defines a small range of attitude parameters, such as pitch, yaw, and twist, around a given attitude state point. It describes regions where the attitudes are "almost identical or change very little." When a server parses consecutive time windows, if it finds that the attitude state points of multiple time windows fall within such a small range, it can be considered that the components within these time windows are essentially maintained within the same specific attitude neighborhood. For example, a pitch angle within 10 degrees plus or minus 0.2 degrees, and a yaw angle within 0 degrees plus or minus 0.1 degrees. An anomalous energy trajectory refers to the attitude evolution path formed by sequentially connecting attitude state points within a specific attitude neighborhood that have continuous energy accumulation and a phase-locked relationship with rapid wind direction oscillations in the attitude energy memory cloud. This path reflects the "trajectory" of self-sustaining micro-oscillations in attitude space.

[0029] Furthermore, the server first reads the attitude parameters of the attitude state points corresponding to each time window from the attitude energy memory cloud map, such as the pitch, yaw, and torsion angles of the arched steel component, and records these attitude parameters as attitude parameter vectors. Then, it measures the attitude similarity between attitude state points based on the degree of difference between the attitude parameter vectors, and assigns attitude state points with high similarity to the same specific attitude neighborhood. The weighted Euclidean distance can be used to measure the difference in attitude parameters; for example, let the attitude parameter vector of the i-th attitude state point be... The attitude parameter vector of the j-th attitude state point is The attitude distance between the two can be expressed as:

[0030]

[0031] in, , , Let represent the attitude parameters of the i-th attitude state point in the directions of pitch, yaw, and twist, respectively. , , This represents the attitude parameters corresponding to the j-th attitude state point. , , These are the weighting coefficients for the three attitude parameters, ranging from zero to one. They are used to adjust the influence of each attitude degree of freedom on the attitude similarity according to engineering needs. The server presets an attitude distance threshold to determine the similarity of the three attitude parameters. A group of attitude state points below the threshold are classified into the same specific attitude neighborhood, and the attitude state points belonging to the same specific attitude neighborhood are sorted from early to late according to the timestamp, forming a time evolution sequence arranged in chronological order, thereby ensuring that all attitude state points in the time evolution sequence are in the specific attitude neighborhood with small changes in the attitude space.

[0032] For each time window in the time evolution sequence, the server extracts the characteristics of the wind direction oscillation trajectory and the local micro-vibration energy distribution within that time window, and marks the times of typical events in the wind direction oscillation trajectory, such as the moment when the wind direction oscillates to a certain extreme value, and the time points when the energy peaks or energy increases significantly in the local micro-vibration energy distribution. For each time window belonging to the same specific attitude neighborhood, the server calculates the relative time offset between each pair of "typical wind direction oscillation events" and "typical local micro-vibration energy events" and constructs a relative time offset sequence. When the relative time offset sequence remains within a preset phase offset range in multiple consecutive time windows, such as fluctuating within a certain small range, it indicates that the response relationship between the wind direction oscillation trajectory and the local micro-vibration energy distribution has become stable within that specific attitude neighborhood. That is, after each oscillation of the wind direction to a certain similar state, the local micro-vibration energy will increase after an approximately fixed time offset. At this point, the server recognizes the phenomenon that the relative time offset between the "wind direction oscillation trajectory event and the local micro-vibration energy event" remains stable within multiple time windows as a phase-locked relationship between the wind direction oscillation trajectory and the local micro-vibration energy distribution, and marks the time evolution sequence with a phase-locked mark, providing a prerequisite for subsequent judgment on whether the time evolution sequence constitutes a continuous energy accumulation trajectory.

[0033] In the attitude energy memory cloud map, the server concatenates the attitude state points in the time evolution sequence in chronological order to obtain a trajectory in the attitude space. It then statistically analyzes the local micro-vibration energy level of each attitude state point and accumulates or averages the local micro-vibration energy of all attitude state points in the time evolution sequence. If the local micro-vibration energy of multiple consecutive time windows in the neighborhood of a specific attitude is significantly higher than the historical average level or global average level of the same area, and the overall attitude descent remains within a small range of variation in the same time period, it indicates that a temporally continuous and spatially concentrated energy accumulation has been formed in the neighborhood of that specific attitude. The server then marks the trajectory of the time evolution sequence in the attitude space at this time as a continuous energy accumulation trajectory. Subsequently, the server examines the geometry of the continuous energy accumulation trajectory in the attitude space. If it finds that the trajectory exhibits a repetitive spiraling trend in the attitude space composed of pitch, yaw, and twist angles, or that the attitude distance between the beginning and end attitude state points of the trajectory is lower than the preset closure threshold, or that it repeatedly loops around to approach a closed shape within a limited attitude region, then the continuous energy accumulation trajectory is further identified as an abnormal energy track. It is believed that there is a self-sustaining micro-oscillation state in the specific attitude neighborhood corresponding to the abnormal energy track, formed by the coupling of rapid wind oscillation and local micro-vibration, providing a clear target for subsequent track shearing and random rewriting.

[0034] S130. If it is determined that the abnormal energy trajectory tends to be closed or nearly closed in the attitude space, the shear attitude offset is calculated based on the phase position of the abnormal energy trajectory and the rapid swing trend of the wind direction. By applying coordinated tension micropulses to the slings, coordinating the tower crane trolley displacement adjustment and component height changes, the steel component attitude is made to cross the windward angle zone where the abnormal energy trajectory is located within a preset time, and the abnormal energy trajectory is sheared in the attitude energy memory cloud map.

[0035] Specifically, attitude space refers to a multi-dimensional parameter space used to describe the overall attitude state of a large-span thin-walled arched corrugated steel component. It includes at least attitude parameters such as pitch angle, yaw angle, and torsion angle, with each attitude state point corresponding to a coordinate position in the attitude space. As time progresses, the attitude of the steel component continuously changes, and the attitude state points move within the attitude space, forming a trajectory. Phase position refers to the relative temporal position of the anomalous energy trajectory on the time axis compared to the rapid wind oscillation cycle, reflecting "which stage of the self-excited cycle is currently in." The phase position describes where the anomalous energy trajectory is currently located within such a cycle. The rapid wind oscillation trend refers to the changing trend of the wind direction relative to the structure's oscillation direction, amplitude, and speed in the current and short-term future timeframe, used to predict "which way," "how much," and "how fast" the wind will oscillate in the future. The rapid wind oscillation trend is obtained by analyzing the changes in wind oscillation characteristics over a recent period.

[0036] Shear attitude shift refers to the server calculating a set of target attitude changes based on the current phase position of the abnormal energy track and the rapid oscillation trend of the wind direction after identifying an abnormal energy track tending towards a closed or near-closed distribution. This is used to quickly shift the overall attitude of the steel component from the area of ​​the current abnormal energy track to a safe attitude area outside the abnormal energy track. Applying coordinated tension micro-pulses to the slings refers to the use of small-amplitude, time-coordinated pulse adjustments of the tension of multiple slings during shear attitude shift, causing multiple sling points to synergistically affect the component and achieve the desired attitude change. Tower crane trolley displacement adjustment refers to the use of small displacements of the trolley on the tower crane boom along the tower crane track during shear attitude shift to change the horizontal projection position of the slings in the plane, thus coordinating with the attitude of the steel component. Component height change refers to the use of the hoisting system to raise and lower the slings, causing the large-span thin-walled arched corrugated steel component to move vertically by a certain height, thereby changing the local wind field characteristics of the component or altering its attitude response to the combined forces of gravity and wind load.

[0037] The preset time refers to the length of a time window pre-set by the control strategy when performing a shear attitude shift. This time window constrains the attitude adjustment to be completed within a specific timeframe, ensuring the shear action is rapid enough to interrupt the development of abnormal energy trajectories and prevent the self-sustaining micro-oscillations from accumulating further. The windward angle zone refers to the set of attitudes within a certain range in attitude space defined by the angle between the principal plane of the steel component and the current wind direction. This set corresponds to the "windward attitude region" where aerodynamic effects are more concentrated. When a thin-walled arched component is in a certain windward angle zone, local boundary layer separation and vortex trapping are more likely to occur, thus more easily triggering aerodynamic self-sustaining micro-oscillations.

[0038] Furthermore, the server first represents the sequence of attitude state points of the anomalous energy trajectory within the observation time interval as a discrete attitude evolution curve, and denotes the attitude parameter vector at each moment as... This includes attitude parameters such as pitch angle, yaw angle, and twist angle. To integrate the closeness of the trajectory's beginning and end with the degree of trajectory wrapping in attitude space, the server constructs a closure measure function, combining the difference in the beginning and end attitude parameters with the overall discrete "curvature energy" of the trajectory into a single index. For example, it can be defined as:

[0039]

[0040] in, and These represent the values ​​of the k-th attitude parameter, such as pitch, yaw, or twist, at the initial and final attitude states of the anomalous energy trajectory. These are the weighting coefficients for each attitude parameter direction, used to adjust the influence of different attitude degrees of freedom on the degree of head-to-tail fit, with values ​​ranging from zero to one. This is the wraparound weighting coefficient, used to balance the relative importance of the closeness of the beginning and end and the degree of curvature inside the trajectory. Its value ranges from zero to one. This represents the attitude parameter vector corresponding to the trajectory at the nth discrete time. The time weight for the nth sampling point ranges from zero to one and can be adjusted with time or energy level. This represents the L2 norm, used to measure the deviation between an intermediate attitude state point and the midpoint of the line connecting the preceding and following attitude state points. The server will... Compared with a preset closure threshold, when When the trajectory remains below the closure threshold for a period of time, it indicates that the attitude parameters at the beginning and end of the trajectory are close and there is obvious wrapping characteristics inside. Therefore, the abnormal energy trajectory is determined to be a near-closed or closed distribution in the attitude space, and enters the subsequent shearing attitude offset calculation stage.

[0041] The server abstracts the rapid oscillation of wind direction into a time-varying wind direction phase function, normalizing the relative position of the wind direction within one oscillation cycle in each time window into a phase parameter. Furthermore, by utilizing the correspondence between "attitude state point - time - wind direction oscillation characteristics" in the attitude energy memory cloud map, the wind direction phase corresponding to the current attitude state point of the abnormal energy trajectory is marked as... Subsequently, the server selects several recent wind direction oscillation cycles within a preset time period and generates a time series of wind direction angles. Weighted smoothing extrapolation is performed within this interval to construct a short-term wind direction prediction trajectory, for example:

[0042]

[0043] in, This represents the prediction of wind direction and angle at a future time t; This represents the measured wind direction angle at the m-th time step before the current moment; The time decay weighting coefficient gradually decreases as m increases and satisfies all The sum is between zero and one; The time sampling interval is specified. The server then calculates the predicted wind direction trajectory. Combined with current and potential attitude parameters, the range of windward angles formed by wind direction and component attitude in the short term is calculated. The set of attitudes with windward angles in the pre-defined "aerodynamic sensitive zone" is marked as the windward angle zone distribution, thereby obtaining the windward attitude region where the abnormal energy trajectory may continue to hover in the next period of time.

[0044] The server revolves around the current attitude state point in attitude space. Construct a set of feasible attitudes jointly defined by attitude change amplitude, safety constraints, and device motion range, and denote this set as . Simultaneously, the server filters the attitude energy memory cloud map for all attitude state points whose long-term energy levels are below the energy threshold and which are not marked as high-risk energy areas, denoted as... Then and The intersection operation yields a set of candidate targets that are both reachable and avoidable from the windward angle zone. Based on this, the server selects the target attitude state point using an optimization criterion with an energy penalty term, for example:

[0045]

[0046] in, The attitude parameter vector of the target attitude state point; This is a diagonal weighting matrix that applies different weights to each attitude component, used to reflect that some attitude degrees of freedom have higher adjustment costs or smaller safety margins compared to other degrees of freedom; This is the energy penalty weighting coefficient, used to balance the influence of attitude adjustment amplitude and cloud map energy distribution; For attitude energy memory cloud map in attitude state The energy density index at a given location is used to represent the degree of accumulation of historical local micro-vibration energy at that attitude. The above optimization results provide the target attitude state point that simultaneously satisfies both "minimum attitude offset cost" and "low local energy density" among the candidate attitudes. The server then converts the attitude change vector... It is explicitly calibrated as shear attitude offset and used for subsequent control variable solving.

[0047] The server can combine all adjustable control increments into a control vector. This includes minute changes in the tension of each sling, changes in the displacement of the tower crane trolley along the track, and changes in the lifting height of the components; the shear attitude offset vector Treating attitude change as a constraint target, the attitude-control sensitivity matrix is ​​obtained through local linearization or experimental identification. And construct an optimization problem with regularization and security cost terms, in Solving under constraints, for example:

[0048]

[0049] in, This represents the optimized combination of coordinated control variables; This is the attitude-control sensitivity matrix, where each element represents the linear sensitivity of a certain control component to a certain attitude parameter component, which can be obtained through simulation or field calibration. This is the control quantity weight matrix, used to highlight the suppression of the change amplitude of certain control quantities in the optimization process, such as limiting the excessive change of tension in a single sling or the excessive displacement of the tower crane trolley. The regularization term weight coefficient is used to limit the overall size of the control quantity, making the solution smoother and safer. and These are the lower and upper limits of the control vector, respectively, reflecting engineering safety constraints related to changes in sling tension, tower crane trolley travel, and component height. The server will obtain... Within a preset time period, the system issues micro-pulse commands for sling tension, micro-step commands for tower crane trolley displacement, and commands for hoisting system height adjustment in segments, causing the steel components to gradually shift their posture within that time period and eventually cross out of the windward angle zone where the abnormal energy track is located.

[0050] After the shearing action is completed, the server uses the latest acquired attitude state points. The attitude energy memory cloud map is updated based on the characteristics of its local micro-vibration energy and wind direction sway, targeting the attitude state point set on the original anomalous energy trajectory. The set of attitude state points near the new attitude state point after shearing A selective memory reconstruction method can be used, for example, for each pose state. Define the cut memory attribute vector:

[0051]

[0052] in, Indicates the attitude state after shearing. The corresponding memory attribute vectors include overall attitude slowdown memory, local micro-vibration energy memory, and wind direction sway feature memory; Indicates the attitude state before shearing. Historical memory attribute vector at the location; Indicates the attitude state after the shearing is completed. The current memory attribute vector is calculated based on the latest time window data; The shearing selection function takes a value close to one at attitude state points that belong to the subsequent extension path of the sheared trajectory, and a value close to zero at attitude state points that are unrelated to the anomalous energy trajectory. The intermediate region can be smoothly transitioned according to the engineering strategy.

[0053] S140. Based on the current energy density distribution and current wind direction swing trend of the sheared attitude energy memory cloud map, controlled random disturbances are applied to the cable tension distribution and tower crane trolley displacement through a random rewrite channel introduced within the preset amplitude and time constraints, so that the attitude state point of the sheared steel component falls into the energy-sparse region and prevents the formation of a new periodic self-excited track.

[0054] Specifically, the preset amplitude and timing constraint range refers to a set of control boundary conditions set for the amplitude of changes in sling tension, tower crane trolley displacement, and the timing of these changes during the design of the random rewrite channel, in order to ensure hoisting safety and reasonable structural stress. The preset amplitude constraint range specifies the maximum increase or decrease in sling tension, the maximum distance the tower crane trolley displacement can deviate, and the maximum allowable displacement of component attitude during each controlled random disturbance, to avoid structural instability or overload caused by excessive single disturbance. The preset timing constraint range specifies the time interval at which controlled random disturbances can occur, the duration of each disturbance, and the time period during which disturbances are prohibited during critical construction phases. The random rewrite channel refers to the introduction of a calculated and constrained random disturbance sequence into the sling tension distribution and tower crane trolley displacement within the preset amplitude and timing constraint range after shearing, in order to prevent the attitude state point from evolving along the original abnormal energy trajectory or a similar trajectory again. This "rewrites" the attitude evolution path in attitude space, thereby disrupting potential new energy accumulation trends. The random rewrite channel is not completely random. Instead, it determines the specific perturbation combination through pseudo-random or probabilistic selection within a space jointly defined by energy-sparse regions, feasible attitude regions, and safety control constraints.

[0055] Energy-sparse regions refer to attitude regions in the sheared attitude energy memory cloud map where the long-term local micro-vibration energy level is low, energy accumulation is not obvious, and historically there have been few or almost no abnormal energy trajectories passing through. These regions appear as "lighter color" or "energy indices significantly lower than the overall average" on the cloud map, and the corresponding attitude state points were not easily strongly coupled to rapid wind oscillations in past operating conditions. High-energy-risk regions refer to attitude regions in the attitude energy memory cloud map where, due to a long-term significant increase in local micro-vibration energy, a stable phase relationship between wind oscillation characteristics and local micro-vibration energy distribution, and minimal variation in the overall attitude modulus within this neighborhood, energy continues to accumulate, forming obvious high-energy bands or clusters. This region corresponds to the risk point of aerodynamic self-sustaining micro-oscillation in thin-walled arched corrugated steel components under specific windward angle combinations, and is the location where the structural attitude is most likely to enter an unfavorable vibration state under aerodynamic-structural coupling.

[0056] Furthermore, the server divides the sheared monitoring time interval and rewrites the sheared attitude state points corresponding to each time window within that interval, along with their associated local micro-vibration responses and wind direction observation data, into the sheared attitude energy memory cloud map. To more accurately identify energy accumulation and depletion areas within a local region, the server performs weighted smoothing and spatial diffusion processing on the energy density of each attitude state unit within the attitude neighborhood of the sheared attitude state point. For example, the weighted energy density of any attitude state unit can be written as:

[0057]

[0058] in, This represents a certain attitude state unit in attitude space, corresponding to a set of pitch, yaw and twist angle parameters; This represents the local micro-vibration response amplitude of the i-th aerodynamically sensitive observation node within the attitude neighborhood during the n-th time window; This represents the weighting coefficient of the i-th aerodynamically sensitive observation node, used to reflect the importance of sensors at different locations in identifying the degree of danger. Its value ranges from zero to one and can be calibrated based on experience. This represents the time weight coefficient of the nth time window, which reflects the principle that the closer the time window is to the current moment, the greater its weight. Its value ranges from zero to one and gradually decreases with the time window number. The number of time windows included in the statistics. This represents the number of aerodynamically sensitive observation nodes participating in the calculation within the attitude neighborhood after shearing. The server compares the values ​​of each attitude state unit. Compared with the global reference energy level, areas with significantly higher energy levels that remain high over time are marked as energy accumulation areas, while areas with energy levels that are consistently lower than the reference level and have small fluctuations are marked as energy sparse areas. At the same time, combined with the local statistical results of wind direction oscillation characteristics within the time period after shearing, a joint characterization of "current energy density distribution and current wind direction oscillation trend" is formed in the attitude neighborhood of the attitude state point after shearing, providing basic information for the guidance target of subsequent random rewrite channels.

[0059] The server uses the attitude parameter vector corresponding to the cut attitude state point. Centered on the target component, and based on the maximum allowable attitude deviation range and the collision safety clearance between the component and surrounding structures, a feasible region for attitude change is determined. This feasible region is represented in attitude space as an attitude candidate region jointly defined by multiple constraints. For example, the attitude candidate region can be defined as:

[0060]

[0061] in, This represents the set of attitude state points contained in the attitude candidate region; A vector representing the attitude parameters of a candidate attitude state point; is a weighted matrix in the direction of attitude parameters, and is a diagonal matrix used to emphasize certain attitude components, such as pitch angle, which has a greater impact on safety. This represents the maximum allowable weighted radius for attitude changes, used to limit the maximum offset of the attitude state point after shearing during random rewriting. This represents the set of safe attitudes after being filtered by geometric constraints, component-to-device spacing constraints, and specification limitations. In the attitude energy memory cloud map, the server will belong to... Furthermore, the attitude state units that simultaneously satisfy the conditions of having a corresponding energy density that is a low-energy region and whose historical records have not been marked as regions traversed by abnormal energy trajectories form a target attitude candidate set, which is used to represent the preferred attitude region that is "safe, low-energy, and has not been self-locked in history", providing the expected convergence attitude for the random rewrite channel.

[0062] The server aggregates all adjustable control increments into a control vector. Its components include the tension increment of each sling and the displacement increment of the tower crane trolley on the track. Based on this, a controlled random generation mechanism is constructed to reflect randomness while always satisfying the control amplitude and timing constraints. The sampling process of candidate control quantities in each discrete random disturbance sub-time slice can be described as follows:

[0063]

[0064] in, This represents the j-th group of candidate control vectors generated in the k-th random disturbance sub-time slice; This indicates that the expression follows a zero-mean, multidimensional covariance matrix. Gaussian random vectors; The covariance matrix is ​​set according to the information security level and the allowable disturbance intensity in the kth sub-time slice, and is used to control the random disturbance amplitude and correlation in each control quantity dimension. This is the control quantity mapping matrix, used to convert a standardized random vector into actual control quantity increments that satisfy control quantity magnitude and direction constraints. The server generates... Then, it is substituted into the attitude-control mapping model to estimate the attitude change caused by the control variable, and it is determined whether the attitude change can push the sheared attitude state point to the target attitude candidate set within a finite number of steps. If the estimation result shows that the attitude converges towards the target attitude candidate set, the corresponding control variable vector is marked as an element of the effective perturbation candidate set; otherwise, it is discarded, and finally an effective perturbation candidate set that "meets the constraints and has the ability to advance to the energy-sparse region" is formed.

[0065] The server needs to comprehensively consider the distance of the current attitude from the target attitude candidate set, the energy density corresponding to the current attitude, and the coupling degree between the current wind direction swing trend and potential self-excited paths. It needs to construct a cost function to evaluate the merits of each perturbation combination and then form a weighted random selection probability based on this. For example, for each effective perturbation combination... Assign a value:

[0066]

[0067] in, To apply perturbation combination in the kth sub-time slice The attitude state point at the next moment is obtained through post-prediction; The set of candidate poses from pose state points to target poses The distance metric function is used to characterize the degree to which the attitude moves closer to or further away from the energy-sparse target region; The energy density of the sheared attitude energy memory cloud at the predicted attitude state point is used to reflect whether the attitude is located in a potentially high-energy region. The coupling cost between the wind direction swing phase and the attitude state point is used to measure whether the disturbance is likely to form a new phase-locking condition together with the current wind direction phase. , , These are weighting coefficients used to balance the importance of distance convergence, energy avoidance, and wind direction coupling. The server constructs a selection probability for each perturbation combination based on this cost function, for example, using an exponential weighting method.

[0068]

[0069] in, Temperature is a parameter that controls the sharpness of the probability distribution. When the value is smaller, a preference is given to combinations of perturbations with lower costs. When the value is large, the selection becomes more random. The server randomly selects a combination of disturbances from the effective disturbance candidate set according to this probability distribution, and applies the corresponding sling tension increment and tower crane trolley displacement increment to the hoisting system, so that the attitude state point after shearing gradually migrates to the energy-sparse region under the action of a series of sub-time slices.

[0070] The server needs to determine whether the attitude state point is gradually converging to an energy-sparse region, and whether a new, similar closed trajectory trend is forming under the cumulative effect of random perturbations. To this end, the server calculates the "energy-distance-closure tendency" index of the attitude trajectory in the attitude space within each longer monitoring time window, and dynamically adjusts the perturbation amplitude and perturbation strategy accordingly. For example, an adjustment function composed of the trajectory convergence index and the closure tendency index can be defined:

[0071]

[0072] in, This represents a trajectory segment composed of attitude state points within the current monitoring time window; It represents the average distance of the trajectory segment relative to the target pose candidate set, and is used to determine whether the whole is moving towards a region with low energy. This is an average energy index representing the residence of a trajectory segment in a low-energy region, used to reflect whether it has stabilized in the low-energy region; It indicates the degree of wrapping of a trajectory segment in the attitude space. For example, it can be calculated by combining the trajectory curvature, the cumulative value of the direction change, and the difference between the first and last attitude values, and is used to identify whether the trajectory shows signs of spiraling or tending to close. , , To adjust the weighting coefficients, the server adjusts them according to the data within each monitoring time window. Dynamically adjust the changing trend and randomly rewrite the channel covariance matrix The overall magnitude, when When the trajectory has significantly approached a region of low energy and the degree of looping is low, gradually reduce the perturbation amplitude until only a minimal perturbation is retained to stabilize the attitude state point; when When the displayed trajectory shows obvious signs of looping or close proximity, the perturbation components in certain directions are amplified or the perturbation selection weights are changed to deliberately disrupt the current trajectory inertial trend and avoid generating new periodic self-excited trajectories in the attitude space, thereby achieving continuous guidance and risk suppression of the attitude evolution after shearing.

[0073] S150. Construct a two-layer attitude representation structure consisting of the main attitude trajectory and the abnormal trajectory layer. The main attitude trajectory is used as the basis for hoisting control based on the long-term window data and geometric constraints of the main attitude observation node. The abnormal trajectory layer is used to determine whether track shearing is triggered based on the short-term window data of the aerodynamically sensitive observation node. This way, the self-sustaining micro-oscillation is restricted to the internal circulation of the abnormal trajectory layer without affecting the main attitude trajectory.

[0074] Specifically, the server first represents the measured attitude of each master attitude observation node using attitude parameter vectors in a unified structural coordinate system, and denotes the discrete observation sequence within a long time window as... Each of them Including arched components at time The attitude parameters include pitch, yaw, and twist angles. To extract the initial master attitude trajectory that evolves slowly over time, the server performs time-smoothing and rigid-body consistency fitting on the discrete sequence, and the master attitude estimate at each time step is written as the solution to the following optimization problem:

[0075]

[0076] in, Indicates at time The corresponding initial master attitude trajectory attitude parameter vector; Indicates time The set of neighborhood time indices that participate in smoothing within a long window; For a moment Corresponding observation at time The time weights in master pose estimation range from zero to one and vary with... The value decreases as it increases, used to highlight observations in the nearest time period; is the attitude component weighting matrix, and is a diagonal matrix used to reflect the relative importance of pitch, yaw, and twist angles in the main attitude estimation; It represents the rigid body constraint relationship determined by the arrangement of slings, the geometric properties of components, and construction safety constraints, such as the support conditions at both ends of the arch axis, the geometric consistency between the lifting points, and the overall rigid body motion assumptions, etc. These are the regularization weights for rigid body constraints, used to balance the trade-off between fitting the observed data and satisfying the geometric constraints. The server performs this for all time intervals. Solving the above problem yields the initial master attitude trajectory that evolves slowly over time. Based on this, secondary corrections are made to attitude points that do not meet the local stress or safety envelope requirements, and attitudes exceeding the safety envelope are projected back into the feasible attitude domain, thereby obtaining the main attitude trajectory as the basis for hoisting control. .

[0077] The server targets each short time window. Within the observation window, the local micro-vibration energy distribution and wind direction sway phase are extracted, and combined with the overall attitude easing, it is determined whether the time window meets the conditions for local micro-vibration energy accumulation, phase locking, and overall attitude easing stability. Taking the k-th short time window as an example, the server detects the high-speed vibration signal of the i-th aerodynamically sensitive observation node within that time window. Energy calculations are performed, and the energy of each node is weighted and superimposed to obtain the local micro-vibration energy index corresponding to this time window:

[0078]

[0079] in, This represents the local micro-vibration energy index within the k-th short time window; This refers to the number of aerodynamically sensitive observation nodes; is the weighting coefficient for the i-th aerodynamically sensitive observation node, used to reflect the importance of different sensing locations; For the i-th node at time... Vibration signals; The baseline vibration average for this node over a longer timescale is used to eliminate static bias. The server also uses data from wind direction and speed observation nodes to calculate the wind direction oscillation phase within this time window. For example, phase parameters are obtained by normalizing the wind direction angle sequence to one oscillation period; and the corresponding overall attitude mitigation is calculated using data from the master attitude observation nodes. This is used to measure whether the overall attitude remains essentially unchanged within a short time window. The server checks conditions within consecutive short time windows. Continuously above the energy threshold Maintain a relatively fixed offset from adjacent time windows. The set of time indices all below the attitude buffer threshold, and the attitude state points corresponding to these time windows that meet the conditions. By sequentially piecing them together, initial anomalous trajectories are formed in attitude space, reflecting the coupling characteristics of local micro-vibration energy accumulation and wind direction sway. When multiple adjacent initial anomalous trajectory segments meet the requirements of continuity and topological similarity in attitude space, the server merges them into a portion of the anomalous trajectory layer, thereby obtaining a set of anomalous trajectory layer paths driven by high-frequency data within a short time window, which is used for subsequent track shearing trigger determination.

[0080] The server expands the attitude energy memory cloud map into a field structure with multi-channel memory, storing each attitude state point... The memory at time t is represented as a joint state containing the main attitude channel and the abnormal trajectory channel, for example, it can be written as:

[0081]

[0082] in, This indicates the main attitude channel in attitude state. The memory vector at time t contains information such as the number of times the attitude point dwells on the main attitude trajectory, the main attitude slack statistics, and the geometric constraint margin related to hoisting control. This represents the memory vector of the anomalous trajectory channel under the same attitude state, containing information such as whether the attitude point was previously marked as part of the anomalous energy trajectory, corresponding local micro-vibration energy statistics, and wind direction phase-locking statistics. When updating the attitude energy memory cloud map, the server only updates attitude state points belonging to the main attitude trajectory. The passage, and not in Channels accumulate abnormal energy memory; for attitude state points belonging to the abnormal trajectory layer, only... The channel accumulates local micro-vibration energy and phase-locking information without changing the The server uses the main attitude information for hoisting control. Through this "channel decoupling" approach, the server forms two independent paths in the same attitude energy memory cloud map: one is the main attitude trajectory that evolves slowly over a long time window, used as a reference for attitude planning, control command generation, and safety constraints; the other is an abnormal trajectory layer driven by a short time window, used as a criterion for monitoring local micro-vibrations, identifying abnormal energy trajectories, and triggering trajectory shearing. Since the high-energy memory related to self-sustaining micro-oscillations is confined to the abnormal trajectory layer channel and does not accumulate in the main attitude channel, the main attitude trajectory will not be chronically biased and contaminated during long-term evolution, thus achieving a two-layer attitude representation structure of "macroscopic attitude stability and control, and closed-loop management of micro-self-excited behavior within the abnormal trajectory layer".

[0083] S160. Based on the sheared attitude energy memory cloud map, delineate high-risk energy areas and low-energy areas, control the main attitude trajectory to pass through the low-energy areas, and set a limit range for the dwell time when passing through the high-risk energy areas. If the dwell time exceeds the limit range and the sheared attitude energy memory cloud map shows an abnormal energy track, then priority is given to triggering track shearing and random rewriting to prevent the self-sustaining micro-oscillation from evolving into a chronic bias of the main attitude trajectory.

[0084] Specifically, the server first selects a statistical time interval after the cut, and records the local micro-vibration energy of all attitude state units within this time interval as... Furthermore, a global baseline energy and local risk indicators are constructed in the time and attitude dimensions. The global baseline energy can first be calculated using a weighted method:

[0085]

[0086] in, This represents the attitude parameter vector of any attitude state unit in the attitude space; This represents the center time of the nth time window within the statistical time interval; This represents the attitude state unit within the time window. The corresponding local micro-vibration energy; is the time weight coefficient for the nth time window, which reflects that the closer to the current moment, the greater the weight. Its value ranges from zero to one and gradually decreases as time moves towards the past. This represents the number of time windows included in the statistics. Based on this, the server constructs a risk index for each attitude state unit that considers the correlation between historical energy levels and anomalous energy trajectories, for example, it can be written as:

[0087]

[0088] in, Represents attitude state unit Energy risk indicators; This represents the time-weighted accumulation of local micro-vibration energy of the attitude state unit within the statistical time interval; This is the relative energy level weighting coefficient, used to control the proportion of the energy item in the overall risk; Represents attitude state unit Historically, the intensity index associated with anomalous energy trajectories can be obtained by combining the number of times the attitude state unit is traversed by the anomalous energy trajectory and the phase locking intensity within the corresponding time period. The value can be normalized to between zero and one. This is the anomaly correlation weighting coefficient, used to measure the impact of historical anomalous energy trajectories on current risk. The server sets a risk threshold. With lower threshold , for satisfying Furthermore, the corresponding local micro-vibration energy is consistently higher than [a certain value] within multiple consecutive time windows. The attitude state unit is marked as a high-energy-risk region, for those that meet the following conditions. Furthermore, attitude state units that do not form obvious energy accumulation and do not have abnormal energy trajectories within the statistical time are marked as energy-sparse regions, thus forming a distribution of high-risk energy regions and energy-sparse regions with quantitative risk labels on the sheared attitude energy memory cloud map.

[0089] During the main attitude trajectory planning and execution process, in order to control the main attitude trajectory to preferentially traverse energy-sparse regions and set restrictions on high-energy-risk regions to be traversed, the server constructs the main attitude trajectory planning problem into a path optimization problem with energy risk weights in the attitude space. The attitude state points of the main attitude trajectory at discrete time nodes are represented as follows: During the planning process, the server selects a trajectory by minimizing the combined cost function of the energy and risk terms. For example, it can construct:

[0090]

[0091] in, The path cost represents the main attitude trajectory; This represents the attitude parameter vector of the main attitude trajectory at the k-th planning node; This represents the cost of attitude change between adjacent trajectory nodes, used to avoid overly abrupt attitude jumps. This is an indicator function for high-risk energy regions, when the attitude state point... If the energy zone is high-risk, use 1; otherwise, use 0. For energy-sparse regions, the indicator function is defined when the attitude state point... If the region is energy-sparse, take 1; otherwise, take 0. The penalty weight for crossing high-energy-risk areas is used to minimize the time a trajectory spends in these areas. To assign a reward weight to areas with low energy, the trajectory optimization process favors these areas as attitude paths. For unavoidable high-energy-risk areas, the server records each high-energy-risk area during the path planning phase based on the intersection of the trajectory and the high-energy-risk area. The trajectory segment traversed by the master attitude trajectory, and a limit on the allowed cumulative dwell time in this high-risk energy area is defined, for example, it can be set as follows:

[0092]

[0093] in, Indicates high-risk energy areas The time limit for stay; Based on the allowable dwell time constant; High-risk energy area Risk indicators for all attitude state units The weighted average is used to reflect the overall risk level of the area; This is a time compression factor used to shorten the allowed dwell time in high-risk energy areas with higher levels of danger. Through the aforementioned cost function and dwell time constraints, the main attitude trajectory prioritizes paths traversing energy-sparse areas during the planning phase, while imposing explicit time constraints on necessary high-risk energy areas. This provides a quantitative basis for real-time monitoring and triggering mechanisms during the execution phase.

[0094] During actual hoisting operations, if the dwell time of an attitude state point within a high-energy-risk area exceeds the limit, or if the sheared attitude energy memory cloud map shows signs of abnormal energy trajectories within the high-energy-risk area, the server needs to prioritize triggering the trajectory shearing and random rewrite channel to prevent self-sustaining micro-oscillations from developing into a chronic bias against the main attitude trajectory within the high-energy-risk area. Therefore, during the execution phase, the server performs a process for each high-energy-risk area... Accumulate the dwell time within the region in real time and construct a region dwell time function:

[0095]

[0096] in, This represents the actual attitude state point from the start of hoisting to time t. In high-risk energy areas The total time spent in the area; Indicates time Actual attitude state points of steel components; For the region indication function, when the attitude state point This area is a high-risk energy zone. If the condition is met, a value of 1 is used; otherwise, a value of 0 is used. Simultaneously, the server monitors the high-risk energy regions for potentially forming new anomalous energy trajectories within the sheared attitude energy memory cloud map, for example, by constructing a local anomalous trajectory indication function:

[0097]

[0098] in, High-risk energy area Indicators of anomalous energy orbital signatures at time t; This is the time average of the local micro-vibration energy in the high-risk energy region over a few recent time windows, used to measure whether the energy is showing a renewed tendency to accumulate. It is a phase consistency index between wind direction oscillation characteristics and local micro-vibration energy peaks within the same time period, used to reflect whether new phase-locking behavior has occurred; and This is a weighting coefficient used to balance the influence of energy intensity and phase coupling strength. When the server detects any high-risk energy region that satisfies... Or simultaneously satisfy When the condition is met, among which To pre-determine the threshold for abnormal trajectory indications, i.e., to identify a significant risk of the re-development of self-sustaining micro-oscillations within the high-energy region, the system immediately triggers the trajectory shearing module and the random rewrite channel module. This causes the current attitude state point to undergo shearing attitude offset at the control level and is subsequently pulled to a low-energy region through the random rewrite channel. This prevents the formation of new periodic self-excited trajectories within the high-energy region and avoids long-term chronic bias effects on the main attitude trajectory.

[0099] This application also provides a hoisting attitude control device for a large-span thin-walled arched corrugated steel structure. Referring to Figure 2, which is a schematic diagram of the modules of a hoisting attitude control device for a large-span thin-walled arched corrugated steel structure provided in an embodiment of this application, the device is a server. The server includes an acquisition module 21 and a processing module 22. The acquisition module 21 is used to acquire the overall attitude slow change, local micro-vibration response, and wind direction and speed oscillation trajectory under a preset wind speed by using the main attitude observation node, aerodynamic sensitive observation node, and wind direction and speed observation node deployed on the large-span thin-walled arched corrugated steel member, and to construct an attitude energy memory that includes wind direction oscillation characteristics, local micro-vibration energy distribution, and overall attitude slowdown. Cloud map; Processing module 22 is used to analyze the correlation between wind direction sway characteristics, local micro-vibration energy distribution and overall attitude slack in a continuous time window based on the attitude energy memory cloud map, and to identify abnormal energy trajectories that form continuous energy accumulation in a specific attitude neighborhood and are phase-locked with the rapid sway of the wind direction; Processing module 22 is also used to calculate shear attitude offset based on the phase position of the abnormal energy trajectory and the rapid sway trend of the wind direction if it is determined that the abnormal energy trajectory tends to be closed or nearly closed in the attitude space, and to apply coordinated tension micro-pulses to the slings, coordinate with the tower crane trolley displacement adjustment and component height changes, so that the steel component attitude crosses the abnormal within a preset time. The processing module 22 is used to determine the windward angle zone where the energy trajectory is located and to shear abnormal energy trajectories in the attitude energy memory cloud map. The processing module 22 is also used to apply controlled random disturbances to the sling tension distribution and tower crane trolley displacement through a random rewrite channel introduced within a preset amplitude and time constraint range, based on the current energy density distribution and current wind direction oscillation trend of the sheared attitude energy memory cloud map. This ensures that the attitude state point of the sheared steel component falls into an energy-sparse region and prevents the formation of new periodic self-excited trajectories. The processing module 22 is also used to construct a two-layer attitude representation structure composed of the main attitude trajectory and the abnormal trajectory layer, using the main attitude trajectory as the hoisting control based on the long-term window data and geometric constraints of the main attitude observation nodes. Based on this, the abnormal trajectory layer is used to determine whether to trigger track shearing based on short-time window data from aerodynamically sensitive observation nodes, so as to limit the self-sustaining micro-oscillation within the abnormal trajectory layer and not affect the main attitude trajectory. The processing module 22 is also used to delineate high-risk energy areas and low-energy areas based on the attitude energy memory cloud map after shearing, control the main attitude trajectory to cross the low-energy area and set a limit range for the dwell time in the high-risk energy area. If the dwell time exceeds the limit range and the attitude energy memory cloud map after shearing shows the appearance of an abnormal energy track, track shearing and random rewriting are triggered first to prevent the self-sustaining micro-oscillation from evolving into a chronic bias of the main attitude trajectory.

[0100] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0101] This application also provides an electronic device. Referring to Figure 3, which is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, the electronic device may include: at least one processor 31, at least one network interface 34, a user interface 33, a memory 35, and at least one communication bus 32.

[0102] The communication bus 32 is used to enable communication between these components.

[0103] The user interface 33 may include a display screen and a camera. Optionally, the user interface 33 may also include a standard wired interface and a wireless interface.

[0104] The network interface 34 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0105] The processor 31 may include one or more processing cores. The processor 31 connects to various parts of the server via various interfaces and lines, executing instructions, programs, code sets, or instruction sets stored in the memory 35, and calling data stored in the memory 35 to perform various server functions and process data. Optionally, the processor 31 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 31 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 31 and may be implemented as a separate chip.

[0106] The memory 35 may include random access memory (RAM) or read-only memory. Optionally, the memory 35 may include a non-transitory computer-readable storage medium. The memory 35 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 35 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 35 may also be at least one storage device located away from the aforementioned processor 31. As shown in Figure 3, the memory 35, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a method of hoisting attitude control for a large-span thin-walled arched corrugated steel structure.

[0107] In the electronic device shown in Figure 3, the user interface 33 is mainly used to provide an input interface for the user and obtain the user input data; while the processor 31 can be used to call the application program stored in the memory 35 for a large-span thin-walled arched corrugated steel structure hoisting attitude control method. When executed by one or more processors, the electronic device executes one or more methods as described in the above embodiments.

[0108] This application also provides a non-transitory computer-readable storage medium storing instructions. When executed by one or more processors, these instructions cause an electronic device to perform one or more of the methods described in the above embodiments.

[0109] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for controlling the hoisting posture of a large-span thin-walled arched corrugated steel structure, characterized in that, The method includes: acquiring the overall slow attitude change, local micro-vibration response, and wind direction and speed trajectory under a preset wind speed by setting up main attitude observation nodes, aerodynamic sensitive observation nodes, and wind direction and speed observation nodes on a large-span thin-walled arched corrugated steel member, and constructing an attitude energy memory cloud map containing wind direction sway characteristics, local micro-vibration energy distribution, and overall attitude slowdown; analyzing the correlation between the wind direction sway characteristics, the local micro-vibration energy distribution, and the overall attitude slowdown within a continuous time window based on the attitude energy memory cloud map, and identifying an abnormal energy track that forms continuous energy accumulation in a specific attitude neighborhood and is phase-locked with the rapid wind direction sway; if it is determined that the abnormal energy track tends to be closed or nearly closed in the attitude space, calculating the shear attitude offset based on the phase position of the abnormal energy track and the rapid wind direction sway trend, and applying coordinated tension micro-pulses to the slings, coordinating with the tower crane trolley displacement adjustment and component height changes, so that the steel member attitude crosses the windward angle zone where the abnormal energy track is located within a preset time, and shearing the abnormal energy track in the attitude energy memory cloud map; and based on the sheared attitude energy memory cloud map... Based on the current energy density distribution and current wind direction oscillation trend, controlled random perturbations are applied to the sling tension distribution and tower crane trolley displacement through a random rewrite channel introduced within a preset amplitude and time constraint range. This ensures that the attitude state point of the sheared steel component falls into an energy-sparse region and prevents the formation of new periodic self-excited trajectories. A two-layer attitude representation structure consisting of a main attitude trajectory and an abnormal trajectory layer is constructed. The main attitude trajectory is used as the lifting control basis based on the long-term window data and geometric constraints of the main attitude observation node, while the abnormal trajectory layer is used based on the short-term window data of the aerodynamically sensitive observation node. To determine whether to trigger track shearing, the self-sustaining micro-oscillation is confined to the internal loop of the abnormal trajectory layer without affecting the main attitude trajectory. Based on the attitude energy memory cloud map after shearing, high-risk energy areas and low-energy areas are delineated. The main attitude trajectory is controlled to traverse the low-energy areas, and a limit is set on the dwell time in the high-risk energy areas. If the dwell time exceeds the limit range during actual hoisting and the attitude energy memory cloud map after shearing shows the appearance of an abnormal energy track, track shearing and random rewriting are triggered first to prevent the self-sustaining micro-oscillation from evolving into a chronic bias of the main attitude trajectory.

2. The method for controlling the hoisting posture of a large-span thin-walled arched corrugated steel structure according to claim 1, characterized in that, The method involves deploying main attitude observation nodes, aerodynamic sensitive observation nodes, and wind direction and speed observation nodes on a large-span thin-walled arched corrugated steel member to acquire data on slow changes in overall attitude, local micro-vibration response, and rapid wind direction oscillation trajectory at a preset wind speed. An attitude energy memory cloud map is then constructed, incorporating wind direction oscillation characteristics, local micro-vibration energy distribution, and overall attitude mitigation. Specifically, under a unified structural coordinate system and a unified time synchronization reference, the overall attitude mitigation acquired by the main attitude observation nodes, the local micro-vibration energy distribution acquired by the aerodynamic sensitive observation nodes, and the wind direction oscillation characteristics acquired by the wind direction and speed observation nodes are synchronously sliced ​​according to multiple overlapping time windows. The attitude state point corresponding to each time window is used as a storage unit for the attitude energy memory cloud map. This storage unit records the overall attitude mitigation attribute, local micro-vibration energy distribution attribute, and wind direction oscillation characteristic attribute of the attitude state point within the time window. As the time window is updated along the time axis, the storage unit is time-weighted and accumulated to form an attitude energy memory cloud map with historical coupling characteristics.

3. The method for controlling the hoisting posture of a large-span thin-walled arched corrugated steel structure according to claim 1, characterized in that, The process of analyzing the correlation between the wind direction sway characteristics, the local micro-vibration energy distribution, and the overall attitude mitigation based on the attitude energy memory cloud map within a continuous time window, and identifying anomalous energy trajectories that form continuous energy accumulation within a specific attitude neighborhood and are phase-locked with the rapid wind direction sway, specifically includes: classifying attitude state points with overall attitude mitigation attributes, local micro-vibration energy distribution attributes, and wind direction sway characteristics into the specific attitude neighborhood according to the similarity of attitude parameters within the continuous time window; forming a time evolution sequence for attitude state points belonging to the same specific attitude neighborhood in chronological order; and in the time evolution sequence, By comparing the time correspondence between the wind direction oscillation trajectory and the local micro-vibration energy distribution, and when the relative time offset remains within a preset phase offset range in multiple consecutive time windows, a phase-locked relationship is determined between the wind direction oscillation trajectory and the local micro-vibration energy distribution. When the phase-locked relationship is established and the overall attitude easing remains within a slow change range in multiple consecutive time windows, the trajectory of the time evolution sequence in the attitude space is determined as a continuous energy accumulation trajectory. When the continuous energy accumulation trajectory exhibits a geometric shape of repeated spiraling, tending to close, or near-closed, the continuous energy accumulation trajectory is determined as the abnormal energy trajectory.

4. The method for controlling the hoisting posture of a large-span thin-walled arched corrugated steel structure according to claim 1, characterized in that, If it is determined that the abnormal energy trajectory tends to be closed or nearly closed in the attitude space, then the shearing attitude offset is calculated based on the phase position of the abnormal energy trajectory and the rapid oscillation trend of the wind direction. Furthermore, by applying coordinated tension micro-pulses to the slings, coordinating with the tower crane trolley displacement adjustment and component height changes, the steel component's attitude is made to cross the windward angle zone where the abnormal energy trajectory is located within a preset time, and the abnormal energy trajectory is sheared in the attitude energy memory cloud map. Specifically, this includes: continuously tracking the attitude evolution curve of the abnormal energy trajectory in the attitude energy memory cloud map; when the abnormal energy trajectory exhibits a looping pattern in the attitude space and the difference between the attitude parameters at the beginning and end of the trajectory is lower than a preset closure threshold, the abnormal energy trajectory is determined to be tending to be closed or nearly closed; based on the abnormal energy trajectory in the current time window... The phase position is read to identify the rapid oscillation characteristics of the corresponding wind direction, and the windward angle distribution of the abnormal energy track is predicted by combining the rapid oscillation trend of the wind direction with the preset time period. By selecting a target attitude state point in the attitude space that can cross the windward angle distribution and is located in an energy-sparse region, the attitude change vector from the current attitude state point to the target attitude state point is determined as the shear attitude offset. Based on the shear attitude offset, a combination of coordinated control quantities for sling tension micropulses, tower crane trolley displacement adjustment, and component height change that satisfy geometric constraints and mechanical equilibrium conditions is constructed. The hoisting device is controlled to execute the combination of coordinated control quantities within the preset time period so that the steel component attitude crosses the windward angle zone where the abnormal energy track is located. The abnormal energy track is sheared in the attitude energy memory cloud map to obtain the sheared attitude energy memory cloud map.

5. The method for controlling the hoisting posture of a large-span thin-walled arched corrugated steel structure according to claim 1, characterized in that, The process involves applying controlled random disturbances to the cable tension distribution and tower crane trolley displacement using a random rewrite channel introduced within a preset amplitude and time constraint range, based on the current energy density distribution and wind direction oscillation trend of the sheared attitude energy memory cloud map. This aims to ensure that the attitude state points of the sheared steel components fall into energy-sparse regions and prevent the formation of new periodic self-excited trajectories. Specifically, this includes: updating the local micro-vibration energy distribution and wind direction oscillation characteristics of the sheared attitude state points based on the sheared attitude energy memory cloud map, and identifying energy accumulation and energy-sparse regions in the energy density distribution of the attitude neighborhood where the sheared attitude state points are located; under the premise of satisfying attitude change amplitude constraints and construction safety constraints, constructing an attitude candidate region centered on the sheared attitude state points, and selecting attitude state units corresponding to energy-sparse regions and not associated with the history of abnormal energy trajectories as the target attitude candidate set within the attitude candidate region; and within a preset amplitude range... Within the time constraints, a controlled random disturbance sequence is generated for the tension distribution of the sling and the displacement of the tower crane trolley. Disturbance combinations that can push the sheared attitude state point towards the target attitude candidate set are selected as effective disturbance candidate sets. In the random disturbance sub-time slice, based on the position of the current attitude state point in the sheared attitude energy memory cloud map and the current wind direction oscillation characteristics, a disturbance combination is selected from the effective disturbance candidate set using a weighted random method and applied to the sling and tower crane trolley to migrate the sheared attitude state point to an energy-sparse region. The trajectory shape of the attitude state point after the controlled random disturbance is monitored within a continuous time window. When the trajectory of the attitude state point after the controlled random disturbance tends towards an energy-sparse region, the disturbance amplitude is reduced to stabilize the attitude state point. When the trajectory of the attitude state point after the controlled random disturbance shows signs of hovering or tending towards closure, the random disturbance strategy is adjusted to avoid forming a new periodic self-excited trajectory.

6. The method for controlling the hoisting posture of a large-span thin-walled arched corrugated steel structure according to claim 1, characterized in that, The construction of a two-layer attitude representation structure consisting of a main attitude trajectory and an abnormal trajectory layer, uses the main attitude trajectory based on long-term window data and geometric constraints from the main attitude observation nodes as the basis for hoisting control, and uses the abnormal trajectory layer based on short-term window data from the aerodynamically sensitive observation nodes to determine whether track shearing is triggered, so as to limit the self-sustaining micro-oscillation to a loop within the abnormal trajectory layer without affecting the main attitude trajectory. Specifically, this includes: mapping the long-term window data obtained from the main attitude observation nodes to an initial main attitude trajectory that evolves slowly over time, and performing rigid body constraint correction on the main attitude trajectory based on the sling arrangement, component geometric characteristics, and construction safety constraints to obtain the main attitude trajectory; and mapping the long-term window data obtained from the main attitude observation nodes to an initial main attitude trajectory that evolves slowly over time. The short-time window data acquired by the aerodynamically sensitive observation node is mapped to an initial abnormal trajectory that reflects the coupling characteristics of local micro-vibration energy distribution and wind direction sway in real time. Within multiple consecutive short-time windows, the initial abnormal trajectories that meet the conditions of local micro-vibration energy accumulation, phase locking, and overall attitude slow stabilization are concatenated to form the abnormal trajectory layer. The main attitude trajectory and the abnormal trajectory layer are recorded in the attitude energy memory cloud map with independent paths. The main attitude trajectory is used to construct the basis for hoisting control and serve as an attitude constraint reference. The abnormal trajectory layer is used to monitor local micro-vibration behavior and trigger track shearing, so as to limit the self-sustaining micro-sway to the internal circulation of the abnormal trajectory layer without affecting the main attitude trajectory.

7. The method for controlling the hoisting posture of a large-span thin-walled arched corrugated steel structure according to claim 1, characterized in that, The process involves defining high-risk and low-energy regions based on the sheared attitude energy memory cloud map, controlling the main attitude trajectory to traverse the low-energy regions, and setting a limit on the dwell time in the high-risk regions. During actual hoisting, if the dwell time exceeds the limit and the sheared attitude energy memory cloud map shows an abnormal energy trajectory, trajectory shearing and random rewriting are preferentially triggered to prevent self-sustaining micro-oscillations from evolving into a chronic bias against the main attitude trajectory. Specifically, this includes maintaining attitudes with energy higher than the global baseline and previously associated with abnormal energy trajectories within multiple consecutive time windows, based on the updated local micro-vibration energy distribution in the sheared attitude energy memory cloud map. The attitude state unit is marked as the energy high-risk area, and the attitude state unit that maintains energy below the global reference and does not form energy accumulation is marked as the energy sparse area. During the planning and execution of the main attitude trajectory, the main attitude trajectory is controlled to preferentially pass through the energy sparse area, and the restriction range is set for the energy high-risk area to be passed through. When the dwell time of the attitude state point in the energy high-risk area exceeds the restriction range during the actual hoisting process, or when the attitude energy memory cloud map after shearing shows signs of abnormal energy trajectory in the energy high-risk area, the trajectory shearing and random rewrite channel is triggered preferentially to avoid the self-sustaining micro oscillation forming a chronic bias on the main attitude trajectory in the energy high-risk area.

8. A hoisting attitude control device for a large-span thin-walled arched corrugated steel structure, characterized in that, The device is used to execute the hoisting attitude control method for a large-span thin-walled arched corrugated steel structure as described in any one of claims 1 to 7. The device includes an acquisition module and a processing module. The acquisition module is used to acquire, through main attitude observation nodes, aerodynamic sensitive observation nodes, and wind direction and speed observation nodes deployed on the large-span thin-walled arched corrugated steel member, the overall attitude slow change, local micro-vibration response, and wind direction rapid oscillation trajectory at a preset wind speed, respectively, and construct an attitude energy memory cloud map containing wind direction oscillation characteristics, local micro-vibration energy distribution, and overall attitude slowdown. The processing module is used to analyze the wind direction oscillation within a continuous time window based on the attitude energy memory cloud map. The processing module is used to determine the correlation between dynamic characteristics, the local micro-vibration energy distribution, and the overall attitude easing, and to identify an abnormal energy trajectory that forms a continuous energy accumulation within a specific attitude neighborhood and is phase-locked with the rapid oscillation of the wind direction. If the abnormal energy trajectory is determined to be tending towards a closed or near-closed distribution in the attitude space, the module calculates the shear attitude offset based on the phase position of the abnormal energy trajectory and the rapid oscillation trend of the wind direction. It also applies coordinated tension micro-pulses to the slings, coordinates with tower crane trolley displacement adjustments and component height changes, so that the steel component's attitude crosses the windward angle zone where the abnormal energy trajectory is located within a preset time, and records this information in the attitude energy memory cloud map. The processing module is further configured to, based on the current energy density distribution and current wind direction oscillation trend of the sheared attitude energy memory cloud map, apply controlled random disturbances to the sling tension distribution and tower crane trolley displacement through a random rewrite channel introduced within a preset amplitude and time constraint range, so that the attitude state point of the sheared steel component falls into an energy-sparse region and prevents the formation of a new periodic self-excited trajectory; the processing module is also configured to construct a two-layer attitude representation structure composed of a main attitude trajectory and an abnormal trajectory layer, using the main attitude trajectory based on the long-term window data and geometric constraints of the main attitude observation node as the basis for hoisting control, and using the abnormal trajectory layer based on the... The short-time window data from the aerodynamically sensitive observation node is used to determine whether to trigger track shearing, so as to limit the self-sustaining micro-oscillation to the internal circulation of the abnormal trajectory layer without affecting the main attitude trajectory. The processing module is also used to delineate high-energy risk areas and low-energy risk areas based on the sheared attitude energy memory cloud map, control the main attitude trajectory to pass through the low-energy risk areas, and set a limit range for the dwell time in the high-energy risk areas. If the dwell time exceeds the limit range and the sheared attitude energy memory cloud map shows the appearance of an abnormal energy trajectory, track shearing and random rewriting are triggered first to prevent the self-sustaining micro-oscillation from evolving into a chronic bias of the main attitude trajectory.

9. An electronic device, characterized in that, The electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. The user interface and the network interface are both used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 7.

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