Intelligent wind power regulation gating method and system for full life cycle

Abstract

The invention discloses a full-life-cycle-oriented intelligent wind power regulation gating method and system, and relates to the technical field of electromechanical control, and the method comprises the steps: constructing a single-degree-of-freedom vibration structure, marking a traction balance point, executing multi-mode detection and laminar flow decomposition to determine effective wind power, and replacing wind power data in multi-mode data; based on the single-degree-of-freedom vibration structure, a gating adjustment module is triggered to carry out gating branch matching and wind power adjustment decision making, and a gating strategy is determined; traction control is carried out through the first micromotor, the second micromotor drives the mechanical damping band-type brake to carry out impedance control, and stable gating management is achieved. The technical problems that an existing door control system is poor in adaptability, low in control precision and high in energy consumption in the complex wind power environment are solved, and the technical effects that through intelligent wind power adjustment and dynamic control, the adaptability and control precision of the door control system in the complex wind power environment are improved, and energy consumption is reduced are achieved.

Description

Technical Field

[0001] The present invention relates to the field of electromechanical control technology, and in particular to a full life cycle-oriented intelligent wind power regulation gate control method and system. Background Art

[0002] In modern industrial and architectural applications, door control systems often operate in complex and changing environments, particularly those affected by wind, which presents numerous challenges to stable door operation. Traditional door control systems, which mostly utilize simple mechanical structures or basic sensor feedback, lack the ability to accurately sense and dynamically adjust to complex environmental factors like wind. For example, in strong or gusty winds, doors are prone to shaking, misalignment, or even damage, which not only impacts the user experience but also poses potential safety risks. Furthermore, traditional systems struggle to adapt to environmental changes over long periods of operation, lacking adaptive learning and optimization capabilities, making efficient energy-saving management impossible. Summary of the Invention

[0003] This application provides an intelligent wind regulation gating method and system for the entire life cycle, which is used to solve the technical problems of poor adaptability, low control accuracy and high energy consumption of existing gating systems in complex wind environments.

[0004] The first aspect of the present application provides an intelligent wind force regulation gating method for the entire life cycle, the method comprising: constructing a single-degree-of-freedom vibration structure based on the door motion mode of the target door body, wherein the single-degree-of-freedom vibration structure is marked with a traction balance point; performing multi-mode detection of the target door body, triggering a laminar flow decomposer, performing laminar flow decomposition based on a single degree of freedom on the wind data according to the wind turbulence state, determining the effective wind force, and replacing the wind force data in the multi-modal data, wherein the laminar flow decomposer is based on the wind portrait and the single-degree-of-freedom vibration structure as the effective wind direction; triggering a gating adjustment module for the multi-modal data, performing gated branch matching based on the gated state, executing a wind force regulation gating decision based on the mechanical motion of the single-degree-of-freedom vibration structure, determining a gating strategy, driving and controlling the motor and the mechanical damping brake, and performing steady-state gating management; wherein, traction control is performed according to the first micromotor, and the mechanical damping brake is driven according to the second micromotor to perform impedance control.

[0005] The second aspect of the present application provides an intelligent wind force regulating door control system for the entire life cycle, the system comprising: a single degree of freedom vibration structure construction module, the single degree of freedom vibration structure construction module is used to construct a single degree of freedom vibration structure based on the door motion mode of the target door body, wherein the single degree of freedom vibration structure is marked with a traction balance point; an effective wind force determination module, the effective wind force determination module is used to perform multi-mode detection of the target door body, trigger a laminar decomposer, perform single-degree-of-freedom laminar decomposition on the wind data according to the wind turbulence state, determine the effective wind force, and replace the multi-modal data. Wind data, wherein the laminar decomposer is based on the wind portrait and the single-degree-of-freedom vibration structure is the effective wind direction; a steady-state gating management module, the steady-state gating management module is used to trigger the gating adjustment module for the multimodal data, perform gated branch matching based on the gated state, execute wind adjustment gating decisions based on the mechanical motion of the single-degree-of-freedom vibration structure, determine the gating strategy, drive and control the motor and the mechanical damping brake, and execute steady-state gating management; wherein, traction control is performed according to the first micromotor, and the mechanical damping brake is driven according to the second micromotor to perform impedance control.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The intelligent wind power regulation gating method and system for the entire life cycle provided by this application relates to the field of electromechanical control technology. By constructing a single-degree-of-freedom vibration structure and identifying the traction balance point, the wind data is processed in combination with multi-mode detection and laminar decomposition technology to determine the effective wind power, and then the gating branches are matched and decided according to the gating state. The dual motors are used for traction control and impedance control respectively to achieve precise wind power regulation and stable gating management, which solves the technical problems of poor adaptability, low control accuracy and high energy consumption of existing gating systems in complex wind environments, and realizes the technical effect of improving the adaptability and control accuracy of the gating system in complex wind environments and reducing energy consumption through intelligent wind power regulation and dynamic control. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic diagram of the process flow of the intelligent wind power regulation gating method for the entire life cycle provided in an embodiment of the present application; Figure 2 Schematic diagram of the structure of the intelligent wind power regulation door control system for the entire life cycle provided in the embodiment of the present application.

[0009] Description of the accompanying drawings: single-degree-of-freedom vibration structure construction module 11, effective wind force determination module 12, steady-state gating management module 13. DETAILED DESCRIPTION

[0010] This application provides an intelligent wind regulation gating method and system for the entire life cycle, which is used to solve the technical problems of poor adaptability, low control accuracy and high energy consumption of existing gating systems in complex wind environments.

[0011] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0012] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0013] Example 1, as Figure 1 As shown, the present application provides a full life cycle intelligent wind power regulation gating method, the method comprising: P10: Based on the door motion mode of the target door body, a single-degree-of-freedom vibration structure is constructed, wherein the single-degree-of-freedom vibration structure is marked with a traction balance point.

[0014] Furthermore, step P10 in the embodiment of the present application further includes: P11: The door motion mode is any one of sliding motion, swinging motion and rotational motion; P12: Based on the door motion mode, the target door body is reconstructed into lightweight three-dimensional motion to determine the single-degree-of-freedom vibration structure; P13: The traction balance point is determined based on the motor specifications, mechanical resistance and wind resistance to constrain the single-degree-of-freedom vibration structure.

[0015] It should be understood that first, an in-depth analysis is performed on the motion pattern of the target door body to construct a single-degree-of-freedom vibration structure and identify the traction balance point.

[0016] Specifically, the target door's motion mode must be identified. This step is the starting point for constructing a single-degree-of-freedom vibration structure. Door motion modes typically include sliding, swinging, and rotational motion. Sliding motion involves the door moving along a linear track and is suitable for applications with limited space, such as common sliding doors. Swinging motion involves the door rotating about a fixed axis and is suitable for applications requiring a larger opening, such as swing doors. Rotational motion involves the door rotating about a central axis and is suitable for applications requiring continuous motion, such as revolving doors. Accurately identifying the target door's motion mode provides a clear direction for subsequent vibration structure design.

[0017] After determining the door's motion pattern, the next step is to perform lightweight 3D kinematic reconstruction of the target door. This process utilizes mathematical modeling and computer-aided design (CAD) techniques to accurately reconstruct the door's motion trajectory and mechanical properties. The goal of lightweight 3D kinematic reconstruction is to improve design efficiency and reduce computing resource consumption while maintaining design accuracy. 3D reconstruction of the door's motion allows for precise determination of specific parameters of the single-degree-of-freedom vibrating structure, such as frequency, amplitude, and damping coefficient.

[0018] During the construction of a single-degree-of-freedom vibrating structure, it is necessary to determine the traction equilibrium point and apply constraints to it. The traction equilibrium point is the stable equilibrium position of the vibrating structure under specific conditions, and its determination requires comprehensive consideration of multiple key factors. First, motor specifications are a key factor influencing the stability of the traction equilibrium point. Parameters such as motor power, torque, and speed directly determine the door's motion and the stability of the vibrating structure. Therefore, selecting the appropriate motor specifications is fundamental to ensuring the stability of the traction equilibrium point. Second, mechanical resistance is also a crucial factor to consider. During movement, the door is subject to mechanical resistance, including rail friction and bearing resistance. These resistances affect the door's motion performance and the dynamic characteristics of the vibrating structure. Accurate calculation and analysis of mechanical resistance can provide an important reference for determining the traction equilibrium point. Finally, wind resistance is also a significant factor influencing the traction equilibrium point. In windy environments, wind resistance significantly affects the movement of the door and the stability of the vibrating structure. Wind profiling technology can accurately analyze the distribution and magnitude of wind resistance, thereby determining the traction equilibrium point.

[0019] Through the above steps, a stable single-degree-of-freedom vibration structure that adapts to the target door motion pattern can be constructed, and the traction balance point can be identified. The determination and constraint of the traction balance point not only ensures the stability of the vibration structure but also provides the data foundation for subsequent wind speed regulation and door control management.

[0020] P20: Perform multi-modal detection of the target door body, trigger the laminar decomposer, perform single-degree-of-freedom laminar decomposition on the wind data according to the wind turbulence state, determine the effective wind force, and replace the wind data in the multi-modal data. The laminar decomposer is based on the wind portrait and the single-degree-of-freedom vibrating structure as the effective wind direction.

[0021] Furthermore, to perform multi-mode detection of the target door, step P20 in this embodiment of the application further includes: P21: Connect a multi-mode sensor, wherein the multi-mode sensor includes at least a motor current sensor, a door accelerometer, and a sonic anemometer; P22: Perform same-frequency detection on the target door based on the multi-mode sensor to determine multi-modal data, wherein the multi-modal data includes real-time traction, door motion status, and wind data.

[0022] Optionally, the wind data is processed by multi-modal detection and laminar decomposition to determine the effective wind and replace the wind data in the multi-modal data.

[0023] When performing multimodal inspection of a target door, the first step is to connect multimodal sensors. These sensors include at least a motor current sensor, a door accelerometer, and an acoustic anemometer. The motor current sensor monitors motor current changes in real time, indirectly reflecting the traction applied to the door during movement. The door accelerometer measures door acceleration, thereby determining its motion state, including speed, position, and vibration. The acoustic anemometer specifically measures wind speed and direction, providing a direct basis for acquiring wind data. By working together, these sensors can comprehensively collect multimodal data related to door movement and the wind environment.

[0024] After the multi-mode sensor connection is complete, the target door needs to be subjected to synchronous frequency detection. This process is achieved by synchronously collecting data such as motor current, door acceleration, and wind speed, ensuring that this data is consistent in time, thereby accurately reflecting the door's motion state and wind environment at a specific moment. Through synchronous frequency detection, multimodal data including real-time traction, door motion state, and wind data can be determined. Real-time traction reflects the force applied by the motor when driving the door, the door motion state includes the door's speed, position, and vibration, and the wind data directly reflects the wind speed and direction in the door's environment.

[0025] After acquiring the multimodal data, the laminar decomposer is triggered. The function of the laminar decomposer is to perform laminar decomposition of the wind data based on a single degree of freedom according to the wind turbulence state. This process can analyze the wind portrait and, combined with the characteristics of the single-degree-of-freedom vibrating structure, decompose the complex wind data into effective wind and other interference components. The wind portrait is a description of wind characteristics, including parameters such as wind speed, wind direction, and turbulence intensity. By analyzing the wind portrait, the characteristics of the wind can be identified more accurately. The single-degree-of-freedom vibrating structure provides a reference for the direction of the effective wind force, ensuring that the decomposed effective wind force is consistent with the direction of movement of the door body.

[0026] Ultimately, the effective wind force determined by the laminar decomposer replaces the original wind data in the multimodal data, ensuring that subsequent gating and adjustment decisions are based on more accurate and reliable wind data. Determining the effective wind force is crucial to the entire wind adjustment process, directly impacting the stability and adjustment accuracy of the gating system. This series of detection and processing steps enables precise measurement and effective decomposition of wind data, providing solid data support for subsequent gating and adjustment.

[0027] Furthermore, the wind data is subjected to single-degree-of-freedom laminar decomposition based on the wind turbulence state to determine the effective wind force. Step P20 of the embodiment of the present application further includes: P23: Based on the wind turbulence state, a multivariate wind portrait is constructed, wherein the wind turbulence state is based on at least seasonal division; P24: For the multivariate wind portrait, a laminar decomposer is constructed with the single-degree-of-freedom vibration structure as the effective force direction; P25: Based on the laminar decomposer, the wind data in the multimodal data is subjected to laminar decomposition and merged based on the effective force direction to determine the effective wind force.

[0028] Specifically, the processing of wind data can be further refined, especially the laminar decomposition and determination of effective wind power under turbulent wind conditions.

[0029] After performing multimodal inspection of the target door and acquiring multimodal data, it is first necessary to construct a multivariate wind portrait based on the wind turbulence state. The wind turbulence state here is at least based on seasonal divisions, because the wind characteristics of different seasons vary significantly. For example, there may be more gusts in spring, strong convective winds in summer, stable monsoons in autumn, and cold northerly winds in winter. By analyzing these seasonal wind characteristics, a detailed wind portrait can be constructed, which includes parameters such as wind speed, wind direction, turbulence intensity, and wind duration. These portraits not only reflect the average characteristics of the wind, but also cover the transient changes and turbulence characteristics of the wind, providing a basis for subsequent wind data processing.

[0030] After constructing the multivariate wind force profiles, the next step is to construct a laminar flow decomposer based on these profiles, using the single-degree-of-freedom vibrating structure as the effective force direction. The force direction of the single-degree-of-freedom vibrating structure refers to the force in the direction of door movement, which is the key force direction for door movement. The laminar flow decomposer is designed to decompose complex wind force data into multiple unidirectional force lines, which reflect the wind force components in different directions. This decomposition can decompose complex vortices into multiple unidirectional force lines and further merge them in the force direction of the single-degree-of-freedom vibrating structure.

[0031] Finally, based on the constructed laminar decomposer, the wind data in the multimodal data is subjected to laminar decomposition and merging based on the effective force direction. This process needs to take into account the direction of movement of the door body, that is, whether the door is open or closed, and merge the decomposed unidirectional force lines into an effective wind force. For example, if the door body is moving in the closing direction, then all force lines in the closing direction will be merged into one effective wind force; if the door body is moving in the opening direction, then all force lines in the opening direction will be merged into one effective wind force. Through the above decomposition and merging process, the effective force exerted by the wind on the door body, that is, the effective wind force, is finally determined. This effective wind force data can be directly used for subsequent door control adjustment decisions.

[0032] P30: For the multimodal data, the gate adjustment module is triggered, the gate branch matching is performed in the gate state, the wind force adjustment gate decision is executed under the mechanical motion of the single-degree-of-freedom vibration structure, the gate strategy is determined, the motor and the mechanical damping brake are driven and controlled, and steady-state gate management is performed; wherein, traction control is performed according to the first micromotor, and the mechanical damping brake is driven according to the second micromotor to perform impedance control.

[0033] Specifically, the gating adjustment module can be used to match gate branches based on multimodal data and gating status, and execute wind power adjustment gating decisions, ultimately determining the gating strategy, driving and controlling the motor and mechanical damping brake, and achieving stable gating management.

[0034] Specifically, after processing wind data and determining effective wind speed, the next step is to trigger the gate control module based on multimodal data. This multimodal data, including real-time traction, door motion status, and processed effective wind speed, provides comprehensive input for gate control. The gate control module analyzes and processes this multimodal data based on the current gate state, selecting the appropriate gate branch for matching.

[0035] Gating branch matching selects the most appropriate gating strategy for the current operating conditions based on the current gating state (such as door opening degree and movement direction) and key parameters from multimodal data. This process comprehensively considers factors such as the door's movement pattern, wind speed, and direction to ensure the accuracy and reliability of gating decisions. For example, if the door is open and the wind is strong, the gating control module may select a strategy that enhances door stability; if the door is closed and the wind is weak, it may choose an energy-saving gating strategy.

[0036] After the gating branches are matched, a wind-regulating gating decision is made based on the mechanical motion of the SDOF vibrating structure. This decision-making process determines the optimal gating strategy based on the mechanical properties of the SDOF vibrating structure and the current wind conditions. The optimal gating strategy is one that adjusts the door's motion parameters (such as speed, acceleration, and traction) to ensure stable operation and minimize energy consumption under the current wind conditions. The SDOF vibrating structure provides a stable mechanical model for the door's motion. By analyzing the dynamic response of this model under varying wind conditions, it is possible to determine how to adjust the door's motion parameters (such as speed and acceleration). Exemplarily, a wind force assessment is first performed, and the impact of the current wind force on the movement of the door body is evaluated based on the effective wind force determined by the laminar flow decomposer; then parameter adjustment is performed, and based on the mechanical model of the single-degree-of-freedom vibration structure, the optimal values ​​of the door body motion parameters (such as speed and acceleration) under the current wind force are calculated. For example, if the wind force is large, the door body movement speed is appropriately reduced to reduce wind resistance; if the wind force is small, the speed can be appropriately increased to improve operating efficiency; then energy consumption optimization is performed, and the output torque of the motor and the damping coefficient of the mechanical damping brake are adjusted to ensure that the door body operates stably while minimizing energy consumption. For example, when the door body approaches the target position, the motor output torque is gradually reduced, and the damping force of the damping brake is used to stop the door body smoothly; finally, real-time feedback and adjustment are performed. During the movement of the door body, the movement state of the door body and wind force changes are monitored in real time, and the control parameters are dynamically adjusted according to the feedback information to ensure that the door body always operates according to the optimal strategy to achieve the best wind force regulation effect.

[0037] After determining the gate control strategy, the motor and mechanical damping brake need to be driven and controlled to achieve stable gate control. Specifically, the first micromotor is responsible for traction control, adjusting the motor's output torque and speed according to the gate control strategy to ensure that the door moves along the predetermined trajectory and speed. For example, if the door needs to open quickly, the first micromotor will increase its output torque and increase its speed; if the door needs to close slowly, the output torque will be reduced accordingly, slowing its speed.

[0038] At the same time, a second micromotor drives the mechanical damping brake for impedance control. The mechanical damping brake provides appropriate damping force during door movement to prevent excessive vibration or unstable movement caused by wind or other external forces. The second micromotor adjusts the damping brake's damping coefficient based on the door control strategy, achieving precise control of door movement. For example, when wind speed is high, the damping brake increases the damping force to enhance door stability; when wind speed is low, the damping brake decreases the damping force to reduce energy consumption.

[0039] By controlling the traction of the first micromotor and the impedance of the second micromotor, the entire door control system achieves stable door control. This dual-motor coordinated control approach not only improves the stability and reliability of the door control system, but also dynamically adjusts the control strategy according to different operating conditions, ensuring stable operation of the door in various wind environments.

[0040] Furthermore, before triggering the gate control adjustment module, the construction of the gate control adjustment module includes: P31a: Obtain gated adjustment records, and determine gated adjustment samples of mechanical state-gated state by performing windage stripping decomposition; P32a: Use the gated adjustment samples to perform mechanical control training on the single-degree-of-freedom vibration structure, determine the gated adjustment module, and embed the gated adjustment module into the gated system platform.

[0041] In a possible embodiment of the present application, in order to ensure that the gating adjustment module can efficiently and accurately execute the wind power adjustment gating decision, the construction process of the gating adjustment module can be further refined.

[0042] Before triggering the gate control module, it must be built. The first step in this process is to obtain gate control records. These records contain operating data of the gate control system under different operating conditions, such as the door's motion state, motor output parameters, wind speed data, and corresponding control actions. By analyzing these records, we can extract sample data related to the mechanical and gate control states. This sample data forms the foundation for building the gate control module.

[0043] Next, the acquired gate control records are subjected to windage stripping decomposition. The purpose of windage stripping decomposition is to isolate the effect of windage on the door's motion from the complex gate control records. By analyzing the variation of windage, the relationship between the mechanical state and the gate control state can be more accurately determined. For example, in a specific wind environment, the door's motion may be significantly affected by windage. Through windage stripping decomposition, this effect can be clearly identified and included as part of the gate control sample.

[0044] Based on the gated control samples obtained after windage stripping and decomposition, mechanical control training is performed on the single-degree-of-freedom vibrating structure. The goal of this training process is to optimize the gated control strategy using the sample data, enabling it to make accurate control decisions based on the current mechanical and gating states. Mechanical control training involves modeling and simulating the dynamic characteristics of the single-degree-of-freedom vibrating structure. By continuously adjusting the control parameters, the gated control module can adapt to different operating conditions and achieve optimal wind control results. Exemplarily, in order to achieve optimal wind force regulation, the mechanical control training process may include the following specific steps: first, modeling and simulation are performed to establish a mechanical model of a single-degree-of-freedom vibration structure, including parameters such as the mass, damping coefficient, and spring stiffness of the door body, and the dynamic response of the door body under different wind forces is simulated through simulation software; then, parameter initialization is performed to set initial control parameters, such as the initial output torque, initial speed, and initial damping coefficient of the damping brake of the motor; then, dynamic adjustment is performed to evaluate the motion stability and energy consumption of the door body under the current control parameters through simulation analysis, and the control parameters are dynamically adjusted based on the evaluation results. For example, if it is found that the door body vibrates more under a certain wind force, the damping coefficient is increased to improve stability; if the energy consumption is too high, the motor output torque is adjusted to reduce energy consumption; then, iterative optimization is performed, and the above steps are repeated to continuously optimize the control parameters until a parameter combination is found that ensures stable door movement and the lowest energy consumption under the current wind conditions; finally, the training results are stored, and the optimized control parameters are stored as part of the gate adjustment module for actual gate operation to achieve the best wind force regulation effect.

[0045] After mechanical control training, the specific parameters and control strategy of the gate control module can be determined. The final step is to embed the trained gate control module into the gate control system's middleware. The gate control system middleware is the control core of the entire gate control system, responsible for coordinating the operation of various subsystems and modules. Embedding the gate control module into the middleware ensures that it can obtain necessary data in real time during system operation and make fast and accurate adjustment decisions based on this data.

[0046] Through the above steps, the constructed gating adjustment module can make accurate wind adjustment decisions based on the real-time mechanical state and gating state, combined with the characteristics of the single-degree-of-freedom vibration structure, thereby improving the intelligence level of the gating system and enhancing its adaptability and stability in complex wind environments.

[0047] Furthermore, step P32a of the embodiment of the present application further includes: P32-1a: The gating state includes a stable state and a motion state, and the motion state includes a door-open state and a door-closed state; P32-2a: The gating adjustment samples are divided according to the stable state, the door-open state and the door-closed state, and the single-degree-of-freedom vibration structure is used as the motion scene to perform multi-threaded mechanical control training under the state thread, determine the first gating branch, the second gating branch and the third gating branch, and perform branch parallel integration to determine the gating adjustment module.

[0048] Optionally, the construction process of the gate control module can be further refined, especially in the mechanical control training stage, by performing more detailed division and multi-threaded training for different gate states to achieve more accurate gate control.

[0049] First, clarify the classification of door control states. According to the embodiment of the present application, the door control state includes a stable state and a motion state, and the motion state is further subdivided into a door-open state and a door-closed state. The stable state refers to the state in which the door body is stationary and not disturbed by external forces; the door-open state refers to the dynamic state in which the door body is in the process of opening; and the door-closed state refers to the dynamic state in which the door body is in the process of closing. The division of these states is based on the actual operating requirements and mechanical characteristics of the door body, providing a clear classification basis for subsequent mechanical control training.

[0050] Based on the aforementioned classification of gate states, gate adjustment samples are categorized. These samples are generated by decomposing gate adjustment records using windage stripping. They contain the motion data of the door under different mechanical conditions and the corresponding adjustment actions. Categorizing these samples into stable, open, and closed states ensures a more targeted and accurate training process.

[0051] Next, multi-threaded mechanical control training is performed under state threads using the single-degree-of-freedom vibrating structure as the motion scenario. Multi-threaded mechanical control training involves simultaneous mechanical control training under different gating states (stable, door-open, and door-closed) to ensure that the gate control module can achieve optimal control in each state. The single-degree-of-freedom vibrating structure provides a stable mechanical model for the door's motion. By performing multi-threaded training under different state threads, the control strategies for the stable, door-open, and door-closed states can be simultaneously optimized. Specifically, the first gating branch is trained for sample data in the stable state; the second gating branch is trained for sample data in the door-open state; and the third gating branch is trained for sample data in the door-closed state. Each branch contains the optimal control strategy for the corresponding state, enabling precise adjustment decisions based on the current mechanical and gating states. Exemplarily, state division is first performed, and the gate adjustment samples are divided according to the gate state (stable state, door open state, door closed state); then thread allocation is performed, and an independent thread is assigned to each state, and mechanical control training is performed separately; then parameter regulation is performed, and in each thread, the control parameters are dynamically adjusted according to the mechanical model and wind conditions of the current state. For example, in the door open state, the traction and speed parameters are adjusted to ensure that the door body can be opened quickly and stably; in the door closed state, the damping coefficient is adjusted to ensure that the door body can be closed smoothly; then synchronous optimization is performed, and the control parameters in different states are optimized simultaneously through multi-threaded parallel processing to improve training efficiency; finally, integration and verification are performed, and the optimized control parameters of each thread are integrated to form a complete gate adjustment module, and the effectiveness and reliability of the module are verified through simulation and actual testing.

[0052] Finally, parallel branch integration is performed, integrating the first, second, and third gating branches to form a complete gating control module. This parallel branch integration ensures that the gating control module can seamlessly switch between different states, achieving comprehensive control of door movement. This parallel integration approach allows the gating control module to quickly select the appropriate control branch based on the real-time gating state, thereby achieving efficient and stable gating management.

[0053] Furthermore, to determine the gating strategy, step P30 of the embodiment of the present application further includes: P31: According to the door movement state, the gating adjustment module is branch matched to determine the target gating branch; P32: The real-time traction, door movement state and effective wind resistance in the multimodal data are input into the target gating branch, and the traction and resistance balance decision under the gating movement is executed to determine the gating strategy.

[0054] It should be understood that in order to achieve accurate gating strategy determination, the relevant steps can be further refined. When executing the wind-adjusted gating decision based on the mechanical motion of the single-degree-of-freedom vibrating structure, the gating adjustment module is first branch-matched according to the actual motion state of the door body. The motion state of the door body mainly includes the stable state, the door-open state and the door-closed state. The stable state refers to the state in which the door body is stationary and not disturbed by external forces; the door-open state refers to the dynamic state of the door body in the process of opening; the door-closed state refers to the dynamic state of the door body in the process of closing. The division of these states can be based on the actual operating requirements and mechanical characteristics of the door body, providing a clear classification basis for the subsequent generation of gating strategies.

[0055] Specifically, the corresponding gating branch is selected based on the actual motion state of the door. For example, if the door is opening, the second gating branch trained for the open state is selected; if the door is closing, the third gating branch trained for the closed state is selected; if the door is stationary, the first gating branch trained for the stable state is selected. This ensures that the gating control module can select the most appropriate control strategy based on the current operating conditions.

[0056] After determining the target gating branch, the real-time traction, door motion state, and effective wind resistance from the multimodal data are input into the target gating branch. These data are key inputs to the gating control decision-making process. The real-time traction reflects the motor's output capacity, the door motion state provides information on the door's current speed and position, and the effective wind resistance is processed wind data, directly reflecting the wind's impact on the door's motion. Based on the input multimodal data, the target gating branch makes decisions on the balance between traction and resistance during the gating motion. The core of this decision-making process is to dynamically adjust the motor's output torque and the damping coefficient of the mechanical damping brake to ensure a balance between traction and resistance in the door's current motion state. For example, if the real-time traction exceeds the effective wind resistance, the gating strategy may reduce the motor's output torque to save energy; if the real-time traction is less than the effective wind resistance, the gating strategy may increase the motor's output torque to ensure smooth door movement.

[0057] Through these balancing decisions, a specific door control strategy is ultimately determined. This strategy includes motor control parameters (such as output torque and speed) and mechanical damping brake control parameters (such as the damping coefficient), ensuring stable and efficient door operation under the current motion state. This entire process not only improves the intelligence level of the door control system but also enhances its adaptability and stability under complex operating conditions, providing a solid technical foundation for the successful implementation of intelligent wind-power-adjusted door control methods.

[0058] Furthermore, the motor and the mechanical damping brake are driven and controlled. Step P30 of the embodiment of the present application further includes: P33: Decompose the gating strategy to determine the traction strategy and the impedance strategy; P34: Use the traction parameter control mechanism to convert the traction strategy into a parameter control to determine the first parameter control; P35: Use the mechanical characteristics of the mechanical damping brake to convert the impedance strategy into a parameter control to determine the second parameter control; P36: Synchronize the first parameter control and the second parameter control with a time stamp, and perform drive control in response to the first micromotor and the second micromotor.

[0059] Specifically, in order to achieve precise drive control of the motor and mechanical damping brake, the gating strategy can be decomposed during the execution of steady-state gating management, and parameter control conversion can be performed separately for the traction strategy and impedance strategy, ultimately achieving synchronous drive control of the dual motors.

[0060] Specifically, after determining the gate control strategy, the first step is to decompose it into the traction strategy and the impedance strategy. The traction strategy primarily involves controlling the motor's output torque and speed, ensuring that the door moves along the predetermined trajectory and speed. The impedance strategy involves adjusting the damping coefficient of the mechanical damping brake to control the damping force during door movement and prevent excessive vibration or unstable movement. By decomposing the gate control strategy into the traction strategy and the impedance strategy, the operating status of the motor and the mechanical damping brake can be more precisely controlled.

[0061] Next, the traction strategy is converted into control parameters. Based on the traction control mechanism, the traction strategy is converted into specific control parameters, known as the first control parameters. The traction control mechanism includes parameters such as the motor's power output, speed regulation, and torque control. For example, if the gating strategy requires increased traction during the gate opening process, the first control parameters would include the required increased motor output torque and the corresponding speed adjustment parameters. In this way, the traction strategy is converted into specific control instructions that the motor can directly execute.

[0062] Simultaneously, the impedance strategy is converted into specific control parameters based on the mechanical characteristics of the mechanical damping brake. These parameters, known as the second control parameters, include the damping coefficient, response time, and operating range. For example, if the gate control strategy requires increased damping force during the closing process, the second control parameters will include the damping coefficient and corresponding response time parameters that need to be adjusted for the mechanical damping brake. In this way, the impedance strategy is converted into specific control instructions that can be directly executed by the mechanical damping brake.

[0063] After determining the primary and secondary control parameters, they need to be synchronized with each other using a timestamp. This synchronization ensures that the first and second micromotors maintain time synchronization when executing drive control. For example, if the door requires both increased traction and damping force adjustment during opening, the first and second micromotors must execute the corresponding control commands at the same time. By adding synchronized timestamps to the primary and second control parameters, the dual motors can work together, ensuring stable and reliable door movement.

[0064] Finally, drive control is performed in response to the first and second micromotors. The first micromotor performs traction control based on the first control parameter, adjusting the motor's output torque and speed to ensure the door moves along the predetermined trajectory and speed. The second micromotor, based on the second control parameter, drives the mechanical damping brake, adjusting the damping coefficient and controlling the damping force during door movement. Through the coordinated control of the two motors, stable door control is achieved, ensuring stable and reliable operation under various operating conditions.

[0065] Furthermore, after performing the steady-state gating management, the embodiment of the present application further includes step P40a, which further includes: P41a: Based on the periodic gating records, the abnormal gating features of non-steady gating are mined; P42a: Based on the abnormal gating features, the gating adjustment module is updated and learned.

[0066] Optionally, after executing steady-state gate control management, this embodiment of the present application further introduces step P40a to achieve continuous optimization and improvement of the gate control system. During gate control system operation, periodic gate control records can be regularly generated. These records contain operating data of the gate control system during each cycle, such as motor output parameters, door motion status, wind speed data, and corresponding adjustment actions. These records are an important basis for evaluating gate control system performance. To further improve system stability and reliability, in-depth analysis of these periodic gate control records is required.

[0067] First, based on periodic gating records, we mine abnormal gating features that indicate non-steady gating. Non-steady gating refers to situations in which the gating system fails to achieve the expected steady state. For example, problems such as door jitter, uneven speed, or position deviation occur during door movement. By analyzing periodic gating records, we can identify these abnormalities and extract their associated features. These abnormal gating features may include sudden changes in motor current, abnormal fluctuations in door acceleration, and unusual changes in wind speed data. By mining these features, we can more accurately identify potential problems in system operation.

[0068] Next, based on the discovered abnormal gating features, the gating control module undergoes updated learning. This process uses the abnormal gating features as new training samples and feeds them into the gating control module, retraining and optimizing it. The goal of this updated learning process is to enable the gating control module to adjust its control strategy based on the new data, thereby better responding to similar abnormal situations. For example, if a door is prone to vibrating under certain wind conditions, the updated gating control module will automatically adjust the motor's output torque and damping coefficient under similar conditions to reduce vibration and ensure stable door movement.

[0069] Through the above steps, periodic recording, analysis, and updated learning can further enhance the system's adaptability and reliability. This process reflects the continuous optimization characteristics of the intelligent door control system, which can continuously adjust and improve the control strategy based on actual operating data, ensuring that the door control system can maintain efficient and stable operation under various complex operating conditions.

[0070] In summary, the embodiments of the present application have at least the following technical effects: This application constructs a single-degree-of-freedom vibration structure and performs laminar decomposition based on wind turbulence state, which can accurately process wind data and determine the effective wind force, thereby improving the stability of the door body in complex wind environments and achieving high-precision wind force regulation; based on multi-modal data, the gate branch matching and wind force regulation decision-making can realize dynamic adjustment of the door body movement to ensure accurate gate management under different working conditions; through periodic gate record analysis and update learning, it can identify abnormal characteristics of non-steady gate control, and optimize the gate control adjustment module accordingly to improve the system's adaptability and long-term operation reliability; by dynamically adjusting the motor output torque and the damping coefficient of the mechanical damping brake, the balance of traction and resistance is achieved, energy consumption is reduced, the energy-saving effect of the system is improved, and the efficient and stable operation of the gate control system throughout its life cycle is ensured.

[0071] The technical effect of improving the adaptability and control accuracy of the door control system in complex wind environments and reducing energy consumption is achieved through intelligent wind regulation and dynamic control.

[0072] The second embodiment is based on the same inventive concept as the intelligent wind power regulation gate control method for the entire life cycle in the above embodiment. Figure 2 As shown, the present application provides an intelligent wind power regulation door control system for the entire life cycle. The system and method embodiments in the present application are based on the same inventive concept. The system includes: A single-degree-of-freedom vibration structure construction module 11 is used to construct a single-degree-of-freedom vibration structure based on the door motion mode of the target door body, wherein the single-degree-of-freedom vibration structure is marked with a traction balance point.

[0073] The effective wind force determination module 12 is used to perform multi-mode detection of the target door body, trigger the laminar decomposer, perform laminar decomposition based on single degree of freedom on the wind data according to the wind turbulence state, determine the effective wind force, and replace the wind force data in the multi-modal data, wherein the laminar decomposer is based on the wind portrait and takes the single degree of freedom vibrating structure as the effective wind direction.

[0074] The steady-state gating management module 13 is used to trigger the gating adjustment module for the multimodal data, perform gating branch matching based on the gating state, execute wind force adjustment gating decision based on the mechanical motion of the single-degree-of-freedom vibrating structure, determine the gating strategy, drive and control the motor and the mechanical damping brake, and perform steady-state gating management; wherein, traction control is performed according to the first micromotor, and the mechanical damping brake is driven according to the second micromotor to perform impedance control.

[0075] Furthermore, the single-degree-of-freedom vibration structure building module 11 is further configured to perform the following steps: The door motion mode is any one of sliding motion, swinging motion and rotational motion; according to the door motion mode, the target door body is reconstructed into lightweight three-dimensional motion to determine the single-degree-of-freedom vibration structure; the traction balance point is determined based on the motor specifications, mechanical resistance and wind resistance to constrain the single-degree-of-freedom vibration structure.

[0076] Furthermore, the effective wind force determination module 12 is further configured to perform the following steps: Connect a multi-mode sensor, wherein the multi-mode sensor includes at least a motor current sensor, a door body accelerometer, and a sonic anemometer; perform same-frequency detection on the target door body according to the multi-mode sensor to determine multi-modal data, wherein the multi-modal data includes real-time traction, door body motion status and wind data.

[0077] Furthermore, the effective wind force determination module 12 is further configured to perform the following steps: A multivariate wind portrait is constructed based on the wind turbulence state, wherein the wind turbulence state is based on at least seasonal division; for the multivariate wind portrait, a laminar decomposer is constructed with the single-degree-of-freedom vibrating structure as the effective force direction; based on the laminar decomposer, the wind data in the multimodal data are subjected to laminar decomposition and merging based on the effective force direction to determine the effective wind force.

[0078] Furthermore, the steady-state gating management module 13 is further configured to perform the following steps: Obtain gated adjustment records, determine gated adjustment samples of the mechanical state-gated state by performing windage stripping decomposition; use the gated adjustment samples to perform mechanical control training on the single-degree-of-freedom vibration structure, determine a gated adjustment module, and embed the gated adjustment module into the gated system platform.

[0079] Furthermore, the steady-state gating management module 13 is further configured to perform the following steps: The gate adjustment samples are divided according to the stable state, the door open state and the door closed state, and the single-degree-of-freedom vibration structure is used as the motion scene to perform multi-threaded mechanical control training under the state thread to determine the first gate branch, the second gate branch and the third gate branch, and perform branch parallel integration to determine the gate adjustment module.

[0080] Furthermore, the steady-state gating management module 13 is further configured to perform the following steps: According to the door movement state, the gating adjustment module is branch matched to determine the target gating branch; the real-time traction, door movement state and effective wind resistance in the multimodal data are input into the target gating branch, and the balance decision of traction and resistance under the gating movement is executed to determine the gating strategy.

[0081] Furthermore, the steady-state gating management module 13 is further configured to perform the following steps: The gating strategy is decomposed to determine a traction strategy and an impedance strategy; the traction strategy is converted into a first parameter control using a traction parameter control mechanism; the impedance strategy is converted into a second parameter control using the mechanical characteristics of a mechanical damping brake; the first and second parameter controls are synchronized with time stamps, and drive control is performed in response to the first micromotor and the second micromotor.

[0082] Furthermore, the system further includes an update learning module, configured to perform the following steps: According to the periodic gating records, the abnormal gating features of the non-steady gating are mined; and according to the abnormal gating features, the gating adjustment module is updated and learned.

[0083] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0084] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0085] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. An intelligent wind power regulation gating method for the entire life cycle, characterized in that: The method comprises: Based on the door motion mode of the target door body, a single-degree-of-freedom vibration structure is constructed, wherein the single-degree-of-freedom vibration structure is marked with a traction balance point; Perform multimodal detection of the target door body, trigger the laminar decomposer, perform single-degree-of-freedom laminar decomposition on the wind data based on the wind turbulence state, determine the effective wind force, and replace the wind force data in the multimodal data, wherein the laminar decomposer is based on the wind portrait and uses the single-degree-of-freedom vibrating structure as the effective wind direction; Based on the multimodal data, the gate adjustment module is triggered to match the gate branches with the gate state, execute the wind force adjustment gate control decision under the mechanical motion of the single-degree-of-freedom vibrating structure, determine the gate control strategy, drive and control the motor and mechanical damping brake, and perform steady-state gate control management; The traction control is performed according to the first micromotor, and the impedance control is performed by driving the mechanical damping brake according to the second micromotor.

2. The intelligent wind power regulation and gating method for the entire life cycle according to claim 1 is characterized in that: The door movement mode is any one of sliding movement, casement movement and rotation movement; Performing lightweight three-dimensional motion reconstruction on the target door body according to the door motion pattern to determine the single-degree-of-freedom vibration structure; The traction balance point is determined based on the motor specifications, mechanical resistance, and wind resistance, and the single-degree-of-freedom vibration structure is constrained.

3. The intelligent wind power regulation and gating method for the entire life cycle according to claim 1 is characterized in that: Perform multi-modal detection of the target portal, including: Connecting a multi-mode sensor, wherein the multi-mode sensor includes at least a motor current sensor, a door accelerometer, and a sonic anemometer; According to the multi-mode sensor, the target door body is subjected to same-frequency detection to determine multi-modal data, wherein the multi-modal data includes real-time traction, door body motion state and wind data.

4. The intelligent wind power regulation and gating method for the entire life cycle according to claim 1 is characterized in that: Based on the wind turbulence state, the wind data is subjected to single-degree-of-freedom laminar decomposition to determine the effective wind force, including: Constructing a multivariate wind profile based on wind turbulence states, where the wind turbulence states are at least seasonally classified; According to the multivariate wind force portrait, a laminar flow decomposer is constructed with the single-degree-of-freedom vibration structure as the effective force direction; According to the laminar decomposer, the wind data in the multimodal data are subjected to laminar decomposition and merged based on effective force directions to determine effective wind force.

5. The intelligent wind power regulation and gating method for the entire life cycle according to claim 1 is characterized in that: Before triggering the gate control adjustment module, the construction of the gate control adjustment module includes: Obtain gated regulation records, and determine gated regulation samples of mechanical state-gated state by performing windage stripping decomposition; The gated adjustment sample is used to perform mechanical control training on the single-degree-of-freedom vibration structure, determine a gated adjustment module, and embed the gated adjustment module into the gated system platform.

6. The intelligent wind power regulation and gating method for the entire life cycle according to claim 5, characterized in that: The door control state includes a stable state and a motion state, and the motion state includes a door open state and a door closed state; The gate adjustment samples are divided according to the stable state, the door open state and the door closed state, and the single-degree-of-freedom vibration structure is used as the motion scene to perform multi-threaded mechanical control training under the state thread to determine the first gate branch, the second gate branch and the third gate branch, and perform branch parallel integration to determine the gate adjustment module.

7. The intelligent wind power regulation and gating method for the entire life cycle according to claim 6, characterized in that: Determine the gating strategy, including: According to the door movement state, the gate control adjustment module is branch matched to determine the target gate control branch; The real-time traction, door motion state and effective wind resistance in the multimodal data are input into the target gating branch, and a balanced decision of traction and resistance under the gating motion is executed to determine the gating strategy.

8. The intelligent wind power regulation and gating method for the entire life cycle according to claim 7, characterized in that: Drive control of the motor and mechanical damping brake, including: Decomposing the gating strategy to determine a pulling strategy and an impedance strategy; Performing parameter control conversion on the traction strategy using a traction parameter control mechanism to determine a first parameter control; Performing parameter control conversion on the impedance strategy based on the mechanical characteristics of the mechanical damping brake to determine a second parameter control; The first parameter control and the second parameter control are synchronously time-stamped, and drive control is performed in response to the first micromotor and the second micromotor.

9. The intelligent wind power regulation and gating method for the entire life cycle according to claim 1, characterized in that: After implementing steady-state gating management, including: Based on periodic gating records, we can mine abnormal gating features of non-stationary gating. The gating adjustment module is updated and learned according to the abnormal gating feature.

10. The intelligent wind power regulation door control system for the entire life cycle is characterized by: The system comprises: a single-degree-of-freedom vibration structure construction module, the single-degree-of-freedom vibration structure construction module being used to construct a single-degree-of-freedom vibration structure based on a door motion mode of a target door body, wherein the single-degree-of-freedom vibration structure is marked with a traction balance point; An effective wind force determination module is used to perform multimodal detection of the target door body, trigger a laminar flow decomposer, perform single-degree-of-freedom laminar flow decomposition on the wind data based on the wind turbulence state, determine the effective wind force, and replace the wind force data in the multimodal data. The laminar flow decomposer is based on the wind force profile and uses the single-degree-of-freedom vibrating structure as the effective wind direction; a steady-state gating management module, which is used to trigger the gating adjustment module based on the multimodal data, perform gating branch matching based on the gating state, execute wind force adjustment gating decisions based on the mechanical motion of the single-degree-of-freedom vibrating structure, determine the gating strategy, drive and control the motor and mechanical damping brake, and perform steady-state gating management; The traction control is performed according to the first micromotor, and the impedance control is performed by driving the mechanical damping brake according to the second micromotor.

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