Intelligent wind regulating door control method and system facing whole life cycle

Abstract

The application discloses a full-life-cycle-oriented intelligent wind regulation door control method and system, relates to the technical field of electromechanical control, and comprises the following steps: a single-degree-of-freedom vibration structure is constructed, and a traction balance point is identified; multi-mode detection and laminar flow decomposition are performed to determine effective wind power, and wind power data in multi-mode data is replaced; based on the single-degree-of-freedom vibration structure, a door control adjustment module is triggered to perform door control branch matching and wind power adjustment decision, and a door control strategy is determined; traction control is performed through a first micro motor, and impedance control is performed through a second micro motor driving a mechanical damping brake, so that stable and constant door control management is realized. The application solves the technical problems that the existing door control system has poor adaptability, low control precision and high energy consumption in a complex wind power environment, achieves the technical effects that the adaptability and control precision of the door control system in the complex wind power environment are improved through intelligent wind power adjustment and dynamic control, and energy consumption is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electromechanical control, in particular to a full-life-cycle-oriented intelligent wind regulation door control method and system. BACKGROUND

[0002] In modern industrial and building applications, door control systems are often in complex and variable environments, especially affected by wind, which brings many challenges to the stable operation of the door body. Traditional door control systems mostly use simple mechanical structures or basic sensor feedback, lacking precise perception and dynamic adjustment capabilities for complex environmental factors such as wind. For example, in strong wind or gusty environments, the door body is prone to shaking, position deviation, and even damage, which not only affects the user experience, but also may cause safety hazards. In addition, traditional systems are difficult to adapt to environmental changes in long-term operation, lacking self-adaptive learning and optimization capabilities, and cannot achieve efficient and energy-saving management. SUMMARY

[0003] The present application provides a full-life-cycle-oriented intelligent wind regulation door control method and system to solve the technical problems of poor adaptability, low control precision, and high energy consumption of existing door control systems in complex wind environments.

[0004] In a first aspect, the present application provides a full-life-cycle-oriented intelligent wind regulation door control method, which comprises: based on the door motion mode of a target door body, constructing a single-degree-of-freedom vibration structure, 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 resolver, performing single-degree-of-freedom-based laminar flow decomposition on wind data according to wind turbulence state to determine effective wind, and replacing wind data in multi-modal data, wherein the laminar flow resolver is based on wind portrait and single-degree-of-freedom vibration structure as effective wind direction; for the multi-modal data, triggering a door control adjustment module to perform door control branch matching in a door control state, performing wind regulation door control decision under the mechanical motion based on the single-degree-of-freedom vibration structure, determining the door control strategy, driving control of the motor and mechanical damping brake, and performing stable door control management; wherein the traction control is performed according to the first micro motor, and the impedance control is performed according to the second micro motor driving the mechanical damping brake.

[0005] In a second aspect of the present application, a full-life-cycle-oriented intelligent wind regulation door control system is provided, which comprises: a single-degree-of-freedom vibration structure construction module, configured to construct a single-degree-of-freedom vibration structure based on a door movement 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, configured to perform multi-mode detection of the target door body, trigger a laminar flow resolver, perform single-degree-of-freedom-based laminar flow decomposition on wind force data according to wind turbulence states, determine effective wind force, and replace wind force data in multi-mode data, wherein the laminar flow resolver is based on a wind force image and the single-degree-of-freedom vibration structure is an effective wind direction; and a steady door control management module, configured to trigger a door control adjustment module for the multi-mode data, perform door control branch matching in a door control state, perform wind regulation door control decision-making in a mechanical movement based on the single-degree-of-freedom vibration structure, determine a door control strategy, and drive control of a motor and a mechanical damping brake to perform steady door control management; wherein the first micro motor is used for traction control, and the second micro motor is used for impedance control of the mechanical damping brake.

[0006] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0007] The full-life-cycle-oriented intelligent wind regulation door control method and system provided in the present application relate to electromechanical control technology. The single-degree-of-freedom vibration structure is constructed and the traction balance point is marked. The wind force data is processed by combining the multi-mode detection and the laminar flow decomposition technology to determine the effective wind force. The door control branch matching and decision-making are performed according to the door control state. The double motors are used for traction control and impedance control, respectively, to realize accurate wind regulation and steady door control management. The technical problem of poor adaptability, low control precision, and high energy consumption of the existing door control system in a complex wind environment is solved. The technical effect of improving the adaptability and control precision of the door control system in the complex wind environment and reducing the energy consumption by intelligent wind regulation and dynamic control is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0009] Figure 1 The full-life-cycle-oriented intelligent wind regulation door control method flowchart provided in the embodiments of the present application is shown in the figure.

[0010] Figure 2A structure diagram of a full-life-cycle-oriented intelligent wind regulation door control system is provided in the embodiments of the present application.

[0011] The reference signs are explained as follows: single-degree-of-freedom vibration structure construction module 11, effective wind force determination module 12, and steady door control management module 13. DETAILED DESCRIPTION

[0012] The present application provides a full-life-cycle-oriented intelligent wind regulation door control method and system, which are used to solve the technical problems of poor adaptability, low control precision, and high energy consumption of the existing door control system in a complex wind environment.

[0013] The technical solutions in the embodiments of the present application will be clearly and completely described in the embodiments of the present application in combination with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0014] It should be noted that the terms "first", "second", and the like in the specification and the above drawings of the present application are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, 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 "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0015] Embodiment one, as shown in the present application, provides a full-life-cycle-oriented intelligent wind regulation door control method, which comprises the following steps: Figure 1

[0016] P10: constructing a single-degree-of-freedom vibration structure based on a door movement mode of a target door body, wherein the single-degree-of-freedom vibration structure is identified with a traction balance point.

[0017] Further, the step P10 of the embodiments of the present application further comprises:

[0018] P11: the door movement mode is any one of sliding movement, flat opening movement, and rotating movement; P12: performing lightweight three-dimensional motion reconstruction on the target door body according to the door movement mode to determine the single-degree-of-freedom vibration structure; and P13: determining a traction balance point based on motor specifications, mechanical resistance, and wind resistance to constrain the single-degree-of-freedom vibration structure.​

[0019] It should be understood that the motion pattern of the target door body is first analyzed in depth, thereby constructing a single-degree-of-freedom vibration structure and identifying a traction balance point.

[0020] Specifically, it is necessary to first determine the motion pattern of the target door body, which is the starting point of constructing a single-degree-of-freedom vibration structure. The motion pattern of the door body generally includes sliding motion, swing motion, and rotating motion. The sliding motion refers to the movement of the door body along a straight track, which is suitable for scenarios with limited space, such as common sliding doors. The swing motion refers to the rotation of the door body around a fixed axis, which is suitable for scenarios requiring a larger opening, such as swing doors. The rotating motion refers to the rotation of the door body around the central axis, which is suitable for scenarios requiring continuous motion, such as rotating doors. By accurately identifying the motion pattern of the target door body, a clear direction can be provided for subsequent vibration structure design.

[0021] After determining the motion pattern of the door body, the next step is to perform lightweight three-dimensional motion reconstruction on the target door body. This process can be achieved through mathematical modeling and computer-aided design (CAD) techniques to accurately reconstruct the motion trajectory and mechanical properties of the door body. The purpose of lightweight three-dimensional motion reconstruction is to improve design efficiency and reduce the consumption of computing resources while ensuring design accuracy. Through three-dimensional reconstruction of the door body motion, the specific parameters of the single-degree-of-freedom vibration structure can be accurately determined, such as vibration frequency, amplitude, and damping coefficient.

[0022] In the process of constructing a single-degree-of-freedom vibration structure, it is also necessary to determine the traction balance point and constrain it. The traction balance point is the stable equilibrium position of the vibration structure under certain conditions, and its determination needs to consider multiple key factors. First, the motor specification is one of the key factors affecting the stability of the traction balance point. The power, torque, and speed of the motor directly determine the motion ability of the door body and the stability of the vibration structure. Therefore, selecting the appropriate motor specification is the basis for ensuring the stability of the traction balance point. Second, mechanical resistance is also an important factor to consider. The door body will be affected by mechanical resistance during movement, including track friction, bearing resistance, etc. These resistances will affect the motion performance of the door body and the dynamic characteristics of the vibration structure. By accurately calculating and analyzing the mechanical resistance, important references can be provided for the determination of the traction balance point. Finally, wind resistance is also one of the important factors affecting the traction balance point. In a wind environment, wind resistance has a significant impact on the motion of the door body and the stability of the vibration structure. Through wind portrait technology, the distribution and size of wind resistance can be accurately analyzed to determine the traction balance point.

[0023] Through the above steps, a single-degree-of-freedom vibration structure that is stable and adaptive to the target door body motion mode can be constructed, and the traction equilibrium point is identified. The determination and constraint of the traction equilibrium point not only ensure the stability of the vibration structure, but also provide a data basis for subsequent wind regulation and door management.

[0024] P20: performing multi-mode detection of the target door body, triggering a laminar flow resolver, performing single-degree-of-freedom-based laminar flow decomposition on wind data according to wind turbulence states to determine effective wind and replace wind data in multi-modal data, wherein the laminar flow resolver takes wind images as the basis and takes a single-degree-of-freedom vibration structure as the effective wind direction.

[0025] Further, performing multi-mode detection of the target door body, the embodiment of the application step P20 further comprises:

[0026] P21: connecting multi-mode sensors, wherein the multi-mode sensors at least include motor current sensors, door body accelerometers, and acoustic anemometers; P22: performing same-frequency detection on the target door body according to the multi-mode sensors to determine multi-modal data, wherein the multi-modal data includes real-time traction, door body motion state, and wind data.

[0027] Optionally, through multi-mode detection and laminar flow decomposition, wind data is processed to determine effective wind and replace wind data in multi-modal data.

[0028] When performing multi-mode detection of the target door body, multi-mode sensors need to be connected first. These sensors at least include motor current sensors, door body accelerometers, and acoustic anemometers. The motor current sensor is used to monitor the current change of the motor in real time, thereby indirectly reflecting the traction force received by the door body during motion; the door body accelerometer is used to measure the acceleration of the door body, thereby determining the motion state of the door body, including speed, position, and vibration; the acoustic anemometer is specifically used to measure wind speed and direction, providing a direct basis for wind data acquisition. Through the cooperative work of these sensors, multi-modal data related to door body motion and wind environment can be comprehensively collected.

[0029] After the multi-mode sensors are connected, the target door body needs to be detected at the same frequency next. This process is to synchronize the collection of motor current, door body acceleration, and wind speed data, ensuring that these data are consistent in time, so as to accurately reflect the motion state of the door body at a specific moment and the wind environment it is in. Through same-frequency detection, multi-modal data including real-time traction, door body motion state, and wind data can be determined. Real-time traction reflects the force exerted by the motor when driving the door body to move, the door body motion state includes the speed, position, and vibration of the door body, and the wind data directly reflects the wind speed and direction in the environment where the door body is located.

[0030] After acquiring the multi-modal data, the laminar flow resolver is triggered. The role of the laminar flow resolver is to perform single-degree-of-freedom-based laminar flow decomposition on the wind force data according to the wind turbulence state. This process can be achieved by analyzing the wind force image, combining the characteristics of the single-degree-of-freedom vibration structure, and decomposing the complex wind force data into effective wind force and other interference components. The wind force image is a description of the wind force characteristics, including wind speed, wind direction, turbulence intensity, and other parameters. Through the analysis of the wind force image, the characteristics of the wind force can be more accurately identified. The single-degree-of-freedom vibration structure provides a reference for the direction of the effective wind force, ensuring that the decomposed effective wind force is consistent with the movement direction of the door body.

[0031] Finally, the effective wind force determined by the laminar flow resolver will replace the original wind force data in the multi-modal data to ensure that the subsequent door control adjustment decisions can be based on more accurate and reliable wind force data. The determination of the effective wind force is the key to the entire wind force adjustment process, which directly affects the stability and adjustment accuracy of the door control system. Through this series of detection and processing steps, accurate measurement and effective decomposition of wind force data can be achieved, providing solid data support for subsequent door control adjustment.

[0032] Further, according to the wind turbulence state, single-degree-of-freedom-based laminar flow decomposition is performed to determine the effective wind force. The step P20 of the embodiment of the present application further includes:

[0033] P23: constructing a multi-element wind force image according to the wind turbulence state, wherein the wind turbulence state is at least based on seasonal division; P24: constructing a laminar flow resolver with the single-degree-of-freedom vibration structure as the effective force direction for the multi-element wind force image; P25: performing laminar flow decomposition and effective force direction-based merging on the wind force data in the multi-modal data according to the laminar flow resolver to determine the effective wind force.

[0034] Specifically, the processing process of the wind force data can be further refined, especially the laminar flow decomposition under the wind turbulence state and the determination of the effective wind force.

[0035] After performing multi-modal detection of the target door body and acquiring multi-modal data, a multi-element wind force image needs to be constructed according to the wind turbulence state. The wind turbulence state here is at least based on seasonal division because the wind characteristics in different seasons are significantly different. For example, there may be more gusts in spring, stronger convective winds in summer, stable monsoons in autumn, and cold north winds in winter. By analyzing these seasonal wind characteristics, a detailed wind force image can be constructed, which contains parameters such as wind speed, wind direction, turbulence intensity, and wind duration. These images 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 force data processing.

[0036] After constructing the multi-element wind image, the next step is to construct a laminar flow resolver for these images, with the single-degree-of-freedom vibration structure as the effective force direction. The force direction of the single-degree-of-freedom vibration structure refers to the force in the direction of door movement, which is the key force direction of door movement. The purpose of designing the laminar flow resolver is to decompose complex wind data into multiple single-direction force lines, which reflect the components of wind force in different directions. Through this decomposition, complex rotational flow can be decomposed into multiple single-direction force lines, and further combined in the force direction of the single-degree-of-freedom vibration structure.

[0037] Finally, according to the constructed laminar flow resolver, the wind data in the multi-modal data is subjected to laminar flow decomposition and combination based on the effective force direction. This process needs to consider the movement direction of the door, i.e. whether the door is in the opening or closing state, and combine the decomposed single-direction force lines into an effective wind force. For example, if the door is moving in the closing direction, all the force lines in the closing direction will be combined into an effective wind force; if the door is moving in the opening direction, all the force lines in the opening direction will be combined into an effective wind force. Through the above decomposition and combination process, the effective application force of wind on the door, i.e. the effective wind force, is finally determined. This effective wind force data can be directly used for subsequent door control adjustment decision.

[0038] P30: For the multi-modal data, a door control adjustment module is triggered to perform door control branch matching in the door control state, execute wind force adjustment door control decision based on the mechanical movement of the single-degree-of-freedom vibration structure, determine the door control strategy, drive control the motor and mechanical damping brake, and perform stable door control management; wherein the first micro motor is used for traction control, and the second micro motor is used to drive the mechanical damping brake for impedance control.

[0039] Specifically, the door control adjustment module can be used to perform door control branch matching based on multi-modal data and door control state, and execute wind force adjustment door control decision to finally determine the door control strategy, drive control the motor and mechanical damping brake, and realize stable door control management.

[0040] Specifically, after the processing of wind force data and the determination of effective wind force, the next step is to trigger the door control adjustment module for the multi-modal data. These multi-modal data include real-time traction force, door movement state and processed effective wind force, etc., providing comprehensive input information for door control adjustment. The role of the door control adjustment module is to analyze and process these multi-modal data according to the current door control state, so as to select the appropriate door control branch for matching.

[0041] Gating branch matching is based on the current gating state (such as the degree of opening of the door, the direction of movement, etc.) and key parameters in multi-modal data to select the most suitable gating strategy for the current working condition. This process needs to consider factors such as the movement mode of the door body, wind size and direction, etc. to ensure the accuracy and reliability of the gating decision. For example, if the door body is in an open state and the wind is strong, the gating adjustment module may choose a strategy that can enhance the stability of the door body; if the door body is in a closed state and the wind is weak, it may choose an energy-saving gating strategy.

[0042] After the gating branch matching is completed, the wind regulation gating decision under the mechanical motion of the single degree of freedom vibration structure is executed. This decision-making process can determine the optimal gating strategy based on the mechanical properties of the single degree of freedom vibration structure and the current wind conditions. The optimal gating strategy refers to the control strategy that can make the door body run stably and minimize energy consumption by adjusting the movement parameters (such as speed, acceleration, traction, etc.) of the door body under the current wind conditions. The single degree of freedom vibration structure provides a stable mechanical model for the movement of the door body, and by analyzing the dynamic response of this model under different wind forces, it can determine how to adjust the movement parameters (such as speed, acceleration, etc.) of the door body. For example, first, wind assessment is performed to evaluate the impact of the current wind on the movement of the door body based on the effective wind force determined by the laminar flow resolver; then, parameter adjustment is performed to calculate the optimal values of the movement parameters (such as speed, acceleration) of the door body under the current wind force based on the mechanical model of the single degree of freedom vibration structure, for example, if the wind is strong, the speed of the door body can be appropriately reduced to reduce wind resistance; if the wind is weak, the speed can be appropriately increased to improve operating efficiency; then, energy consumption optimization is performed to ensure that the door body runs stably while minimizing energy consumption by adjusting the output torque of the motor and the damping coefficient of the mechanical damping brake, for example, when the door body approaches the target position, gradually reduce the output torque of the motor, and use the damping force of the damping brake to stop the door body smoothly; finally, real-time feedback and adjustment are performed to monitor the movement state of the door body and the change of wind force in real time during the movement of the door body, and dynamically adjust the control parameters according to the feedback information to ensure that the door body always runs according to the optimal strategy to achieve the best wind regulation effect.

[0043] After the gating strategy is determined, the motor and mechanical damping brake need to be driven and controlled to achieve stable and constant gating management. Specifically, the first micro motor is responsible for traction control and adjusts the output torque and speed of the motor according to the gating strategy to ensure that the door body moves according to the predetermined trajectory and speed. For example, if the door body needs to be quickly opened, the first micro motor will increase the output torque to increase the movement speed of the door body; if the door body needs to be slowly closed, the output torque will be correspondingly reduced to reduce the movement speed.

[0044] Meanwhile, the second micro motor drives the mechanical damping brake to control the impedance. The role of the mechanical damping brake is to provide appropriate damping force during the movement of the door body to prevent excessive vibration or unstable movement of the door body due to wind force or other external forces. The second micro motor adjusts the damping coefficient of the damping brake according to the door control strategy, thereby realizing precise control of the movement of the door body. For example, when the wind force is large, the damping brake increases the damping force to enhance the stability of the door body; when the wind force is small, the damping brake reduces the damping force to reduce energy consumption.

[0045] Through the traction control of the first micro motor and the impedance control of the second micro motor, the entire door control system can realize stable and constant door control management. This dual-motor cooperative control method not only improves the stability and reliability of the door control system, but also dynamically adjusts the control strategy according to different working conditions to ensure stable operation of the door body in various wind environments.

[0046] Further, before triggering the door control adjustment module, the construction of the door control adjustment module includes:

[0047] P31a: Obtain door control adjustment records, and determine door control adjustment samples of the mechanical state-door control state by performing wind resistance stripping decomposition; P32a: Perform mechanical control training on the single-degree-of-freedom vibration structure with the door control adjustment samples to determine a door control adjustment module, and embed the door control adjustment module in the door control system.

[0048] In a possible embodiment of the present application, in order to ensure that the door control adjustment module can efficiently and accurately execute wind force adjustment door control decisions, the construction process of the door control adjustment module can be further refined.

[0049] Before triggering the door control adjustment module, the door control adjustment module needs to be constructed first. The first step of the construction process is to obtain door control adjustment records. These records contain the running data of the door control system under different working conditions, such as the movement state of the door body, the output parameters of the motor, the wind force data, and the corresponding adjustment actions. By analyzing these records, sample data related to the mechanical state and the door control state can be extracted, which are the basis for constructing the door control adjustment module.

[0050] Next, the obtained door control adjustment records are subjected to wind resistance stripping decomposition. The purpose of wind resistance stripping decomposition is to separate the influence of wind resistance on the movement of the door body from complex door control adjustment records. By analyzing the variation law of wind resistance, the relationship between the mechanical state and the door control state can be more accurately determined. For example, in a certain wind environment, the movement state of the door body may be significantly affected by wind resistance. Through wind resistance stripping decomposition, this influence can be clearly identified and included as part of the door control adjustment samples.

[0051] Based on the wind resistance peeling decomposition obtained by the gating regulation sample, the mechanical control training of the single degree of freedom vibration structure is carried out. The purpose of this training process is to optimize the gating regulation strategy through sample data, so that it can make accurate adjustment decisions according to the current mechanical state and gating state. The mechanical control training involves modeling and simulation of the dynamic characteristics of the single degree of freedom vibration structure, and by continuously adjusting the control parameters, the gating regulation module can adapt to different working conditions to achieve the best wind power regulation effect. For example, to achieve the best wind power regulation, the mechanical control training process can include the following specific steps: first, modeling and simulation, establish the mechanical model of the single degree of freedom vibration structure, including the mass, damping coefficient, spring stiffness and other parameters of the door body, and simulate the dynamic response of the door body under different wind forces through simulation software; then, parameter initialization, set the initial control parameters, such as the initial output torque of the motor, the initial speed, the initial damping coefficient of the damping brake, etc.; then, dynamic adjustment, through simulation analysis, evaluate the motion stability and energy consumption of the door body under the current control parameters, and according to the evaluation results, dynamically adjust the control parameters, for example, if it is found that the door body vibrates greatly under a certain wind force, increase the damping coefficient to improve stability; if the energy consumption is too high, adjust the motor output torque to reduce energy consumption; then, iterative optimization, repeat the above steps, continuously optimize the control parameters, until the parameter combination that makes the door body motion stable and has the lowest energy consumption under the current wind force condition is found; finally, store the training results, store the optimized control parameters as part of the gating regulation module for actual gating operation to achieve the best wind power regulation effect.

[0052] After the mechanical control training, the specific parameters and control strategy of the gating regulation module can be determined. The last step is to embed the trained gating regulation module in the middle station of the gating system. The middle station of the gating system is the control core of the entire gating system, responsible for coordinating the operation of various subsystems and modules. Embedding the gating regulation module in the middle station can ensure that it can obtain the necessary data in real time during system operation and make quick and accurate adjustment decisions based on these data.

[0053] Through the above steps, the gating regulation module constructed can make accurate wind power regulation decisions based on real-time mechanical state and gating state, combined with the characteristics of the single degree of freedom vibration structure, improve the intelligent level of the gating system, and enhance its adaptability and stability in complex wind power environment.

[0054] Further, the step P32a of the embodiment of the present application further includes:

[0055] P32-1a: the gating state includes a stable state and a motion state, and the motion state includes a door opening state and a door closing state; P32-2a: the gating adjustment sample is divided into the stable state, the door opening state and the door closing state, the single-degree-of-freedom vibration structure is taken as a motion scene, multi-thread mechanical control training under a state thread is performed, a first gating branch, a second gating branch and a third gating branch are determined, and the gating adjustment module is determined by branch parallel integration.

[0056] Optionally, the construction process of the gating adjustment module can be further refined, especially in the mechanical control training phase, different gating states are further divided and multi-thread training is performed to achieve more accurate gating adjustment.

[0057] First of all, the classification of the gating state is clear. According to the embodiments of the present application, the gating state includes a stable state and a motion state, and the motion state is further divided into a door opening state and a door closing state. The stable state refers to the state that the door body is in a static state and is not disturbed by external force; the door opening state refers to the dynamic state of the door body in the opening process; the door closing state refers to the dynamic state of the door body in the closing process. The classification of these states is based on the actual running demand and mechanical characteristics of the door body, which provides a clear classification basis for the subsequent mechanical control training.

[0058] Based on the above classification of the gating state, the gating adjustment sample is classified and processed. The gating adjustment sample is obtained by wind resistance stripping and decomposition of the gating adjustment record, and contains the motion data and corresponding adjustment actions of the door body under different mechanical states. Dividing these samples according to the stable state, the door opening state and the door closing state can ensure that the training process is more targeted and accurate.

[0059] Next, taking a single degree of freedom vibration structure as the motion scenario, multi-threaded mechanical control training under state threads is carried out. Multi-threaded mechanical control training refers to simultaneously carrying out mechanical control training under different gating states (stable state, gate opening state, gate closing state) to ensure that the gating adjustment module can achieve optimal control under various states. The single degree of freedom vibration structure provides a stable mechanical model for the motion of the door body. By carrying out multi-threaded training under different state threads, the control strategies for the stable state, the gate opening state and the gate closing state can be optimized simultaneously. Specifically, the first gating branch is trained for the sample data of the stable state; the second gating branch is trained for the sample data of the gate opening state; and the third gating branch is trained for the sample data of the gate closing state. Each branch contains the optimal control strategy for the corresponding state, and can make accurate adjustment decisions according to the current mechanical state and gating state. For example, first, the state is divided, and the gating adjustment samples are divided according to the gating state (stable state, gate opening state, gate closing state); then, thread allocation is performed, and each state is allocated an independent thread for mechanical control training; 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 gate opening state, the traction force and speed parameters are mainly adjusted to ensure that the door body can be quickly and stably opened; in the gate closing state, the damping coefficient is mainly adjusted to ensure that the door body can be smoothly closed; then, synchronous optimization is performed, and the control parameters under different states are simultaneously optimized through multi-thread parallel processing to improve training efficiency; finally, integration and verification are performed, the control parameters optimized by each thread are integrated to form a complete gating adjustment module, and the effectiveness and reliability of the module are verified through simulation and actual test.

[0060] Finally, branch parallel integration is performed to integrate the first gating branch, the second gating branch and the third gating branch to form a complete gating adjustment module. The purpose of branch parallel integration is to ensure that the gating adjustment module can seamlessly switch between different states and achieve comprehensive control of the door body motion. Through this parallel integration method, the gating adjustment module can quickly select the corresponding control branch according to the real-time gating state, thereby achieving efficient and stable gating management.

[0061] Further, the gating strategy is determined, and the step P30 of the embodiment of the application further includes:

[0062] P31: According to the door body motion state, branch matching is performed on the gating adjustment module to determine a target gating branch; P32: The real-time traction force, door body motion state and effective wind resistance in the multi-modal data are input into the target gating branch to execute the balance decision of traction and resistance under the gating motion, and the gating strategy is determined.

[0063] It should be understood that in order to achieve accurate gating strategy determination, the relevant steps can be further refined. In the execution of the wind regulation gating decision based on the mechanical motion of the single degree of freedom vibration structure, first, the gating adjustment module is branched matched according to the actual motion state of the door. The motion state of the door mainly includes stable state, door opening state and door closing state. The stable state refers to the state that the door is static and not disturbed by external force; the door opening state refers to the dynamic state of the door in the opening process; the door closing state refers to the dynamic state of the door in the closing process. The classification of these states can be based on the actual running demand and mechanical characteristics of the door, and provide clear classification basis for subsequent gating strategy generation.

[0064] Specifically, according to the actual motion state of the current door, the corresponding gating branch is selected. For example, if the door is in the opening process, the second gating branch trained for the door opening state is selected; if the door is in the closing process, the third gating branch trained for the door closing state is selected; if the door is in the static state, the first gating branch trained for the stable state is selected, so as to ensure that the gating adjustment module can select the most suitable control strategy according to the current working condition.

[0065] After determining the target gating branch, the real-time traction force, the door motion state and the effective wind resistance in the multi-modal data are input into the target gating branch. These data are the key inputs of the gating adjustment decision, the real-time traction force reflects the output capability of the motor, the door motion state provides the current speed and position information of the door, and the effective wind resistance is the processed wind force data, which directly reflects the influence of wind on the door motion. The target gating branch executes the balancing decision of traction and resistance under the gating motion according to the input multi-modal data. The core of this decision process is to dynamically adjust the output torque of the motor and the damping coefficient of the mechanical damping brake, so as to ensure that the door can realize the balance between traction and resistance under the current motion state. For example, if the real-time traction force is greater than the effective wind resistance, the gating strategy may reduce the output torque of the motor to save energy; if the real-time traction force is less than the effective wind resistance, the gating strategy may increase the output torque of the motor to ensure that the door can move smoothly.

[0066] Through the above balancing decision, the specific gating strategy is finally determined. This strategy includes the control parameters of the motor (such as output torque, speed, etc.) and the control parameters of the mechanical damping brake (such as damping coefficient, etc.), which ensures that the door can realize stable and efficient operation under the current motion state. The whole process not only improves the intelligent level of the gating system, but also enhances its adaptability and stability under complex working conditions, providing a solid technical guarantee for the successful implementation of the intelligent wind regulation gating method.

[0067] Further, the motor and the mechanical damping brake are driven and controlled, and the step P30 of the embodiment of the application further comprises:

[0068] P33: decompose the gating strategy to determine the traction strategy and the impedance strategy; P34: perform parameter control conversion on the traction strategy based on a traction parameter control mechanism to determine a first parameter control; P35: perform parameter control conversion on the impedance strategy based on the mechanical characteristics of the mechanical damping brake to determine a second parameter control; P36: synchronize the first parameter control and the second parameter control with time stamps, and perform driving control of the first micro motor and the second micro motor in response.

[0069] Specifically, to achieve precise driving control of the motor and the mechanical damping brake, the gating strategy can be decomposed during the execution of the steady gating management, and parameter control conversion can be performed on the traction strategy and the impedance strategy respectively, to finally achieve synchronous driving control of the double motors.

[0070] Specifically, after determining the gating strategy, the gating strategy needs to be decomposed first to clarify the traction strategy and the impedance strategy. The traction strategy mainly involves the output torque and speed control of the motor, which is used to ensure that the door body can move according to the predetermined trajectory and speed; the impedance strategy involves the adjustment of the damping coefficient of the mechanical damping brake, which is used to control the damping force during the movement of the door body to prevent excessive vibration or unstable movement. By decomposing the gating strategy into the traction strategy and the impedance strategy, the working state of the motor and the mechanical damping brake can be more accurately controlled.

[0071] Next, parameter control conversion is performed on the traction strategy. Based on the traction parameter control mechanism, the traction strategy is converted into specific control parameters, i.e., the first parameter control. The traction parameter control mechanism includes parameters such as power output, speed adjustment, and torque control of the motor. For example, if the gating strategy requires increasing the traction force during the opening process, the first parameter control will include the output torque that the motor needs to increase and the corresponding speed adjustment parameters. In this way, the traction strategy is converted into specific control instructions that the motor can directly execute.

[0072] At the same time, parameter control conversion is performed on the impedance strategy. Based on the mechanical characteristics of the mechanical damping brake, the impedance strategy is converted into specific control parameters, i.e., the second parameter control. The mechanical characteristics of the mechanical damping brake include parameters such as damping coefficient, response time, and working range. For example, if the gating strategy requires increasing the damping force during the closing process, the second parameter control will include the damping coefficient that the mechanical damping brake needs to adjust and the corresponding response time parameters. In this way, the impedance strategy is converted into specific control instructions that the mechanical damping brake can directly execute.

[0073] After determining the first control parameter and the second control parameter, it is necessary to synchronize the time stamps of the two control parameters. The purpose of synchronizing the time stamps is to ensure that the first micro motor and the second micro motor can maintain synchronization in time when performing driving control. For example, if the door body needs to increase traction and adjust damping force simultaneously during opening, the first micro motor and the second micro motor need to execute the corresponding control instructions at the same time point. By adding synchronization time stamps to the first control parameter and the second control parameter, the cooperative work of the double motors can be realized, and the stability and reliability of the door body movement can be ensured.

[0074] Finally, in response to the driving control of the first micro motor and the second micro motor. The first micro motor executes traction control according to the first control parameter, adjusts the output torque and speed of the motor, and ensures that the door body can move according to the predetermined trajectory and speed; the second micro motor drives the mechanical damping brake according to the second control parameter, adjusts the damping coefficient, and controls the damping force during the movement of the door body. Through the cooperative control of the double motors, stable and constant door control management is realized, and the door body can stably and reliably operate under various working conditions.

[0075] Further, after performing stable and constant door control management, the embodiment of the present application further includes step P40a, which further includes:

[0076] P41a: According to the periodic door control record, the abnormal door control characteristics of the non-stable and constant door control are mined; P42a: According to the abnormal door control characteristics, the door control adjustment module is updated and learned.

[0077] Optionally, after performing stable and constant door control management, the embodiment of the present application further introduces step P40a to realize the continuous optimization and improvement of the door control system. During the operation of the door control system, periodic door control records can be generated regularly, which contain the operation data of the door control system in each period, such as the output parameters of the motor, the movement state of the door body, the wind data and the corresponding adjustment actions. These records are important basis for evaluating the performance of the door control system. In order to further improve the stability and reliability of the system, it is necessary to analyze these periodic door control records in depth.

[0078] Firstly, according to the periodic door control record, the abnormal door control characteristics of the non-stable and constant door control are mined. Non-stable and constant door control refers to the situation that the door control system fails to achieve the expected stable and constant state under certain conditions, such as the door body shaking, speed uneven or position deviation during movement. By analyzing the periodic door control record, these abnormal situations can be identified, and the related features can be extracted. These abnormal door control characteristics may include sudden changes in motor current, abnormal fluctuations in door body acceleration, abnormal changes in wind data, etc. By mining these characteristics, potential problems in system operation can be more accurately identified.

[0079] Next, according to the abnormal gating features mined, the gating adjustment module is updated and learned. This process is to input the abnormal gating features as new training samples into the gating adjustment module to retrain and optimize it. The purpose of updating and learning is to enable the gating adjustment module to adjust its control strategy according to new data, so as to better cope with similar abnormal situations. For example, if it is found that the door body is prone to shaking under a certain wind condition, the updated and learned gating adjustment module will be able to automatically adjust the output torque and damping coefficient of the motor under similar conditions to reduce shaking and ensure stable movement of the door body.

[0080] Through the above steps, through periodic record analysis and updating learning, the adaptive ability and reliability of the system can be further improved. This process embodies the continuous optimization characteristics of the intelligent gating system, which can continuously adjust and improve the control strategy according to the actual running data, and ensure that the gating system can maintain efficient and stable operation under various complex working conditions.

[0081] In summary, the embodiments of the present application have at least the following technical effects:

[0082] The present application can accurately process wind data by constructing a single degree of freedom vibration structure and performing laminar flow decomposition based on wind turbulence state, determine effective wind force, thereby improving the stability of the door body in complex wind environment, and realize high-precision wind force regulation; based on the gating branch matching and wind force regulation decision of multi-modal data, the dynamic adjustment of the door body movement is realized, and accurate gating management under different working conditions is ensured; through periodic gating record analysis and updating learning, abnormal features of non-steady gating can be identified, and the gating adjustment module can be optimized accordingly, improving the adaptability and long-term operation reliability of the system; by dynamically adjusting the output torque of the motor and the damping coefficient of the mechanical damping brake, the balance of traction force and resistance is realized, energy consumption is reduced, and the energy saving effect of the system is improved, ensuring efficient and stable operation of the gating system throughout its life cycle.

[0083] The technical effects of improving the adaptability and control precision of the gating system in complex wind environment and reducing energy consumption through intelligent wind force regulation and dynamic control are achieved.

[0084] Embodiment two, based on the same inventive concept as the intelligent wind force regulation gating method in the foregoing embodiments, as shown in Figure 2 The present application provides a life cycle-oriented intelligent wind force regulation gating system, and the system and method embodiments in the present application are based on the same inventive concept. The system comprises:

[0085] A single-degree-of-freedom vibration structure construction module 11 is configured 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 identified with a traction balance point.

[0086] An effective wind force determination module 12 is configured to perform multi-mode detection of the target door body, trigger a laminar flow resolver, perform single-degree-of-freedom-based laminar flow decomposition on wind force data according to a wind turbulence state, determine an effective wind force, and replace wind force data in multi-mode data, wherein the laminar flow resolver is based on a wind force image and the single-degree-of-freedom vibration structure is an effective wind direction.

[0087] A steady door control management module 13 is configured to trigger a door control adjustment module for the multi-mode data, perform door control branch matching in a door control state, perform wind force adjustment door control decisions based on single-degree-of-freedom vibration structure mechanical motion, determine a door control strategy, drive control of a motor and a mechanical damping brake, and perform steady door control management; wherein the first micro motor is used for traction control, and the second micro motor is used for impedance control of the mechanical damping brake.

[0088] Further, the single-degree-of-freedom vibration structure construction module 11 is further configured to perform the following steps:

[0089] The door motion mode is any one of sliding motion, flat opening motion, and rotating motion; according to the door motion mode, a lightweight three-dimensional motion reconstruction is performed on the target door body to determine the single-degree-of-freedom vibration structure; and a traction balance point is determined based on motor specifications, mechanical resistance, and wind resistance to constrain the single-degree-of-freedom vibration structure.

[0090] Further, the effective wind force determination module 12 is further configured to perform the following steps:

[0091] A multi-mode sensor is connected, wherein the multi-mode sensor at least includes a motor current sensor, a door body accelerometer, and an acoustic wave anemometer; according to the multi-mode sensor, a same frequency detection is performed on the target door body to determine multi-mode data, wherein the multi-mode data includes real-time traction force, door body motion state, and wind force data.

[0092] Further, the effective wind force determination module 12 is further configured to perform the following steps:

[0093] A multi-element wind force image is constructed according to a wind turbulence state, wherein the wind turbulence state is at least based on seasonal division; a laminar flow resolver is constructed for the multi-element wind force image with the single-degree-of-freedom vibration structure as an effective force direction; and the laminar flow resolver is used to perform laminar flow decomposition and effective force direction-based combination on wind force data in the multi-mode data to determine an effective wind force.

[0094] Further, the stable gating management module 13 is further used to execute the following steps:

[0095] Obtain the gating adjustment record, determine the gating adjustment sample of the mechanical state-gating state by performing wind resistance stripping decomposition; perform mechanical control training on the single degree of freedom vibration structure with the gating adjustment sample, determine the gating adjustment module, and embed the gating adjustment module in the gating system.

[0096] Further, the stable gating management module 13 is further used to execute the following steps:

[0097] Divide the gating adjustment sample according to the stable state, the door open state and the door closed state, perform multi-threaded mechanical control training under the state thread with the single degree of freedom vibration structure as the motion scene, 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.

[0098] Further, the stable gating management module 13 is further used to execute the following steps:

[0099] According to the door body motion state, branch matching is performed on the gating adjustment module to determine the target gating branch; the real-time traction force, the door body motion state and the effective wind resistance in the multi-modal data are input into the target gating branch to execute the balance decision of the traction and the resistance under the gating motion, and the gating strategy is determined.

[0100] Further, the stable gating management module 13 is further used to execute the following steps:

[0101] Decompose the gating strategy to determine the traction strategy and the impedance strategy; perform parameter control conversion on the traction strategy with a traction parameter control mechanism to determine the first parameter control; perform parameter control conversion on the impedance strategy according to the mechanical characteristics of the mechanical damping brake to determine the second parameter control; synchronize the first parameter control and the second parameter control with a time stamp identifier, and respond to the driving control of the first micro motor and the second micro motor.

[0102] Further, the system further comprises an updating learning module, which is used to execute the following steps:

[0103] According to the periodic gating record, abnormal gating characteristics of non-stable gating are mined; and according to the abnormal gating characteristics, the gating adjustment module is updated and learned.

[0104] It should be noted that the above-mentioned embodiment sequences of the present application are merely for description only, but not for representing the advantages and disadvantages of the embodiments. And the above-mentioned embodiments of the present specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0105] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0106] The specification and drawings are merely exemplary of the present application, and any and all modifications, variations, combinations or equivalents that are within the scope of the present application should be considered covered by the present application. Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A life cycle oriented intelligent wind regulating door control method, characterized in that, The method comprises: Based on the door movement mode of the target door body, a single degree of freedom vibration structure is constructed, wherein the single degree of freedom vibration structure identifies a traction balance point; Performing multi-mode detection of the target door body, triggering a laminar flow resolver, performing single degree of freedom-based laminar flow decomposition on wind force data according to wind turbulence state to determine effective wind force, and replacing wind force data in multi-modal data, wherein the laminar flow resolver is based on wind force image and the single degree of freedom vibration structure is effective wind direction; For the multi-modal data, a gating adjustment module is triggered to perform gating branch matching in a gating state, to perform wind force adjustment gating decision under mechanical movement based on the single degree of freedom vibration structure, to determine a gating strategy, and to drive and control the motor and mechanical damping brake to perform stable gating management; Wherein, according to the first micro motor, the traction control is performed, and according to the second micro motor, the mechanical damping brake is driven to perform impedance control; The multi-mode sensor at least includes a motor current sensor, a door body accelerometer, and a sound wave anemometer. According to the wind turbulence state, a multi-element wind force image is constructed, wherein the wind turbulence state is at least divided based on seasonality; and for the multi-element wind force image, a laminar flow resolver is constructed with the single degree of freedom vibration structure as the effective force direction. The force direction of the single degree of freedom vibration structure refers to the force in the door movement direction.

2. The lifecycle-oriented intelligent wind conditioning gate method of claim 1, wherein, The door movement mode is any one of sliding movement, flat opening movement, and rotating movement. According to the door movement mode, a lightweight three-dimensional motion reconstruction is performed on the target door body to determine the single degree of freedom vibration structure; The traction balance point is determined based on motor specifications, mechanical resistance, and wind resistance, and the single degree of freedom vibration structure is constrained.

3. The lifecycle-oriented intelligent wind conditioning gate method of claim 1, wherein, Performing multi-mode detection of the target door body comprises: According to the multi-mode sensor, same frequency detection is performed on the target door body to determine multi-modal data, wherein the multi-modal data includes real-time traction force, door body movement state, and wind force data.

4. The lifecycle-oriented intelligent wind conditioning gate method of claim 1, wherein, Performing single degree of freedom-based laminar flow decomposition on wind force data according to wind turbulence state to determine effective wind force comprises: According to the laminar flow resolver, laminar flow decomposition is performed on wind force data in the multi-modal data and the effective force direction-based merging is performed to determine effective wind force.

5. The lifecycle-oriented intelligent wind conditioning gate method of claim 1, wherein, Before triggering the gating adjustment module, the construction of the gating adjustment module comprises: Obtaining gating adjustment records, and determining gating adjustment samples of mechanical state-gating state by performing wind resistance stripping decomposition; According to the gating adjustment samples, mechanical control training is performed on the single degree of freedom vibration structure to determine the gating adjustment module, and the gating adjustment module is embedded in the gating system.

6. The lifecycle-oriented intelligent wind conditioning gate method of claim 5, wherein, The gating state includes stable state and movement state, and the movement state includes door opening state and door closing state; According to the stable state, the door opening state, and the door closing state, the gating adjustment samples are divided, multi-threaded mechanical control training is performed on the single degree of freedom vibration structure as the movement scene under the state thread to determine the first gating branch, the second gating branch, and the third gating branch, and branch parallel integration is performed to determine the gating adjustment module.

7. The lifecycle-oriented intelligent wind conditioning gate method of claim 6, wherein, Determining the gating strategy comprises: According to the door body motion state, the door control adjustment module is branch matched to determine a target door control branch; The real-time traction, the door body motion state and the effective wind resistance in the multi-modal data are input into the target door control branch to execute the balance decision of the traction and the resistance under the door control motion and determine a door control strategy.

8. The lifecycle-oriented intelligent wind conditioning gate method of claim 7, wherein, The motor and the mechanical damping brake are driven and controlled, including: The door control strategy is decomposed to determine a traction strategy and an impedance strategy; The traction strategy is parameter-controlled and converted to determine a first parameter control according to the traction parameter control mechanism; The impedance strategy is parameter-controlled and converted to determine a second parameter control according to the mechanical characteristics of the mechanical damping brake; The first parameter control and the second parameter control are identified by a synchronous time stamp, and the first micro motor and the second micro motor are driven and controlled in response.

9. The lifecycle-oriented intelligent wind conditioning gate method of claim 1, wherein, After executing the steady door control management, including: According to the periodic door control record, the abnormal door control characteristics of the non-steady door control are mined; According to the abnormal door control characteristics, the door control adjustment module is updated and learned.

10. A smart wind regulating door system oriented to the whole life cycle, characterized in that, The system is used to execute the intelligent wind regulation door control method for the whole life cycle according to any one of claims 1 to 9, and the system includes: A 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 a target door body, wherein the single degree of freedom vibration structure is identified with a traction balance point; An effective wind power determination module is used to execute multi-mode detection of a target door body, trigger a laminar flow resolver, execute single degree of freedom based laminar flow decomposition according to the wind turbulence state of wind power data, determine effective wind power, and replace the wind power data in the multi-modal data, wherein the laminar flow resolver is based on wind power image and the single degree of freedom vibration structure is an effective wind direction; A steady door control management module is used to trigger a door control adjustment module for the multi-modal data, perform door control branch matching according to the door control state, execute wind regulation door control decision under the mechanical motion based on the single degree of freedom vibration structure, determine a door control strategy, drive and control the motor and the mechanical damping brake, and execute steady door control management. According to the first micro motor, the traction is controlled, and according to the second micro motor, the mechanical damping brake is driven to control the impedance.

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